Reviewerly/exHarmony huggingface dataset:
Conceptual Bayesian networks for contaminated site ecological risk assessment and remediation support

The causal pathways of stressors that lead to impacts on individuals, populations, and communities of organisms are useful to know for designing alternatives that manage or remediate ecological risks. The ecological risk assessment (ERA) framework (USEPA, 1998b) can help to identify and prioritize management of risks. One key product of the problem formulation step in an ERA, that captures and represents causal knowledge, is the conceptual site model (CSM). The CSM is a graphical depiction of the risk environment that traces the fate and transport pathways of contaminants from sources of contamination (e.g., a leaking storage tank) to receptors (i.e., the ecological endpoints of concern in the risk assessment). The CSM guides the development of methods for assessing ecological risk scenarios and for remediation design alternatives. The qualitative and quantitative aspects of Bayesian networks may support CSM development and risk characterization. Bayesian networks provide a graphical platform geared toward probabilistic modeling making them important candidates for calculating risks in environmental assessments. The diagrammatic representation of causal Bayesian networks (i.e., the directed acyclic graphs) also adds explanatory depth for developing the evidence-base for risk characterization and remediation interventions. We call these qualitative graphs conceptual Bayesian networks (CBNs). The components of CBNs can be used to represent the variables and relationships between sources of contamination, media transfer, bioaccumulation, and risk. The connections help to compose, piece together, and explore hypothesized relationships that bring about high-risk scenarios. Causal pathway analysis of the CBNs provides visualizations of exposure pathways from initial and intermediate sources to receptors. Remediation options that would interrupt or stop the transport of contaminants to ecological receptors can then be identified. Even if the CBN is not quantified, the structures can support mechanistic and statistical designs for exposure and effects analysis and risk characterization and evaluate information needs for resolving uncertainties. This paper will examine these and other unexplored benefits of CBNs to assessment and management of contaminated sites.
Indian sign language recognition using wearable sensors and multi-label classification

Sign language recognition is often carried out using hierarchical classification approach to reduce complexity and enhance accuracy. In this paper, mutli-label classification is proposed for categorization of a sign based on its lexical attributes followed by final classification of the sign. Results are presented for classification of 100 isolated signs from the Indian sign language recorded using multiple surface electromyogram and inertial measurement units on both the forearms of 10 different signers. Signals from both the hands are processed in an integrated manner to identify static or dynamic state of the two hands. Moreover, symmetry in the motion of two hands is also utilized for sign categorization using novel features. In the classic tree-based categorization of signs, there is error propagation, which results in a classification error of 6.22%. Whereas in the proposed mutli-label classification approach, error propagation is avoided and the average classification error of 2.73% is observed.
Part-Guided Attention Learning for Vehicle Instance Retrieval

Vehicle instance retrieval (IR) often requires one to recognize the fine-grained visual differences between vehicles. Besides the holistic appearance of vehicles which is easily affected by the viewpoint variation and distortion, vehicle parts also provide crucial cues to differentiate near-identical vehicles. Motivated by these observations, we introduce a <i>Part-Guided Attention Network</i> (PGAN) to pinpoint the prominent part regions and effectively combine the global and local information for discriminative feature learning. PGAN first detects the locations of different part components and salient regions regardless of the vehicle identity, which serves as the <i>bottom-up attention</i> to narrow down the possible searching regions. To estimate the importance of detected parts, we propose a <i>Part Attention Module</i> (PAM) to adaptively locate the most discriminative regions with high-attention weights and suppress the distraction of irrelevant parts with relatively low weights. The PAM is guided by the identification loss and therefore provides <i>top-down attention</i> that enables attention to be calculated at the level of car parts and other salient regions. Finally, we aggregate the global appearance and local features together to improve the feature performance further. The PGAN combines part-guided bottom-up and top-down attention, global and local visual features in an end-to-end framework. Extensive experiments demonstrate that the proposed method achieves new state-of-the-art vehicle IR performance on four large-scale benchmark datasets.<xref ref-type="fn" rid="fn1"><sup>1</sup></xref>
Local quaternion polar harmonic Fourier moments-based multiple zero-watermarking scheme for color medical images

Zero-watermarking schemes are currently dedicated to resisting general desynchronization attacks, such as rotation and scaling, but cannot effectively resist complex desynchronization attacks, such as translation, cropping, length–width ratio changing, UnZign, and zero-padding. To this end, a robust zero-watermarking scheme resistant to complex desynchronization attacks was proposed in this paper based on local feature regions (LFRs) and quaternion polar harmonic Fourier moments (QPHFMs). First, the stable feature points of original color medical images were extracted with a speeded-up robust feature (SURF) operator; then, the stable LFRs were obtained through a feature scale-adaptive process and scale-standardized tests. Finally, the QPHFMs of the LFRs were calculated, and multiple feature sequences were constructed using their magnitudes and binarized to generate multiple zero-watermarks. The experimental results indicate that the proposed scheme can not only resist common image processing attacks and general desynchronization attacks but also robustly resist complex desynchronization attacks, such as translation, cropping, length–width ratio changing, UnZign, and zero-padding. The proposed scheme is superior to the state-of-the-art LFR-based multiple embedded watermarking schemes and zero-watermarking schemes.
Differentiable Joint Pruning and Quantization for Hardware Efficiency

We present a differentiable joint pruning and quantization (DJPQ) scheme. We frame neural network compression as a joint gradient-based optimization problem, trading off between model pruning and quantization automatically for hardware efficiency. DJPQ incorporates variational information bottleneck based structured pruning and mixed-bit precision quantization into a single differentiable loss function. In contrast to previous works which consider pruning and quantization separately, our method enables users to find the optimal trade-off between both in a single training procedure. To utilize the method for more efficient hardware inference, we extend DJPQ to integrate structured pruning with power-of-two bit-restricted quantization. We show that DJPQ significantly reduces the number of Bit-Operations (BOPs) for several networks while maintaining the top-1 accuracy of original floating-point models (e.g., 53\(\times \) BOPs reduction in ResNet18 on ImageNet, 43\(\times \) in MobileNetV2). Compared to the conventional two-stage approach, which optimizes pruning and quantization independently, our scheme outperforms in terms of both accuracy and BOPs. Even when considering bit-restricted quantization, DJPQ achieves larger compression ratios and better accuracy than the two-stage approach.
Research on How Human Intelligence, Consciousness, and Cognitive Computing Affect the Development of Artificial Intelligence

Artificial intelligence (AI) is essentially the simulation of human intelligence. Today’s AI can only simulate, replace, extend, or expand part of human intelligence. In the future, the research and development of cutting-edge technologies such as brain-computer interface (BCI) together with the development of the human brain will eventually usher in a strong AI era, when AI can simulate and replace human’s imagination, emotion, intuition, potential, tacit knowledge, and other kinds of personalized intelligence. Breakthroughs in algorithms represented by cognitive computing promote the continuous penetration of AI into fields such as education, commerce, and medical treatment to build up AI service space. As to human concern, namely, who controls whom between humankind and intelligent machines, the answer is that AI can only become a service provider for human beings, demonstrating the value rationality of following ethics.
Multilevel thresholding image segmentation based on energy curve with harmony Search Algorithm

Image segmentation is a process of portion image into regions. From image segmentation schemes available, multilevel thresholding on the histogram is a highly established method. Otsu’s method is a significant multilevel thresholding technique, in this multiple threshold levels selected on histogram and group the pixels of an image into different regions. The optimized threshold levels computed with an Optimized technique by maximizing the inter-class variance. Methods with histograms are incapable to possess spatial details of contextual information for finding optimal threshold levels. As a remedy, a novel method proposed the Energy Curve is used instead of a histogram with Otsu’s method and Harmony Search Algorithm to compute optimized gray levels. The proposed method experimented on several benchmark images, and results compared with various optimization algorithms with histogram by Dunn Index, DB Index, SD Index, mean of fitness and PSNR, comparison clarifies that the proposed method is superior to histogram-based methods.
Learning morphological operators for skin detection

Human skin detection, i.e. the process of discriminating "skin" and "non-skin" pixel in an image or a video, is a very important task for several applications including face detection, video surveillance, body tracking, hand gesture recognition, and many others. Skin detection has been widely studied from the research community resulting in several methods based on hand-crafted rules or deep learning. In this work we propose a novel post-processing approach for skin detectors based on trained morphological operators. The first step, consisting in skin segmentation, is performed according to an existing skin detection approach, and then a second step is carried out consisting in the application of a set of morphological operators to refine the resulting mask. Extensive experimental evaluation, performed considering two different detection approaches (one based on deep learning and a handcrafted one), carried on 10 different datasets confirms the quality of the proposed method. To encourage future comparisons the MATLAB source code is freely available in the GitHub repository: https://github.com/LorisNanni.
Practical relevance of software engineering research: synthesizing the community’s voice

Software engineering (SE) research should be relevant to industrial practice. There have been regular discussions in the SE community on this issue since the 1980's, led by pioneers such as Robert Glass. As we recently passed the milestone of "50 years of software engineering", some recent positive efforts have been made in this direction, e.g., establishing "industrial" tracks in several SE conferences. However, many researchers and practitioners believe that we, as a community, are still struggling with research relevance and utility. The goal of this paper is to synthesize the evidence and experience-based opinions shared on this topic so far in the SE community, and to encourage the community to further reflect and act on the research relevance. For this purpose, we have conducted a Multi-vocal Literature Review (MLR) of 54 systematically-selected sources (papers and non peer-reviewed articles). Instead of relying on and considering the individual opinions on research relevance, mentioned in each of the sources, the MLR aims to synthesize and provide the "holistic" view on the topic. The highlights of our MLR findings are as follows. The top three root causes of low relevance, discussed in the community, are: (1) Researchers having simplistic views (or wrong assumptions) about SE in practice; (2) Lack of connection with industry; and (3) Wrong identification of research problems. The top three suggestions for improving research relevance are: (1) Using appropriate research approaches such as action-research; (2) Choosing relevant research problems; and (3) Collaborating with industry. By synthesizing all the discussions on this important topic so far, this paper aims to encourage further discussions and actions in the community to increase our collective efforts to improve the research relevance.
Joint Person Objectness and Repulsion for Person Search

Person search targets to search the probe person from the unconstrainted scene images, which can be treated as the combination of person detection and person matching. However, the existing methods based on the Detection-Matching framework ignore the person objectness and repulsion (OR) which are both beneficial to reduce the effect of distractor images. In this paper, we propose an OR similarity by jointly considering the objectness and repulsion information. Besides the traditional visual similarity term, the OR similarity also contains an objectness term and a repulsion term. The objectness term can reduce the similarity of distractor images that not contain a person and boost the performance of person search by improving the ranking of positive samples. Because the probe person has a different person ID with its \emph{neighbors}, the gallery images having a higher similarity with the \emph{neighbors of probe} should have a lower similarity with the probe person. Based on this repulsion constraint, the repulsion term is proposed to reduce the similarity of distractor images that are not most similar to the probe person. Treating the Faster R-CNN as the person detector, the OR similarity is evaluated on PRW and CUHK-SYSU datasets by the Detection-Matching framework with six description models. The extensive experiments demonstrate that the proposed OR similarity can effectively reduce the similarity of distractor samples and further boost the performance of person search, e.g., improve the mAP from 92.32% to 93.23% for CUHK-SYSY dataset, and from 50.91% to 52.30% for PRW datasets.
A comprehensive comparative study of clustering-based unsupervised defect prediction models

Software defect prediction recommends the most defect-prone software modules for optimization of the test resource allocation. The limitation of the extensively-studied supervised defect prediction methods is that they require labeled software modules which are not always available. An alternative solution is to apply clustering-based unsupervised models to the unlabeled defect data, called Clustering-based Unsupervised Defect Prediction (CUDP). However, there are few studies to explore the impacts of clustering-based models on defect prediction performance. In this work, we performed a large-scale empirical study on 40 unsupervised models to fill this gap. We chose an open-source dataset including 27 project versions with 3 types of features. The experimental results show that (1) different clustering-based models have significant performance differences and the performance of models in the instance-violation-score-based clustering family is obviously superior to that of models in hierarchy-based, density-based, grid-based, sequence-based, and hybrid-based clustering families; (2) the models in the instance-violation-score-based clustering family achieves competitive performance compared with typical supervised models; (3) the impacts of feature types on the performance of the models are related to the indicators used; and (4)the clustering-based unsupervised models do not always achieve better performance on defect data with the combination of the 3 types of features.
Deep context modeling for multi-turn response selection in dialogue systems

Multi-turn response selection is a major task in building intelligent dialogue systems. Most existing works focus on modeling the semantic relationship between the utterances and the candidate response with neural networks like RNNs and various attention mechanisms. In this paper, we study how to leverage the advantage of pre-trained language models (PTMs) to multi-turn response selection in retrieval-based chatbots. We propose a deep context modeling architecture (DCM) for multi-turn response selection by utilizing BERT as the context encoder. DCM is formulated as a four-module architecture, namely contextual encoder, utterance-to-response interaction, features aggregation, and response selection. Moreover, in DCM, we introduce the next utterance prediction as a pre-training scheme based on BERT, aiming to adapt general BERT to accommodate the inherent context continuity underlying the multi-turn dialogue. Taking BERT as the backbone encoder, we then investigate a variety of strategies to perform response selection with comprehensive comparisons. Empirical results on three public datasets from two different languages show that our proposed model outperforms existing promising models significantly, pushing recall to 86.8% (+5.2% improvement over BERT) on Ubuntu Dialogue corpus, recall to 68.5% (+6.4% improvement over BERT) on E-Commerce Dialogue corpus, MAP and MRR to 61.6% and 64.9% respectively (+2.3% and 1.8% improvement over BERT) on Douban Conversation corpus, achieving new state-of-the-art performance for multi-turn response selection.
BioSec: A Biometric Authentication Framework for Secure and Private Communication Among Edge Devices in IoT and Industry 4.0

To address the privacy and security related challenges in Internet of Things (IoT) environment, we proposed a biometric method called BioSec to provide authentication in IoT integrated with edge consumer electronics with fingerprint authentication. Further, we ensured the security of biometric data both in the transmission channel and database with the standard encryption method. In this way, BioSec ensures secure and private communication among edge devices in IoT and Industry 4.0. Finally, we have compared three encryption methods used to protect biometric templates in terms of processing times and identified that AES-128-bit key encryption method outperforms others.
An adaptive fault detector strategy for scientific workflow scheduling based on improved differential evolution algorithm in cloud

With the increasing popularity and acceptance of cloud computing, it is being applied in services like executing large-scale applications, where cloud environment is selected by the scientific associations to easily execute the computation intensive workflows. However, cloud computing can have higher failure rates due to the larger number of servers and components filled with the intensive workloads. These failures may lead to the unavailability of virtual machines (VMs) for computation. Hence, this issue of fault occurrences can be tolerated by adopting an effective and efficient fault tolerant strategy. The goal of our research in this paper is to develop an adaptive fault detector strategy based on Improved Differential Evolution (IDE) algorithm in cloud computing that can minimize the energy consumption, the makespan, the total cost and, at the same time, tolerate up faults when scheduling scientific workflows. This proposed work applies an adaptive network-based fuzzy inference system (ANFIS) prediction model to proactively control resource load fluctuation that increases the failure prediction accuracy before fault/failure occurrence. In addition, it applies a reactive fault tolerance technique for when a processor fails and the scheduler must allocate a new VM to execute the workflow tasks. The experimental results show that compared with existing techniques, the proposed approach significantly improves the overall scheduling performance, achieves a higher degree of fault tolerance with high HyperVolume (HV) compared with the ICFWS, IDE, and ACO algorithms, minimizes the makespan, the energy consumption and task fault ratio, and reduces the total cost.
Cybersecurity Impact over Bigdata and IoT Growth

Big Data and IoT based applications are promising and being necessary for almost all the fields. IoT applications provide us with beneficial services, and also they gather and transmit data to Big Data databases where data can be stored and analyzed. Big Data and IoT started to be involved in smart homes, smart healthcare, education, shopping and even in agriculture field. These Big Data and IoT based applications are growing rapidly. The more these technologies are giving us great applications and making our life better; the more cybersecurity attacks start against them. These applications are the target for attackers due to the useful and massive amount of data they have. Cybersecurity is a significant issue for these technologies. Cybersecurity threats and attacks can stop these technologies from growing, which is considered to be a negative point for us and these promising technologies. Cybersecurity threats weaken these technologies to gain full access over the user's data. Understanding the possible applications and benefits that we could learn from these technologies is important Also, understanding and being aware of the possible threats that could threaten the various Big Data and IoT based applications is more critical Understanding the possible cybersecurity attacks and threats can help us to know about how to protect these technologies and applications from cybersecurity attacks. This research presents critical cybersecurity impacts in the form of security threats, and attacks that could be initiated against Big Data and IoT based applications and affect their growth. These impacts are elaborated using a case study of a healthcare system with its possible cybersecurity attacks, which shows the relation between cybersecurity attacks and the growth of Big Data and IoT technologies.
Research and Implementation of Cross-Chain Transaction Model Based on Improved Hash-Locking

Blockchain is a decentralized, trust-free distributed ledger technology which has been applied in various fields such as finance, supply chain, and asset management. However, the network isolation between blockchains has limited their interoperability in asset exchange and business collaboration since it forms blockchain islands. Cross-chain is an important technology aiming to realize the interoperability between blockchains, and has become one of the hottest research topics in this area. This paper proposes a cross-chain transaction model based on improved hash locking consulted by the notary and users, which can solve the security problems in traditional hash locking. It can prevent malicious participants from creating large traffic blocking channels based on the key of unlocking condition. At the same time, the scheme of notary multi-signature is designed to solve the problem of lack of trust in traditional model. The transaction process of cross-chain, key agreement, cooperation mechanism of cross-chain, and security analysis based on the model is given in detail. Experiments of cross-chain transactions are implemented in Ethereum private chain, and prove that the proposed model has good application abilities.