Human-Centered Technologies for Inclusive Collection and Analysis of Public-Generated Data

The meteoric rise in the popularity of public engagement platforms such as social media, customer review websites, and public input solicitation efforts strives for establishing an inclusive environment for the public to share their thoughts, ideas, opinions, and experiences. Many decisions made at a personal, local, or national scale are often fueled by data generated by the public. As such, inclusive collection, analysis, sensemaking, and utilization of pubic-generated data are crucial to support the exercise of successful decision-making processes. However, people often struggle to engage, participate, and share their opinions due to inaccessibility, the rigidity of traditional public engagement methods, and the lack of options to provide opinions while avoiding potential confrontations. Concurrently, data analysts and decision-makers grapple with the challenges of analyzing, sensemaking, and making informed decisions based on public-generated data, which includes high dimensionality, ambiguity present in human language, and a lack of tools and techniques catered to their needs. Novel technological interventions are therefore necessary to enable the public to share their input without barriers and allow decision-makers to capture, forage, peruse, and sublimate public-generated data into concrete and actionable insights. The goal of this dissertation is to demonstrate how human-centered approaches involve the stakeholders in the design, development, and evaluation of tools and techniques that can lead to inclusive, effective, and efficient approaches to public-generated data collection and analysis to support informed decision-making. To that end, in this dissertation, I first addressed the challenges of empowering the public to share their opinions by exploring two major opinion-sharing avenues --- social media and public consultation. To learn more about people\u27s social media experiences and challenges, I built two technology probes and conducted a qualitative exploratory study with 16 participants. This study is followed up by exploring the challenges of inclusive participation during public consultations such as town halls. Based on a formative study with 66 participants and 20 organizers, I designed and developed CommunityClick to enable reticent share their opinions silently and anonymously during town halls. Equipped with the knowledge and experiences from these works, I designed, developed, and evaluated technologies and methods to facilitate and accelerate informed data-driven decision-making based on increased public-generated data. Based on interviews with 14 analysts and decision-makers in the civic domain, I built a visual analytics system CommunityClick that can facilitate public input analysis by surfacing hidden insights, people\u27s reflections, and priorities. Leveraging the lessons learned during this work, I created a visual text analytics system that supports serendipitous discovery and balanced analysis of textual data to help make informed decisions. In this work, I contribute an understanding of how people collect and analyze public-generated data to fuel their decisions when they have increased exposure to alternative avenues for opinion-sharing. Through a series of human-centered studies, I highlight the challenges that inhibit inclusivity in opinion sharing and shortcomings of existing methods that prevent decision-makers to account for comprehensive public input that includes marginalized or unpopular opinions. To address these challenges, I designed, developed, and evaluated a collection of interactive systems including CommunityClick, CommunityPulse, and Serendyze. Through a rigorous set of evaluation strategies which include creativity sessions, controlled lab studies, in-the-wild deployment, and field experiments, I involved stakeholders to assess the effectiveness and utility of the built systems. Through the empirical evidence from these studies, I demonstrate how alternative designs for social media could enhance people\u27s social media experiences and enable them to make new connections with others to share opinions. In addition, I show how CommunityClick can be utilized to enable reticent attendees during public consultation to share their opinions while avoiding unwanted confrontation and allowing organizers to capture and account for silent feedback. I highlight how CommunityPulse allowed analysts and decision-makers to examine public input from multiple angles for an accelerated analysis and more informed decision-making. Furthermore, I demonstrate how supporting serendipitous discovery and balanced analysis using Serendyze can lead to more informed data-driven decision-making. I conclude the dissertation with a discussion on future avenues to expand this research including the facilitation of multi-user collaborative analysis, integration of multi-modal signals in the analysis of public-generated data, and potential adoption strategies for decision-support systems designed for inclusive collection and analysis of public-generated data

Computational investigation of small-molecule human tissue transglutaminase inhibitors

Human tissue transglutaminase (TG2) catalyses transamidation and deamidation reactions through a nucleophilic cysteine residue (CYS277). TG2 activity was found to increase in celiac disease, cystic fibrosis, neurodegenerative disorders and cancer. For this, TG2 has received much focus as a target for drug discovery and many inhibitors have been designed and tested. The most important of these have an electrophilic warhead that reacts covalently with CYS277 resulting in an irreversible inhibition of TG2. The work presented in this thesis aimed at the development of computational methods that could aid in the design and testing of potential TG2 inhibitors. 3-D models of TG2 active site were developed starting from published X-ray crystal structures by means of docking experiments with known irreversible inhibitors followed by molecular dynamics (MD) simulations. The models were validated by additional docking runs and MD simulations involving a larger set of compounds with a range of activities against TG2. The models performed reasonably well in the validation process and were, therefore, chosen as active site models of TG2. No straightforward correlation could be found to rank the compounds based on their activities. This was the rationale for the next stage of the work, where the mechanism of inhibition of TG2 by two classes of inhibitors was studied. The covalent-bond-forming events for the inhibitors bearing acrylamide warheads were followed by applying quantum mechanics/molecular mechanics (QM/MM) umbrella sampling MD simulations to the reaction. The produced activation energies correlated well with the biological activities for the inhibitors and a mechanism with an oxyanion intermediate was proposed. The mechanism of inhibition by compounds having sulfonium ion warheads was investigated using reaction path experiments, where a transition state was first identified and verified and was used as a starting point for the reaction path. The activation energies again produced a reasonable correlation with biological activity and an SN2 mechanism was suggested for this inhibition.On a different level, two allosteric inhibitors proposed in the literature were docked into an allosteric site in TG2 predicted by a collaborator from the University of Strathclyde, and docking complexes were subjected to accelerated MD (aMD) to inspect whether the binding would induce significant conformational changes in TG2. The binding of one inhibitor in the predicted site caused bending in TG2 structure that could be a starting event for complete TG2 inactivation. The other inhibitor seemed to produce a similar effect when bound to the original GDP binding site. An even more profound conformational change was reported due to the binding of GDP in its original binding site. aMD, for the simulation times used (400-1000 nanoseconds), was able to represent some large conformational changes in TG2 brought about by the binding of allosteric inhibitors. To sum up, the work presented in this thesis was successful in applying various computational approaches to the analysis of inhibition of TG2 with irreversible and allosteric inhibitors

Adsorption of some Metal Ions from Aqueous Solution on Iraqi Rice Bran and Its Relation to the Physical Properties of these Metal Ions

Adsorption studies were carried out to test the ability of the Iraqi rice bran (Amber type) to adsorb some metals divalent cations (Cd2+, Co2+, Cu2+, Fe2+, Ni2+, Pb2+, and Zn2+) as an alternative tool to remove these pollutants from water. The Concentrations of these ions in water were measured using flame and flamless atomic absorption spectrophotometry techniques. The applicability of the adsorption isotherm on Langmuir or Freundlisch equation were tested and found to be dependent on the type of ions. The results showed different adsorptive behavior and different capacities of the adsorption of the ions on the surface of the bran. The correlation between the amounts adsorbed and different cation parameters including (electronegativity, ionic radius, and the second ionization potential) were tested. This study showed the applicability of bran, as a cheap and available waste materials, to remove different cations from solution

Unsupervised video summarization framework using keyframe extraction and video skimming

Video is one of the robust sources of information and the consumption of online and offline videos has reached an unprecedented level in the last few years. A fundamental challenge of extracting information from videos is a viewer has to go through the complete video to understand the context, as opposed to an image where the viewer can extract information from a single frame. Apart from context understanding, it almost impossible to create a universal summarized video for everyone, as everyone has their own bias of keyframe, e.g; In a soccer game, a coach person might consider those frames which consist of information on player placement, techniques, etc; however, a person with less knowledge about a soccer game, will focus more on frames which consist of goals and score-board. Therefore, if we were to tackle problem video summarization through a supervised learning path, it will require extensive personalized labeling of data. In this paper, we attempt to solve video summarization through unsupervised learning by employing traditional vision-based algorithmic methodologies for accurate feature extraction from video frames. We have also proposed a deep learning-based feature extraction followed by multiple clustering methods to find an effective way of summarizing a video by interesting key-frame extraction. We have compared the performance of these approaches on the SumMe dataset and showcased that using deep learning-based feature extraction has been proven to perform better in case of dynamic viewpoint videos.Comment: 5 pages, 3 figures. Technical Repor

The Effect of Vertical Loads and the Pile Shape on Pile Group Response under Lateral Two-Way Cyclic Loading

This paper is presented the lateral dynamic response of pile groups embedded in dry sand under influence of vertical loads and the pile shape in-group, which are subjected to the lateral two-way cyclic loads. The laboratory typical tests with pile groups (2×1) have an aluminum-pipe (i.e. circular, square) pile, embedded length to diameter of pile ratio (L/D=40) and spacing to diameter ratio (S/D) of 3, 5, 7 and 9 are used with different cyclic-load ratio (CLR) 0.4, 0.6 and 0.8. The experimental results are revealed that both the vertical and lateral pile capacity and displacement is significantly affected by the cyclic-loading factors i.e. (number of cycles, cyclic load ratio, and shape of pile) .In this study, important design references are presented. Which are explained that the response of the pile groups under cyclic lateral loading are clear affected by the attendance of vertical load and pile shape. Where, it is reduction the lateral displacement of group piles head and increase lateral capacity about (50) % compared without vertical loads. On the other side, the pile shape is a well affected to the pile response where the level of decline in lateral displacement at the pile groups head in the square pile is more than circular pile about 20 % at the same load intensity

Assessment knowledge of female academic staff in Kirkuk- Technical Institute to tetanus toxiod vaccination

Background:Tetanus is a preventable disease and no age is immune unless there is  a previous protection with tetanus toxiod  immunization which is a highly effective and the immunity lasts several years. Objective: To assess  the female academic staff   knowledge regarding the vaccination status with tetanus toxoid. Patients and Methods: A  cross- sectional study  was  conducted in Kirkuk Technical Institute   for  the  period  from 1st   January /2014    till the  end of  May    2014. A randomly selected sample from different  scientific departments  and a special questionnaire form was prepared by direct  interviewing with the study  sample and 100  female academic staff      were participated  in the study after receiving  a verbal  consent from them before establishing  the study. Results: The results revealed  that 43.0% of study female academic staff aged ( 41-50  ) years  , (53.0% ) having  a master  certificate  , (61.0%) of them are  assistant lecturers , and (81.3%) of study female staff  were vaccinated with tetanus toxiod     ,and  73.6% of them  are  completed their vaccination schedule with tetanus toxiod during their reproductive life. Conclusion: The study concluded that  female staff who are not completed their vaccination status with tetanus toxiod because of over load working.                                                                                                          

Predictor Corrector Parallel Based on the Geometric Mean Runge-Kutta Formula for Solving Initial Value Problems

The purpose of this study is to present a new connection of the famous Runge-Kutta methods by using more than one old technique and obtain a new method, which has acceptable results for solving Initial Value problems

A Symptamatic Urinary Tract Infection Among Kirkuk Technical Institute Students Attending Primary Health Center

Background: Urinary tract infection is regarded as one of the most common health problem affecting female in all age groups. Objective: To study the presences of a symptomatic urinary tract infection among female students attending primary health center belongs to Kirkuk Technical Institute. Patients and Methods: Across- sectional study was done and a randomly selected   sample   (80 female students) from different scientific depts. in Kirkuk Technical Institute after receiving their agreements to participate in the study which was started from 1st December 2015 till 1st April 2016. A special questionnaire sheet prepared for this purpose. A general urine exam was done for each student and for the infected females, urine culture and sensitivity was performed. Results: The results show that 80 female students were included in the study, 86.6 % of them aged   (18- 20) years and there was a highly significant relationship between the age group < 18 years and the presence of infection with a p value = 0.000. More than  half of infected female students  (65.4%) having bacteria(bacteriuria)  while 15.4% having pus cell (pyuria) and 80.8% of them having positive urine culture which was related to the presence of Escherichia coli (71.5%) . Conclusion: Escherichia coli was the frequent bacteria among positive growth in infected females

MODIFIED HILL CLIMBING MAXIMUM POWER POINT TRACKING CONTROL METHOD FOR SATELLITE ELECTRICAL POWER SUPPLY SYSTEM

The increasing of  efficiency of the satellite electrical power supply system (PSS) is important engineering task. Modern satellite PSS widely use DC-DC converters under pulse-width controlling. Existing methods of maximum power point tracking of such systems not always can  to search surely for maximum power point if there are existing  some local maximums of power. It can be if illumination of few non-oriented solar panels are different. This paper present the modeling of modified hill climbing (HC) maximum power point tracking (MPPT). The aspect of the method is choosing initial point through open circuit voltage coefficient and next tracking by using of special function through digital differentiation of measured values of output power with scaling on actual power and special empirical coefficient. The simulation results revealed robust tracking of the main peak power at a sufficiently rapid convergence

Editable User Profiles for Controllable Text Recommendation

Methods for making high-quality recommendations often rely on learning latent representations from interaction data. These methods, while performant, do not provide ready mechanisms for users to control the recommendation they receive. Our work tackles this problem by proposing LACE, a novel concept value bottleneck model for controllable text recommendations. LACE represents each user with a succinct set of human-readable concepts through retrieval given user-interacted documents and learns personalized representations of the concepts based on user documents. This concept based user profile is then leveraged to make recommendations. The design of our model affords control over the recommendations through a number of intuitive interactions with a transparent user profile. We first establish the quality of recommendations obtained from LACE in an offline evaluation on three recommendation tasks spanning six datasets in warm-start, cold-start, and zero-shot setups. Next, we validate the controllability of LACE under simulated user interactions. Finally, we implement LACE in an interactive controllable recommender system and conduct a user study to demonstrate that users are able to improve the quality of recommendations they receive through interactions with an editable user profile.Comment: Accepted to SIGIR 2023; Pre-print, camera-ready to follo