Bidirectional job matching through unsupervised feature learning

Job matching is a process that involves decision to whether a job vacancy is relevant, given the profile of job seeker and vice versa. It requires thorough understanding of job seeker and vacancy in order to match them bidirectionally. Bidirectional matching through measuring the degree of semantic similarity of job descriptions in vacancies and candidate job seekers has been a challenging task in the job recruitment industry. The challenges are associated with i) lack of information due to job seeker inability or resistance to provide sufficient data, ii) difficulty in modeling job seekers and/or vacancies and iii) the complexity of matching process itself. Fortunately, the Internet and advancement of information technology provide opportunities that help deal with these challenges. Availability of huge online data about job descriptions which has been entered by job seekers and job holders can be utilized to understand job seekers. Large volume of online vacancy data can be exploited to portray the current demand of the job market. Prevalence of technological advancements to handle the size of big data and the complexities of the matching process makes job matching feasible to address. Understanding the job seeker presupposes obtaining more information about the job seeker directly from himself, i.e., through resumé and web survey, or through others, i.e., from social network. This research investigates tools and techniques, and implements a web-based user interface, i.e., a context-aware Dynamic Text Field (DTF), that allows users to enter data of their choice but with guidance using autocompletion. Moreover, this study identifies methods that measure the skills, expertise and experience of a job seeker and investigates the importance of using social networking data as input to user modeling that determines the strength of skills to be used for recommending matching job vacancies. In addition to job seekers, job matching requires understanding and modeling of vacancies. Though online vacancies are publicly available, due to overwhelming volume of data, job seekers are not able to easily find relevant vacancy for their skill or are unable to analyze the requirements to estimate its relevance. Analyzing vacancies as well as optimizing the matching process, on one hand, and exploiting the available opportunities of big data and technological advancement, on the other hand, are of paramount importance to pursue a novel approach of job matching. This research employs solutions that learn from data (as opposed to rules) because they perform better at handling job seekers and vacancy data in the ever changing market. One of the methods to address these challenges is applying Machine Learning – data-intensive techniques to model job seekers and vacancies – and get a better matching. It explores matching job vacancies with job seekers using data from online vacancies, occupational standards, resumés, job seeker’s self-assessment, and social network data. Deep unsupervised feature learning, which is a kind of machine learning, is applied to develop a novel bidirectional matching of job seeker and vacancy through modeling the former using data from self-assessment, resumé parsing and social network, and the latter using vacancy parsing and enriching it via occupational standards. The choice of the data is based on its suitability to model different aspects of job seeker and vacancy. Machine learning is chosen because of the dynamics of job market, i.e, the jobs change so frequently that developing rules is practically infeasible, whereas learning from data is feasible with the availability of large online data, and robust computational resources. The results of this endeavor are i) development of algorithm for context-aware DTF for user profile survey; ii) a new technique to measure skill relevance using social network-enhanced job seeker modeling; iii) improved relevance ranking of job vacancies through feature enriching by job titles and descriptions from standard occupations.Job-Matching ist ein Prozess der angesichts des Profils eines Arbeitsuchenden überprüft inwiefern eine Stellenausschreibung maßgeblich ist und umgekehrt. Bidirektionales Matching durch Messung der semantischen Ähnlichkeit von Berufsbeschreibungen in Stellenangeboten und Bewerberprofilen ist eine fortlaufende Herausforderung für Arbeitsvermittlungseinrichtungen. Diese Herausforderungen sind i)der Mangel an Informationen aufgrund der Unfähigkeit oder dem Widerstand des Arbeitssuchenden ausreichende Daten zur Verfügung zu stellen, ii)Schwierigkeiten bei der Modellierung von Arbeitsuchenden und Stellenangeboten und iii)mit der Komplexität des Matching-Prozesses selbst assoziiert. Glücklicherweise bieten das Internet und die Weiterentwicklung der Informationstechnologie Möglichkeiten mit diesen Herausforderungen umzugehen. Ein großes Volumen an Online-Stellenangeboten kann zudem genutzt werden, um den aktuellen Bedarf des Arbeitsmarktes darzustellen. Das Verständnis über den Arbeitssuchenden setzt voraus, dass weitergehende Informationen direkt von oder über diesen ermittelt werden, zum Beispiel durch dessen Lebenslauf, eine Suche im Web, oder durch andere Kanäle wie etwa soziale Netzwerke. Die vorgelegte Arbeit untersucht Werkzeuge und Techniken zur Ermittlung und Erfassung weiterer Informationen und implementiert hierzu eine webbasierte Benutzeroberfläche in Form eines kontextbezogenen dynamischen Textfelds (DTF), in welches Benutzer Daten ihrer Wahl eintragen können und dabei durch Funktionen zur Autovervollständigung unterstützt werden. Außerdem identifiziert die vorgelegte Dissertation Methoden die die Fähigkeiten, Fachkenntnisse und Erfahrungen eines Arbeitssuchenden messen. Des Weiteren wird die Bedeutung der Verwendung von Daten aus Sozialen Netzwerken als Input für die Benutzer-Modellierung untersucht, um davon abhängig die Stärke der Fähigkeiten zu bestimmen und diese letztendlich für die Empfehlung passender Stellenangebote zu verwenden. Zusätzlich zu dem Verständnis über den Arbeitsuchenden, erfordert der Job-Matching Prozess das Verständnis von Stellenangeboten und deren Strukturierung bzw. Modellierung. Obwohl Online-Stellenangebote öffentlich zugänglich sind, sind diese aufgrund der überwältigenden Datenmengen unüberschaubar, so dass die Arbeitsuchenden nur schwierig in der Lage die passende Stelle für ihre Fähigkeiten zu ermitteln und zu bewerten. Gerade die Analyse von Stellenangeboten sowie die Optimierung des Matching-Prozesses einerseits aber auch die Nutzung der vorhandenen Chancen von Big Data und technologischer Weiterentwicklungen andererseits sind von größter Bedeutung, um einen neuartigen Ansatz im Job-Matching zu verfolgen. Die vorgelegte Dissertation setzt Lösungen ein, die aus Daten (im Gegensatz zu Regeln) lernen, um auf diese Weise besser auf einen sich ständig ändernden Arbeitsmarkt hinsichtlich der Zusammenführung von Arbeitssuchenden und offenen Stellenangeboten reagieren zu können. Angewendete Methoden um diese Herausforderungen zu bewältigen sind Techniken aus dem Bereich des Maschinellen Lernens, welche eine Analyse und Verarbeitung von datenintensiven Sammlungen realisieren und ein besseres Matching zwischen Arbeitsuchenden und Anforderungen in Stellenangeboten ermöglichen. Hierzu wurde erforscht wie Daten aus Online-Stellenausschreibungen, Beschreibungen von Berufsbildern, Lebensläufen, Selbsteinschätzung des Arbeitssuchenden und Daten aus sozialen Netzwerken analysiert und für ein besseres Matching zwischen Stellenangeboten und Arbeitsuchenden eingesetzt werden können. Eine Form des maschinellen Lernens ist die Methode des „Deep Unsupervised Feature Learning“. Diese wurde dazu eingesetzt ein neuartiges bidirektionales Matching von Arbeitsuchenden und Stellenangeboten zu entwickeln, indem es ein Modell mit Hilfe von Daten aus Selbsteinschätzung, Resumé-Parsing und sozialen Netzwerken modelliert. Zusätzlich erfolgt ein Parsing der Stellenbeschreibungen und eine Anreicherung mit allgemeinen Anforderungen auf Basis der Berufsbilder. Die Wahl der Daten beruht auf ihrer Eignung verschiedene Aspekte des Arbeitsuchenden und der offenen Stellen zu modellieren. Maschinelles Lernen als Analysemethode wird u.a. aufgrund der Dynamik des Arbeitsmarktes gewählt, da sich Arbeitsanforderungen so häufig ändern, dass die Entwicklung von Regeln praktisch unmöglich ist, während das Lernen aus Daten durch die Verfügbarkeit von großen Online-Datensammlungen und robusten Rechenressourcen möglich ist. Die Ergebnisse der Forschungsfragen sind i)die Entwicklung eines kontext-sensitiven DTF-Algorithmus für Benutzerprofil-Umfragen; ii)eine neue Technik zur Messung der Qualifikationsrelevanz durch die Modellierung von Arbeitssuchenden mit Hilfe von Daten sozialer Netzwerke; iii)ein verbessertes Relevanz-Ranking von offenen Stellen durch eine Feature-Anreicherung mittels Berufsbezeichnung und Beschreibungen aus Berufsbildern

Determinant Factors Affect the Implementation of Laboratory Work in Science Subjects at Secondary Schools in Bale Zone, Ethiopia

The research is financed by Madda Walabu University, Bale Robe, Ethiopia Abstract Laboratory work as a teaching and learning science is prominence in the Ethiopian curriculum for secondary school. It is emphasized that students should be given opportunities to develop the ability to search for answers to questions, plan, and conduct, interpret and present results. Moreover, students should also be encouraged to use their science knowledge to communicate, argument and present conclusions. But incorporating laboratory work curriculum and implementing in real context are different things. Because of different factors it is not implemented in most cases in Ethiopian. Hence, the objective of this research was to determine factors that affect the implementation of laboratory work in science subjects at Secondary Schools in Bale Zone. There are about 57 secondary schools found in Bale zone and from these 6 schools from pastoralist and 5 schools from pastoralist a total sample size of 11 schools using stratified sampling method. Primary data was gathered from teachers, school principals and students. Secondary data were collected from natural science books (physics, chemistry and biology), documents such as annual plans, laboratory reports, annual reports and exam papers. The find of research has showed that the major hindering factors for laboratory works to be functional are shortage lab technician and resources (lab materials, chemicals, well organized and separated Laboratory room) and large class size. The educational offices should seriously plan and enforce the provision of the required facilities for the schools and professional support for the teachers. The school environment should be facilitated to handle the implementation of the Laboratory works. Keywords: Determinant factors, Laboratory work, science subjects, Secondary Schools. DOI: 10.7176/JEP/10-13-09 Publication date:May 31st 201

Effect Analysis on Laboratory Work Skill Training Provided by Madda Walabu University for Secondary Schools (2013-2017G.C) in Bale Zone, South East Ethiopia

The research is financed by Madda Walabu University, Bale Robe, Ethiopia Abstract Incorporating Laboratory work and implementing in real context are different things. As one of community service Madda Walabu University gave training on practical skills for teachers by identifying the problems related to implication of laboratory work. The project title was “Awareness and Skill Training for natural science teachers in Secondary and preparatory School from 2013-2015G.C”. Hence, the objective of this research was to analyze effect of training on the implementation laboratory work. Among 57 secondary schools in Bale zone 15 schools received the training and 42 did not. For comparison purposes 6 trained and 6 not trained schools were selected which comprise 20% form the total schools. Primary data were gathered from 72 teachers and 12 principals using in depth interview and the questionnaires for 404 Grade 10 students. Secondary data were collected from documents such as annual plans and laboratory reports, annual reports. A descriptive survey has been conducted to analyze the effect of the training on schools laboratory works. The result of the study has showed that there are no significant changes were found in implementation of laboratory works in schools as a result of the trainings. The major hindering factors for laboratory works to be functional are shortage lab technician and resources (lab materials, chemicals, well organized and separated Laboratory room) and large class size. Unless these barriers are tackled prior to the training, it is impossible to expect achievement in objectives wanted by providing only the training. Prior to the training the school environment should be facilitated to handle the implementation of the Laboratory works. The skill training is not required in the schools with poor facilities. The educational offices should seriously plan and enforce the provision of the required facilities for the schools and professional support for the teachers. Keywords: Laboratory work Training provided, Secondary Schools, result of the training DOI: 10.7176/JEP/10-13-08 Publication date:May 31st 201

Secondary School Students’ Beliefs Towards Learning Physics and Its Influencing Factors

Physics is considered as one of the most prevailing and problematic subject by the students in the natural science. Students believe physics as a difficult subject during high school and become more when they reach university. This paper deals with the students' beliefs about physics learning and relations with their practices based on contemporary literatures. Beliefs have generally been perceived through the personal experiences and interactions with immediate environment and setting. The objective of this article is to find out students' beliefs about physics learning and influencing factors and hence, has been reviewed systematically from over seventy different findings done. These beliefs are internally built in a person and can be difficult to alter. These lead us to perceive how students‘physics beliefs can shape their behaviors as to how they relate to learning physics. Student’ beliefs toward physics are both positive and negative. Students who demonstrated positive beliefs tended to enjoy and learn effectively when they clearly understood physics well. Conversely, students with negative attitudes usually put less effort into their learning process. Most students disliked learning physics because it is believed to be difficult. Different factors have been examined from over hundred articles reviews that influence students’ beliefs towards learning physics. These are the students’ self-concept, self-efficacy and confidence contribute highly students’ beliefs towards learning physics and intern affect for success or failure physics subject. Secondly teachers’ personal experiences affect approaches to teaching; experience with schooling and instruction influences beliefs about children’s learning and the role of teacher and formal knowledge in the context of pedagogical knowledge has been found to influence teacher beliefs. Teachers who do not provide support or show patience can have a negative impact on students’ achievement. Studies have also shown that a positive correlation between a disadvantaged school environment and learners’ beliefs towards physics at school. Cultural beliefs also influenced the scientific world in the most of the student’s beliefs in creating misconceptions of students in describing, understanding, interpreting and predicting natural phenomena in physics classroom. Keywords: students’ belief, physics subject and secondary schools DOI: 10.7176/RHSS/10-7-05 Publication date: April 30th 2020

Estimating Global Solar Radiation in Bale Robe Town Using Angstrom-Prescott and Hargreaves-Samani Models

The demand for energy, consumption of forest for firewood and charcoal, and the pollution rate of atmosphere are increasing in alarming rate as worldwide particularly in developing countries like Ethiopia. Following hydro energy the forest is the main source of energy for the communities of Robe town. The town is growing rapidly and facing high shortage of energy source from household to organization level. Therefore, for many reasons like solar energy which is free of environmental pollution, renewable and abundantly accessible has to be considered and studied for the development of project on energy harvesting. The first objective of this study was to determine seasonal value of global solar radiation GSR by using Angstrom-Prescott AP and Hargreaves-Samani HS models in Bale Robe town. The second one is to estimate variation between the two models for this town. The data were obtained from Robe meteorological station which was measured over a period of the year 2010 to 2014. The measured data of the daily sunshine duration and daily maximum and minimum temperature were used to estimate seasonal mean values of GSR and their percentage difference between Angstrom-Prescott AP and Hargreaves-Samani HS models in Bale Robe town and analyzed using linear regression. The findings of the study in general, revealed that, GSR in different seasons summer, autumn, winter and spring season were 18.80 MJm-2day-1, 22.76 MJm-2day-1, 26.64 MJm-2day-1 and 23.94 MJm-2day-1 respectively using Angstrom-Prescott model. Using Hargreaves-Samani model the value of GSR found to be in summer, autumn, winter and spring seasons were 20.60 MJm-2day-1, 24.42 MJm-2day-1, 28.60 MJm-1day-1 and 26.12 MJm-2day-1 respectively. The results of the study showed that, there were peak values of GSR estimated in winter and spring and low value of global solar radiation has been observed in summer and autumn in both models. The percentage difference between the two models showed that AP and HS models were favorable models that predict seasonal value of GSR in Bale Robe town in the absence of instrumental installations which are important to measure GSR directly. Based on the finding, peak value of GSR obtained in a seasons of winter and spring with AP and HS models. Hence, both models used successfully to estimate seasonal value of GSR with relative accuracy. Keywords: GSR in Robe, sunshine duration, air temperature, AP and HS mode

Practice and Challenges Facing Practical Work Implementation in Natural Science Subjects at Secondary Schools

Practical work is very crucial for teaching and learning process in school science and good quality of practical work implementation helps to develop pupils’ understanding of scientific processes and concepts. However, it has been shown that practical work in science subjects almost ignored or not effectively implemented in secondary schools in many countries of the world because of different factors. The main purpose of this article was to identify challenges facing implementing practical work in natural science subjects and its practice at secondary schools in different areas of the world based on different published works. The most recent and majorly the last 15 years that published in reputable journals have been critical reviewed and used as a direct source. Hence, the dominant factors frequently that indicated in most findings special in developing countries to implement practical activities of natural science subjects in secondary schools are problems related to school resource are: lab equipment and supply, laboratory manuals, laboratory rooms, class size and ICT access.  The second ranked determinant is problem related to teachers and technicians related issues which include: teacher’s perception and motivation, teachers’ skills competence, teachers work experience, the laboratory technicians, job satisfaction and teachers work load. The other factors are exams and assessments, curriculum and educational administrations are identified as different factors. However, each of them affect the implementation of practical work with varies degree from school to school and also among different countries. The factors affecting in developed countries and developing are somewhat different. The implementation process of practical work in science education is still limited in Ethiopian schools and students perform poorly in science subjects. Keywords: Practical Work, Secondary Schools, practice, challenges. DOI: 10.7176/JEP/10-31-01 Publication date: November 30th 201

AI Lifecycle Zero-Touch Orchestration within the Edge-to-Cloud Continuum for Industry 5.0

The advancements in human-centered artificial intelligence (HCAI) systems for Industry 5.0 is a new phase of industrialization that places the worker at the center of the production process and uses new technologies to increase prosperity beyond jobs and growth. HCAI presents new objectives that were unreachable by either humans or machines alone, but this also comes with a new set of challenges. Our proposed method accomplishes this through the knowlEdge architecture, which enables human operators to implement AI solutions using a zero-touch framework. It relies on containerized AI model training and execution, supported by a robust data pipeline and rounded off with human feedback and evaluation interfaces. The result is a platform built from a number of components, spanning all major areas of the AI lifecycle. We outline both the architectural concepts and implementation guidelines and explain how they advance HCAI systems and Industry 5.0. In this article, we address the problems we encountered while implementing the ideas within the edge-to-cloud continuum. Further improvements to our approach may enhance the use of AI in Industry 5.0 and strengthen trust in AI systems

Barriers and facilitators of maternal health care services use among pastoralist women in Ethiopia: Systems thinking perspective

We explored the barriers and facilitators of maternal health care service use among women in the pastoralist region of Ethiopia. We used a mixed methods design—focus group discussions, key informant interviews, review of the literature and Participatory Ethnographic Evaluation Research (PEER) methods followed by a household survey among randomly chosen pastoralist women of reproductive age (n = 1,499). We used multi-variable regression analyses, and a p value ≤ 0.05 was set to determine statistical significance. In addition, we analysed qualitative data thematically and developed a causal loop diagram using dynamic synthesis methodology to analyse non-linearity, intricate relationships of the variable of interests. In this study, 20.6% of women used modern contraceptive methods, 44.6% had four or more antenatal visits and 38.4% of sampled women received skilled delivery services. We observed multiple individual and community related factors such as education, income and women’s and their partner’s knowledge, perceptions, husband approval, social norms and value-expectations and providers’ gender preferences and health systems factors such as access to health facilities, place of living, provider's cultural competency skills, supplies, delivery positions, economic and political stability, and provider's attitude were linked to maternal health care services utilization among women in pastoralist regions. Approaches towards pastoralists’ health care delivery systems should be responsive to their cultural and political ecology and human agency

AfriSenti: A Twitter Sentiment Analysis Benchmark for African Languages

Africa is home to over 2000 languages from over six language families and has the highest linguistic diversity among all continents. This includes 75 languages with at least one million speakers each. Yet, there is little NLP research conducted on African languages. Crucial in enabling such research is the availability of high-quality annotated datasets. In this paper, we introduce AfriSenti, which consists of 14 sentiment datasets of 110,000+ tweets in 14 African languages (Amharic, Algerian Arabic, Hausa, Igbo, Kinyarwanda, Moroccan Arabic, Mozambican Portuguese, Nigerian Pidgin, Oromo, Swahili, Tigrinya, Twi, Xitsonga, and Yor\`ub\'a) from four language families annotated by native speakers. The data is used in SemEval 2023 Task 12, the first Afro-centric SemEval shared task. We describe the data collection methodology, annotation process, and related challenges when curating each of the datasets. We conduct experiments with different sentiment classification baselines and discuss their usefulness. We hope AfriSenti enables new work on under-represented languages. The dataset is available at https://github.com/afrisenti-semeval/afrisent-semeval-2023 and can also be loaded as a huggingface datasets (https://huggingface.co/datasets/shmuhammad/AfriSenti).Comment: 15 pages, 6 Figures, 9 Table