Reviewerly/exHarmony huggingface dataset:
The state-of-practice in requirements specification: an extended interview study at 12 companies

Abstract Requirements specification is a core activity in the requirements engineering phase of a software development project. Researchers have contributed extensively to the field of requirements specification, but the extent to which their proposals have been adopted in practice remains unclear. We gathered evidence about the state of practice in requirements specification by focussing on the artefacts used in this activity, the application of templates or guidelines, how requirements are structured in the specification document, what tools practitioners use to specify requirements, and what challenges they face. We conducted an interview-based survey study involving 24 practitioners from 12 different Swedish IT companies. We recorded the interviews and analysed these recordings, primarily by using qualitative methods. Natural language constitutes the main specification artefact but is usually accompanied by some other type of instrument. Most requirements specifications use templates or guidelines, although they seldom follow any fixed standard. Requirements are always structured in the document according to the main functionalities of the system or to project areas or system parts. Different types of tools, including MS Office tools, are used, either individually or combined, in the compilation of requirements specifications. We also note that challenges related to the use of natural language (dealing with ambiguity, inconsistency, and incompleteness) are the most frequent challenges that practitioners face in the compilation of requirements specifications. These findings are contextualized in terms of demographic factors related to the individual interviewees, the organization they are affiliated with, and the project they selected to discuss during our interviews. A number of our findings have been previously reported in related studies. These findings show that, in spite of the large number of notations, models and tools proposed from academia for improving requirements specification, practitioners still mainly rely on plain natural language and general-purpose tool support. We expect more empirical studies in this area in order to better understand the reason of this low adoption of research results.
Evaluating the Pertinence of Pose Estimation model for Sign Language Translation

Sign Language is the natural language used by a community that is hearing impaired. It is necessary to convert this language to a commonly understandable form as it is used by a comparatively small part of society. The automatic Sign Language interpreters can convert the signs into text or audio by interpreting the hand movements and the corresponding facial expression. These two modalities work in tandem to give complete meaning to each word. In verbal communication, emotions can be conveyed by changing the tone and pitch of the voice, but in sign language, emotions are expressed using nonmanual movements that include body posture and facial muscle movements. Each such subtle moment should be considered as a feature and extracted using different models. This paper proposes three different models that can be used for varying levels of sign language. The first test was carried out using the Convex Hull-based Sign Language Recognition (SLR) finger spelling sign language, next using a Convolution Neural Network-based Sign Language Recognition (CNN-SLR) for fingerspelling sign language, and finally pose-based SLR for word-level sign language. The experiments show that the pose-based SLR model that captures features using landmark or key points has better SLR accuracy than Convex Hull and CNN-based SLR models.
Blind light field image quality assessment with tensor color domain and 3D shearlet transform

Blind light field image quality assessment (LFIQA) models are able to automatically evaluate the perceptual quality of the distorted light field image (LFI) under conditions where the original LFI is not available. Most existing blind LFIQA models predict the quality of the distorted LFI by separately measuring spatial quality and angular consistency, while ignoring the correlation between the two. This paper proposes a blind LFI quality assessment method based on tensor color domain and three-dimensional (3D) shearlet transform. The traditional color space conversion only handles color 2D images, so a color space conversion based on tensor decomposition is designed to extract the color information of the distorted LFI, that is the 5D tensor, from the tensor color domain, and three 4D tensor slices are obtained. Each slice is reshaped into 3D pseudo-video, and which are converted into 3D shearlet domain, respectively. 3D shearlet coefficients are able to represent the local spatial-angular information of the distorted LFI, and the perceptual features based on 3D shearlet transform are extracted for LFI quality prediction. Finally, the perceptual feature vector is fed into the support vector regression to predict the quality of the LFI. Extensive experimental results on three databases have proved the superiority of the proposed method, and it is competitive with the start-of-the-art 2D image quality assessment, video quality assessment and LFI quality assessment methods.
Performance of Background Oriented Schlieren with different background patterns and image processing techniques

Synthetic tests of the accuracy, spatial resolution and robustness with respect to large gradient of the apparent displacement are performed for Background Oriented Schlieren (BOS) measurements using various background patterns and image processing techniques, including multi-pass cross-correlation, iterative Horn–Schunck and Lucas–Kanade optical flow algorithms and Fourier Transform Profilometry (FTP). It is shown that FTP with periodic sinusoidal or binary fringe pattern outperforms all the other methods. With optimal pattern period λpattern=6−15pix it is able to resolve perturbations with wavelength down to λpattern2 and to obtain reliable results for images with displacement gradient up to 0.9 pix/pix. Better performance of FTP, compared to multi-pass cross-correlation, is confirmed for real experimental measurements of water temperature in natural convection near a heated vertical plate. FTP advantages and limitations, including possible nonlocal error, associated with phase unwrapping, are discussed.
LGSA: A next POI prediction method by using local and global interest with spatiotemporal awareness

Predicting the next Point-of-Interest (POI) is a persistent issue in the realm of Location-Based Social Networks (LBSN). To discover the user’s dynamic interests and the dependence between different POIs in user trajectory sequences, self-attention based methods have been applied recently. These methods, however, limit local dependence in the customized spatiotemporal region and do not take the special time period in trajectory sequence into account. To this end, we propose a spatiotemporal awareness model with global and local interest (LGSA) for next POI prediction. According to the geographic distance and time interval, each user’s trajectory sequence is separated into individualized spatiotemporal regions, and the dependence between POIs check-in by user in these regions is learned from the local view. Besides, we use a non-invasive way to fuse the user’s trajectory sequence and the time period of the sequence, and mine the user’s dynamic preferences on the time period from the global view. Extensive experiments on three real-world datasets show that LGSA outperforms state-of-the-art methods.
Cryptocurrency hacking incidents and the price dynamics of Bitcoin spot and futures

The increasing hacking incidents of cryptocurrency may adversely affect cryptocurrency development and influence the Bitcoin price dynamics. We investigate the effect of hacking incidents and the hack-stolen funds on the return, volatility, and price discovery of Bitcoin spot and futures. We find that the presence of hacking incidents and an increase in stolen funds decrease Bitcoin spot and futures returns and increase their volatility contemporaneously. We also find that Bitcoin futures dominate in price discovery, but hacking incidents reduce the relative price discovery of Bitcoin futures. This is the first study to document the relation between hacking incidents and the price dynamics of Bitcoin spot and futures.
Fans Optimizer: A human-inspired optimizer for mechanical design problems optimization

This paper proposes a human-inspired algorithm for optimizing various practical engineering problems, specifically, the Fans Optimization (FO) algorithm that is inspired by the Fans economy fused in the entertainment domain. Opposing current algorithms, the FO algorithm introduces a Multi-groups mechanism (Cooperation–Competition) and a Two-characteristic individual update mechanism to balance the exploration and exploitation (E&E). Additionally, the multi-phase optimization algorithm includes Roles information and Fans-community, Fans-community Switching, Resource Competition, Information Sharing, and K-Update. In the experiments, the FO algorithm is first compared with 12 meta-heuristic algorithms in four groups of benchmark functions (selected from the IEEE CEC and GECCO competitions), demonstrating that the FO algorithm has a superior convergence performance and E&E. Moreover, the Williams test prove the superiority of the FO algorithm over the competitor algorithms. Additionally, eight class practical engineering problems verify the FO’s practical engineering applicability. Finally, the FO algorithm solves the inverse kinematic solution problem of the 9-degree of freedom serial robot arm (9-DFSRA) , demonstrating superior results over the competitor schemes. Overall, the test results prove that the FO algorithm is superior to its competitor algorithms for complex engineering problems.
Online Adaptation of Robots Controlled by Nanowire Networks: A Preliminary Study

The ability to adapt to changes and unexpected situations is a commonly acknowledged hallmark of autonomy and intelligence. In this work we take inspiration from biology for the definition of a robot able to continuously adapt to changes. Specifically, we define its control structure and the mechanism used to perform the adaptation. The former is based on the Reservoir Computing framework, on which the latter acts. The result is the design of an Online Adaptive Reservoir Computing system based on a novel memristive reservoir: the Nanowire Network. Finally, the robot is tested on three different tasks taking place in different arenas. The results are then discussed and compared with a baseline algorithm.
Cervical precancerous lesion classification using quantum invasive weed optimization with deep learning on biomedical pap smear images

Abstract Biomedical imaging devices, in general, have been made and used a lot lately to examine the insides of the body during diagnostic and analytic procedures. Biomedical imaging gives accurate information about metabolites, which can be used to find and classify diseases because it is not invasive. For the study of cervical cancer (CC), the pap smear is a crucial type of biological imaging. CC is a crucial reason to enhance the rate of women's mortalities. Proper screening of pap smear images is critical for assisting in the early detection and analysis of CC. Computer‐aided systems for cancerous cell recognition need well established artificial intelligence (AI) methods. In this study, we introduce an automated Cervical Precancerous Lesion Classification using Quantum Invasive Weed Optimization with Deep Learning (CPLC‐QIWODL) on biomedical pap smear images. The presented CPLC‐QIWODL technique examines the pap smear images for cervical cancer classification. To do so, the presented CPLC‐QIWODL technique pre‐processes the biomedical images using a Gabor filtering (GF) approach. Moreover, the CPLC‐QIWODL technique uses a deep convolutional neural network‐based SqueezeNet system for feature extraction. Furthermore, the hyperparameter tuning of the SqueezeNet methodology takes place using the QIWO technique, showing the novelty of the work. Finally, to classify CC, the deep variational autoencoder (DVAE) model is applied. The experimental result analysis of the CPLC‐QIWODL technique is tested using a benchmark medical image database. Extensive comparative results demonstrated the enhanced outcomes of the CPLC‐QIWODL technique over other existing algorithms, with a maximum accuracy of 99.07%.
Ranking loss and sequestering learning for reducing image search bias in histopathology

Recently, deep learning has started to play an essential role in healthcare applications, including image search in digital pathology. Despite the recent progress in computer vision, significant issues remain for image searching in histopathology archives. A well-known problem is AI bias and lack of generalization. A more particular shortcoming of deep models is the ignorance toward search functionality. The former affects every model, the latter only search and matching. Due to the lack of ranking-based learning, researchers must train models based on the classification error and then use the resultant embedding for image search purposes. Moreover, deep models appear to be prone to internal bias even if using a large image repository of various hospitals. This paper proposes two novel ideas to improve image search performance. First, we use a ranking loss function to guide feature extraction toward the matching-oriented nature of the search. By forcing the model to learn the ranking of matched outputs, the representation learning is customized toward image search instead of learning a class label. Second, we introduce the concept of sequestering learning to enhance the generalization of feature extraction. By excluding the images of the input hospital from the matched outputs, i.e., sequestering the input domain, the institutional bias is reduced. The proposed ideas are implemented and validated through the largest public dataset of whole slide images. The experiments demonstrate superior results compare to the-state-of-art.
Multiple Blocks, Multiple Rounds Method for Image Cryptography

In this research paper a simple, flexible, secure and efficient method of digital image cryptography will be introduced. The method will enhance some features of standard methods like DES and AES. The introduced method will divide the source data into blocks, the block size will be variable, the number of rounds will be also variable and they will be determined by the user when selecting the private key. The proposed method will use a complicated private key, which will provide a huge key space capable to resist hacking attacks, the produced decrypted data will be very sensitive to the selected private key, any minor changes in the key will be considered as a hacking attempt by producing damaged decrypted data. The private key will be used to divide the image into blocks, to set the number of rounds and to generate the required for rounds secret keys. The private key will contain the values of chaotic parameters to run two chaotic logistic map models to generate two2D chaotic keys; these keys will be converted to decimal integers to apply the required XORing operations. The proposed method will be tested and implemented using various images; the obtained results will be used to prove the enhancement provided by this method: quality of cryptography, security, speed and sensitivity.
Chinese Named Entity Recognition Method Based on Dictionary Semantic Knowledge Enhancement

Chinese Named Entity Recognition is the fundamental technology in the field of the Chinese Natural Language Process. It is extensively adopted into information extraction, intelligent question answering, and knowledge graph. Nevertheless, due to the diversity and complexity of Chinese, most Chinese NER methods fail to sufficiently capture the character granularity semantics, which affects the performance of the Chinese NER. In this work, we propose DSKE-Chinese NER: Chinese Named Entity Recognition based on Dictionary Semantic Knowledge Enhancement. We novelly integrate the semantic information of character granularity into the vector space of characters and acquire the vector representation containing semantic information by the attention mechanism. In addition, we verify the appropriate number of semantic layers through the comparative experiment. Experiments on public Chinese datasets such as Weibo, Resume and MSRA show that the model outperforms character-based LSTM baselines.
Strategies for the Analysis of Large Social Media Corpora: Sampling and Keyword Extraction Methods

In the context of the COVID-19 pandemic, social media platforms such as Twitter have been of great importance for users to exchange news, ideas, and perceptions. Researchers from fields such as discourse analysis and the social sciences have resorted to this content to explore public opinion and stance on this topic, and they have tried to gather information through the compilation of large-scale corpora. However, the size of such corpora is both an advantage and a drawback, as simple text retrieval techniques and tools may prove to be impractical or altogether incapable of handling such masses of data. This study provides methodological and practical cues on how to manage the contents of a large-scale social media corpus such as Chen et al. (JMIR Public Health Surveill 6(2):e19273, 2020) COVID-19 corpus. We compare and evaluate, in terms of efficiency and efficacy, available methods to handle such a large corpus. First, we compare different sample sizes to assess whether it is possible to achieve similar results despite the size difference and evaluate sampling methods following a specific data management approach to storing the original corpus. Second, we examine two keyword extraction methodologies commonly used to obtain a compact representation of the main subject and topics of a text: the traditional method used in corpus linguistics, which compares word frequencies using a reference corpus, and graph-based techniques as developed in Natural Language Processing tasks. The methods and strategies discussed in this study enable valuable quantitative and qualitative analyses of an otherwise intractable mass of social media data.
Text Classification Using Hybridization of Meta-Heuristic Algorithm with Neural Network

Sentiment analysis is one of the important applications of text mining which categorized the text into sentiments. This research work presents the sentiment categorization of text which consists of pre-processing, feature vector extraction, feature selection, and classification steps. The grey wolf technique is used in this work for feature selection to extract the prominent text features from the feature vector. Further, deep learning neural network is used to classify the text into positive and negative sentiments. The validation of the proposed model is done on publicly available Tweeter and movie review datasets namely sentiment140 and Imdb, respectively. The highest 94.16%, 95.81%, 92.87%, and 95.22% of precision, accuracy, recall, and f-measures have achieved, respectively.
The role of immersive technologies in value creation in marketing

Technological innovations and their potential as a tool for creating added value in marketing have long been underused, or insufficiently researched. This paper describes the use of immersive technologies and their potential for application in marketing. The use of immersive technologies in marketing has great potential and has attracted a lot of attention in the last few years. Incorporating the use of the concept of immersive technologies is increasingly becoming the subject of research in business and marketing. Initiating the use of immersive technologies in modern business is becoming the root of managing and creating a completely new customer experience. Reality can be shaped by using this type of technological innovation and as such immersive technologies with their role create an extraordinary position in communication with users. The use of immersive technologies, especially the use of Virtual Reality (VR), Augmented Reality (AR) and Mixed Reality (MR) has been used successfully in the entertainment sector for some time, but it becomes clear that there is a much greater need for immersive technologies in various fields and industries to recognize them as a huge potential for value creation in marketing.
Deep Learning for Sarcasm Identification in News Headlines

Sarcasm is a mode of expression whereby individuals communicate their positive or negative sentiments through words contrary to their intent. This communication style is prevalent in news headlines and social media platforms, making it increasingly challenging for individuals to detect sarcasm accurately. To mitigate this challenge, developing an intelligent system that can detect sarcasm in headlines and news is imperative. This research paper proposes a deep learning architecture-based model for sarcasm identification in news headlines. The proposed model has three main objectives: (1) to comprehend the original meaning of the text or headlines, (2) to learn the nature of sarcasm, and (3) to detect sarcasm in the text or headlines. Previous studies on sarcasm detection have utilized datasets of tweets and employed hashtags to differentiate between ordinary and sarcastic tweets depending on the limited dataset. However, these datasets were prone to noise regarding language and tags. In contrast, using multiple datasets in this study provides a comprehensive understanding of sarcasm detection in online communication. By incorporating different types of sarcasm from the Sarcasm Corpus V2 from Baskin Engineering and sarcastic news headlines from The Onion and HuffPost, the study aims to develop a model that can generalize well across different contexts. The proposed model uses LSTM to capture temporal dependencies, while the proposed model utilizes a GlobalMaxPool1D layer for better feature extraction. The model was evaluated on training and test data with an accuracy score of 0.999 and 0.925, respectively.
Seeing the unseen: Wifi-based 2D human pose estimation via an evolving attentive spatial-Frequency network

Camera-based human pose estimation has become popular due to its wide applications and easy implementation. However, it is not applicable under several circumstances such as poor illumination, occlusion, and private protection. In this work, we utilize Wifi signals to estimate 2D human poses, which is challenging because Wifi signals are abstract and contain limited information. To address these challenges, we develop an evolving attentive spatial-frequency network to discover the relationship between signal variation and body movement for Wifi-based 2D human pose estimation. By first taking dilated CSI sequences as inputs, a spatial-frequency encoder is then introduced to effectively integrate static spatial information and dynamic frequency information from CSI signals. Finally, we design an evolving attention module to enable our model to attend to certain channels of features. Due to a lack of benchmarks, we propose two Wifi-based human pose estimation datasets, the General Pose Estimation dataset (GPE) and Specific Pose Estimation dataset (SPE), which have been released as a public download at project page. Extensive experiments on the proposed datasets show that our model outperforms the state-of-the-art method by at least 16% in terms of PCK@20 (percentage of correct keypoints).