Reviewerly/exHarmony huggingface dataset:
Mobile learning applications for the elderly: eliciting requirements based on pedagogical and accessibility guidelines

In this research full paper we present a set of requirements for an m-learning application for the elderly. Currently, the elderly population has been growing considerably. This fact leads to the country and society adapt and create new opportunities for this audience, since they can present changes and challenges of aging. One of this challenges involves the educational area. Education can take place formally or informally, with face-to-face or distance classes and, in this context, emerges the concept of mobile learning (or m-learning). This modality of learning has generated great expectations for educators and learners, since it has benefits related to the flexibility of learning, especially regarding the adaptation with respect to space and time of use. Based on this potential for flexibility and adaptation, one of the main goals of m-learning is to democratize access to education. Thus, m-learning applications must be adequate and structured to serve the general population, including the elderly. Considering this audience, it is necessary to develop accessible and pedagogically appropriate m-learning applications, since these users may have different skills in comparison with younger students. Elderly people may have their physical, sensory, perception and cognitive abilities compromised due to the aging process and, therefore, m-learning applications must be developed based on these needs. In this paper we present the requirements elicitation process for a m-learning app, focused on elderly users. The requirements were created based on works that involve eliciting requirements for m-learning in general, pedagogical pattern language for this type of domain and pedagogical and accessibility guidelines for m-learning app focused on the elderly. At the very end, such requirements will support the development of a crossword mobile application to the elderly, which should contribute to the learning process of such audience.
Multi-target threat assessment in unmanned cluster confrontation scenarios

Threat assessment is a key module of the unmanned intelligent platform decision-making system. This paper mainly studies the target threat assessment in multiparty unmanned cluster confrontation scenarios. First, a three-level threat assessment functional model is established, and the target intent is regarded as a tactical threat, the threat assessment index system has been improved. Then the Adaboost algorithm is introduced into the threat assessment problem, and on this basis, the Adaboost algorithm is improved by building a various meta-learner algorithm library, which reduces the algorithm's error rate. Finally, the threat assessment based on improved Adaboost is simulated, and the results show that the algorithm can be used in the threat assessment of multi-party unmanned cluster confrontation scenarios.
Composed clustering of non-relational data with mixed types using K-modes and K-prototypes algorithms

The current available structured data for the majority of AI projects contains mixed categorical and numerical types. This mixture of data types drives the researchers to continually invent and develop new algorithms capable of handling these issues. K-Modes and K-prototypes are good examples of these innovative algorithms for clustering purposes. However, many fields are generating non-relational data that offers flexible schema and enables faster computing. The problem with this data is that it needs an advanced preprocessing step in order to make it ready to use by clustering algorithms. We propose a composed clustering that can fix the issue of document-based categorical attributes, by using K-Modes to cluster the modalities of these attributes and standardize them, then applying K-Prototypes for clustering individuals of the dataset using all numerical and preprocessed categorical attributes. We tested our approach on a real-world database used by human resource agents to cluster applicants based on information extracted from their resumes, with results showing good performances of our approach.
Hardware acceleration of robot scene perception algorithms

Hybrid machine learning algorithms that combine deep learning with probabilistic inference techniques provide highly accurate scene perception for robot manipulation. In particular, a 2-stage approach that combines object detection using convolutional neural networks with Monte-Carlo sampling for pose estimation has been shown to perform particularly well under adversarial scenarios. Unfortunately, this accuracy comes at the cost of high computational complexity, which affects runtime, resource utilization, and energy consumption. This paper describes various challenges in developing complexity-aware techniques for robust robot perception and presents a novel hardware accelerator that addresses these challenge. Experimental results show our design is at least 30% faster and consumes 97% less energy compared to an implementation on a high-end GPU. Compared to a low-power GPU implementation, our design is 95% faster while consuming 96% less energy, demonstrating that accurate, energy-efficient scene perception is possible in real time with targeted hardware acceleration.
Macros for Coding Information Encryption &amp; Decryption in Trusted Platform Module

This article describes the process assembling of Encryption & Decryption In Trusted Platform Module of encoding information in NM640X ®. Encoding of information, carried out in assembly language according to Gost 28147-89. It is a realisation of standard GOST 28147-89- Russian state symmetric key block cipher. GOST 28147-89 has 64-bit to access the kernel, trust, and allocated memory in the BlockSize and 256-bit KeySize.
Learning Co-Occurrence of Laughter and Topics in Conversational Interactions

This paper describes experiments to learn laughter co-occurrences with dialogue contributions. The dialogue data belongs to the special type of First Encounter Dialogues where the interlocutors meet each other for the first time and where laughter mainly functions as a sign of politeness or relief of embarrassment. The earlier studies have shown that there is a correlation between the speaker's utterance content (topic) and non-verbal communication (laughter and body movement) while in this paper we seek to learn the correlations via a neural model. The results show that there seems to be a weak correlation in our data.
Academic motivation of students in the period of digital transformation of the educational environment

Abstract The article presents the results of an empirical study of students of one of the leading financial and economic universities in the country, the main goal of which was to identify the features of motivation for educational activities in the higher education system based on an assessment of a number of psychological variables associated with motivation. At the same time, the authors used a number of psychological diagnostic techniques developed by experts in previous studies: life satisfaction, resilience, etc. The central place in the study is to identify the direction of academic motivation: internal, external and motivation in accordance with the theory of self-determination, which is widespread in modern psychological science personalities of E. Desi and R. Ryan. The study-involved students of various courses and areas of training for bachelor’s and master’s degrees, full-time and part-time studies, as well as those receiving higher education on a budgetary and paid basis. The recommendations developed by the authors to increase the level of motivation of students with low indicators of intrinsic motivation for educational activity are given.
Deep SAS: A Deep Signature-based API Specification Learning Approach

The number and variety of Web APIs is growing exponentially. Software engineers need to expend a significant amount of time and effort reading and understanding the accompanying documentation. In addition, system automation may use API to interact with each other. However, this is not always a simple task since the API documentation of a provider can be anything from a single HTML page description through to a complex structure with information spanning several pages. Understanding this wide variety of API documentation structures and styles is therefore a labor intensive and error-prone task for engineers. By providing a machine-learning platform that can extract and standardize API usage information, however, we believe we can accelerate the creation of API-enabled systems by using automation to simplify the task of understanding. In this paper we introduce a novel approach to automating and standardizing usage information about APIs, combining several machine-learning algorithms in order to extract key attributes from API documentation and generate a machine readable Open API Specification (OAS). We develop i) a content-based learning model that identifies the context of a block of extracted API features; ii) a signature-based machine learning model that recognizes a sequence of successful/unsuccessful extracted API endpoints; and iii) a deep mapping model that pinpoints fine-grained mapping of extracted API attributes to OAS objects. Results of our experiments show that the proposed approach successfully works with an accuracy of 99%, 94% and 97% for content-based learning, signature-based learning, and Deep Mapping of API attributes respectively. We then use the models to produce OAS compliant API Specifications for more than 2,585 public APIs, validate them via API calls and finally deploy the validated APIs to the RunMyProcess software automation platform.
Interprocedural Context-Unbounded Program Analysis Using Observation Sequences

A classical result by Ramalingam about synchronization-sensitive interprocedural program analysis implies that reachability for concurrent threads running recursive procedures is undecidable. A technique proposed by Qadeer and Rehof, to bound the number of context switches allowed between the threads, leads to an incomplete solution that is, however, believed to catch “most bugs” in practice, as errors tend to occur within few contexts. The question of whether the technique can also prove the absence of bugs at least in some cases has remained largely open. Toward closing this gap, we introduce in this article the generic verification paradigm of observation sequences for resource-parameterized programs. Such a sequence observes how increasing the resource parameter affects the reachability of states satisfying a given property. The goal is to show that increases beyond some “cutoff” parameter value have no impact on the reachability—the sequence has converged . This allows us to conclude that the property holds for all parameter values. We applied this paradigm to the context- unbounded program analysis problem, choosing the resource to be the number of permitted thread context switches. The result is a partially correct interprocedural reachability analysis technique for concurrent shared-memory programs. Our technique may not terminate but is able to both refute and prove context-unbounded safety for such programs. We demonstrate the effectiveness and efficiency of the technique using a variety of benchmark programs. The safe instances cannot be proved safe by earlier, context-bounded methods.
A Short History of KeY

This paper describes the story of the first nine years of the KeY project, its original goals, the people involved, its setbacks, but also its occasional failures and blind alleys. It is deliberately written in a more personal style, but tries to meet scientific standards of correctness and completeness.
HAR-MI with COSTE in Handling Multi-Class Imbalance

The class imbalance problem is a serious problem in machine learning. This problem can occur in two-class and multi-class problems. This problem can result in low accuracy and not obtaining information regarding the minority class. Approaches to overcome this problem often use a combination of the Data-Level Approach and Algorithm-Level Approach, which is often referred to as a Hybrid Approach. One of the Hybrid Approach methods to solve the multi-class imbalance problem is the Hybrid Approach Redefinition-Multiclass Imbalance (HAR-MI). The sampling method used in the HAR-MI is the Oversampling method. Oversampling tends to be chosen because Undersampling can eliminate useful information, but Oversampling often causes Over-Fitting. Therefore, this study will modify the HAR-MI approach by using the Complexity-based OverSampling TEchnique (COSTE). COSTE will rank the instances based on the measurement of complexity so that it will provide better results and prevent Over-Fitting. The results showed that HAR-MI with COSTE gave better results than the classic HAR-MI.
Incorporating Uncertainty into Unsupervised Machine Learning for Cyber-Physical Systems

In the field of Cyber-Physical Systems (CPS), the early detection of anomalies is crucial to avoid future faulty behaviors, e.g. preventing downtimes or decreasing product qualities. As a solution, unsupervised machine learning can be used to learn models of the historic system behavior and consequentially detect deviations from these models. Since CPS data are high dimensional time series, suitable approaches such as Long Short-Term Memory (LSTM) neural networks are good solution candidates.CPSs have specific requirements for such machine learning algorithms. Learned models must be especially useful in closed control loops, i.e. without human supervision. For this, it is essential that the uncertainty about model predictions is also part of the learned models. In order to incorporate such uncertainties, we modify the mean squared error loss function used by LSTM. This paper also analyses the solution on artificial and real data.
Optimization of LNN Reversible Circuits Using an Analytic Sifting Method

In this paper, we propose an analytic approach to the variable sifting based on weighting of qubits and gates. The proposed scheme allows us to optimally sift gates (multi-control single-target reversible gates) within a linear number of steps of computation and provides, in general, that a smaller number of SWAP gates are required to transform a reversible circuit into an Linear Nearest Neighbor (LNN) model than other competing approaches. The method is analyzed for two different models of implementations; it is verified on the experimental data and results are compared with the state-of-the-art algorithms for the design of LNN circuits.
A Triple Adversary Network Driven by Hybrid High-Order Attention for Domain Adaptation

How to bridge the knowledge gap between the annotated source domain and the unlabeled target domain is a basic challenge to domain adaptation. The existing approaches can relieve this gap by feature alignments across domains; however, aligning non-transferable features may lead to negative shift confusing the knowledge learning on target domains. In this paper, a triple adversary network is proposed on the basis of a high-order attention, hopefully to solve the problem. The proposed architecture focuses on the detailed feature alignment by a hybrid high-order attention using a fast iteration algorithm. In addition, an orthogonal loss of two complementary modules is applied to constrain the mutual exclusion of foreground and background features. Finally, a triple adversarial strategy is introduced to further improve the training convergence for the composed architectures. Numeric experiments on datasets of Digits, Office-31 and Office-home illuminate that the proposed network can effectively improve the state-of-art domain adaptations with superior transferring performance.
Patterns for Development of Safety-Critical Systems with Agile

In safety-critical systems keeping complete trace of requirements and detailed testing is an extremely relevant part of software development life cycle. Safety standards like ISO 26262, DO178C and many others prescribe that critical requirements must be completely traceable. These standards also demand detailed and regression testing of system. Here we present some patterns that deal with these concerns in an agile way. First set of patterns describe the key mechanism to list the sources of safety requirements and a mechanism for traceability of those requirements. It uses an approach that satisfies safety standards and adapts agile behavior where possible. The second pattern is about test automation for safety-critical systems, which complements our first set of patterns. It decreases the amount of documentation required for traceability and testing of features but without any compromise on essential testing. These patterns will facilitate the team to perform requirement's traceability and regular, rigorous testing in a timely and cost efficient manner.
Augmented and Virtual Reality and Its Applications

Augmented reality (AR) overlies manually made materials directly over the real-world materials. This chapter addresses the technological and design frameworks required to create realistic motion tracking environments, realistic audio, 3D graphical interactions, multimodal sensory integration, and user interfaces and games using virtual reality to augmented reality. Similarly, the portfolio required to build a personal VR or AR application is detailed. Virtual and augmented reality industry committed innovative technologies that can be explored in the field of entertainment, education, training, medical and industrial innovation, and the development are explored. Augmented reality (AR) allows the physical world to be enhanced by incorporating digital knowledge in real time created by virtual machine. Few applications that have used augmented and virtual reality in real-world applications are discussed.
Adaptive Multi-modal Fusion Instance Segmentation for CAEVs in Complex Conditions: Dataset, Framework and Verifications

Abstract Current works of environmental perception for connected autonomous electrified vehicles (CAEVs) mainly focus on the object detection task in good weather and illumination conditions, they often perform poorly in adverse scenarios and have a vague scene parsing ability. This paper aims to develop an end-to-end sharpening mixture of experts (SMoE) fusion framework to improve the robustness and accuracy of the perception systems for CAEVs in complex illumination and weather conditions. Three original contributions make our work distinctive from the existing relevant literature. The Complex KITTI dataset is introduced which consists of 7481 pairs of modified KITTI RGB images and the generated LiDAR dense depth maps, and this dataset is fine annotated in instance-level with the proposed semi-automatic annotation method. The SMoE fusion approach is devised to adaptively learn the robust kernels from complementary modalities. Comprehensive comparative experiments are implemented, and the results show that the proposed SMoE framework yield significant improvements over the other fusion techniques in adverse environmental conditions. This research proposes a SMoE fusion framework to improve the scene parsing ability of the perception systems for CAEVs in adverse conditions.
Single depth map super-resolution via a deep feedback network

Existing depth map-based super-resolution (SR) methods cannot achieve satisfactory results in depth map detail restoration. For example, boundaries of the depth map are always difficult to reconstruct effectively from the low-resolution (LR) guided depth map particularly at big magnification factors. In this paper, we present a novel super-resolution method for single depth map by introducing a deep feedback network (DFN), which can effectively enhance the feature representations at depth boundaries that utilize iterative up-sampling and down-sampling operations, building a deep feedback mechanism by projecting high-resolution (HR) representations to low-resolution spatial domain and then back-projecting to high-resolution spatial domain. The deep feedback (DF) block imitates the process of image degradation and reconstruction iteratively. The rich intermediate high-resolution features effectively tackle the problem of depth boundary ambiguity in depth map super-resolution. Extensive experimental results on the benchmark datasets show that our proposed DFN outperforms the state-of-the-art methods.
Deep Priors Inside an Unrolled and Adaptive Deconvolution Model

Image deconvolution is an essential but ill-posed problem even if the degradation kernel is known. Recently, learning based methods have demonstrated superior image restoration quality in comparison to traditional methods which are typically based on empirical statistics and parameter adjustment. Though coming up with outstanding performance, most of the plug-and-play priors are trained in a specific degradation model, leading to inferior performance on restoring high-frequency components. To address this problem, a deblurring architecture that adopts (1) adaptive deconvolution modules and (2) learning based image prior solvers is proposed. The adaptive deconvolution module adjusts the regularization weight locally to well process both smooth and non-smooth regions. Moreover, a cascade made of image priors is learned from the mapping between intermediates thus robust to arbitrary noise, aliasing, and artifact. According to our analysis, the proposed architecture can achieve a significant improvement on the convergence rate and result in an even better restoration performance.
Short Text Embedding Autoencoders With Attention-Based Neighborhood Preservation

Shortness and sparsity often plague short text representation for clustering and classification. A popular solution is to extract meaningful low-dimensional embeddings as short text representation via various Dimensionality Reduction technology. However, the existing methods, such as topic models and neural networks, discover low-dimensional embeddings from the whole training sets without considering the geometrical information of short text manifold, resulting in an inability to provide a discriminative embedding of short text. In this paper, we propose a manifold-regularized method, namely Short Texts Embedding AutoEncoders (STE-AEs), aiming to incorporate the semantics from the neighborhood into a regularization training of AutoEncoders (AEs) to extract discriminative low-dimensional short text embeddings. STE-AEs first determines semantics neighborhood via an attention-based weighted matching distance and then preserves the local geometrical structure by incorporating a minimization of the weighted cross-entropy of nearby texts' embeddings into a regularization training of AEs. Finally, the encoder can act as a parametrized mapping function between observations and embeddings. Furthermore, based on the activation values of the encoder for the training set, STE-AEs employs a regression model of Random Forest (RF) to determine the feature importance so as to find certain informative and readable words for embeddings interpretation. Through extensive experiments on three real-world short text corpuses, the evidence demonstrate that STE-AEs can capture the intrinsic discriminative explanatory factors, improving the performance of short text clustering and classification. Moreover, some understandable words can be efficiently discovered to promote the interpretability of low-dimensional embeddings.
Variational Quantum Denoising Technique for Medical Images

A novel variational restoration framework for the medical images corrupted by quantum, or Poisson, noise is proposed in this research paper. The considered approach is using a variational scheme that leads to a nonlinear fourth-order PDE-based model. That partial differential equation model is then solved numerically by developing a consistent finite difference-based numerical approximation scheme converging to its variational solution. The obtained numerical algorithm removes successfully the quantum noise from the medical images, preserves their details, and outperforms other shot noise filtering solutions.