Miracle-dz/UniFAR-FTUs huggingface dataset:
Context-Aware Data and Task Placement in Edge Computing Environments

Computationally intensive tasks of IoT applications can be offloaded to powerful devices in the edge. Code offloading reduces energy consumption and increases performance. However, applications that use face recognition, machine learning, or image rendering, rely on large amounts of data. The transfer of this data leads to latencies which contradicts the responsiveness required by many pervasive applications. As a solution, decoupling the data from the tasks allows to apply new scheduling strategies that place data on remote devices before the actual task execution. Grid computing approaches use this technique effectively, however, edge computing introduces further challenges such as device fluctuation and heterogeneity.In this paper, we propose a data management approach for edge computing environments that decouples data placement from task scheduling. We present a multi-level scheduler, which places data on resource providers in the system considering multiple context dimensions. The scheduler allocates tasks according to the current context and observes the state during runtime. If required, the system adjusts the number of data copies to optimize the trade-off between execution latencies and data management overhead. The paper has three contributions: (1) a context-aware multi-level scheduler, (2) the integration of four data placement, three task scheduling, and three runtime adaptation algorithms, (3) an evaluation in a real-world testbed.
New improvements in the optimization of the launcher ascent trajectory through the HJB approach

This study completes a previous one presented at EUCASS 2015. We investigate the resolution of the launcher ascent trajectory problem by the so-called Hamilton-Jacobi- Bellman (HJB) approach. The method gives a global optimum without any initialization procedure; nevertheless, the computational cost increases quickly for high dimension problems. To mitigate this, the payload mass was excluded from the parameters managed by the HJB equations. A new formulation is proposed to consider explicitly this parameter, while limiting the computation time. An illustration is proposed on a heavy class launcher, for a GEO mission. This study has been performed in the frame of the CNES Launchers Research Technology program.
A novel privacy- and integrity-preserving approach for multidimensional data range queries in two-tiered wireless sensor networks

To address the problem of storage nodes in wireless sensor networks being vulnerable to attack, this article proposes a new range query protocol, WQuery, based on multidimensional data privacy and integrity protection. To ensure the privacy of multidimensional data, the WQuery protocol encodes multidimensional sensing data and user query requests based on a bucket-partitioning scheme and sequential encryption mechanism at the sensor node and sink node. Therefore, the storage nodes cannot sense the real value of sensing data and query requests under the premise of correctly executing the request. To ensure the integrity of the multidimensional data query results, WQuery includes an integrity verification scheme based on the new tree-to-destination data structure. The scheme translates the actual query scope of a user request into a matched set of bucket labels and verifies the integrity of query results through the tree-to-destination data structure. Simulation results show that the tree-to-destination scheme is flexible, easy to implement, and highly secure, thereby effectively reducing energy consumption.
An Analysis on the Applicability of Meta-Heuristic Searching Techniques for Automated Test Data Generation in Automatic Programming Assessment

Automatic Programming Assessment (APA) has been gaining lots of attention among researchers mainly to support automated grading and marking of students’ programming assignments or exercises systematically. APA is commonly identified as a method that can enhance accuracy, efficiency and consistency as well as providing instant feedback on students’ programming solutions. In achieving APA, test data generation process is very important so as to perform a dynamic testing on students’ assignment. In software testing field, many researches that focus on test data generation have demonstrated the successful of adoption of Meta-Heuristic Search Techniques (MHST) so as to enhance the procedure of deriving adequate test data for efficient testing. Nonetheless, thus far the researches on APA have not yet usefully exploited the techniques accordingly to include a better quality program testing coverage. Therefore, this study has conducted a comparative evaluation to identify any applicable MHST to support efficient Automated Test Data Generation (ATDG) in executing a dynamic-functional testing in APA. Several recent MHST are included in the comparative evaluation combining both the local and global search algorithms ranging from the year of 2000 until 2018. Result of this study suggests that the hybridization of Cuckoo Search with Tabu Search and lévy flight as one of promising MHST to be applied, as it’s outperforms other MHST with regards to number of iterations and range of inputs.
Microinteractions: Designing with Details

Its the little things that turn a good digital product into a great one. With this practical book, youll learn how to design effective microinteractions: the small details that exist inside and around features. How can users change a setting? How do they turn on mute, or know they have a new email message? Through vivid, real-world examples from todays devices and applications, author Dan Saffer walks you through a microinteractions essential parts, then shows you how to use them in a mobile app, a web widget, and an appliance. Youll quickly discover how microinteractions can change a product from one thats tolerated into one thats treasured.Explore a microinteractions structure: triggers, rules, feedback, modes, and loops Learn the types of triggers that initiate a microinteraction Create simple rules that define how your microinteraction can be used Help users understand the rules with feedback, using graphics, sounds, and vibrations Use modes to let users set preferences or modify a microinteraction Extend a microinteractions life with loops, such as Get data every 30 seconds
On Phenomenography and Researching Online Tutor Support

Rapid developments of affordable information and communication technologies are seen as assisting a shift in pedagogy attitudes from a teacher-centred/content orientation to a student-centred/learning orientation, and combined with the various forms of technology-mediated interaction, influencing the roles and experiences for both online tutors and students. Within this context, there have been calls from researchers (Gibson, 2003; McCartan, 2000) to address the need of understanding the student’s perspective in technology-supported education settings. This paper contributes to the debate on how to understand and research the described experiences students give to phenomenon and aims to present the potential contribution of the phenomenographic approach (Marton, 1981; Marton & Booth 1997) for researching networked management learning phenomena on a theoretical and empirical level by connecting this theoretical discussion with current research on students experiences of online tutor support. Discussion of the considerations of using a phenomenographic approach within the context of this research is highlighted in this paper.
Detection of Industrial Machine Work Errors using LVQ Neural Network

In the world of industry, the utilization of technology machinery industry is one of the most important factors to facilitate the employment of human. However, an industrial machine does not work regardless of fault that can inhibit the production process and cause harm to the industry. This paper aims to detect errors with the industrial machine work to analyze the movement of industrial machinery in a video, at this stage of the process of preprocessing, image resizes, do segmentation method thresholding, and the morphological operations with the opening operation. The further step, the feature extraction performed by converting a binary image into vector data is used as input data in the classification process using Algorithm Learning Vector Quantization (LVQ) Neural Network version 1 and version 2. Research results obtained detection accuracy reached 100% for training using LVQ1 much higher than the results of the training using LVQ2 with an accuracy of only 67.59%.
Mobile Crowdsourcing in Smart Cities: Technologies, Applications, and Future Challenges

Local administrations and governments aim at leveraging wireless communications and Internet of Things (IoT) technologies to manage the city infrastructures and enhance the public services in an efficient and sustainable manner. Furthermore, they strive to adopt smart and cost-effective mobile applications to deal with major urbanization problems, such as natural disasters, pollution, and traffic congestion. Mobile crowdsourcing (MCS) is known as a key emerging paradigm for enabling smart cities, which integrates the wisdom of dynamic crowds with mobile devices to provide decentralized ubiquitous services and applications. Using MCS solutions, residents (i.e., mobile carriers) play the role of active workers who generate a wealth of crowdsourced data to significantly promote the development of smart cities. In this paper, we present an overview of state-of-the-art technologies and applications of MCS in smart cities. First, we provide an overview of MCS in smart cities and highlight its major characteristics. Second, we introduce the general architecture of MCS and its enabling technologies. Third, we study novel applications of MCS in smart cities. Finally, we discuss several open problems and future research challenges in the context of MCS in smart cities.
How Important is Communication Style in Chat Reference?

Abstract Chat operators sometimes struggle to find a communication style that balances approachability and warmth with professionalism and authority. Both formal and informal communication styles have benefits and drawbacks that may affect patron satisfaction. Consequently, it may be difficult for chat operators to decide how to communicate with users, and for chat service managers to decide if communication style should be part of a service’s training and policies. This article reports on a study of chat transcripts from a collaborative chat reference service in Ontario, Canada. We compared operator communication style with users’ willingness to return, and included user and query type as moderating variables. The study used two samples: 299 randomly selected interactions and 217 interactions with negative or neutral exit survey responses. Chi square tests of independence showed a significant relationship between communication style and willingness to return in the dissatisfied sample, but not the random sample. There was a significant relationship between informality and willingness to return for faculty members and for research questions but only in the dissatisfied sample. We conclude that an operator’s choice to communicate either more formally or informally will not make users more satisfied, but it can help avoid leaving them dissatisfied, especially for faculty members and during research-intensive questions.
ACTIVATION DETECTION IN FMRI IMAGES USING NONLINEAR MODELS: WIENER-HAMMERSTEIN AND NARMA

Functional magnetic resonance imaging (fMRI) is widely used for investigation of brain neural activity. This imaging technique obtains signals and images from human brain’s response to prescheduled tasks. Several studies on blood oxygenation level-dependent (BOLD) signal responses demonstrate nonlinear behavior in response to a stimulus. In this paper we investigate nonlinear modeling of BOLD signal activity to model the nonlinear and time variant behaviors of this physiological system. For this purpose two categories of nonlinear methods are considered, first those one with emphasis on physiological parameters which affect BOLD response and methods model the input and output of system without any refer to all the hidden state variables (physiological parameters. Balloon model is analzyed and a new approach for activation detection based on this model is introduced. In addition, the Hammerstein-Wiener, NARMA and Volterra kernels are investigated as nonlinear and nonphysiological methods and their ability in detection of activation detection are compared. The Activation detection methods have been applied on the two data sets (real and synthetic). For synthetic data and threshold equal to 0.45, the Jaccard index for WienerHammerstein, NARMA, and Volterra model was 0.9, 1.0, and 0.91, respectively. In real dataset and for optimal threshold (0.35, 0.4, and 0.45) the same index was 0.85, 0.90, and 0.87, respectively.
Generating Fluent Adversarial Examples for Natural Languages

Efficiently building an adversarial attacker for natural language processing (NLP) tasks is a real challenge. Firstly, as the sentence space is discrete, it is difficult to make small perturbations along the direction of gradients. Secondly, the fluency of the generated examples cannot be guaranteed. In this paper, we propose MHA, which addresses both problems by performing Metropolis-Hastings sampling, whose proposal is designed with the guidance of gradients. Experiments on IMDB and SNLI show that our proposed MHAoutperforms the baseline model on attacking capability. Adversarial training with MHA also leads to better robustness and performance.
Implicit Learning of Musical Performance Parameters

This paper describes our attempt to make the Hidden Markov Model (HMM) score following system developed at IRCAM sensible to past experiences in order to adapt itself to a certain style of performance of musicians on a particular piece. We focus mostly on the aspects of the implemented machine learning technic pertaining to the style of performance of the score follower. To this end, a new observation modeling based on Gaussian Mixture Models is developed which is trainable using a novel learning algorithm we would call automatic discriminative training. The novelty of this system lies in the fact that this method, unlike classical methods for HMM training, is not concerned with modeling the music signal but with correctly choosing the sequence of music events that was performed.
A Comparative Study of DCT and DWT Image Compression Techniques combined with Huffman coding

لقد شاع استخدام تقنيات ضغط الصور لتخزين البيانات ونقلها، الأمر الذي يتطلب حيزا تخزينيا كبيرا وسرعة نقل عالية. ويؤدي النمو السريع للصور عالية الجودة إلى تنامي الطلب على تقنيات فعالة لتخزين البيانات وتبادلها عبر الانترنت. في هذه الورقة البحثية، نقدم دراسة مقارنة بين خوارزميات تقنيتي DCT و DWT مع استخدام ترميز هوفمان. والمقارنة في هذه الدراسة مبنية على خسمة عوامل: معدل الضغط، ومتوسط مربع الخطأ (MSE)، وأعلى نسب الأشارة إلى الضجيج (PSNR)، ومقياس عامل التشابه البنيوي (SSIM)، وزمن الضغط/ازالة الضغط.
Strategies for Streaming Media Applications Using TCP-Friendly Rate Control

This document discusses strategies for using streaming media applications with unreliable congestion-controlled transport protocols such as the Datagram Congestion Control Protocol (DCCP) or the RTP Profile for TCP Friendly Rate Control. Of particular interest is how media streams, which have their own transmit rate requirements, can be adapted to the varying and sometimes conflicting transmit rate requirements of congestion control protocols such as TCP-Friendly Rate Control (TFRC).
Streaming stochastic variational Bayes; An improved approach for Bayesian inference with data streams

Online learning is an essential tool for predictive analysis based on continuous, endless data streams. Adopting Bayesian inference for online settings allows hierarchical modeling while representing the uncertainty of model parameters. Existing online inference techniques are motivated by either the traditional Bayesian updating or the stochastic optimizations. However, traditional Bayesian updating suffers from overconfidence posteriors, where posterior variance becomes too inadequate to adapt to new changes to the posterior. On the other hand, stochastic optimization of variational objective demands exhausting additional analysis to optimize a hyperparameter that controls the posterior variance. In this paper, we present ''Streaming Stochastic Variational Bayes" (SSVB)—a novel online approximation inference framework for data streaming to address the aforementioned shortcomings of the current state-of-the-art. SSVB adjusts its posterior variance duly without any user-specified hyperparameters while efficiently accommodating the drifting patterns to the posteriors. Moreover, SSVB can be easily adopted by practitioners for a wide range of models (i.e. simple regression models to complex hierarchical models) with little additional analysis. We appraised the performance of SSVB against Population Variational Inference (PVI), Stochastic Variational Inference (SVI) and Black-box Streaming Variational Bayes (BB-SVB) using two non-conjugate probabilistic models; multinomial logistic regression and linear mixed effect model. Furthermore, we also discuss the significant accuracy gain with SSVB based inference against conventional online learning models for each task.
Efficient Encoding and Decoding of Binaural Sound with Resonance Audio

Resonance Audio is an open source project designed for creating and controlling dynamic spatial sound in Virtual & Augmented Reality (VR/AR), gaming or video experiences. It also provides integrations with popular game development platforms and digital audio workstations (as a preview plugin). Resonance Audio binaural decoder is used in YouTube to provide binaural rendering of 360/VR videos. This paper describes the core sound spatialization algorithms used in Resonance Audio and can be treated as a companion to the Resonance Audio C++ / MATLAB library source code.
Fast and Low Bit-Rate AMBTC Image Compression Scheme

Abstract In this paper, a low bit rate image compression scheme based on the absolute moment block truncation coding (AMBTC) is presented. In this scheme, a combination of three techniques, four-level block prediction technique, residual block coding technique and interpolative bit plane coding technique, are used. Experimental results show that the proposed scheme achieves better image quality at low bit rate with less computational cost than the existing low bit rate scheme.
Study on the 3D Reconstruction Technology Based on Point Cloud

With the rapid development of various three dimensional scanning devices, the 3D point cloud data of many objects in the real world can be easily obtained. Therefore, research on 3D reconstruction technology based on point cloud has important practical significance. In this paper, first the research background of point cloud reconstruction is analyzed; and then the typical methods for reconstruction are presented and discussed; finally the performance of these methods are analyzed and compared qualitatively, and the development trend in this field is prospected.
Inducing Document Structure for Aspect-based Summarization

Automatic summarization is typically treated as a 1-to-1 mapping from document to summary. Documents such as news articles, however, are structured and often cover multiple topics or aspects; and readers may be interested in only some of them. We tackle the task of aspect-based summarization, where, given a document and a target aspect, our models generate a summary centered around the aspect. We induce latent document structure jointly with an abstractive summarization objective, and train our models in a scalable synthetic setup. In addition to improvements in summarization over topic-agnostic baselines, we demonstrate the benefit of the learnt document structure: we show that our models (a) learn to accurately segment documents by aspect; (b) can leverage the structure to produce both abstractive and extractive aspect-based summaries; and (c) that structure is particularly advantageous for summarizing long documents. All results transfer from synthetic training documents to natural news articles from CNN/Daily Mail and RCV1.
Making A pplications in KSWorld

We report on our experiences in creating a GUI framework called KSWorld, which supports an interactive and declarative manner of application writing. The framework embodies direct manipulation, a high degree of loose coupling, and time-aware execution derived from Functional Reactive Programming (FRP). We also describe how a universal document editor was developed in this framework. The fields, or slots, of graphical widgets in KSWorld are reactive variables. Definitions of such variables can be added or modified in a localized manner, allowing on-the-fly customization of the visual and behavioral aspects of widgets and entire applications. Thus the KSWorld environment supports highly exploratory application building: a user constructs the appearance interactively with direct manipulation, then attaches and refines reactive-variable definitions to achieve the desired overall behavior. We also show that the system scales up sufficiently to support a universal document editor. About 10,000 lines of code were needed to build the framework, the FRP evaluator, the document model and the editor, including the implementation of the special language created for KSWorld.
A new Gaussian elimination-based algorithm for parallel solution of linear equations

A variant of Gaussian elimination (GE) method called the successive Gaussian elimination (SGE) algorithm, for the parallel solution of linear equations, is presented. Unlike the conventional GE algorithm, the SGE algorithm does not have a separate back-substitution phase-which requires O(N) steps using O(N) processors or O(log/sub 2//sup 2/N) steps using O(N/sup 3/) processors-for solving a system of N linear algebraic equations. The SGE algorithm replaces the back-substitution phase by only one step-division-and possesses numerical stability through partial pivoting. Finally, an efficient scheduling scheme for assigning the computational tasks in the SGE algorithm on to the processors in a multiprocessor system is given. >
Determination of the evaluation criteria for agritourism via delphi and analytic hierarchy process methods: a case study in Turkey

The population is decreasing in the regions where agricultural lands are prevalent, agricultural areas are not used effectively and efficiently, and both the afore-said regions and the people living there show a recession with each passing day in terms of economic, social and cultural aspects. This study examines from a different angle the agritourism which can be considered as an alternative option for the creation of a significant impact which may decelerate, stop or even reverse the above-mentioned recession. Accordingly, the main objective of the study is to determine and weight the evaluation criteria for agritourism. Decision-making techniques such as Delphi and Analytic Hierarchy Process methods have been used for this purpose. 52 evaluation criteria have been determined by common consent through a Delphi study consisting of three rounds. The Analytic Hierarchy Process (AHP) method has been used for the determination of the priorities in the specified criteria and for the calculation of their weights. In this way, it has been aimed to contribute to the studies that will be carried out in order to determine the existence of agritourism potentials in any region.