Miracle-dz/UniFAR-FTUs huggingface dataset:
A Task Offloading Framework for Energy Saving on Mobile Devices using Cloud Computing

Over the last decade, mobile devices have become popular among people, and their number is ever growing because of the computing functionality they offer beyond primary voice communication. However, mobile devices are unable to accommodate most of the computing demand as long as they suffer the limited energy supply caused by the capacity of their small battery to store only a relatively small amount of energy. The literature describes several specialist techniques proposed in academia and industry that save the mobile device energy and solve this problem to some extent but not satisfactorily. Task offloading from mobile devices to cloud computing is a promising technique for tackling the problem especially with the emergence of high-speed wireless networks and the ubiquitous resources from the cloud computing. Since task offloading is in its nascent age, it lacks evaluation and development in-depth studies. In this dissertation, we proposed an offloading framework to make task offloading possible to save energy for mobile devices. We achieved a great deal of progress toward developing a realistic offloading framework. First, we examined the feasibility of exploiting the offloading technique to save mobile device energy using the cloud as the place to execute the task instead of executing it on the mobile device. Our evaluation study reveals that the offloading does not always save energy; in cases where the energy for the computation is less than the energy for communication no energy is saved. Therefore, the need for the offloading decision is vital to make the offloading beneficial. Second, we developed mathematical models for the energy consumption of a mobile device and its applications. These models were then used to develop mathematical models that estimate the energy consumption on the networking and the computing activities at the application level. We modelled the energy consumption of the networking activity for the Transmission Control Protocol (TCP) over Wireless Local Area Network (WLAN), the Third Generation (3G), and the Fourth Generation (4G) of mobile telecommunication networks. Furthermore, we modelled the energy consumption of the computing activity for the mobile multi-core Central Processing Unit (CPU) and storage unit. Third, we identified and classified the system parameters affecting the offloading decision and built our offloading framework based on them. In addition, we implemented and validated the proposed framework experimentally using a real mobile device, cloud, and application. The experimental results reveal that task offloading is beneficial for mobile devices given that in some cases it saves more than 70% of the energy required to execute a task. Additionally, our energy models accurately estimate the energy consumption for the networking and computing activities. This accuracy allows the offloading framework to make the correct decision as to whether or not offloading a given task saves energy.
A Novel Design and Implementation of Hybrid Lut/Multiplexer For Fpga Logic Architectures

Hybrid configurable logic block architectures for field-programmable gate arrays that contain a mixture of lookup tables and hardened multiplexers are evaluated toward the goal of higher logic density and area reduction. Multiple hybrid configurable logic block architectures, both nonfracturable and fracturable with varying MUX:LUT logic element ratios are evaluated across two benchmark suites (VTR and CHStone) using a custom tool flow consisting of LegUp-HLS, Odin-II front-end synthesis, ABC logic synthesis and technology mapping, and VPR for packing, placement, routing, and architecture exploration. Technology mapping optimizations that target the proposed architectures are also implemented within ABC. Experimentally, we show that for nonfracturable architectures, without any mapper optimizations, we naturally save up to ∼ 8% area postplace and route; both accounting for complex logic block and routing area while maintaining mapping depth. With architecture-aware technology mapper optimizations in ABC, additional area is saved, post-place-and-route. For fracturable architectures, experiments show that only marginal gains are seen after place-and-route up to ∼ 2%. For both nonfracturable and fracturable architectures, we see minimal impact on timing performance for the architectures with best area-efficiency.
Mixed Integer Linear Programming for Maintenance Scheduling in Power System Planning

This paper discussed the merit of mixed-integer linear programming (MILP)-based approach against Lagrangian relaxation (LR)-based approach in solving generation and transmission maintenance scheduling problem. MILP provides a straightforward solution by formulating coupling constraints equations so that these sub-problems can be solved simultaneously without involving multipliers. In LR-based approach, generation and transmission maintenance scheduling, and security-constrained unit commitment have been solved individually and the integration was realized through a series of multipliers which has caused computational burden to the system. Numerical case studies were evaluated on the 6-bus system. A comparative study is carried out between the MILP and LR approaches. Simulation results indicate that the maintenance schedule derived by the proposed MILP approach outperforms the LR in terms of operational cost savings and gap tolerance. The operating cost could be saved up to 5% and the gap tolerance achieved is 0.01% as compared to 0.14% by LR.
Building an Online Community: Student Teachers’ Perceptions on the Advantages of Using Social Networking Services in A Teacher Education Program

This inquiry examined student teachers' perceptions on the advantages of using Social Networking Services (SNS) in an English teacher education program at a public university in Jambi, Indonesia to ease the communication, supervision, discussion, and report submissions between supervisors and student teachers. The networking types included in the program are Whatsapp, Telegram, Email, and Google Form. The method of the research was qualitative through using focus group discussions as the technique of collecting data involving forty-two student teachers. We organized our analysis and discussion around their perceptions and the contexts in which the advantages they perceived emerge. The analyses of the texts revealed that two salient themes with their sub-themes related to the advantages of using Social Networking Services (SNS) in a teacher education program were social interaction (peer discussion and platform to interact with supervisors or lecturers) and learning motivation and experience supports (self-directed learning, promotes critical thinking, content engagement). Some pedagogical and social implications are also discussed.
Features of Gaze Control Systems

Severely disabled people will often spend a significant part of their waking day using gaze control. Technology has a positive impact on many areas of their life. What simple features do people who have severe and complex disabilities need to use gaze control technology? In this chapter, we consider features that are enhancing the effective use of this innovative and rapidly growing method of computer control. It also provides practical hints in finding and choosing the best gaze control system for each individual. DOI: 10.4018/978-1-61350-098-9.ch004
Diagnosis of Motor Habits during Backward Fall with Usage of Rotating Training Simulator

There are jobs with high risk of a fall. It seems reasonable to create a device for diagnosis and improving safe fall skills for workers. The aim of the present study was a verifica tion of usability of a rotating training simulator for assessing motor habits during a fall caused by an external force by conducting validation procedure. Material and Methods: the participants were chosen from a group of 128 students of physical education of the University of Zielona Góra. Predictive validity was determined by comparing results of immediate fall test (IFT) to forced fall test (FFT). Repeatability was determined by conduction test/retest conditions. Reliability was also determined by comparing grades given by two observers with those given by an expert. Results: the acquired results show that there were no significant differences between results of IFT and FFT tests conditions and also no significant differences between test/retest conditions separetly for IFT and FFT, alongside with moderate correlation of its results. Good and excellent reliability ICC values were obtained for observers and experts (from r = 0.853 to 1.00). Summary: the obtained results show that the rotating training simulator is a valid and reliable tool for diagnosing motor habits during a fall caused by an external force.
A Multi-Objective Evolutionary Algorithm Based Optimization Model for Network-on-Chip Synthesis

Network on chip (NoC) is a new paradigm for design core based system on chip (SoC) which supports high degree of reusability and provides increase computation power. This paper addresses the problem of topological mapping of intellectual properties (IPs) on the tiles of a mesh-based NoC in two systematic steps using multi-objective evolutionary algorithm. The main objective is to obtain the Pareto mappings that minimized the energy consumption (computational and communicational) and link bandwidth requirement under performance constraints. The evaluation performed on three randomly generated benchmarks and a real application (M-JPEG encoder) to conform the efficiency, accuracy and scalability of the proposed approach. Our proposed approach saves up to (15%-20%) of energy and bandwidth requirements in comparison to the existing approaches
Visual-Linguistic Semantic Alignment: Fusing Human Gaze and Spoken Narratives for Image Region Annotation

Advanced image-based application systems such as image retrieval and visual question answering depend heavily on semantic image region annotation. However, improvements in image region annotation are limited because of our inability to understand how humans, the end users, process these images and image regions. In this work, we expand a framework for capturing image region annotations where interpreting an image is influenced by the end user’s visual perception skills, conceptual knowledge, and task-oriented goals. Human image understanding is reflected by individuals’ visual and linguistic behaviors, but the meaningful computational integration and interpretation of their multimodal representations (e.g. gaze, text) remain a challenge. Our work explores the hypothesis that eye movements can help us understand experts’ perceptual processes and that spoken language descriptions can reveal conceptual elements of image inspection tasks. We propose that there exists a meaningful relation between gaze, spoken narratives, and image content. Using unsupervised bitext alignment, we create meaningful mappings between participants’ eye movements (which reveal key areas of images) and spoken descriptions of those images. The resulting alignments are then used to annotate image regions with concept labels. Our alignment accuracy exceeds baseline alignments that are obtained using both simultaneous and a fixed-delay temporal correspondence. Additionally, comparison of alignment accuracy between a method that identifies clusters in the images based on eye movements and a method that identifies
Gaussian Isoperimetry for Multiple Sets

We discuss a few conjectural Gaussian isoperimetric inequalities where traditional symmetrization or heat flow methods fail. Generally speaking, we are trying to maximize a convex function on a convex domain. So, any method that tries to maximize a function by moving in the direction of higher gradient should fail. Symmetrization and heat flow methods seem to fall into this category, so we need to develop new approaches to our problems. The central object of study is the Ornstein-Uhlenbeck operator, i.e. the heat operator on Euclidean space equipped with the Gaussian measure. In this setting, Fourier analysis with respect to Hermite polynomials becomes most relevant. Due to the relation of the hypercube and the Gaussian measure via Central Limit Theorems, these Gaussian isoperimetric results imply inequalities for discrete functions, which then have applications to theoretical computer science. The first problem we discuss asks for the maximum of the sum squared firstorder Hermite-Fourier coefficients over all partitions of Euclidean space. The second problem we discuss asks for the most noise stable partitions of Euclidean space. The latter problem is a generalization of the so-called Gaussian double bubble problem. We give partial solutions to these two problems, and we also give some negative results for the latter problem.
A Content Analysis of Virtual Reality Studies in Foreign Language Education

The use of Virtual Reality in education has been highly prevalent in recent years. In nearly every domain of education, integration of Virtual Reality has been ongoing including foreign language teaching and learning. There is a huge shift in teaching and learning facilities from conventional classrooms to interactive ones. What makes Virtual Reality special in comparison to other technologies are immersion, interaction and involvement properties. Therefore, the purpose of this study was to analyze the studies on foreign language learning and teaching through virtual reality technology and to highlight the current trends on this topic, thus propose some suggestions for future researchers of this field. Document analysis method was used in this study. Like other analytical methods in a qualitative research, document analysis requires that data be examined and interpreted in order to elicit meaning, gain understanding, and develop empirical knowledge. 40 research papers published between the years 1995 and 2015 were scanned in terms of data collection tools, research design, sample, sample size, data analysis method, and the topic of the paper. The results of the study revealed that document analysis was in the first place in terms of data collection tool and half of the studies were qualitative. In addition, undergraduate population was primarily the focus of attention. As from sample size, 101-300 size was primarily used. Moreover, effectiveness of virtual reality and game-based learning were the two outstanding topics of these studies.
Similarity Learning and Stochastic Language Models for Tree-Represented Music

Similarity computation is a difficult issue in music information retrieval tasks, because it tries to emulate the special ability that humans show for pattern recognition in general, and particularly in the presence of noisy data. A number of works have addressed the problem of what is the best representation for symbolic music in this context. The tree representation, using rhythm for defining the tree structure and pitch information for leaf and node labelling has proven to be effective in melodic similarity computation. In this dissertation we try to built a system that allowed to classify and generate melodies using the information from the tree encoding, capturing the inherent dependencies which are inside this kind of structure, and improving the current methods in terms of accuracy and running time. In this way, we try to find more efficient methods that is key to use the tree structure in large datasets. First, we study the possibilities of the tree edit similarity to classify melodies using a new approach for estimate the weights of the edit operations. Once the possibilities of the cited approach are studied, an alternative approach is used. For that a grammatical inference approach is used to infer tree languages. The inference of these languages give us the possibility to use them to classify new trees (melodies).
Optimizing Mixed Fuzzy-Rule Formation by Controlled Evolutionary Strategy

—Machine learning algorithms are heavily applied to address many challenges in various ﬁelds. This paper speciﬁcally takes a look at use cases from the health sector, as well as the industry 4.0 sector. In both cases, the knowledge about the clas-siﬁcation process is as important as the classiﬁcation itself. One current problem is the disregard of expert knowledge provided by adept human beings. In practice, it is possible and also feasible to learn similar knowledge with machine learning algorithms like artiﬁcial neural networks (ANNs) or support vector machines (SVMs). However, time and money could be saved if this expert knowledge was used directly. Right now, this is only possible with more transparent algorithms like rule-based systems or decision trees, where knowledge can be incorporated relatively easily. The approach of this paper shows that rules generated by a mixed fuzzy-rule formation algorithm can be optimized by applying a controlled evolutionary strategy while maintaining the interpretability of the decision-making process. The evaluation is performed by executing the evolutionary strategy proposed in this paper on data from two different industries.
Obstacles Perceived by Physical Education Teachers to Integrating ICT.

Teachers find difficulties and barriers about integrating technology in the physical education classroom. Therefore, the aim of this study is to analyse the perception of PE teachers regarding obstacles to integrating ICT and its relation with their age. The methodology followed was quantitative and descriptive in nature. The participants were 400 secondary education PE teachers. A face-to-face standardised interview was used through “EFYTICS” questionnaire. Results of the study showed that the most frequently perceived obstacles were: loss of time spent on physical activity, lack of resources, investment in time and training, unsuitable use, lack of knowledge, and technical problems. By age, these same obstacles were perceived, but in a different order. Improvement actions should be established so as to better integrate ICT, mainly to deal with those obstacles that are more greatly perceived by teachers, in order to use ICT in an educational way in the classroom and to take advantage of all of the benefits they offer to the subject.
Evaluating Cultural Dimensions to Design Better User Interfaces

approved: ______________________________________________________ Kenneth H. Funk II Cultural differences between designers and users is an important aspect to consider when designing products for other countries. However, many prominent authors in the human factors engineering field have failed to specifically address how to identify and utilize cultural differences in user interface (UI) design. This research evaluated if design guidelines based upon Hofstede’s cultural dimension model led to valid UI requirements. The two dimensions this study focused on were Power Distance and Uncertainty Avoidance. The researcher interpreted the related literature to create a set of guidelines for each dimension. These guidelines centered on how information was communicated between the UI and the user. The guidelines were then applied to the case study of a medical diagnostic application to create four sets of UI component pairs. An online survey was used to test for correlations between the usability of the UI component pairs and variations in the cultural dimension scores for the participants. Overall, it was found that the usability of the UI component pairs did not significantly correlate with the cultural dimension scores. This suggested that UI design guidelines, based on Hofstede’s cultural dimension model, did not produce valid UI requirements for communication of information between the UI and the user. However, the results of the study did provide evidence for when and how product designers should incorporate culturally localized design features. Additionally, the results suggested several avenues for future research. ©Copyright by Robert Miller June 3, 2016 All Rights Reserved Evaluating Cultural Dimensions to Design Better User Interfaces
PRINCIPLES OF LEXICAL MINIMUM SELECTION FOR TEACHING GENERAL TECHNICAL ENGLISH TO ENGINEERING STUDENTS

The article covers problems related to selecting lexical material for teaching engineers-to-be and surveys the history of the problem. It contains the definition of the "lexical minimum" as a term, and analyses principles of selecting the lexical minimum based both on topics covered by English for Technical Purposes program and requirements for the B2+ level which is the target level of studying English during the first two years on technical faculties. The article explains the importance of selecting the lexical minimum as it makes up the base for exercises used to form lexical competence in reading, speaking, listening, writing and translating. The absence of the lexical minimum makes it much more difficult for a teacher to control and evaluate the formation of the foreign language competence. Taking into account that selection of lexical minimum must be carried out with consideration of many factors (such as program requirements, learning environment and language skills) and certain principles and procedures, it is necessary to draw up an algorithm of actions to select lexical units. The article has four parts: an introduction explaining the topicality, aims and objectives of the article, two parts with the main research material and conclusions.
Sentiment analysis: a comparison of deep learning neural network algorithm with SVM and naϊve Bayes for Indonesian text

Deep learning is a new era of machine learning techniques that essentially imitate the structure and function of the human brain. It is a development of deeper Artificial Neural Network (ANN) that uses more than one hidden layer. Deep Learning Neural Network has a great ability on recognizing patterns from various data types such as picture, audio, text, and many more. In this paper, the authors tries to measure that algorithm’s ability by applying it into the text classification. The classification task herein is done by considering the content of sentiment in a text which is also called as sentiment analysis. By using several combinations of text preprocessing and feature extraction techniques, we aim to compare the precise modelling results of Deep Learning Neural Network with the other two commonly used algorithms, the Naϊve Bayes and Support Vector Machine (SVM). This algorithm comparison uses Indonesian text data with balanced and unbalanced sentiment composition. Based on the experimental simulation, Deep Learning Neural Network clearly outperforms the Naϊve Bayes and SVM and offers a better F-1 Score while for the best feature extraction technique which improves that modelling result is Bigram.
Phonology, Orthography, and Decoding Skills Within and Across English and Chinese

ABSTRACT The current study examined the construct of word-reading skills within and across English and Chinese. One hundred forty-three 5th graders who were native Chinese (Mandarin) speakers learning English as a second language completed 19 tasks representing phonological awareness, orthographic knowledge, and word decoding in both English and Chinese. Confirmatory factor analyses were conducted within each language, and the results showed that the best-fitting model for both languages was the 3-factor model involving phonological awareness, orthographic knowledge, and word decoding. Across the 2 languages, the language-specific model fit better than other models with 1 or 2 language-general factors. These results suggest that word reading is not a unified, language-general ability within and across English and Chinese among Chinese children learning English as a second language. Instruction and intervention may need to target phonological awareness, orthographic knowledge, and word decoding in each language.