Reviewerly/exHarmony huggingface dataset:
Towards building robust DNN applications

Data augmentation techniques that increase the amount of training data by adding realistic transformations are used in machine learning to improve the level of accuracy. Recent studies have demonstrated that data augmentation techniques improve the robustness of image classification models with open datasets; however, it has yet to be investigated whether these techniques are effective for industrial datasets. In this study, we investigate the feasibility of data augmentation techniques for industrial use. We evaluate data augmentation techniques in image classification and object detection tasks using an industrial in-house graphical user interface dataset. As the results indicate, the genetic algorithm-based data augmentation technique outperforms two random-based methods in terms of the robustness of the image classification model. In addition, through this evaluation and interviews with the developers, we learned following two lessons: data augmentation techniques should (1) maintain the training speed to avoid slowing the development and (2) include extensibility for a variety of tasks.
Ternary Propagation-Based Local Search for more Bit-Precise Reasoning

Current state of the art for reasoning about quantifier-free bit-vector constraints in Satisfiability Modulo Theories (SMT) is a technique called bit-blasting, an eager translation into propositional logic (SAT). While efficient in practice, it may not scale for large bit-widths when the input size cannot be sufficiently reduced with preprocessing techniques. A recent propagation-based local search procedure was shown to be effective on hard satisfiable instances, in particular in combination with bit-blasting in a sequential portfolio setting. However, a major weakness of this approach is its obliviousness to bits that can be simplified to constant values. In this paper, we generalize propagation-based local search with respect to such constant bits to ternary values. We further extend the procedure to handle more bit-vector operators, and introduce heuristics for more precise inverse value computation via bound tightening for inequality constraints. We provide an extensive experimental evaluation and show that the presented techniques yield a considerable improvement in performance.
Hot question prediction in Stack Overflow

Stack Overflow is a very popular programming question and answer community. Some questions become hot, and receive high views, which are of widespread concern to developers. Finding hot questions early can give priority to recommend potential hot questions to answers, thereby shortening the response time. Besides, the hot question prediction is also helpful for making advertising plan, planning advertising campaigns and estimating costs. Therefore, it is important to predict hot questions. The authors propose the VSAF method which analyses the View amount changes, Answer amount changes and Score changes soon after questions' creation based on Fully convolutional neural network. The performance of the VSAF method based on a training set and two different test sets has been evaluated. The training set has 1600 hot questions and 1600 cold questions. The random test set has 381 hot questions and 2819 cold questions, while the balanced test set has 400 hot questions and 400 cold questions. The experimental results show that using the balanced test set, VSAF achieves Accuracy, F1hot and F1cold of 80%, 77.77% and 81.81%, which outperforms the baseline approach by 25.59%, 21.52% and 29.04%, respectively. Using the random test set for evaluation, VSAF achieves Accuracy, F1hot and F1cold of 84.91%, 53.96% and 90.97%, which outperforms the baseline approach by 31.83%, 84.16% and 19.35%, respectively. The VSAF method significantly outperforms the state-of-the-art approach on hot question prediction.
Proof Theory of Partially Normal Skew Monoidal Categories

The skew monoidal categories of Szlachányi are a weakening of monoidal categories where the three structural laws of left and right unitality and associativity are not required to be isomorphisms but merely transformations in a particular direction.In previous work, we showed that the free skew monoidal category on a set of generating objects can be concretely presented as a sequent calculus.This calculus enjoys cut elimination and admits focusing, i.e. a subsystem of canonical derivations, which solves the coherence problem for skew monoidal categories.In this paper, we develop sequent calculi for partially normal skew monoidal categories, which are skew monoidal categories with one or more structural laws invertible.Each normality condition leads to additional inference rules and equations on them.We prove cut elimination and we show that the calculi admit focusing.The result is a family of sequent calculi between those of skew monoidal categories and (fully normal) monoidal categories.On the level of derivability, these define 8 weakenings of the I,⊗ fragment of intuitionistic non-commutative linear logic.
The 2-adic complexity of Yu-Gong sequences with interleaved structure and optimal autocorrelation magnitude

In 2008, a class of binary sequences of period $$N=4(2^k-1)(2^k+1)$$ with optimal autocorrelation magnitude has been presented by Yu and Gong based on an m-sequence, the perfect sequence (0, 1, 1, 1) of period 4 and interleaving technique. In this paper, we study the 2-adic complexity of these sequences. Our result shows that it is larger than $$N-2\lceil \mathrm {log}_2N\rceil +4$$ (which is far larger than N/2) and could attain the maximum value N if suitable parameters are chosen, i.e., the 2-adic complexity of this class of interleaved sequences is large enough to resist the Rational Approximation Algorithm.
Digital Repository "Geologyscience.Ru": Open Access To Scientific Publications On Russian Geology

The article describes new approaches related to the collection of data from heterogeneous information systems of access to scientific publications using open international standards and protocols for the formation of systems of open access to scientific geological publications. Based on developed and adapted approaches and technological solutions, a set of programs of information and analytical system of access to scientific publications has been implemented, implementing functions of collection, search, cataloguing, filtering and management of scientific publications and their metadata.
Federated Imitation Learning: A Privacy Considered Imitation Learning Framework for Cloud Robotic Systems with Heterogeneous Sensor Data

Humans are capable of learning a new behavior by observing others perform the skill. Robots can also implement this by imitation learning. Furthermore, if with external guidance, humans will master the new behavior more efficiently. So how can robots implement this? To address the issue, we present Federated Imitation Learning (FIL) in the paper. Firstly, a knowledge fusion algorithm deployed on the cloud for fusing knowledge from local robots is presented. Then, effective transfer learning methods in FIL are introduced. With FIL, a robot is capable of utilizing knowledge from other robots to increase its imitation learning. FIL considers information privacy and data heterogeneity when robots share knowledge. It is suitable to be deployed in cloud robotic systems. Finally, we conduct experiments of a simplified self-driving task for robots (cars). The experimental results demonstrate that FIL is capable of increasing imitation learning of local robots in cloud robotic systems.
A denoising and enhancing method framework for 4D ultrasound images of human fetal heart

4D ultrasound images of human fetal heart are important for medical applications such as evaluation of fetal heart function and early diagnosis of congenital heart diseases. However, due to the high noise and low contrast characteristics in fetal ultrasound images, denoising and enhancements are important.In this paper, a special method framework for denoising and enhancing is proposed. It consists of a 4D-NLM (non-local means) denoising method for 4D fetal heart ultrasound image sequence, which takes advantage of context similar information in neighboring images to denoise the target image, and an enhancing method called the Adaptive Clipping for Each Histogram Pillar (ACEHP), which is designed to enhance myocardial spaces to distinguish them from blood spaces.Denoising and enhancing experiments show that 4D-NLM method has better denoising effect than several classical and state-of-the-art methods such as NLM and WNNM. Similarly, ACEHP method can keep noise level low while enhancing myocardial regions better than several classical and state-of-the-art methods such as CLAHE and SVDDWT. Furthermore, in the volume rendering after the combined "4D-NLM+ACEHP" processing, the cardiac lumen is clear and the boundary is neat. The Entropy value that can be achieved by our method framework (4D-NLM+ACEHP) is 4.84.Our new framework can thus provide important improvements to clinical fetal heart ultrasound images.
Energy-Efficient Accelerator Design With Tile-Based Row-Independent Compressed Memory for Sparse Compressed Convolutional Neural Networks

Deep convolutional neural networks (CNNs) are difficult to be fully deployed to edge devices because of both memory-intensive and computation-intensive workloads. The energy efficiency of CNNs is dominated by convolution computation and off-chip memory (DRAM) accesses, especially for DRAM accesses. In this article, an energy-efficient accelerator is proposed for sparse compressed CNNs by reducing DRAM accesses and eliminating zero-operand computation. Weight compression is utilized for sparse compressed CNNs to reduce the required memory capacity/bandwidth and a large portion of connections. Thus, a tile-based row-independent compression (TRC) method with relative indexing memory is adopted for storing none-zero terms. Additionally, the workloads are distributed based on channels to increase the degree of task parallelism, and all-row-to-all-row non-zero element multiplication is adopted for skipping redundant computation. The simulation results over the dense accelerator show that the proposed accelerator achieves $1.79\times$ speedup and reduces 23.51%, 69.53%, 88.67% on-chip memory size, energy, and DRAM accesses of VGG-16.
Big Data Compression in Smart Grids via Optimal Singular Value Decomposition

The smart grid is a fully automatic delivery grid for electricity power with a two-way reliable flow of electricity and information among different equipment on the grid. With the rapid development of smart grids, smart meters and sensors are used to monitor the system and provide a wide reporting which produce a huge amount of data in various part of the grid. To logical manage this trouble, the presented paper proposes a new lossy data compression approach for big data compression. In the proposed method, at the first step, the optimal singular value decomposition (OSVD) is applied to a matrix that achieves the optimal number of singular values to the sending process and the other ones will be neglected. This goal is done due to the quality of retrieved data and the rate of compression ratio. In the presented scheme, to implementation of the optimization framework, various intelligent optimization methods are used to determine the number of optimal values in the elimination stage. The efficiency and capabilities of the proposed method are examined using the experimental dataset of several residential microgrid consumers and market dataset. Simulation results show the high performance and efficiency of the proposed model in smart grids with big data.
The Review of Mobile Applications and Wireless Technologies in Sustaining K-12 Schools during COVID-19

Mobile-learning (M-Learning) is no longer a futuristic concept in E-learning.The disruptions induced by the mandatory social distancing norms amidst the COVID-19 pandemic has brought it to the centre stage as it facilitates seamless delivery of education to mobile-users on the go.The ever increasing penetration of low-cost smartphones and remarkable advancement in wireless communication technologies has made M-learning ubiquitous and affordable.This paper highlights the roles that mass-adoption of mobile phone devices have played in sustaining K-12 schools.We review the impact that latest developments in communication technology and the deployment of LTE-WiFi coexistence, 5G, and Wi-Fi6 networks will have on M-learning applications.Further, we highlight the challenges that need to be addressed so that the higher penetration of mobile phones and deployment of wireless network technologies can be optimally harnessed for an improved M-learning experience.
La enseñanza de STEM en Educación Primaria desde una perspectiva de género.

The aim of this study is to identify the needs of 141 students and 67 teachers from seven Primary Education schools in relation to STEM teaching, detecting possible gender differences and similarities. This objective is part of the european CREATEskills project. On the basis of an exploratory and non-experimental research design, two validated questionnaires were used through a dual procedure (expert judgment and pilot study). The results indicate that teachers demand additional resources and training for STEM teaching, while students prefer to use digital resources and conduct experimental work. In relation to gender differences, female teachers adopted a more critical view of STEM teaching in comparison to male teachers, but no significant gender differences were identified among students.
CausalWorld: A Robotic Manipulation Benchmark for Causal Structure and Transfer Learning

Despite recent successes of reinforcement learning (RL), it remains a challenge for agents to transfer learned skills to related environments. To facilitate research addressing this, we propose CausalWorld, a benchmark for causal structure and transfer learning in a robotic manipulation environment. The environment is a simulation of an open-source robotic platform, hence offering the possibility of sim-to-real transfer. Tasks consist of constructing 3D shapes from a given set of blocks - inspired by how children learn to build complex structures. The key strength of CausalWorld is that it provides a combinatorial family of such tasks with common causal structure and underlying factors (including, e.g., robot and object masses, colors, sizes). The user (or the agent) may intervene on all causal variables, which allows for fine-grained control over how similar different tasks (or task distributions) are. One can thus easily define training and evaluation distributions of a desired difficulty level, targeting a specific form of generalization (e.g., only changes in appearance or object mass). Further, this common parametrization facilitates defining curricula by interpolating between an initial and a target task. While users may define their own task distributions, we present eight meaningful distributions as concrete benchmarks, ranging from simple to very challenging, all of which require long-horizon planning and precise low-level motor control at the same time. Finally, we provide baseline results for a subset of these tasks on distinct training curricula and corresponding evaluation protocols, verifying the feasibility of the tasks in this benchmark.
Toward a More Meaningful Transparency: Examining Twitter, Google, and Facebook’s Transparency Reporting and Removal Practices in Russia

Since 2010, telecommunications and internet companies have increased their use of transparency reports to gain user trust. These reports include data regarding government requests for user information, takedowns related to intellectual property, and government mandates to remove content. However, users and civil society criticize them for providing insufficient data about the companies’ content moderation practices and interactions with foreign governments. This paper empirically examines the transparency and removal practices of Twitter, Google, and Facebook in Russia. It identifies shortcomings in transparency reporting and suggests improvements to hold the companies accountable to their own transparency standards.
Data Mining and Artificial Intelligence Techniques Used to Extract Big Data Patterns

A lot of research and analysis has been done that focuses on the implementation, use, and evaluation of artificial intelligence techniques. The analysis is done on different techniques and variations of known methods regarding their characteristics like speed, performance, and effectiveness using scientific methods, statistics and mathematical proofs. On the other end of the spectrum, a lot of research has been done on high-level data mining as well. The research on data mining usually stops at technical implementations and focuses mainly on high-level techniques to manipulate the bulk of data to be mined. The physical implementation is usually abstracted and left for libraries to optimize. In order to use this research in the area of big data, the areas of AI and Data mining need to be conjoined so that the appropriate knowledge from both technical and conceptual areas is used. The purpose of this literature review is to systematically review the research done on both the technical and conceptual ends of the spectrum and to find the overlapping techniques. This is needed to get a clear understanding of the entire knowledge extraction process from big data to business value. The research results in a broad view of all techniques and their appropriateness towards big data. In order to make decisions on the techniques used for a specific data mining problem, a broad view of all available solutions is needed. This paper attempts to deliver it by investigating all possibilities and discuss their advantages and disadvantages relating to big data.
The use of partially observable Markov decision processes to optimally implement moving target defense

For moving target defense (MTD) to shift advantage away from cyber attackers, we need techniques which render systems unpredictable but still manageable.We formulate a partially observable Markov decision process (POMDP) which facilitates optimized MTD capable of thwarting cyber attacks without excess overhead.This paper describes POMDP formulation including the use of an absorbing final state and attack penalty scaling factor to abstract defender-defined priorities into the model.An autonomous agent leverages the POMDP to select the optimal defense based on assessed cyber-attack phase.We offer an example formulation wherein attack suppression of greater than 99% and system availability of greater than 94% were maintained even as probability of detection of attack phase dropped to 74%.
DLFT: Data and Layout Aware Fault Tolerance Framework for Big Data Transfer Systems

Various scientific research organizations generate several petabytes of data per year through computational science simulations. These data are often shared by geographically distributed data centers for data analysis. One of the major challenges in distributed environments is failure; hardware, network, and software might fail at any instant. Thus, high-speed and fault tolerant data transfer frameworks are vital for transferring such large data efficiently between the data centers. In this study, we proposed a bloom filter-based data aware probabilistic fault tolerance (DAFT) mechanism that can handle such failures. We also proposed a data and layout aware mechanism for fault tolerance (DLFT) to effectively handle the false positive matches of DAFT. We evaluated the data transfer and recovery time overheads of the proposed fault tolerance mechanisms on the overall data transfer performance. The experimental results demonstrated that the DAFT and DLFT mechanisms exhibit a maximum of 10% and a minimum of 2% recovery time overhead at 80% and 20% fault points respectively. However, we observed minimum to negligible overhead with respect to the overall data transfer rate.
E-ART: A New Encryption Algorithm Based on the Reflection of Binary Search Tree

Data security has become crucial to most enterprise and government applications due to the increasing amount of data generated, collected, and analyzed. Many algorithms have been developed to secure data storage and transmission. However, most existing solutions require multi-round functions to prevent differential and linear attacks. This results in longer execution times and greater memory consumption, which are not suitable for large datasets or delay-sensitive systems. To address these issues, this work proposes a novel algorithm that uses, on one hand, the reflection property of a balanced binary search tree data structure to minimize the overhead, and on the other hand, a dynamic offset to achieve a high security level. The performance and security of the proposed algorithm were compared to Advanced Encryption Standard and Data Encryption Standard symmetric encryption algorithms. The proposed algorithm achieved the lowest running time with comparable memory usage and satisfied the avalanche effect criterion with 50.1%. Furthermore, the randomness of the dynamic offset passed a series of National Institute of Standards and Technology (NIST) statistical tests.
Show, Don’t Tell. Reflections on the Design of Multi-modal Conversational Interfaces

Conversational Agents are the future of Human-Computer Interaction. Technological advancements in Artificial Intelligence and Natural Language Processing allow the development of Conversational Agents that support increasingly complex tasks. When the complexity increases, the conversation alone is no more sufficient to support the interaction effectively, but other modalities must be integrated to relieve the cognitive burden for the final user. To this aim, we define and discuss a set of design principles to create effective multi-modal Conversational Agents. We start from the best practices in literature for multi-modal interaction and uni-modal Conversational Interfaces to see how they apply in our context. Then, we validate our results with an empirical evaluation. Our work sheds light on a largely unexplored field and inspires the future design of such interfaces.
THE CHALLENGES OF TEACHING EFL FOR ADULT LEARNERS: ONLINE LEARNING DURING THE COVID-19 PANDEMIC

This study was conducted to determine the challenges of teaching EFL for adult learners online learning in UIN Mataram during the covid-19 pandemic. This study used a qualitative method. Data collection instruments are questionnaires and interviews. The findings of this study show that there are challenges for lecturers in teaching EFL for adult learners. In conclusion, this paper has shown so many responses about the challenges experienced by the lecturer, and students while studying online, such as; the positive and negative impact of online learning.
Intelligent Video Highlights Generation with Front-Camera Emotion Sensing

In this paper, we present HOMER, a cloud-based system for video highlight generation which enables the automated, relevant, and flexible segmentation of videos. Our system outperforms state-of-the-art solutions by fusing internal video content-based features with the user’s emotion data. While current research mainly focuses on creating video summaries without the use of affective data, our solution achieves the subjective task of detecting highlights by leveraging human emotions. In two separate experiments, including videos filmed with a dual camera setup, and home videos randomly picked from Microsoft’s Video Titles in the Wild (VTW) dataset, HOMER demonstrates an improvement of up to 38% in F1-score from baseline, while not requiring any external hardware. We demonstrated both the portability and scalability of HOMER through the implementation of two smartphone applications.