Forecasting the Spreading of Technologies in Research Communities

Technologies such as algorithms, applications and formats are an important part of the knowledge produced and reused in the research process. Typically, a technology is expected to originate in the context of a research area and then spread and contribute to several other fields. For example, Semantic Web technologies have been successfully adopted by a variety of fields, e.g., Information Retrieval, Human Computer Interaction, Biology, and many others. Unfortunately, the spreading of technologies across research areas may be a slow and inefficient process, since it is easy for researchers to be unaware of potentially relevant solutions produced by other research communities. In this paper, we hypothesise that it is possible to learn typical technology propagation patterns from historical data and to exploit this knowledge i) to anticipate where a technology may be adopted next and ii) to alert relevant stakeholders about emerging and relevant technologies in other fields. To do so, we propose the Technology-Topic Framework, a novel approach which uses a semantically enhanced technology-topic model to forecast the propagation of technologies to research areas. A formal evaluation of the approach on a set of technologies in the Semantic Web and Artificial Intelligence areas has produced excellent results, confirming the validity of our solution

An agent-driven semantical identifier using radial basis neural networks and reinforcement learning

Due to the huge availability of documents in digital form, and the deception possibility raise bound to the essence of digital documents and the way they are spread, the authorship attribution problem has constantly increased its relevance. Nowadays, authorship attribution,for both information retrieval and analysis, has gained great importance in the context of security, trust and copyright preservation. This work proposes an innovative multi-agent driven machine learning technique that has been developed for authorship attribution. By means of a preprocessing for word-grouping and time-period related analysis of the common lexicon, we determine a bias reference level for the recurrence frequency of the words within analysed texts, and then train a Radial Basis Neural Networks (RBPNN)-based classifier to identify the correct author. The main advantage of the proposed approach lies in the generality of the semantic analysis, which can be applied to different contexts and lexical domains, without requiring any modification. Moreover, the proposed system is able to incorporate an external input, meant to tune the classifier, and then self-adjust by means of continuous learning reinforcement.Comment: Published on: Proceedings of the XV Workshop "Dagli Oggetti agli Agenti" (WOA 2014), Catania, Italy, Sepember. 25-26, 201

A hybrid neuro--wavelet predictor for QoS control and stability

For distributed systems to properly react to peaks of requests, their adaptation activities would benefit from the estimation of the amount of requests. This paper proposes a solution to produce a short-term forecast based on data characterising user behaviour of online services. We use \emph{wavelet analysis}, providing compression and denoising on the observed time series of the amount of past user requests; and a \emph{recurrent neural network} trained with observed data and designed so as to provide well-timed estimations of future requests. The said ensemble has the ability to predict the amount of future user requests with a root mean squared error below 0.06\%. Thanks to prediction, advance resource provision can be performed for the duration of a request peak and for just the right amount of resources, hence avoiding over-provisioning and associated costs. Moreover, reliable provision lets users enjoy a level of availability of services unaffected by load variations

Kompetensi pembimbing syarikat bertauliah Sistem Latihan Dual Nasional (SLDN)

Sistem Latihan Dual Nasional (SLDN) merupakan satu sistem latihan dan usahasama antara sektor awam dan sektor swasta dilaksanakan untuk melahirkan tenaga mahir k-worker selari dengan keperluan industri masa kini untuk membangunkan ekonomi negara. Pihak kerajaan dan syarikat swasta menaja pekerja pilihan mereka sebagai pelatih dalam sistem latihan ini bagi mempertingkatkan kebolehan pekerja mereka. Selain itu, pelatih juga terdiri daripada pelajar yang tidak dapat melanjutkan pelajaran ke mana-mana institusi pengajian tinggi awam mahupun swasta. Sistem ini menjalankan pendekatan day release iaitu pelatih menjalani latihan selama empat hari di industri dan satu hari di institusi latihan atau block release iaitu pelatih menjalani latihan kemahiran di industri empat bulan dan satu bulan di institusi latihan mengikut kesesuaian industri tersebut. Kajian berbentuk deskriptif dijalankan untuk melihat melihat tahap kompetensi pembimbing SLDN. Selain itu juga, kajian ini dijalankan bagi melihat perbezaan terhadap tahap pengetahuan, kemahiran dan sikap pembimbing SLDN berdasarkan jantina. Kajian ini juga dibuat bagi menentukan hubungan kompetensi pembimbing berdasarkan pengalaman bekerja. Penyelidikan tinjauan deskriptif ini menggunakan borang soal selidik sebagai instrumen kajian berskala Likert. Seramai 84 orang responden yang terdiri daripada pembimbing syarikat bertauliah SLDN terlibat di dalam kajian ini. Data dianalisis menggunakan SPSS versi 16.0. Hasil analisis mendapati pembimbing mempunyai pengetahuan yang tinggi di samping kemahiran dan sikap. Keputusan inferensi pula menunjukkan tidak terdapat perbezaan antara tahap pengetahuan, kemahiran dan sikap pembimbing berdasarkan jantina manakala analisis korelasi Pearson menunjukkan tidak terdapat hubungan antara kompetensi pembimbing berdasarkan pengalaman bekerja

Kompetensi pembimbing syarikat bertauliah Sistem Latihan Dual Nasional (SLDN)

Sistem Latihan Dual Nasional (SLDN) merupakan satu sistem latihan dan usahasama antara sektor awam dan sektor swasta dilaksanakan untuk melahirkan tenaga mahir k-worker selari dengan keperluan industri masa kini untuk membangunkan ekonomi negara. Pihak kerajaan dan syarikat swasta menaja pekerja pilihan mereka sebagai pelatih dalam sistem latihan ini bagi mempertingkatkan kebolehan pekerja mereka. Selain itu, pelatih juga terdiri daripada pelajar yang tidak dapat melanjutkan pelajaran ke mana-mana institusi pengajian tinggi awam mahupun swasta. Sistem ini menjalankan pendekatan day release iaitu pelatih menjalani latihan selama empat hari di industri dan satu hari di institusi latihan atau block release iaitu pelatih menjalani latihan kemahiran di industri empat bulan dan satu bulan di institusi latihan mengikut kesesuaian industri tersebut. Kajian berbentuk deskriptif dijalankan untuk melihat melihat tahap kompetensi pembimbing SLDN. Selain itu juga, kajian ini dijalankan bagi melihat perbezaan terhadap tahap pengetahuan, kemahiran dan sikap pembimbing SLDN berdasarkan jantina. Kajian ini juga dibuat bagi menentukan hubungan kompetensi pembimbing berdasarkan pengalaman bekerja. Penyelidikan tinjauan deskriptif ini menggunakan borang soal selidik sebagai instrumen kajian berskala Likert. Seramai 84 orang responden yang terdiri daripada pembimbing syarikat bertauliah SLDN terlibat di dalam kajian ini. Data dianalisis menggunakan SPSS versi 16.0. Hasil analisis mendapati pembimbing mempunyai pengetahuan yang tinggi di samping kemahiran dan sikap. Keputusan inferensi pula menunjukkan tidak terdapat perbezaan antara tahap pengetahuan, kemahiran dan sikap pembimbing berdasarkan jantina manakala analisis korelasi Pearson menunjukkan tidak terdapat hubungan antara kompetensi pembimbing berdasarkan pengalaman bekerja

Intelligent systems in manufacturing: current developments and future prospects

Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS