Miracle-dz/UniFAR-FTUs huggingface dataset:
Anomalies in the Behaviour of a Modularity Inducing Problem Domain

Espinosa-Soto and Wagner (2010) introduced a domain with weak assumptions on biology and environment, where modular structures emerge under simple evolutionary processes. We found a number of anomalous behaviours: modularity emerged in this domain, but could not dominate populations as observed in biology. Highly fit, modular solutions exist in the search space, can be readily found by a simple deterministic procedure (and presumably could dominate populations if found), but evolutionary search never found them, despite mutation biases that appear to favour those solutions. Moreover, emergence of modularity was promoted by stochastic dynamicity in the fitness function: a stochastic but fixed fitness function generated much less modular solutions.
Markov Chain Modeling for Multi-Server Clusters

We focus on a cluster server environment, which consists of multiple servers with differ­ ent access price. We analyze on the problem of assigning incoming jobs to a certain server within the cluster in order to achieve load balance while minimizing average response time. We evaluate quantitatively the impact of different assigning schemes on several system per­ formance metrics including the average response time and utilization of each server using simulation and analytical models. The assigning decision for each incoming job is made on the knowledge of the current system state on its arrival time. A typical system state incorporates the information of the expected queue length, access price at each individual server and the incoming job’s type. Consequently, by changing the access price at each server, one can effectively influence the assigning decision a t run time to favor certain performance metrics. In this project, we use the pricing scheme as a way to alleviate congestion condition for priority jobs as well as to gracefully balance the load among all servers within a cluster. For this purpose, we propose a heuristic pricing scheme, which dynamically determines the access price for each server. Through theoretical modeling analysis and simulation results, we conclude th a t the pro­ posed pricing scheme can lessen the congestion problem for priority jobs especially during traffic peak time, while maintaining the load balance nature when the traffic is low. MARKOV CHAIN MODELING FOR MULTI-SERVER CLUSTERS
Modeling Influence Diffusion over Signed Social Networks

In offline or online worlds, many social systems can be represented as signed social networks including both positive and negative relationships. Although a variety of studies on signed social networks have been conducted motivated by the great application value of unique polarity characteristics, how to model the process of influence propagation over signed social networks is still an important problem that remains pretty much open. Currently, a few studies extended traditional diffusion models (e.g., Independent Cascade model and Linear Threshold model) from unsigned social networks to signed social networks for estimating positive and negative influence of user sets. However, all of above extension models are stochastic and descriptive models. In order to ensure the accuracy of estimated influence, existing models require a significant number of Monte-Carlo simulations which are very time-consuming and not scalable. Aiming at this issue, we propose the Polarity-related Linear Influence Diffusion (PLID) model which can quickly and accurately calculate polarity-related influence of user sets without simulations. To validate effectiveness and efficiency of our proposed model, we make use of our PLID model to solve the positive influence maximization problem in signed social networks under rigorous mathematical proofs. Extensive experiments demonstrate that our PLID model and approximation algorithm significantly outperform state-of-the-art methods in terms of positive influence spread and running time, using Epinions and Slashdot datasets.
New Disturbance Rejection Constraint for Redundant Robot Manipulators: An Optimization Perspective

Due to the property of multiple solutions, redundant robot manipulators are usually required to simultaneously achieve multiple objectives in complex applications. The research of robustness for scheme formulation and optimization becomes an increasingly important issue for motion planning of redundant robot manipulators. From the perspective of optimization, a robust hybrid multiobjective (RHMO) scheme with a new disturbance rejection constraint is proposed in this article to achieve simultaneously four objectives together with the suppression of external time-varying disturbances. Theoretical results on the property of disturbance rejection are shown to confirm the effectiveness and robustness of the proposed RHMO scheme with a new disturbance rejection constraint. The RHMO scheme is then reformulated as dynamical quadratic programming with its solution found via the piecewise-linear projection equation neural network. Numerical experiments, tests, and comparisons on the basis of a PA10 manipulator verify the effectiveness, robustness, and superiority of the RHMO scheme with the new constraint for the motion planning and optimization of redundant robot manipulators against time-varying disturbances.
A context-aware tool-set for routing-targeted mutual configuration and optimization of LLNs through bridging virtual and physical worlds

In the history of the WSN, unexpected routing behaviors is always a main issue of the large scale WSN deployments. Considering the high cost of building a real deployment, network simulators are often used in this domain. However, the original problem is still not solved although the era of IoT has been coming. A new concept of LLN is emerging. We realize that, no matter how wonderful the results from the simulation scenarios and thorough testing, the problems, such as bad performance or even severe system failures due to suboptimal routing path, would still happen in the real-world system. Our context-aware tool-set can help to build the simulation topology which is more close to the real network through mapping a serial routing metrics defined by IEFT ROLL working group and the link situation of the actual network. We believe our suggestion of bridging virtual and physical worlds reflected on our proposed tool-set could conduct more precise routing-targeted simulations. Moreover, by close-loop method, the knowledge and analyzed simulation results can lead us to improve the routing topology of the deployed LLN.
Exoskeletons With Virtual Reality, Augmented Reality, and Gamification for Stroke Patients’ Rehabilitation: Systematic Review

Background Robot-assisted therapy has become a promising technology in the field of rehabilitation for poststroke patients with motor disorders. Motivation during the rehabilitation process is a top priority for most stroke survivors. With current advancements in technology there has been the introduction of virtual reality (VR), augmented reality (AR), customizable games, or a combination thereof, that aid robotic therapy in retaining, or increasing the interests of, patients so they keep performing their exercises. However, there are gaps in the evidence regarding the transition from clinical rehabilitation to home-based therapy which calls for an updated synthesis of the literature that showcases this trend. The present review proposes a categorization of these studies according to technologies used, and details research in both upper limb and lower limb applications. Objective The goal of this work was to review the practices and technologies implemented in the rehabilitation of poststroke patients. It aims to assess the effectiveness of exoskeleton robotics in conjunction with any of the three technologies (VR, AR, or gamification) in improving activity and participation in poststroke survivors. Methods A systematic search of the literature on exoskeleton robotics applied with any of the three technologies of interest (VR, AR, or gamification) was performed in the following databases: MEDLINE, EMBASE, Science Direct & The Cochrane Library. Exoskeleton-based studies that did not include any VR, AR or gamification elements were excluded, but publications from the years 2010 to 2017 were included. Results in the form of improvements in the patients’ condition were also recorded and taken into consideration in determining the effectiveness of any of the therapies on the patients. Results Thirty studies were identified based on the inclusion criteria, and this included randomized controlled trials as well as exploratory research pieces. There were a total of about 385 participants across the various studies. The use of technologies such as VR-, AR-, or gamification-based exoskeletons could fill the transition from the clinic to a home-based setting. Our analysis showed that there were general improvements in the motor function of patients using the novel interfacing techniques with exoskeletons. This categorization of studies helps with understanding the scope of rehabilitation therapies that can be successfully arranged for home-based rehabilitation. Conclusions Future studies are necessary to explore various types of customizable games required to retain or increase the motivation of patients going through the individual therapies.
Mining Text, Linking Entities – NLB’s Journey

Connecting collections across institutions and discovering hidden knowledge has always been our goal. Due to disparate data sources, differing levels of quality and granularity in the data sets and varying terminologies used for concepts and entities, this has proved to be a challenge. Barring standardisation across knowledge repositories, which is an insurmountable task, the next best thing will be to explore how text mining techniques together with linked data technology may be of help to support semantic searches. Working on text mining techniques to identify works and entities, the National Library Board of Singapore (NLB) has discovered a way to link its resources to other institutions’ knowledge bases. This approach was implemented across the collections of NLB, the National Archives of Singapore (NAS), and the National Heritage Board (NHB) which manages the museums in Singapore. Conducting a search on the respective portals will enable users of these institutions to retrieve contextualised results from all three institutions. An entity widget was created and deployed on the various portals. An example would be at NHB’s “Roots” page where visitors looking for previously undiscovered heritage connections will be able to supplement their research through the widget. This will link them to NLB and NAS resources defined by events, people, places or organisations identified in NHB’s content. Based on linked data’s resource description framework (RDF) and an entity recognition software, connections between entities are extracted, disambiguated and established. NLB proposes that such a widget can be deployed on the websites of interested institutions. Using the same technical infrastructure, we highlighted the works of prominent Singapore Chinese pioneers in collaboration with the Singapore Federation of Chinese Clan Associations. The collated results form a knowledge base of these illustrious pioneers who can be traced back to China and their connection to overseas Chinese. This paper will also address the issues faced with text mining and entity linking, the importance of named entities creation and how data mining can enhance knowledge discovery. NLB’s efforts can help other libraries to connect with similar institutions in their community.
TRAINING EFFECTIVENESS AND USER EXPERIENCE OF EDUCATION TEACHER TRAINING OF AN OPEN SOURCE LMS USING THIS LMS

E-Learning is another way of teaching and learning, conducted mainly via the Internet. Most of the pedagogical principles that apply to the traditional classroom delivery method also apply to e-Learning, but these principles need to be extended. The views of forty-five (45) teachers, who participated in the large scale training programme for a specific Learning Management System (LMS) (Open eclass) using the same system, have been recorded via the use of a Likert five-scale questionnaire on the Usefulness, the Ease in Use, the Ease in Learning and the Satisfaction as far as this system is concerned. These four factors are considered in this work to determine the overall user experience. In the research it was found that the majority of those questioned stated that the specific LMS is characterized by Usefulness, Ease in Use, Ease in Learning and Satisfaction. It is worth noticing that although these four factors are interrelated, this is not the case with the demographic characteristics of the specimen. In addition, it was found that from Primary and Secondary Education Greek teachers, those who seek their general participation in training programs, especially those related to new technologies, are those who hold a postgraduate degree. Also, they are somewhat above the average of their careers and they serve in Secondary Education, teaching Physical Sciences and they have not attended any other relevant training in this Learning Management System.
Training deep learning models to count based on synthetic data

Training deep convolutional neural networks requires a significant amount of data. Solving the need for real-world training data that is hard and expensive to create, this research project tries to design both a deep convolutional neural network and a synthetic dataset for training. Using synthetic data in training solves the need for big real-world datasets. In this training, a customized deep neural network allows for a more tailored approach to learning and generalizing the training. The context is a regression problem dealing with counting houses on satellite images. As a result, this research presents a combined model able to count houses on images in a real-world testing dataset with an average counting error of 3 for images with a number of houses in range [0, 38]. The combined model consists of a deep convolutional neural network and a linear regression model. This research concludes that creating a custom model is a good, but complicated, way of solving specific counting problems and that the method of creating synthetic data is very important in arriving at a good solution.
ABET-DRIVEN GRADING AND STATISTICAL ONLINE TOOLS FOR COURSE OUTCOMES ASSESSMENT

Working towards ABET accreditation; it is indispensable to conduct course assessment to improve student learning, performance, and the educational process. Assessment determines whether or not the goals of teaching and learning are being met and triggers needed corrective measures. It provides indicators to instructors and course coordinators to assess the attainment of course outcomes (COs) and consequently the overall program outcomes in order to adapt the teaching methodologies and techniques to address any anomalies. In this paper, we report the integration of the course assessment process into the university management system (UMS). Two tools are presented, the course grading tool for individual class sections and the coordinator statistical tool for all offered class sections. The course class section grading tool is used by the instructor to assess the students’ performance on the designated course activities and generate an assessment report. The coordinator statistical tool is used to collect all offered course sections data and provides a report including the course activities statistics. The coordinator uses this report to analyze the results, synthesize the instructor recommendations, and propose corrective actions to close the loop of continuous improvement. Both tools are interrelated and represent an opportunity for instructors and course coordinators to assess their students and consequently make significant and positive changes to teaching and learning based on best practices in higher education. As a case study, data from 31 sections of a basic Electric Circuits course were collected for a detailed statistical analysis.
On Data Sharing Strategy for Decentralized Collaborative Visual-Inertial Simultaneous Localization And Mapping

This article introduces and evaluates two decentralized data sharing algorithms for multi-robot visual-inertial simultaneous localization and mapping (VI-SLAM): Factor Sparsification for Visual-Inertial Packets (FS-VIP) and Min-K-Cover Selection for Visual-Inertial Packets (MKCS-VIP). Both methods make robots regularly build and exchange data packets which describe the successive portions of their map, but rely on distinct paradigms. While FS-VIP builds on consistent marginalization and sparsification techniques, MKCSVIP selects raw visual and inertial information which can best help to perform a faithful and consistent re-estimation while reducing the communication cost. Performances in terms of accuracy and communication loads are evaluated on multi-robot scenarios built on both available (EUROC) and custom datasets (SOTTEVILLE).
Analysis of Work Load and Total Operator Needs on Final Inspection of Work Station

This study aims to determine the number of ideal operators according to the company condition. This research uses work measurement method and analyzed by Yamazumi Chart. The research object is three selected operators from the final inspection work station. The study results concluded that, the new Takt time conditions for 120 seconds, then the company allocates 3 operators, the average work loading or it’s Manpower efficiency in that part is only 60 percent. But if the company allocates 2 operators only, the average work loading or man power efficiency reaches 90 percent. Based on the calculation according to the method used, then the operator needs in that part with the new Takt time conditions are 2 operators. Keywords—manpower; takt time; work measurement; yamazumi chart
Single event upset mitigation for FPGA based low density parity check decoder

With the increasing of data rates and physical limitation defined by channel capacity, communication systems have to be designed with high efficiency and reliability. LDPC codes have emerged over the last decades and became a key component of many commercialized systems as a benefit of their excellent performance and suitability to parallel hardware implementation. Under that scenario, FPGA-based decoders have been exploited since these devices offer rapid prototyping and high levels of parallelism. FPGAs, as any semiconductor device, have become sensitive to radiation due to the continual evolution of fabrication technology, such as device shrinkage, power supply reduction and increasing operating speeds. FPGAs’ cells are especially susceptible to single event upsets (SEUs) and fault tolerance techniques must be applied in order to mitigate their effects. In this work, it is presented a study about the effects of SEUs in an FPGA-based LDPC decoder and it is proposed a selective technique to improve reliability in this specific application.
Using deep learning algorithms to detect violent activities

It is of extensive importance to develop a technique for automatic surveillance video analysis to recognize the presence of violence. In this work, to identify violent videos, we put forward a deep neural network. For extracting frame level features from a video, a convolutional neural network is used with a pre-trained ImageNet model. The characteristics of the frame level are then aggregated using a long short-term memory variant that uses fully connected layers and leaky rectified linear units. Together with the long short-term memory, the convolutional neural network is capable of capturing localized spatio-temporal features that enable the analysis of local motion in the video. The performance is further evaluated in terms of accuracy of recognition on three standard benchmark datasets. In order to determine the capabilities of our proposed model, we also compared our system results with other techniques. The approach proposed outperforms state-of the-art methods while processing the videos in real time.
Web-Based intelligent tutoring for Prolog following the learning theory of constructivism.

Intelligent tutoring systems (ITS) incorporate techniques for transferring knowledge and skills to students. These systems are based on a combination of computer-aided instructions and artificial intelligence techniques. This thesis studies and develops a web-based intelligent tutoring system for Prolog following the learning theory of constructivism. Our thesis studies among others the kind of programming knowledge, which is required in the learning process by learners (programming techniques, program schemata, etc.), how this knowledge is represented, and how this knowledge can be applied in an effective way. The constructivism theory is encapsulated into our system in order to have high quality learning outcomes. This theory is based on the fundamental principles of interaction, construction, assimilation and knowledge consolidation. These four principles have been taken into account in the development of our system. As such, the learning is performed through interaction of the students/learners with the system. The learning process is guided by iteratively constructing solutions on assigned problems. Our system instructs learners by giving guidelines on how they can construct a solution to their problem. In addition, each learner can try a set of exercises and the system checks the correctness of the provided answers. In case of erroneous ones, the system gives appropriate instructions to the student in order to reconstruct a correct answer. Many learning decisions are taken by the learner rather than by the system due to the constructivism theory. The practice and the assessment modules support the principles of assimilation and knowledge consolidation. In the practice module, the learner has to practice the theory he learnt on a set of exercises by choosing a practice method from the available ones. In the assessment module, the learner has to answer a set of exercises on what he has already learnt. The main novelty of our system is its flexibility to adapt to individual student choices and profile, offering a wide range of alternatives and trying to continuously retain the interest of learners. Finally, a preliminary evaluation of our system confirms its usability and its benefits of using it for learning Prolog.