Reviewerly/exHarmony huggingface dataset:
An Efficient-Assembler Whale Optimization Algorithm for DNA Fragment Assembly Problem: Analysis and Validations

The study of deoxyribonucleic acid (DNA) is crucial in many fields, including medicine, biology, zoology, agriculture, and forensics.Since reading a DNA sequence is onerous because of its massive length, it is common in many DNA analysis applications to divide DNA strands into small segments or fragments which, after analysis, must be reassembled.Since this reassembly takes a non-specific polynomial time to solve, the DNA fragment assembly problem (DFAP) is NP-hard.This paper proposes a new assembler for tackling the DFAP based on the overlap-layout-consensus (OLC) approach.The proposed assembler adapts a discrete whale optimization algorithm (DWOA) using standard operators adopted from evolutionary algorithms to simulate the strategy adopted by humpback whales when searching for prey.For the first time, we formulate the behaviors of whales to be applied directly to any discrete optimization problem based on three primary operations: a swap-based best-position operator, an ordered crossover operator, and selection of a random whale operation to perform the exploitation and exploration phases of the algorithm.These operations were carefully designed to preserve the methodology of the original whale algorithm.DFAP is a multi-objective problem that seeks to reach the optimal order of segments that maximizes the overlap score and minimizes the number of contigs (set of overlapping DNA segments) to compose a one-contig DNA strand.Existing local search methods, such as problem aware local search (PALS) many non-conflicting movements (PALS2-many), suffer from being trapped in local optima.Hence, the integration of DWOA with PALS2-many improves the search capability for finding the optimal order of fragments.In addition, we propose a new variation of PALS2-many that achieves simultaneously the two objectives of DFAP.Our proposed DWOA was compared with a number of the most recent robust assemblers: a hybrid crow search algorithm for solving the DFAP (CSA-P2M*Fit), P2M*Fit, and a hybrid genetic algorithm (GA-P2M*Fit).The experimental results and statistical analyses of the proposed DWOA on thirty benchmark instances show that DWOA significantly outperforms those algorithms in reaching fewer contigs, in addition to being competitive with CSA-P2M*Fit and superior to P2M*Fit and GA-P2M*Fit for the overlap score.
Road Obstacle Detection Method Based on an Autoencoder with Semantic Segmentation

Accurate detection of road obstacles is vital for ensuring safe autonomous driving, particularly on highways. However, existing methods tend to perform poorly when analyzing road scenes with complex backgrounds, because supervised approaches cannot detect unknown objects that are not included in the training dataset. Hence, in this study, we propose a road obstacle detection method using an autoencoder with semantic segmentation that was trained with only data from normal road scenes. The proposed method requires only a color image captured by a common in-vehicle camera as input. It then creates a resynthesized image using an autoencoder consisting of a semantic image generator as the encoder and a photographic image generator as the decoder. Extensive experiments demonstrate that the performance of the proposed method is comparable to that of existing methods, even without postprocessing. The proposed method with postprocessing outperformed state-of-the-art methods on the Lost and Found dataset. Further, in evaluations using our Highway Anomaly Dataset, which includes actual and synthetic road obstacles, the proposed method significantly outperformed a supervised method that explicitly learns road obstacles. Thus, the proposed machine-learning-based road obstacle detection method is a practical solution that will advance the development of autonomous driving systems.
Characterizing Efficiency Optimizations in Solidity Smart Contracts

On many blockchain platforms, gas fees have to be paid for deploying and executing smart contracts. These fees depend on the size of the contract code as well as the needed computational steps and required storage space of a smart contract. Because of the large amount of gas cost paid each day, there is an inherent motivation to optimize smart contract code in order to reduce these cost. Within this paper, we discuss the application of 25 strategies for code optimization to Solidity smart contracts. A prototype is developed which detects potential optimizations and partially automatically optimizes the code accordingly. The optimization strategies are evaluated based on 3,018 verified open source smart contracts from etherscan.io. We find 471 rule violations in the test data spread across 204 different contract files.
Lightweight Single-Image Super-Resolution Network with Attentive Auxiliary Feature Learning

Despite convolutional network-based methods have boosted the performance of single image super-resolution (SISR), the huge computation costs restrict their practical applicability. In this paper, we develop a computation efficient yet accurate network based on the proposed attentive auxiliary features (A\(^2\)F) for SISR. Firstly, to explore the features from the bottom layers, the auxiliary feature from all the previous layers are projected into a common space. Then, to better utilize these projected auxiliary features and filter the redundant information, the channel attention is employed to select the most important common feature based on current layer feature. We incorporate these two modules into a block and implement it with a lightweight network. Experimental results on large-scale dataset demonstrate the effectiveness of the proposed model against the state-of-the-art (SOTA) SR methods. Notably, when parameters are less than 320k, A\(^2\)F outperforms SOTA methods for all scales, which proves its ability to better utilize the auxiliary features. Codes are available at https://github.com/wxxxxxxh/A2F-SR.
A lightweight approach to detect malicious/unexpected changes in the error rates of NISQ computers

Despite the current progress in quantum computing, the reliability of quantum computers is very challenging. Near-term quantum computers referred to as Noisy Intermediate-Scale Quantum (NISQ) computers are expected to operate in the presence of errors. To run a quantum circuit on a NISQ computer, the circuit should be mapped to satisfy the physical constraints of the quantum architecture. The mapping process takes into account the error rates of the quantum hardware. It selects physical qubits and their movements, which minimize the circuit error rates. The output of the quantum circuit can be obtained through several runs on NISQ computers.
When enough is enough: Investigating the antecedents and consequences of information security fatigue

Abstract Despite concerns raised by practitioners, the potential downside of the information security demands imposed by organizations on their employees has received limited scholarly attention. Our research focuses on information security fatigue (hereafter security fatigue), which is defined as a socio‐emotional state experienced by an individual who is tired of and disillusioned with security policies and their associated guidelines and procedures. This research delves into the security fatigue concept, investigates its antecedents and reports how fatigue affects employee security policy compliance (and non‐compliance). Since security fatigue is not well articulated in the literature and there is limited understanding of its antecedents and consequences, we take a research approach that affords novel insight into this phenomenon. Specifically, we conduct 38 in‐depth interviews with business and IT professionals, and then use a qualitative approach to construct a model, including seven research propositions, to highlight the key aspects of security fatigue. Our results indicate that four distinct antecedents contribute to security fatigue, which result in three unique consequences. We discuss security fatigue in relation to past theoretical views and related concepts within the security policy compliance literature and identify directions for future research.
Unsupervised face Frontalization for pose-invariant face recognition

Face frontalization aims to normalize profile faces to frontal ones for pose-invariant face recognition. Current works have achieved promising results in face frontalization by using deep learning techniques. However, training deep models of face frontalization usually needs paired training data which is undoubtedly costly and time-consuming to acquire. To address this issue, we propose a Pose Conditional CycleGAN (PCCycleGAN) to generate authentic and identity-preserving frontal face images for pose-invariant face recognition. First, through coupling with a pair of inverse mappings, constraining with cycle consistent loss and using conditional pose label to control specific face pose generation, PCCycleGAN can be trained with unpaired samples. Second, pixel-level loss, feature space perception loss, and identity preserving loss are introduced in PCCycleGAN to help synthesize realistic and identity-preserving frontal face images. Extensive experiments on both constrained Multi-PIE dataset and unconstrained LFW and IJB-A datasets are conducted on face synthesis and pose-invariant face recognition. Results demonstrate that the proposed face frontalization model can synthesize frontal faces with high image quality as well as maintaining the identity information in both the constrained and unconstrained environments. In addition, our method enhances the performance of face recognition on the Multi-PIE, LFW and IJB-A datasets and achieves competitive face recognition performance on LFW and IJB-A datasets.
Phishing Detection using Random Forest, SVM and Neural Network with Backpropagation

Phishing is a common attack used to obtain sensitive information using visually similar websites to that of legitimate websites. With the growing technology, phishing attacks are on the rise. Machine Learning is a very popular approach to detect phishing websites. This paper explains the existing machine learning methods that are used to detect phishing websites. The paper explains the improved Random Forest classification method, SVM classification algorithm and Neural Network with backpropagation classification methods which have been implemented with accuracies of 97.369%, 97.451% and 97.259% respectively.
Phase-Based Classification for Arm Gesture and Gross-Motor Activities Using Histogram of Oriented Gradients

Micro-Doppler spectrograms are a conventional data representation domain for movement recognition such as Human Activity Recognition (HAR) or gesture detection. However, they present the problem of time-frequency resolution trade-offs of Short-Time Fourier Transform (STFT), which may have limitations due to unambiguous Doppler frequency, and the STFT computation may be onerous in constrained embedded environments. We propose in this paper an alternative classification approach based on the radar phase information directly extracted from high-resolution Range Map (RM). This novel approach does not use the aforementioned micro-Doppler processing, and yet achieves equivalent or even superior classification results. This shows a potential advantage for low-latency, real-time applications, or computationally constrained scenarios. The proposed method exploits the Histogram of Oriented Gradients (HOG) algorithm as an effective feature extraction algorithm, specifically its capability to capture the unique shape and patterns present in the wrapped phase domains, such as their contour intensity and distributions. Validation results consistently above 92% demonstrate the effectiveness of this method on two independent datasets of arm gestures and gross-motor activities. These were classified with three algorithms, namely the Nearest Neighbor (NN), the linear Support Vector Machine (SVM), and the Gaussian SVM classifiers using the proposed phase information. Feature fusion of different data domains, e.g. the modulus of the RM fused with the RM phase information, is also investigated and shows classification improvement specifically for the robustness of activity performances, such as the aspect angle and the speed of performance.
A machine-learning approach for automatic classification of volcanic seismicity at La Soufrière Volcano, Guadeloupe

The classification of seismo-volcanic signals is performed manually at La Soufrière Volcano, which is time consuming and can be biased by subjectivity of the operator. We propose here a machine-learning-based model for classification of these signals, to handle large datasets and provide objective and reproducible results. To describe the properties of the signals, we used 104 statistical, entropy, and shape descriptor features computed from the time waveform, the spectrum, and the cepstrum. First, we trained a random forest classifier with a dataset provided by the Observatoire Volcanologique et Sismologique de Guadeloupe that consisted of 845 labeled events that were recorded from 2013 to 2018: 542 volcano-tectonic (VT); 217 Nested; and 86 long period (LP). We obtained an overalll accuracy of 72%. We determined that the VT class includes a variety of signals that cover the VT, Nested and LP classes. After visual inspection of the waveforms and spectral characteristics of the data set, we introduced two new classes: Hybrid and Tornillo. A new random forest classifier was trained with this new information, and we obtained a much better overall accuracy of 82%. The model is very good for recognition of all event classes, except Hybrid events (67% accuracy, 70% precision). Hybrid events are often considered to be a mix of VT and LP events. This can be explained by the nature of this class and the physical processes that include both fracturing and resonating components with different modal frequencies. By analyzing the feature weights and by training a model with the most important features, we show that a subset of the 14 best features is sufficient to obtain a performance that is close to that of the model with the whole feature set. However, these best features are different from the 13 best features obtained for another volcano in Peru, with only one feature common to both sets of best features. Therefore, the model is not universal and it must be trained for each volcano, or it is too specific to the one station used here.
Deep blind image quality assessment based on multiple instance regression

In recent years, the research of image quality assessment (IQA) based on deep learning, especially convolutional neural network (CNN), has made rapid development. The most widely used way to build a CNN-based IQA model is to divide image into patches and conduct a patch-based training. However, this method has a critical defect that the local ground-truth for each patch is not available. This defect leads to a sub-optimal result. To address this issue, we propose a novel deep blind IQA algorithm under the multiple instance regression (MIR) framework. Specifically, we assume each instance (patch) has a certain probability to be the prime instance of the bag (image), which is responsible for the bag label. Then the global quality score of the bag can be computed by the weighted summation of the local quality scores of the instances, where the weights are the probabilities of the instances to be the prime one. To simplify the training procedure, we propose an EM-like algorithm, called conditional EM algorithm, to train the deep MIR IQA model. Experimental results show that the proposed deep MIR IQA algorithm performs better than the traditional deep blind IQA algorithm. Moreover, the proposed algorithm can be used as a unified framework to improve the performance of any patch-based deep IQA models.
A hypergrid based adaptive learning method for detecting data faults in wireless sensor networks

In wireless sensor networks (WSNs), data anomalies/faults often occur due to the limited resources and unreliability of sensor nodes. Many traditional anomaly detection methods are designed in a batch manner, but for the nature of streaming data in WSNs, continuous anomaly detection method is preferred. Existing methods often detect only a single type of faults but cannot detect multiple types of faults that actually are more common in the sensor data. Therefore, this paper provides a Hypergrid based Adaptive Detection of Faults (HADF) method, which adopts hypergrid and statistical analysis to recognize three types of faults in the sensor data, including outliers, stuck-at faults, and noisy faults. HADF is a distributed method running on sensor nodes, which can reduce the influence of concept drift in unstable streaming data through combining both lazy leaning and continuous learning to adaptively update its normal profile. In the experimental study, we have manually inserted different types of faults into two real-world datasets, and the results demonstrate that HADF achieves higher accuracy with reasonable efficiency for detecting the data faults than four counterpart methods.
Research on customer opinion summarization using topic mining and deep neural network

Product reviews are of great commercial value for online shopping market. The identification of customer opinions from product reviews is helpful to improve the marketing decisions of customers, sellers and producers. This paper proposes a novel framework for summarizing customer opinions from product reviews. Firstly, our framework identifies grammatically and semantically meaningful phrases which contain product attributes and their corresponding opinions from original product reviews by using grammar rules and the latent Dirichlet allocation (LDA) model. Secondly, our framework generates readable and simple summaries from the identified phrases automatically by using the deep neural network. The summaries provide users the valuable opinions on product attributes. Moreover, our framework provides an interactive mode for users to choose product attributes which they are interested for generate personalized summaries to help users focus on the most concerned opinions. Experimental results on six datasets demonstrate effectiveness of our framework.
Auxiliary Learning for Relation Extraction

Relation extraction aims to predict a semantic relation between entities in a sentence, which is usually regarded as a classification problem. However, due to the limited relation set, many semantic relations are labeled as a special artificial relation type, termed <inline-formula><tex-math notation="LaTeX">$no\_relation$</tex-math></inline-formula>, if they are beyond the predefined relation set. Existing methods treat this artificial relation type <inline-formula><tex-math notation="LaTeX">$no\_relation$</tex-math></inline-formula> as a common semantic relation without taking its rich semantics into account. In this paper, a novel auxiliary learning method is proposed to excavate the semantic information of <inline-formula><tex-math notation="LaTeX">$no\_relation$</tex-math></inline-formula>, resulting in the improvement of generalization performance. The auxiliary learning method focuses on the model learning phase and introduces a binary classification task that treats the artificial relation type <inline-formula><tex-math notation="LaTeX">$no\_relation$</tex-math></inline-formula> as negative class and the rest semantic types as positive class. The binary classification task, named auxiliary learning task, pays more attention to <inline-formula><tex-math notation="LaTeX">$no\_relation$</tex-math></inline-formula> with a cost-sensitive loss by assigning higher cost on the misclassification of negative samples than positive ones. An additional reward is provided to the main prediction task by the auxiliary learning task, which leads to a better representation for relation extraction. Significant improvements are consistently achieved when state-of-the-art models are equipped with the auxiliary learning task on SemEval-2010 Task 8 and the large-scale TACRED. Especially, new state-of-the-art performance is achieved on SemEval-2010 Task 8 by the proposed method. Meanwhile, the motivation for introducing the auxiliary learning method is further reinforced by extensive experiments.
Organizational Factors Affecting Successful Implementation of Chatbots for Customer Service

While chatbots have become an important part of customer service operations, there is a knowledge gap concerning organizational aspects of chatbot implementation and management. In response to this gap, we present a study of organizational factors affecting successful chatbot implementation. The study involved six organizations that had implemented chatbots for customer service within the last three years. Interviews were conducted with chatbot project owners, managers, developers, and customer service personnel – a total of 14 interviews. Through thematic analysis, five organizational factors were detailed as important for successful chatbot implementation: (1) work and team organization, (2) change management, (3) competencies and competency acquisition, (4) organizational resources, and (5) performance measures. We also present findings on the organizations' motivations and key success criteria for chatbot implementation. Based on the findings we summarize implications for theory and practice and point out directions for future research.
Semantic-Driven Interpretable Deep Multi-Modal Hashing for Large-Scale Multimedia Retrieval

Multi-modal hashing focuses on fusing different modalities and exploring the complementarity of heterogeneous multi-modal data for compact hash learning. However, existing multi-modal hashing methods still suffer from several problems, including: 1) Almost all existing methods generate unexplainable hash codes. They roughly assume that the contribution of each hash code bit to the retrieval results is the same, ignoring the discriminative information embedded in hash learning and semantic similarity in hash retrieval. Moreover, the length of hash code is empirically set, which will cause bit redundancy and affect retrieval accuracy. 2) Most existing methods exploit shallow models which fail to fully capture higher-level correlation of multi-modal data. 3) Most existing methods adopt online hashing strategy based on immutable direct projection, which generates query codes for new samples without considering the differences of semantic categories. In this paper, we propose a Semantic-driven Interpretable Deep Multi-modal Hashing (SIDMH) method to generate interpretable hash codes driven by semantic categories within a deep hashing architecture, which can solve all these three problems in an integrated model. The main contributions are: 1) A novel deep multi-modal hashing network is developed to progressively extract hidden representations of heterogeneous modality features and deeply exploit the complementarity of multi-modal data. 2) Learning interpretable hash codes, with discriminant information of different categories distinctively embedded into hash codes and their different impacts on hash retrieval intuitively explained. Besides, the code length depends on the number of categories in the dataset, which can reduce the bit redundancy and improve the retrieval accuracy. 3) The semantic-driven online hashing strategy encodes the significant branches and discards the negligible branches of each query sample according to the semantics contained in it, therefore it could capture different semantics in dynamic queries. Finally, we consider both the nearest neighbor similarity and semantic similarity of hash codes. Experiments on several public multimedia retrieval datasets validate the superiority of the proposed method.