Reviewerly/exHarmony huggingface dataset:
Link-based multi-verse optimizer for text documents clustering

Text document clustering (TDC) represents a key task in text mining and unsupervised machine learning, which partitions a specific documents' collection into varied K-groups according to certain similarity/dissimilarity criterion. There exists a considerable amount of knowledge in the text clustering field and many attempts were carried out to resolve the TDC problem and improve the learning performance. The multi-verse optimizer algorithm (MVO) is a stochastic population-based algorithm, which was recently introduced and successfully utilized to tackle many optimization problems that are complex. The original MVO performance is limited to the utilization of only the best solution in the exploitation phase (local search capability), which makes it suffer from entrapment in local optima and low convergence rate. This paper aims to propose a novel method of modifying the MVO algorithm called link-based Multi-verse optimizer algorithm (LBMVO) to enhance the exploitation phase in the original MVO. The enhancement involves adding a neighbor operator to the MVO algorithm to enhance the search capability via a novel probability factor, namely neighborhood selection strategy (NSS). The proposed LBMVO's effectiveness was tested on six standard datasets, which are used in the text clustering domain in addition to five standard datasets, which are utilized in the data clustering domain. The experiments revealed that the modified MVO with NSS has boosted the results in terms of error rate, accuracy, recall, precision, F-measure, purity, entropy criteria, and high convergence rate. Generally, LBMVO has outperformed or at least showed that it is profoundly competitive compared with the original MVO algorithm and with widely known clustering techniques like Spectral, Agglomerative, Density-based spatial clustering of applications with noise (DBSCAN), K-means, K-means++ clustering techniques and the optimization algorithms like harmony search (HS), genetic algorithm (GA), particle swarm optimization (PSO), krill herd algorithm (KHA), covariance matrix adaptation evolution strategy (CMAES), coyote optimization algorithm (COA), as well as original MVO.
Deep Joint Depth Estimation and Color Correction From Monocular Underwater Images Based on Unsupervised Adaptation Networks

Degraded visibility and geometrical distortion typically make the underwater vision more intractable than open air vision, which impedes the development of underwater-related machine vision and robotic perception. Therefore, this paper addresses the problem of joint underwater depth estimation and color correction from monocular underwater images, which aims at enjoying the mutual benefits between these two related tasks from a multi-task perspective. Our core ideas lie in our new deep learning architecture. Due to the lack of effective underwater training data, and the weak generalization to the real-world underwater images trained on synthetic data, we consider the problem from a novel perspective of style-level and feature-level adaptation, and propose an unsupervised adaptation network to deal with the joint learning problem. Specifically, a style adaptation network (SAN) is first proposed to learn a style-level transformation to adapt in-air images to the style of underwater domain. Then, we formulate a task network (TN) to jointly estimate the scene depth and correct the color from a single underwater image by learning domain-invariant representations. The whole framework can be trained end-to-end in an adversarial learning manner. Extensive experiments are conducted under air-to-water domain adaptation settings. We show that the proposed method performs favorably against state-of-the-art methods in both depth estimation and color correction tasks.
FPGA Implementation of High-Speed Area-Efficient Processor for Elliptic Curve Point Multiplication Over Prime Field

Developing a high-speed elliptic curve cryptographic (ECC) processor that performs fast point multiplication with low hardware utilization is a crucial demand in the fields of cryptography and network security. This paper presents field-programmable gate array (FPGA) implementation of a high-speed, low-area, side-channel attacks (SCAs) resistant ECC processor over a prime field. The processor supports 256-bit point multiplication on recently recommended twisted Edwards curve, namely, Edwards25519, which is used for a high-security digital signature scheme called Edwards curve digital signature algorithm (EdDSA). The paper proposes novel hardware architectures for point addition and point doubling operations on the twisted Edwards curve, where the processor takes only 516 and 1029 clock cycles to perform each point addition and point doubling, respectively. For a 256-bit key, the proposed ECC processor performs single point multiplication in 1.48 ms, running at a maximum clock frequency of 177.7 MHz in a cycle count of 262 650 with a throughput of 173.2 kbps, utilizing only 8873 slices on the Xilinx Virtex-7 FPGA platform, where the points are represented in projective coordinates. The implemented design is time-area-efficient as it offers fast scalar multiplication with low hardware utilization without compromising the security level.
The social utility of ‘data literacy’

This article examines the social utility of the concept, ‘data literacy’. Recent developments in the processes of datafication challenge long-held assumptions about privacy and the role of both state and commerce in individual lives. Typically, these have been addressed through: regulatory legal constraints underwritten by the nation state but are difficult to enforce at a global level; tactical resistance through forms of self-regulation and technical innovations, and; educational interventions, typically as ‘literacy’, which brings understanding of the new forms of digital control. The article considers the benefit of theorising digital data as a ‘text’ and reviews current educational models of data literacy, categorised here as formal, personal and folk pedagogies of data. The article concludes that while the analogy between print and data has many inconsistencies, the term has rhetorical benefits. However, to become a meaningful strategy ‘data literacy’ requires both a more complete theorisation and complex practical development.
SLDeep: Statement-level software defect prediction using deep-learning model on static code features

Software defect prediction (SDP) seeks to estimate fault-prone areas of the code to focus testing activities on more suspicious portions. Consequently, high-quality software is released with less time and effort. The current SDP techniques however work at coarse-grained units, such as a module or a class, putting some burden on the developers to locate the fault. To address this issue, we propose a new technique called as Statement-Level software defect prediction using Deep-learning model (SLDeep). The significance of SLDeep for intelligent and expert systems is that it demonstrates a novel use of deep-learning models to the solution of a practical problem faced by software developers. To reify our proposal, we defined a suite of 32 statement-level metrics, such as the number of binary and unary operators used in a statement. Then, we applied as learning model, long short-term memory (LSTM). We conducted experiments using 119,989 C/C++ programs within Code4Bench. The programs comprise 2,356,458 lines of code of which 292,064 lines are faulty. The benchmark comprises a diverse set of programs and versions, written by thousands of developers. Therefore, it tends to give a model that can be used for cross-project SDP. In the experiments, our trained model could successfully classify the unseen data (that is, fault-proneness of new statements) with average performance measures 0.979, 0.570, and 0.702 in terms of recall, precision, and accuracy, respectively. These experimental results suggest that SLDeep is effective for statement-level SDP. The impact of this work is twofold. Working at statement-level further alleviates developer's burden in pinpointing the fault locations. Second, cross-project feature of SLDeep helps defect prediction research become more industrially-viable.
Learning target-focusing convolutional regression model for visual object tracking

Discriminative correlation filters (DCFs) have been widely used in the tracking community recently. DCFs-based trackers utilize samples generated by circularly shifting from an image patch to train a ridge regression model, and estimate target location using a response map generated by the correlation filters. However, the generated samples produce some negative effects and the response map is vulnerable to noise interference, which degrades tracking performance. In this paper, to solve the aforementioned drawbacks, we propose a target-focusing convolutional regression (CR) model for visual object tracking tasks (called TFCR). This model uses a target-focusing loss function to alleviate the influence of background noise on the response map of the current tracking image frame, which effectively improves the tracking accuracy. In particular, it can effectively balance the disequilibrium of positive and negative samples by reducing some effects of the negative samples that act on the object appearance model. Extensive experimental results illustrate that our TFCR tracker achieves competitive performance compared with state-of-the-art trackers. The code is available at: https://github.com/deasonyuan/TFCR.
UAV Mission Planning Resistant to Weather Uncertainty

Fleet mission planning for Unmanned Aerial Vehicles (UAVs) is the process of creating flight plans for a specific set of objectives and typically over a time period. Due to the increasing focus on the usage of large UAVs, a key challenge is to conduct mission planning addressing changing weather conditions, collision avoidance, and energy constraints specific to these types of UAVs. This paper presents a declarative approach for solving the complex mission planning resistant to weather uncertainty. The approach has been tested on several examples, analyzing how customer satisfaction is influenced by different values of the mission parameters, such as the fleet size, travel distance, wind direction, and wind speed. Computational experiments show the results that allow assessing alternative strategies of UAV mission planning.
Spatio-Temporal Action Graph Networks

Events defined by the interaction of objects in a scene are often of critical importance; yet important events may have insufficient labeled examples to train a conventional deep model to generalize to future object appearance. Activity recognition models that represent object interactions explicitly have the potential to learn in a more efficient manner than those that represent scenes with global descriptors. We propose a novel inter-object graph representation for activity recognition based on a disentangled graph embedding with direct observation of edge appearance. In contrast to prior efforts, our approach uses explicit appearance for high order relations derived from object-object interaction, formed over regions that are the union of the spatial extent of the constituent objects. We employ a novel factored embedding of the graph structure, disentangling a representation hierarchy formed over spatial dimensions from that found over temporal variation. We demonstrate the effectiveness of our model on the Charades activity recognition benchmark, as well as a new dataset of driving activities focusing on multi-object interactions with near-collision events. Our model offers significantly improved performance compared to baseline approaches without object-graph representations, or with previous graph-based models.
Experimental investigation of silicon and silicon nitride platforms for phase-change photonic in-memory computing

Advances in artificial intelligence have greatly increased demand for data-intensive computing. Integrated photonics is a promising approach to meet this demand in big-data processing due to its potential for wide bandwidth, high speed, low latency, and low-energy computing. Photonic computing using phase-change materials combines the benefits of integrated photonics and co-located data storage, which of late has evolved rapidly as an emerging area of interest. In spite of rapid advances of demonstrations in this field on both silicon and silicon nitride platforms, a clear pathway towards choosing between the two has been lacking. In this paper, we systematically evaluate and compare computation performance of phase-change photonics on a silicon platform and a silicon nitride platform. Our experimental results show that while silicon platforms are superior to silicon nitride in terms of potential for integration, modulation speed, and device footprint, they require trade-offs in terms of energy efficiency. We then successfully demonstrate single-pulse modulation using phase-change optical memory on silicon photonic waveguides and demonstrate efficient programming, memory retention, and readout of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mo>&gt;<!-- > --></mml:mo> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mn>4</mml:mn> </mml:mrow> </mml:math> bits of data per cell. Our approach paves the way for in-memory computing on the silicon photonic platform.
Visual object tracking with adaptive structural convolutional network

Convolutional Neural Networks (CNN) have been demonstrated to achieve state-of-the-art performance in visual object tracking task. However, existing CNN-based trackers usually use holistic target samples to train their networks. Once the target undergoes complicated situations (e.g., occlusion, background clutter, and deformation), the tracking performance degrades badly. In this paper, we propose an adaptive structural convolutional filter model to enhance the robustness of deep regression trackers (named: ASCT). Specifically, we first design a mask set to generate local filters to capture local structures of the target. Meanwhile, we adopt an adaptive weighting fusion strategy for these local filters to adapt to the changes in the target appearance, which can enhance the robustness of the tracker effectively. Besides, we develop an end-to-end trainable network comprising feature extraction, decision making, and model updating modules for effective training. Extensive experimental results on large benchmark datasets demonstrate the effectiveness of the proposed ASCT tracker performs favorably against the state-of-the-art trackers.
The state‐of‐the‐art in container technologies: Application, orchestration and security

Summary Containerization is a lightweight virtualization technology enabling the deployment and execution of distributed applications on cloud, edge/fog, and Internet‐of‐Things platforms. Container technologies are evolving at the speed of light, and there are many open research challenges. In this paper, an extensive literature review is presented that identifies the challenges related to the adoption of container technologies in High Performance Computing, Big Data analytics, and geo‐distributed (Edge, Fog, Internet‐of‐Things) applications. From our study, it emerges that performance, orchestration, and cyber‐security are the main issues. For each challenge, the state‐of‐the‐art solutions are then analyzed. Performance is related to the assessment of the performance footprint of containers and comparison with the footprint of virtual machines and bare metal deployments, the monitoring, the performance prediction, the I/O throughput improvement. Orchestration is related to the selection, the deployment, and the dynamic control of the configuration of multi‐container packaged applications on distributed platforms. The focus of this work is on run‐time adaptation. Cyber‐security is about container isolation, confidentiality of containerized data, and network security. From the analysis of 97 papers, it came out that the state‐of‐the‐art is more mature in the area of performance evaluation and run‐time adaptation rather than in security solutions. However, the main unsolved challenges are I/O throughput optimization, performance prediction, multilayer monitoring, isolation, and data confidentiality (at rest and in transit).
Auto-weighted multi-view co-clustering via fast matrix factorization

Multi-view clustering is a hot research topic in machine learning and pattern recognition, however, it remains high computational complexity when clustering multi-view data sets. Although a number of approaches have been proposed to accelerate the computational efficiency, most of them do not consider the data duality between features and samples. In this paper, we propose a novel co-clustering approach termed as Fast Multi-view Bilateral K-means (FMVBKM), which can implement clustering task on row and column of the input data matrix, simultaneously. Specifically, FMVBKM applies the relaxed K-means clustering technique to multi-view data clustering. In addition, to decrease information loss in matrix factorization, we further introduce a new co-clustering method named as Fast Multi-view Matrix Tri-Factorization (FMVMTF). Extensive experimental results on six benchmark data sets show that the proposed two approaches not only have comparable clustering performance but also present the high computational efficiency, in comparison with state-of-the-art multi-view clustering methods.
Narrative and gaming experience interact to affect presence and cybersickness in virtual reality

Research has established a link between presence and cybersickness in virtual environments, but there is significant disagreement regarding the directionality of the relationship (positive or negative) between the two factors, and if the relationship is modulated by other top-down influences. Several studies have revealed a negative association between the factors, highlighting the prospect that manipulating one factor might affect the other. Here we examined if a top-down factor (narrative context) enhances presence, and whether this effect is associated with a decrease in cybersickness. We analyzed the association between responses to questionnaire measures of cybersickness and presence, as well as the degree to which their relationship was affected by the administration of an ‘enriched’ or ‘minimal’ verbal narrative context. The results of the first experiment, conducted in a controlled laboratory environment, revealed that enriched narrative was associated with increased presence, but that the reductive effect of narrative on cybersickness depended on video gaming experience. We also observed the expected negative association between presence and cybersickness, but only in the enriched narrative group. In a second experiment, conducted with a diverse sample at a public museum, we confirmed our previous finding that presence and cybersickness are negatively correlated, specifically when participants experienced an enriched narrative. We also confirmed the interaction between narrative and gaming experience with respect to cybersickness. These results highlight the complexity of the presence-cybersickness relationship, and confirm that both factors can be modulated in a beneficial manner for virtual reality users by means of top-down interventions.
A Scalable Handwritten Text Recognition System

Many studies on (Offline) Handwritten Text Recognition (HTR) systems have focused on building state-of-the-art models for line recognition on small corpora. However, adding HTR capability to a large scale multilingual OCR system poses new challenges. This paper addresses three problems in building such systems: data, efficiency, and integration. Firstly, one of the biggest challenges is obtaining sufficient amounts of high quality training data. We address the problem by using online handwriting data collected for a large scale production online handwriting recognition system. We describe our image data generation pipeline and study how online data can be used to build HTR models. We show that the data improve the models significantly under the condition where only a small number of real images is available, which is usually the case for HTR models. It enables us to support a new script at substantially lower cost. Secondly, we propose a line recognition model based on neural networks without recurrent connections. The model achieves a comparable accuracy with LSTM-based models while allowing for better parallelism in training and inference. Finally, we present a simple way to integrate HTR models into an OCR system. These constitute a solution to bring HTR capability into a large scale OCR system.
Autonomous Navigation via Deep Reinforcement Learning for Resource Constraint Edge Nodes Using Transfer Learning

Smart and agile drones are fast becoming ubiquitous at the edge of the cloud. The usage of these drones is constrained by their limited power and compute capability. In this paper, we present a Transfer Learning (TL) based approach to reduce on-board computation required to train a deep neural network for autonomous navigation via value-based Deep Reinforcement Learning for a target algorithmic performance. A library of 3D realistic meta-environments is manually designed using Unreal Gaming Engine and the network is trained end-to-end. These trained meta-weights are then used as initializers to the network in a test environment and fine-tuned for the last few fully connected layers. Variation in drone dynamics and environmental characteristics is carried out to show robustness of the approach. Using NVIDIA GPU profiler, it was shown that the energy consumption and training latency is reduced by 3.7$\times$ and 1.8$\times$ respectively without significant degradation in the performance in terms of average distance traveled before crash i.e. Mean Safe Flight (MSF). The approach is also tested on a real environment using DJI Tello drone and similar results were reported. The code for the approach can be found on GitHub: https://github.com/aqeelanwar/DRLwithTL.
Toward Collaborative Inferencing of Deep Neural Networks on Internet-of-Things Devices

Recent advancements in deep neural networks (DNNs) have enabled us to solve traditionally challenging problems. To deploy a service based on DNNs, since DNNs are compute intensive, consumers need to rely on compute resources in the cloud. This approach, in addition to creating a dependency on the high-quality network infrastructure and data centers, raises new privacy concerns because of the sharing of private data. These concerns and challenges limit the widespread use of DNN-based applications, so many researchers and companies are trying to optimize DNNs for fast in-the-edge execution. Executing DNNs is further pushed to the edge with the widespread use of embedded processors and ubiquitous wireless networks in Internet-of-Things (IoT) devices. However, inadequate power and computing resources of edge devices, along with the small number of local requests, limit the use of prevalent optimization techniques such as batch processing. In this article, we enable the utilization of the aggregated computing power of several IoT devices by creating a local collaborative network for a subset of DNNs, visual-based applications. In this approach, IoT devices cooperate to conduct single-batch inferencing in real time while exploiting several new model-parallelism methods, which will be introduced in this article. Our approach enhances the collaborative system by creating a balanced and distributed processing pipeline while adjusting the tasks in real time. For experiments, we deploy a system with up to 10 Raspberry Pis and execute state-of-the-art visual models, such as AlexNet, VGG16, Xception, and C3D.