Reviewerly/exHarmony huggingface dataset:
Investigating replication challenges through multiple replications of an experiment

As Empirical Software Engineering grows in maturity and number of publications, more replications are needed to provide a solid grounding to the evidence found through prior research. However, replication studies are scarce in general and some topics suffer more than others with such scarcity. On top, the challenges associated with replicating empirical studies are not well understood. In this study, we aim to fill this gap by investigating difficulties emerging when replicating an experiment. We used participants with distinct backgrounds to play the role of a research group attempting to replicate an experimental study addressing Highly-Configurable Systems. Seven external close replications in total were performed. After obtaining the quantitative replication results, a focus group session was applied to each group inquiring about the replication experience. We used the grounded theory’s constant comparison method for the qualitative analysis. We have seen in our study that, in the replications performed, most results hold when comparing them with the baseline. However, the participants reported many difficulties in replicating the original study, mostly related to the lack of clarity of the instructions and the presence of defects on replication artifacts. Based on our findings, we provide recommendations that can help mitigate the problems reported. The quality of replication artifacts and the lack of clear instructions might impact an experiment replication. We advocate having good quality replication instructions and well-prepared laboratory packages to foster and enable researchers to perform better replications.
Collision-free motion planning approach for multi-object spectrographs with equal-arm fiber positioners

The fiber positioner plays an important role in carrying the fiber in the Fiber Multi-Object Spectrograph. Most spectrographs such as the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) and the Dark Energy Spectroscopic Instrument (DESI) have thousands of fiber positioners shared working space. However, the overlap of motion areas increases the possibility of collisions for fiber positioners within a confined space. The collision prevents fiber positioners from reaching the designated observation position, which reduces the observation accuracy of the telescope and the service life of fiber positioners. This study proposed a collision-free motion planning approach based on the collision risk function for the fiber positioner. By using this method, a collision-free trajectory can be planned before positioners run, avoiding the discontinuity of trajectory caused by collision avoidance while running. Simulated experiments validated that our method has the advantages of easy operation, complete avoidance of collision, and less accumulation of motion errors compared with the existing anti-collision motion trajectory planning methods.
Dual Optimization of Revenue and Expense in Geo-Distributed Data Centers Using Smart Grid

Exorbitant energy expenses can supersede data center profits. Electricity prices often vary across the geographic regions, caused by gaps in the supply-demand, time of use, and production cost factors. Geo-distributed cloud data centers facilitated by a smart grid and enabled by cloud computing can potentially utilize the spatiotemporal diversity of energy prices to reduce operational expenditure and maximize profit. In this article, we solve the data center profit by formulating it as a constrained multi-objective optimization problem. The proposed solution utilizes an evolutionary algorithm-based higher-level heuristic that optimizes data center revenue and expense objectives simultaneously. The proposed technique provides system managers with trade-off solutions suited to varied operational scenarios. Ours is a multi-step approach, utilizing the optimization scheme to obtain Pareto optimal solutions for the request dispatch and resource allocation problem. When broadly evaluated against a comparative resource optimization scheme, our technique increases revenue while lowering expense and collectively yields a higher profit. It exhibits such performance over a broad range of price changes regardless of the data center's size and utilization level. The extensive simulation results ascertain the effectiveness of the proposed approach across a myriad of system parameters.
Aspect-Level Sentiment Analysis with Local Semantic and Global Syntactic Features Integration

Aspect-level sentiment analysis aims to predict the sentiment polarity toward a specific aspect in a sentence. Most current approaches are based on deep learning and the attention mechanism. However, these models cannot simultaneously include the context semantic information carried by local words and the global syntactic information possibly carried by remote words. In this paper, we propose a local semantic and global syntactic integration scheme, which employs a local focus mechanism over local context words and exploits improved graph convolutional networks over dependency tree to encode global syntactic information. Moreover, multi-head attention is used to capture both the semantic information and also the interactive information between semantics and syntactic features. Experimental results on five datasets show the effectiveness of our model over a series of latest models.
Convolutional Neural Networks Based Dictionary Pair Learning for Visual Tracking

Feature representation is a key component of most visual tracking algorithms. It is difficult to deal with complex appearance changes with low-level hand-crafted features due to weak representation capacities of such features. In this paper, we propose a novel tracking algorithm through combining a joint dictionary pair learning with convolutional neural networks (CNN). We utilize CNN model that is trained on ImageNet-Vid to extract target features. The CNN includes three convolutional layers and two fully connected layers. A dictionary pair learning follows the second fully connected layer. The joint dictionary pair is learned upon extracted deep features by the trained CNN model. The temporal variations of target appearances are learned in the dictionary learning. We use the learned dictionaries to encode target candidates. A linear combination of atoms in the learned dictionary is used to represent target candidates. Extensive experimental evaluations on OTB2015 demonstrate the superior performances against SOTA trackers.
Large-Scale Parallel Alignment Algorithm for SMRT Reads

Single Molecule Real-Time (SMRT) sequencing is one of the popular issues in third-generation sequencing technology. Compared with next-generation sequencing technology, SMRT can detect single molecules and has much longer read lengths, which also leads to a huge increase in the amount of data. As the performance of a single CPU has reached its bottleneck, single-node computing is far from meeting the SMRT sequencing requirements. An alternative solution is parallel computing. It makes the alignment algorithm run on multiple computing nodes, thus greatly decreases the running time. The Regional Hashing-based Alignment Tool (rHAT) is a novel approach developed especially for SMRT sequencing. It has better sensitivity, improved correctness compared with existing sequence alignment tools. However, the original rHAT source can only run on a single node, which dramatically limits its performance. In this article, we developed PrHAT, a parallel sequence alignment version of rHAT. We test PrHAT on simulated and real datasets which the original rHAT used. Our results show that PrHAT reduces the computing wall-time from nearly an hour to several minutes. In the process of increasing the number of nodes from 2 to 16 on aligning large-scale datasets, PrHAT achieves speedups of 1.94–14.87x. The parallel efficiency decreases from 97% to 93%; moreover, its weak scaling remains almost unchanged. Based on PrHAT, we developed OpenPrHAT. It has a similar performance towards PrHAT, but can run on other computing devices like GPU in the platform. We expect that the implementation of PrHAT will promote the development of SMRT in third-generation sequencing technology.
Random sample consensus in decentralized Kalman filter

This paper presents a new decentralized Kalman filter (DKF) framework to detect and isolate faulty nodes in a network of sensors. The Chi-square test is a well-versed fault detection method. However, if error in the predicted state estimate is not within bounds defined by the predicted error covariance matrix, the Chi-square test fails. In order to detect faults (also known as outliers) in measurements, the random sample consensus (RANSAC) algorithm has been widely used in computer vision applications. We propose a novel integration of RANSAC into DKF framework, named as the DKF-RANSAC algorithm, that uses the information and information matrix of the connecting nodes to formulate a hypothesis to detect faulty nodes. An approach for minimizing the iterations in the RANSAC algorithm using the Chi-square test is also presented. The proposed DKF-RANSAC algorithm is validated on a target tracking problem. A simulation study shows that this algorithm identifies the faulty nodes correctly and isolates them, as well as handles incorrect initialization error simultaneously. The proposed DKF-RANSAC algorithm is also compared with the well-known Chi-square test as well as an adaptive DKF.
CHIS: A Novel Hybrid Granularity Identifier Splitting Approach

Information Retrieval (IR) techniques have been widely utilized by a growing number of software maintenance activities. However, there is a mismatch between source code lexicon (especially identifiers) and vocabulary in software artifacts, leading to the inefficiency of IR techniques. Consequently, it is essential to normalize identifiers, whose aim is to parse identifiers into several natural language terms. Identifier splitting significantly impacts on the effectiveness of identifier normalization. Even though researchers have proposed several approaches to split identifiers, three main drawbacks remain to be resolved, including without considering morphemes, over-splitting, and under-splitting. In this paper, we propose a new Character-level Hybrid-granularity Identifier Splitting approach CHIS to resolve the three drawbacks and better split identifiers. CHIS combines the Bidirectional Encoder Representation from Transformers (BERT) and Conditional Random Fields (CRF) to train a deep learning model to split identifiers. In addition, CHIS further employs a pre-processing component and a post-processing component to resolve the morpheme acquisition drawback and the over-splitting as well as the under-splitting drawbacks respectively, thus further improving its performance. Specifically, in the pre-processing component, CHIS obtains and labels the most frequent subwords of the training identifiers as morphemes through the Byte Pair Encoding (BPE) algorithm and the sequence labeling algorithm. In the post-processing component, CHIS iteratively merges and splits the splitting results obtained by the deep learning model to resolve the over-splitting and under-splitting drawbacks. We conduct extensive experiments to show the effectiveness of CHIS. Experimental results show that CHIS achieves the Accuracy of 0.943 on average and outperforms the state-of-the-art approach by 0.085 on average. In addition, the effectiveness of the pre-processing and post-processing components of CHIS are also validated.
Efficient Authentication for Securing Electronic Health Records using Algebraic Structure

The trend among healthcare organizations to outsource EHR data to the cloud creates serious privacy concerns for specific individuals. When healthcare organizations outsource their electronic health records (EHRs) to a hybrid cloud, security and privacy concerns arise since EHRs can be accessed by an endless number of people. A hybrid cloud infrastructure combines public and private clouds. The authors of this study took into account the need for a privacy-preserving access restriction. In traditional electronic health records (EHRs), medical information is frequently controlled independently by several hospitals, making information sharing problematic. Information sharing is a challenge with traditional EHRs, which cloud-based EHRs solve. Cloud-based EHRs, on the other hand, have a centralized issue in the shape of a cloud service center and a key-generation center. The authors of this study suggest an algebraic structure-based authentication technique for electronic health data. The authors used a multi-authority identity-based signature technique that is resistant to collusion assaults from N different authorities.
Comparison of Overlay Design in Between Lightweight Deflectometer and Benkelman Beam Deflection Test Results: A Case Study in India

Strengthening is an essential aspect of the operation, maintenance and management of low volume roads. In India, maintenance of other districts roads and village roads is a very significant task. Benkelman Beam Deflection (BBD) method is laborious and time-consuming because of the state of practice. However, the state-of-art Lightweight Deflectometer (LWD) method was employed to overlay the thickness design of low volume roads (LVR), utilizing a rationale that accounts for the determination of dynamic deflections of thin paved surfaces. This paper aimed to look into the behavior of in-service low-volume roads at a project level and develop a simplified method of designing overlay thickness for low-volume roads using two NDT methods. The results were compared and correlated for low volume roads (LVR). In this paper, responsive stress and strain of thin asphalt pavement beneath layers were analyzed using KENPAVE software. The overlay thickness for different sections ranges from 27.00 mm to 184.39 mm by LWD and BBD. The correlation for deflection values between LWD and BBD was R2 (0.741), while the overlay thickness designed by LWD and BBD showed a high correlation with R2 value of 0.994. This paper also serves to assess the capability of LWD to serve as a substitute to traditional BBD on low volume roads (LVR). The practical limitation of BBD test procedure and analysis may be overcome by conducting more test points using LWD on Indian thin asphalt low volume roads.
'It's on the tip of my tongue'

The tip of the tongue known-item retrieval (TOT-KIR) task involves the 'one-off' retrieval of an item for which a user cannot recall a precise identifier. The emergence of several online communities where users pose known-item queries to other users indicates the inability of existing search systems to answer such queries. Research in this domain is hampered by the lack of large, open or realistic datasets. Prior datasets relied on either annotation by crowd workers, which can be expensive and time-consuming, or generating synthetic queries, which can be unrealistic. Additionally, small datasets make the application of modern (neural) retrieval methods unviable, since they require a large number of data-points. In this paper, we collect the largest dataset yet with 15K query-item pairs in two domains, namely, Movies and Books, from an online community using heuristics, rendering expensive annotation unnecessary while ensuring that queries are realistic. We show that our data collection method is accurate by conducting a data study. We further demonstrate that methods like BM25 fall short of answering such queries, corroborating prior research. The size of the dataset makes neural methods feasible, which we show outperforms lexical baselines, indicating that neural/dense retrieval is superior for the TOT-KIR task.
PPCD-GAN: Progressive Pruning and Class-Aware Distillation for Large-Scale Conditional GANs Compression

We push forward neural network compression research by exploiting a novel challenging task of large-scale conditional generative adversarial networks (GANs) compression. To this end, we propose a gradually shrinking GAN (PPCD-GAN) by introducing progressive pruning residual block (PP-Res) and class-aware distillation. The PP-Res is an extension of the conventional residual block where each convolutional layer is followed by a learnable mask layer to progressively prune network parameters as training proceeds. The class-aware distillation, on the other hand, enhances the stability of training by transferring immense knowledge from a well-trained teacher model through instructive attention maps. We train the pruning and distillation processes simultaneously on a well-known GAN architecture in an end-to-end manner. After training, all redundant parameters as well as the mask layers are discarded, yielding a lighter network while retaining the performance. We comprehensively illustrate, on ImageNet 128 × 128 dataset, PPCD-GAN reduces up to 5.2 ×(81%) parameters against state-of-the-arts while keeping better performance.
A Defense Method Against Facial Adversarial Attacks

Machine Learning (ML) models have impressively performed on perceptual tasks over the past few years. However, these models remain vulnerable to adversarial attacks. An adversarial attack modifies an input by adding small perturbations to cause classifier misclassification. Although many efforts have been spent on training robust forensic models against this type of attack, the mitigation of adversarial attacks and defense against such attacks remain an active problem. We propose a defense method in forensics that mainly focuses on face adversarial attacks detection. The proposed defense method is based on the Private Fast Gradient Sign Method (P-FGSM) and Weighted Local Magnitude Pattern (WLMP) features. Face adversarial attacks are generated based on P-FGSM and extracted WLMP features are provided to Support Vector Machines (SVM) for detection of face adversarial images from the original face images. The proposed defense method is evaluated on a real-world dataset and results show that it detects the adversarial images from the original dataset with an accuracy of 98.75%.
An efficient coding‐based grayscale image automatic colorization method combined with attention mechanism

The development of deep learning provides a new way for solving the colorization problem on the grayscale image. Excellent coding-based methods appear in the automatic image colorization task, avoiding the unsaturated colour effect problem of previous methods based on the L2 loss function. Traditional neural networks come with high computational costs and a large number of parameters. Considering the limitation of memory and computing resources and aiming at lightweight, a novel grey image automatic colorization network is proposed. The basic idea of coding-based methods is used, regarding the colorization task as a pixel-level classification problem, meanwhile redesign and improve the colour encoding and decoding process. This network architecture leverages a lightweight convolution to reduce the computation and combines an efficient attention model to form a residual block as the kernel of the backbone network. Furthermore, an efficient image self-attention mechanism placed at the end of the network is applied to enhance the ultimate colouring results. The method proposed in this paper can maintain the natural colouring effect and significantly reduce the computational amount and network model parameters.
Domain Specific Text Preprocessing for Open Information Extraction

Preprocessing is an integral part of Natural Language Processing (NLP) based applications. Standard preprocessing steps consist of removal of irrelevant, unwanted characters or parts of the text based on several observed patterns, while preserving the original intent of the text. We introduce domain-specific preprocessing to filter domain-irrelevant parts of the text while preserving the intended, semantically relevant meaning and syntactic correctness of the text. For this, we define multiple patterns using the dependency tree that represents the Natural Language text based on its dependency grammar. We applied this technique and the patterns to the United States retirement domain documents for open information extraction task as a pre-cursor for mining business product information and rules, and were able to reduce the document data aka information for analysis and mining by at least 13%, which enhanced the F1-score of relation extraction by a minimum of 16%.
India’s Cyber Security Landscape

With the second-largest Internet user base and a leading Information and Communications Technology industry, India has quickly adopted digital technologies to emerge as a major actor in the global cyberspace. This thrust on digital and information technologies is accompanied by a rapid expansion of cyberthreats such as malware attacks, data breaches and cyber-enabled espionage operations. While there has been no major disruptive cyber-attack, India has faced multiple attacks from China and Pakistan—two countries with which it is locked in protracted border disputes. These two sources of threat are joined by non-state actors such as organised hacking syndicates and terrorist groups which have used the cyberspace to target Indian interests. This paper broadly examines the sources and nature of India’s cyberthreats. It also reviews the government’s policy measures to counter these threats. The paper argues that in tackling these threats, India maintains a precarious balance between its commitment to a liberal multi-stakeholder order in the cyberspace and the pressure to protect its national security interests.
Implementation of Number Theoretic Transform Unit for Polynomial Multiplication of Lattice-based Cryptography

In the post-quantum Internet of Things (loT) era, the use of anti-quantum cryptographic algorithms in a large number of terminals can effectively resist potential quantum computing attacks. The lattice-based cryptography has the characteristics of resisting quantum computing attacks, and will be an alternative to the traditional public-key cryptographic algorithm that is widely deployed now. However, the relatively large time complexity of the algorithm itself will bring considerable computational overhead to the edge computing chip in the IoT terminal. Polynomial multiplication is the most computationally intensive operation in lattice-based cryptographic algorithms, and the research on fast calculation of polynomial multiplication is of great significance. Using fast number theory transformations to speed up polynomial multiplication is a common method. This paper proposes a hardware implementation of a fast number theory transformation. We used multiple arithmetic units in the design to achieve parallel calculations, and implemented it on ASIC. The performance evaluation results show that our implementation performance is greatly improved compared to other implementation, and it can be deployed in edge computing chips to increase computing speed.
Describing Image Focused in Cognitive and Visual Details for Visually Impaired People: An Approach to Generating Inclusive Paragraphs

Several services for people with visual disabilities have emerged recently due to achievements in Assistive Technologies and Artificial Intelligence areas. Despite the growth in assistive systems availability, there is a lack of services that support specific tasks, such as understanding the image context presented in online content, e.g., webinars. Image captioning techniques and their variants are limited as Assistive Technologies as they do not match the needs of visually impaired people when generating specific descriptions. We propose an approach for generating context of webinar images combining a dense captioning technique with a set of filters, to fit the captions in our domain, and a language model for the abstractive summary task. The results demonstrated that we can produce descriptions with higher interpretability and focused on the relevant information for that group of people by combining image analysis methods and neural language models.