Reviewerly/exHarmony huggingface dataset:
Encrypted Data Retrieval and Sharing Scheme in Space–Air–Ground-Integrated Vehicular Networks

As a smart transportation application of the Internet of Things, the Internet of Vehicles (IoV) depresses the chances of traffic accidents, while improving transportation efficiency and user driving experience. However, as the number of vehicles continues to grow, the original ground-based IoV system is difficult to meet the ever-increasing demand. To this end, space–air–ground-integrated network (SAGIN) incorporates satellite systems, aerial network and terrestrial communications. However, because SAGIN integrates multiple network services and communication modes, which makes SAGIN more vulnerable to various types of attacks and security threats. This article first presents the dominating security threats in data storage, transmission and sharing of space–air–ground integrated vehicular network (SAGIVN). Moreover, for guaranteeing the safety and effectiveness of the model, we advance a safe and effective encrypted data retrieval and sharing scheme in SAGIVN (ERDSS) for possible threats, the ERDSS can execute fuzzy retrieval over misspelling keywords and sort results by relevance scores to realize precise retrieval. We perform a comprehensive security discussion and execute experiments based on real-world data sets. The consequences demonstrate that the ERDSS is safe and efficient.
Few-Shot Microscopy Image Cell Segmentation

Automatic cell segmentation in microscopy images works well with the support of deep neural networks trained with full supervision. Collecting and annotating images, though, is not a sustainable solution for every new microscopy database and cell type. Instead, we assume that we can access a plethora of annotated image data sets from different domains (sources) and a limited number of annotated image data sets from the domain of interest (target), where each domain denotes not only different image appearance but also a different type of cell segmentation problem. We pose this problem as meta-learning where the goal is to learn a generic and adaptable few-shot learning model from the available source domain data sets and cell segmentation tasks. The model can be afterwards fine-tuned on the few annotated images of the target domain that contains different image appearance and different cell type. In our meta-learning training, we propose the combination of three objective functions to segment the cells, move the segmentation results away from the classification boundary using cross-domain tasks, and learn an invariant representation between tasks of the source domains. Our experiments on five public databases show promising results from 1- to 10-shot meta-learning using standard segmentation neural network architectures.
A high-quality stitching algorithm based on fisheye images

The fisheye lens has the characteristics of wide viewing angle and low overall cost. Panoramic images can be further generated based on fisheye camera. In this paper, we propose a high-quality fisheye image stitching algorithm(LLBI-AW). In correction part, traditional bilinear interpolation algorithm leads to edge blur, a latitude and longitude-based interpolation algorithm (LLBI) is proposed to address this problem. Direction of the target point 0.is introduced through the latitude and longitude, weights are determined by the distance which between each point and the direction, and then performs interpolation. In fusion part, for the ghost problem in the overlapped area of the image after stitching, a weighted fusion algorithm (AW) based on the arc function is proposed. We establish an arc function to determine weights of left and right images, and then perform fusion calculation. The experimental results show that the information entropy, standard deviation and Brenner gradient of LLBI-AW algorithm are improved compared with the traditional algorithm, and the central region of the stitching image has a natural transition and high image quality.
On the Impact of Gravity Compensation on Reinforcement Learning in Goal-Reaching Tasks for Robotic Manipulators

Advances in machine learning technologies in recent years have facilitated developments in autonomous robotic systems. Designing these autonomous systems typically requires manually specified models of the robotic system and world when using classical control-based strategies, or time consuming and computationally expensive data-driven training when using learning-based strategies. Combination of classical control and learning-based strategies may mitigate both requirements. However, the performance of the combined control system is not obvious given that there are two separate controllers. This paper focuses on one such combination, which uses gravity-compensation together with reinforcement learning (RL). We present a study of the effects of gravity compensation on the performance of two reinforcement learning algorithms when solving reaching tasks using a simulated seven-degree-of-freedom robotic arm. The results of our study demonstrate that gravity compensation coupled with RL can reduce the training required in reaching tasks involving elevated target locations, but not all target locations.
Low‐rank isomap algorithm

Isomap is a well-known nonlinear dimensionality reduction method that highly suffers from computational complexity. Its computational complexity mainly arises from two stages; a) embedding a full graph on the data in the ambient space, and b) a complete eigenvalue decomposition. Although the reduction of the computational complexity of the graphing stage has been investigated by graph processing methods, the eigenvalue decomposition stage remains a bottleneck in the problem. In this paper, we propose the Low-Rank Isomap (LRI) algorithm by introducing a projection operator on the embedded graph from the ambient space to a low-rank latent space to facilitate applying the partial eigenvalue decomposition. This approach leads to reducing the complexity of Isomap to a linear order while preserving the structural information during the dimensionality reduction process as long as the number of observations remains extensively larger than the dimensionality of the ambient space. The superiority of the LRI algorithm compared to some state-of-art algorithms is experimentally verified on facial image clustering in terms of speed and accuracy.
Visual content-enhanced sequential recommendation with feature-level attention

A sequential recommender system (SRS) takes historical user-item interactions as a sequence to predict the next item that may be of interest to a user. Most of existing SRSs make predictions by only considering item IDs, while ignoring other important factors that may also significantly influence users’ choices, such as the visual content of items. This may not always be true in reality since an item’s appearance usually has great visual impact on users and thus plays an important role in users’ choices. In practice, each item’s appearance has often been specifically designed to represent a particular style or look. In addition, users’ preferences with regard to visual aspects, such as styling, color, etc., may be relatively consistent compared with their dynamic preferences on items for a certain period under sequential transaction scenarios. This motivates us to take the items’ visual content into account to build more reliable SRSs. Accordingly, we propose a novel visual content-enhanced sequential recommender system (VCSRS) for improving the performance of sequential recommendations. Particularly, in VCSRS, a feature-level attention module (FAM) is designed to learn the attentive visual representations of an item’s appearance. Moreover, a vision-concentrated recurrent network (VCRN) is devised to model the sequential dependencies between items while incorporating items’ visual representations. Extensive experiments on real-world datasets demonstrated the effectiveness of visual information and showed the superiority of our approach over other representative and state-of-the-art methods.
Analysis of the multi-objective release plan rescheduling problem

Software project development is a dynamic process by nature, since it is common that events such as changes in the software requirements and in the development team occur during the execution of the original project plan. When such disruptive events occur, the project plan needs to be adjusted, and the activity of adjusting the original plan in order to comply with the date and costs constraints of the project is called project rescheduling. This situation is not exclusive to whole-project plan-driven approaches to software development, as it is also present when software projects are developed following agile methodologies such as Scrum. In Scrum, shorter period project plans, called release plans, are used and when disruptive events occur release plan rescheduling needs to be performed. Plan rescheduling can be modeled as a multi-objective optimization problem, however, there have been no studies that investigate the release plan rescheduling problem (RPRP) and its solution by means of multi-objective optimizers. The study presented in this paper intends to address the lack of studies of the release plan rescheduling problem and covers three fundamental topics. First, a model for the release plan rescheduling problem is proposed, which considers metrics that are particularly important to define a release plan, including story points and velocity. Second, three evolutionary multi-objective algorithms are utilized for solving a number of problem instances and an appropriate in-depth analysis of their solutions is provided. Finally, a careful examination of the solutions with respect to their practicability is performed.
A pilot evaluation of technology –Enabled active learning through a Hybrid Augmented and Virtual Reality app

This paper reports on students' learning experience with a hybrid Augmented Reality (AR) and Virtual Reality (VR) app called the Kuching Heritage App (KHA). We investigated the effect of visualisations, time lapse (i.e., past to present), and the witnessing of historical changes on students' learning experience about cultural heritage. This pilot study utilised a mixed methods approach with an initial Technology-Enabled Active Learning (TEAL) Inventory distributed to 59 students, followed by semi-structured interviews with five student participants. Overall, the participants responded positively to the TEAL questionnaire's four domains, particularly in the interactive engagement and problem-solving skills domain. The interest and feedback domain had lower mean values highlighting areas for improvement. The qualitative phase expanded on how students' understanding was enhanced, and their interest sustained through KHA. Pedagogical implications for active learning through technology are discussed.
Planning and Implementation of Large-Scale Online Project-Based Learning and Flipped Classes

In order to prevent the spread of COVID-19, various online collaborative learning environments have been recently designed for project-based learning (PBL) programs and lectures, which are compulsory subjects for more than 500 students. For the PBL programs, 45 groups of 500 students were formed, and an online collaborative learning environment was established with 16 faculty members providing guidance. For the lectures, an online flipped classroom environment was established with 600 students divided into four classrooms, and online peer teaching was realized by about 140 student groups. This paper describes a large-scale PBL learning program with flipped classes built online to encourage the active and autonomous learning of students and ensure education quality during the pandemic.
Alignment Knowledge Distillation for Online Streaming Attention-Based Speech Recognition

This article describes an efficient training method for online streaming attention-based encoder-decoder (AED) automatic speech recognition (ASR) systems. AED models have achieved competitive performance in offline scenarios by jointly optimizing all components. They have recently been extended to an online streaming framework via models such as monotonie chunkwise attention (MoChA). However, the elaborate attention calculation process is not robust against long-form speech utterances. Moreover, the sequence-level training objective and time-restricted streaming encoder cause a nonnegligible delay in token emission during inference. To address these problems, we propose CTC synchronous training (CTC-ST), in which CTC alignments are leveraged as a reference for token boundaries to enable a MoChA model to learn optimal monotonie input-output alignments. We formulate a purely end-to-end training objective to synchronize the boundaries of MoChA to those of CTC. The CTC model shares an encoder with the MoChA model to enhance the encoder representation. Moreover, the proposed method provides alignment information learned in the CTC branch to the attention-based decoder. Therefore, CTC-ST can be regarded as self-distillation of alignment knowledge from CTC to MoChA. Experimental evaluations on a variety of benchmark datasets show that the proposed method significantly reduces recognition errors and emission latency simultaneously. The robustness to long-form and noisy speech is also demonstrated. We compare CTC-ST with several methods that distill alignment knowledge from a hybrid ASR system and show that the CTC-ST can achieve a comparable tradeoff of accuracy and latency without relying on external alignment information.
A Partition Based Gradient Compression Algorithm for Distributed Training in AIoT

Running Deep Neural Networks (DNNs) in distributed Internet of Things (IoT) nodes is a promising scheme to enhance the performance of IoT systems. However, due to the limited computing and communication resources of the IoT nodes, the communication efficiency of the distributed DNN training strategy is a problem demanding a prompt solution. In this paper, an adaptive compression strategy based on gradient partition is proposed to solve the problem of high communication overhead between nodes during the distributed training procedure. Firstly, a neural network is trained to predict the gradient distribution of its parameters. According to the distribution characteristics of the gradient, the gradient is divided into the key region and the sparse region. At the same time, combined with the information entropy of gradient distribution, a reasonable threshold is selected to filter the gradient value in the partition, and only the gradient value greater than the threshold is transmitted and updated, to reduce the traffic and improve the distributed training efficiency. The strategy uses gradient sparsity to achieve the maximum compression ratio of 37.1 times, which improves the training efficiency to a certain extent.
A blockchain based secure communication framework for community interaction

The globalization of the modern world has highly affected the network communication. For the communication mostly closed and centralized systems are used which sometimes causes privacy problems. The associated systems cannot meet the integrity and transparency aspects of the information security. One such framework is “community research applications”. The existing communication frameworks for community interactions are based on the trust of intermediary institutions, for example, a central authority server. It is considered to be at the risk of single-point failure attack. It can be worse as there may be some internal temperament without the knowledge of the concerned authorities. Therefore, designing of a blockchain based framework for the community interaction is highly recommended. In this paper, we propose a blockchain based secure communication framework for community interaction. In the proposed framework, we used the mechanism of blockchain and implemented it with a tamper-proof distributed ledger. We also implemented an additional layer of security using the RAFT algorithm in which a key management systems is deployed to handle the keys securely. On the basis of proposed framework an android application (app) has been developed to check its usability in the real-time environment. The provided security analysis proved the security of the proposed framework against the various types of possible attacks. We compared the performance of the proposed framework with respect to other similar approaches and found that the performance of the proposed framework is better than others.
Quality Properties of Execution Tracing, an Empirical Study

The quality of execution tracing impacts the time to a great extent to locate errors in software components; moreover, execution tracing is the most suitable tool, in the majority of the cases, for doing postmortem analysis of failures in the field. Nevertheless, software product quality models do not adequately consider execution tracing quality at present neither do they define the quality properties of this important entity in an acceptable manner. Defining these quality properties would be the first step towards creating a quality model for execution tracing. The current research fills this gap by identifying and defining the variables, i.e., the quality properties, on the basis of which the quality of execution tracing can be judged. The present study analyses the experiences of software professionals in focus groups at multinational companies, and also scrutinises the literature to elicit the mentioned quality properties. Moreover, the present study also contributes to knowledge with the combination of methods while computing the saturation point for determining the number of the necessary focus groups. Furthermore, to pay special attention to validity, in addition to the the indicators of qualitative research: credibility, transferability, dependability, and confirmability, the authors also considered content, construct, internal and external validity.
Tackling Age-Invariant Face Recognition With Non-Linear PLDA and Pairwise SVM

Face recognition approaches, especially those based on deep learning models, are becoming increasingly attractive for missing person identification, due to their effectiveness and the relative simplicity of obtaining information available for comparison. However, these methods still suffer from large accuracy drops when they have to tackle cross-age recognition, which is the most common condition to face in this specific task. To address these challenges, in this paper we investigate the contribution of different generative and discriminative models that extend the Probabilistic Linear Discriminant Analysis (PLDA) approach. These models aim at disentangling identity from other facial variations (including those due to age effects). As such, they can improve the age invariance characteristics of state-of-the-art deep facial embeddings. In this work, we experiment with a standard PLDA, a non-linear version of PLDA, the Pairwise Support Vector Machine (PSVM), and introduce a nonlinear version of PSVM (NL–PSVM) as a novelty. We thoroughly analyze the proposed models’ performance when addressing cross-age recognition in a large and challenging experimental dataset containing around 2.5 million images of 790,000 individuals. Results on this testbed confirm the challenges in age invariant face recognition, showing significant differences in the effects of aging across embedding models, genders, age ranges, and age gaps. Our experiments show as well the effectiveness of both PLDA and its proposed extensions in reducing the age sensitivity of the facial features, especially when there are significant age differences (more than ten years) between the compared images or when age-related facial changes are more pronounced, such as during the transition from childhood to adolescence or from adolescence to adulthood. Further experiments on three standard cross-age benchmarks (MORPH2, CACD-VS, and FG-NET) confirm the proposed models’ effectiveness.
Blockchain Technologies as a Reflection of Modern Reality: Diversity of Opportunities Versus Security Risks

Digital technologies determine the dynamics of social and economic development of modern states. The emergence of blockchain technologies has created conditions, on the one hand, for the optimization of business processes in various sectors of the national economy, and, on the other hand, for the emergence of new threats to security, primarily information and economic properties, which can call into question the well-being of society and the sustainability of the state. The purpose of the study: to analyze the content characteristics of the blockchain technology, the trends of its implementation in various sectors of the economy and limitations, as well as to justify the blockchain technology as a promising tool for the development of the digital economy and overcoming the factors of global instability. The content characteristics of the blockchain technology are investigated, and their advantages for use in various sectors of the economy are determined. The classification of blockchain applications used in business is given depending on the mechanism of their implementation. The industry structure of the blockchain technology market is analyzed. The countries where blockchain is most actively developing are highlighted-the USA, China, and the UK. The degree of implementation of blockchain technologies in various sectors of the economy is analyzed, as well as the economic benefits of their use are revealed. It is proved that behind the versatility of the blockchain technology, there is a whole range of restrictions and threats that require leveling.
Hyperspectral Image Denoising Using 3-D Geometrical Kernel With Local Similarity Prior

The majority of existing hyperspectral (HS) image denoising methods exploit local similarity in HS images by rearranging them into the matrix or vector forms. As the typical 3-D data, the inherent spatial and spectral properties in HS images should be simultaneously explored for denoising. Therefore, a 3-D geometrical kernel (3DGK) is developed in this article to describe the local structure. The proposed method assumes that the pixel can be represented by other pixels within a 3-D block efficiently owing to the local similarity with adjacent positions. Then, the HS image is modeled by the 3-D kernel regression with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm constraint, in which the local similarity is captured by the proposed 3DGK. To efficiently compute the parameters in 3DGK, geometrical structures, such as scale, shape, and orientation in the 3-D block, are estimated from the gradient information approximately. Finally, the noises are effectively removed while preserving the structures in HS images. Moreover, experimental results on simulated and real datasets demonstrate that the performance of 3DGK is better than those of the methods based on local similarity prior.
A comparative study of test code clones and production code clones

Clones are one of the most widespread code smells, known to negatively affect the evolution of software systems. While there is a large body of research on detecting, managing, and refactoring clones in production code, clones in test code are often neglected in today’s literature. In this paper we provide empirical evidence that further research on clones in test code is warranted. By analysing the clones in five representative open-source systems and comparing production code clones to test code clones, we observe that test code contains twice as many clones as production code. A detailed analysis reveals that most test clones are of Type II and Type III, and that many tests are duplicated multiple times with slight modifications. Moreover, current clone detection tools suffer from false negatives, and that this occurs more frequently in test code than in production code (NiCad = 76%, CPD = 90%, iClones = 12%). So even from a tools perspective, specific fine-tuning for test code is needed.