Reviewerly/exHarmony huggingface dataset:
Team conflict dynamics &amp; conflict management: derivation of a model for software organisations to enhance team performance and software quality

Commercial Software Engineering is a team based activity and therefore success is hugely dependent on whether the team has succeeded in building a cooperative environment and how well the team members get along together. Over the past years, team conflict has increasingly been viewed as a major factor that can cause the failure of a software project. Conflict must be properly managed in the best interest of the project's stakeholders. This research uses team conflict dynamics model to analyse different conflict types and team conflict profiles to produce a framework that can improve project success in software development. An eight stage framework was devised and was tested. From the data gathered it was found that the framework was successful. This framework can be studied by individuals, taught or applied by a mediator and also another benefit is that individuals are encouraged to express themselves and integrate emotional intelligence.
Enhancing Blockchain-Based Processes with Decentralized Oracles

The automation of business processes via blockchain-based systems allows for trust, reliability and accountability of execution. The link that connects modules that operate within the on-chain sphere and the off-chain world is key as processes often involve the handling of physical entities and external services. The components that create that link are named oracles. Numerous studies on oracles and their implementations are arising in the literature. Nevertheless, their availability, integrity and trust could be undermined if centralized architectures are adopted, as taking over an oracle could produce the effect of a supply-chain attack on the whole system. Solutions are emerging that overcome this issue by turning the architecture underneath the oracles into a distributed one. In this paper, we investigate the design and application of oracles, distinguishing their adoption for the in-flow or out-flow of information and according to the initiator of the exchange (hence, pull- or push-based).
Sentiment Analysis-Based Sexual Harassment Detection Using Machine Learning Techniques

Despite intensive efforts to prevent and control malicious human activities, they still pose grave risks and significant challenges in cyber-space. Malicious human activities are an evolving problem, especially with fast-growing technological advances. Sexual harassment or cyberbullying is considered an online malicious human activity that can easily affect legitimate users, governments, or other targets. The primary goal of this research is to propose an approach that could be utilized towards developing detection systems and enhance the classification of the different types of malicious human activities by using machine learning with different algorithms. Experiments showed that combining Term Frequency Inverse Document Frequency (TF-IDF) with machine learning achieved 81 % accuracy rate.
Multi-Scale Convolutional Neural Networks for Classification of Digital Mammograms With Breast Calcifications

Breast cancer is the most commonly diagnosed cancer in women. Mammography is a widely used tool for breast cancer screening. Breast calcifications appear on mammograms as tiny white spots or grainy dots with a variety of clustering structures which may be time-consuming and difficult to identify by the human eye. Recent advances in multi-scale architectures have demonstrated strong feature representation for both large-scale contextual information and small-scale features. We hypothesized that the new architectures could be employed for classification of mammograms with breast calcifications and possibly for localization of breast calcification regions. In this study, we investigated different multi-scale architectures on 1,617 mammograms that contained only breast calcifications locally curated from our institution. Our best multi-scale attention network with hierarchical block-wise and layer-wise feature representation capability achieved an accuracy of 84.34% and an area under the receiver operating characteristic curve (AUROC) of 90.36%. Similar performance was observed across breast density categories. Our network simultaneously processes both craniocaudal (CC) and mediolateral oblique (MLO) views of the breast providing class activation maps that were able to precisely localize breast calcification regions in a weakly supervised manner, indicating the benefits of strong feature representation through the multi-scale architectures.
Multiple attention encoded cascade R-CNN for scene text detection

Inspired by instance segmentation algorithms, researchers have proposed quantity of segmentation-based methods for text detection, achieving remarkable results on scene text with arbitrary orientation and large aspect ratios. Following their success, we believe cascade architecture and extracting contextual information in multiple aspects are powerful to boost performance on the basis of segmentation-based methods, especially in decreasing false positive texts in complex natural scene. Based on such consideration, we propose a multiple-context-aware and cascade CNN structure, which appropriately encodes multiple categories of context information into a cascade R-CNN framework. Specifically, the proposed method consists of two stages, i.e., feature generation and cascade detection. During the first stage, we define ISTK (Isolated Selective Text Kernel) module to refine feature map, which sequentially encodes channel-wise and kernel-size attention information by designing multiple branches and different kernel sizes in isolate form. Afterwards, we build long-range spatial dependencies in feature map via non-local operations. Built on contextual feature map, Cascade Mask R-CNN structure progressively refines accurate boundaries of text instances with multi-stage framework. We conduct comparative experiments on ICDAR2015 and 2017-MLT datasets, where the proposed method outperform comparative methods in terms of effectiveness and efficiency measurements.
bccstego: A Framework for Investigating Network Covert Channels

Modern malware increasingly exploits information hiding to remain undetected while attacking. To this aim, network covert channels, i.e., hidden communication paths established within legitimate flows, can be used to exfiltrate data or exchange commands without getting noticed by firewalls, antivirus, and intrusion detection systems. Since the secret data can be directly injected in various portions of the stream or encoded via suitable alterations of the traffic, spotting hidden communications is a challenging and poorly generalizable task. Moreover, the majority of works addressed IPv4, thus leaving the detection of covert channels targeting IPv6 almost unexplored.
Joint Lossless Image Compression and Encryption Scheme Based on CALIC and Hyperchaotic System

For efficiency and security of image transmission and storage, the joint image compression and encryption method that performs compression and encryption in a single step is a promising solution due to better security. Moreover, on some important occasions, it is necessary to save images in high quality by lossless compression. Thus, a joint lossless image compression and encryption scheme based on a context-based adaptive lossless image codec (CALIC) and hyperchaotic system is proposed to achieve lossless image encryption and compression simultaneously. Making use of the characteristics of CALIC, four encryption locations are designed to realize joint image compression and encryption: encryption for the predicted values of pixels based on gradient-adjusted prediction (GAP), encryption for the final prediction error, encryption for two lines of pixel values needed by prediction mode and encryption for the entropy coding file. Moreover, a new four-dimensional hyperchaotic system and plaintext-related encryption based on table lookup are all used to enhance the security. The security tests show information entropy, correlation and key sensitivity of the proposed methods reach 7.997, 0.01 and 0.4998, respectively. This indicates that the proposed methods have good security. Meanwhile, compared to original CALIC without security, the proposed methods increase the security and reduce the compression ratio by only 6.3%. The test results indicate that the proposed methods have high security and good lossless compression performance.
ICT development, governance quality and the environmental performance: avoidable thresholds from the lower and lower-middle-income countries

Purpose This paper aims to study the relationship between information and communication technology (ICT) readiness, use, and intensity and environmental sustainability factors in the lower and middle lower-income countries from 2012 to 2018. Design/methodology/approach ICT readiness, use and intensity are measured with the impact of ICT on access to basic services, phone penetration and Internet penetration, while CO 2 emissions per capita, fossil fuel energy consumption and methane emissions are used as indicators for air pollution. To achieve this goal, a two-step generalized method of moments (GMM) estimation was performed which thresholds are computed contingent on the validity of tested hypotheses. Findings The results demonstrate that increasing ICT readiness, use and intensity in lower and lower-middle-income countries enhance environmental sustainability by decreasing CO 2 emissions and energy consumption. Research limitations/implications One of the limitations of this study is that the conclusions and policy recommendations do not take into account the specificities of each country. Indeed there are some differences in the growth pattern of ICT in the lower and middle-lower-income countries. Taken together, the authors conclude that increasing ICT has a positive net effect on CO 2 and methane emissions per capita, while increasing the impact of ICT access in basic services has a net negative effect on CO 2 fossil fuel energy consumption and methane emissions. Practical implications The world needs immediate emissions reduction to avoid the long-term danger of climate change. Second, government authorities should give additional efforts in the more pollutant sector such as transport and industry to monitor their energy consumption. Originality/value To explore this issue further, the negative net effects suggest that ICT needs to be further developed beyond the determined thresholds, to attain the required negative net effect on fossil fuel energy consumption.
Importance of Diagonal Gates in Tensor Network Simulations

In this work, we present two techniques that tremendously increase the performance of tensor-network based quantum circuit simulations. The techniques are implemented in the QTensor package and benchmarked using Quantum Approximate Optimization Algorithm (QAOA) circuits. It allowed increasing the size and depth of QAOA circuits, which can be simulated. In particular, we increased QAOA depth p from 2 to 5 and the size of a QAOA circuit N from 180 to 244 qubits. Moreover, we increased the speed of simulations by up to 10 million times. Our work provides important insights into how various techniques can dramatically speed up the simulations of circuits.
Day-ahead to week-ahead solar irradiance prediction using convolutional long short-term memory networks

In this work, a day-ahead to week-ahead solar irradiance prediction mechanism based on convolutional Long Short-Term Memory (LSTM) model is proposed. The system takes hourly irradiance data from several days previous to the prediction day as the input. Then, features are extracted from the input data using one dimensional convolutional filters. The extracted features from different days are concatenated and serve as the input of the LSTM network. The output of the LSTM is further concatenated with selected original data to emphasize its importance and enhance the prediction results. Afterwards, a fully connected layer is used to produce the final prediction output. The proposed framework can be trained using a relatively small amount of training data within the duration of only two months. Therefore, it has the advantage of being applicable in the initial deployment phase when the amount of training data is limited. The proposed system has been validated using a highly challenging dataset collected in Taiwan with tropical and subtropical marine island climate.
MASG-GAN: A multi-view attention superpixel-guided generative adversarial network for efficient and simultaneous histopathology image segmentation and classification

Efficient analysis of Haematoxylin and Eosin stained histopathology images has become a challenge in digital pathology work-flow. We propose a Multi-view Attention Superpixel-guided Generative Adversarial Network (MASG-GAN) to achieve the multi-task learning for nuclei segmentation and benign-malignant tissue classification. Firstly, a novel superpixel segmentation approach driven by Bounded Asymmetric Gaussian Mixture Model (BAGMM) is presented for generating superpixel-prior probability map with high-level semantics. Then, we develop a generator network that integrates student-branch and teacher-branch. Concretely, the teacher-branch takes superpixel-prior probability map as input and guides the student-branch for accurate segmentation and classification. Then in generator, we build a light-weight U-shaped block (LUB) that consists of depthwise separable convolutions with mini encoding-decoding structure to reduce network computational cost. Finally, a Multi-view Attention Module (MVAM) is designed for further enhance the segmentation quality of nuclei with small area and unclear boundary. Extensive experiments on five benchmark datasets demonstrate that our pipeline outperforms some state-of-the-art methods, especially in terms of efficiency.
DQ: Two approaches to measure the degree of decentralization of blockchain

Blockchain technology innovatively removes the need for intermediaries and establishes a trust-less trust system by peer-to-peer networks and distributed ledger technology. It is still difficult to clarify the specific degree of decentralization of blockchain even though the decentralized characteristic of the system is derived from this system structure. Namely, the criteria for determining whether decentralized are not well established and do not reflect the real world well. So we propose two approaches to measure the degree of decentralization of blockchain system that currently exists: Censorship resistance and geographical diversity. This paper outlines the meaning of each decentralization quotient and explains how those indices can be applied in respect of the user protection.
Automatic Speech Recognition for Air Traffic Control Communications

A significant fraction of communications between air traffic controllers and pilots is through speech, via radio channels. Automatic transcription of air traffic control (ATC) communications has the potential to improve system safety, operational performance, and conformance monitoring, and to enhance air traffic controller training. We present an automatic speech recognition model tailored to the ATC domain that can transcribe ATC voice to text. The transcribed text is used to extract operational information such as call-sign and runway number. The models are based on recent improvements in machine learning techniques for speech recognition and natural language processing. We evaluate the performance of the model on diverse datasets.
The Way of the DAO: Toward Decentralizing the Tactile Internet

There has been a growing interest in adapting blockchain technologies to the specific needs of the Internet of Things (IoT) in order to develop a variety of blockchain-based IoT (BIoT) applications such as smart cities and Industry 4.0, where smart contracts play an important role. After briefly reviewing recent progress on BIoT, we explore the symbiosis of blockchain with other key technologies such as artificial intelligence (AI) and robots, while putting our focus on the emerging Tactile Internet for advanced human-to-machine interaction. Our interest is in exploiting the concept of the decentralized autonomous organization (DAO), which executes smart contracts and requires the involvement from humans to perform certain tasks that autonomous AI based software agents and robots themselves cannot do. In our search for synergies between human-agent-robot teamwork (HART) and the complementary strengths of the DAO, AI, and robots, we decentralize the Tactile Internet by leveraging mobile end-user equipment via partially or fully decentralized multi-access edge computing, and crowdsourcing of human expertise to decrease the completion time of physical tasks in the event of unreliable feedback forecasting of teleoperated robots. Finally, we aim at enhancing the human capabilities of unskilled crowd members by using our proposed nudge contract.
A frequency count approach to multi-criteria recommender system based on criteria weighting using particle swarm optimization

Collaborative filtering (CF) is the most successful and extensively used recommendation technique in the field of Recommender Systems (RS). Generally, the collaborative filtering technique suffers from sparsity and cold start problems because of the absence of appropriate rating information for efficient similarity computation among users. To handle such issues and to identify similar users more efficiently, it is necessary to include other useful user–item details into the system through efficient fusion methods. The multi-criteria information has been proved as a beneficial and influential factor for improving the performance of the classical CF technique. In this research, we incorporate multi-criteria ratings into traditional CF technique using a frequency count approach. The similarity among users is computed using a newly derived common rating weight similarity (CRS) measure. Further, different users show different importance to various criteria of an item. Therefore, a particle swarm optimization algorithm is used to learn optimal weights on different criteria's in the process of global similarity computation. The extensive experimental study driven on a benchmark dataset demonstrates significant improvements in prediction and recommendation qualities compared to the most commonly used similarity and heuristic approaches.
Robust Object Tracking Algorithm for Autonomous Vehicles in Complex Scenes

Object tracking is an essential aspect of environmental perception technology for autonomous vehicles. The existing object tracking algorithms can only be applied well to simple scenes. When the scenes become complex, the algorithms have poor tracking performance and insufficient robustness, and the problems of tracking drift and object loss are prone to occur. Therefore, a robust object tracking algorithm for autonomous vehicles in complex scenes is proposed. Firstly, we study the Siam-FC network and related algorithms, and analyze the problems that need to be addressed in object tracking. Secondly, the construction of a double-template Siamese network model based on multi-feature fusion is described, as is the use of the improved MobileNet V2 as the feature extraction backbone network, and the attention mechanism and template online update mechanism are introduced. Finally, relevant experiments were carried out based on public datasets and actual driving videos, with the aim of fully testing the tracking performance of the proposed algorithm on different objects in a variety of complex scenes. The results showed that, compared with other algorithms, the proposed algorithm had high tracking accuracy and speed, demonstrated stronger robustness and anti-interference abilities, and could still accurately track the object in real time without the introduction of complex structures. This algorithm can be effectively applied in intelligent vehicle driving assistance, and it will help to promote the further development and improvement of computer vision technology in the field of environmental perception.
Cross-category Video Highlight Detection via Set-based Learning

Autonomous highlight detection is crucial for enhancing the efficiency of video browsing on social media platforms. To attain this goal in a data-driven way, one may often face the situation where highlight annotations are not available on the target video category used in practice, while the supervision on another video category (named as source video category) is achievable. In such a situation, one can derive an effective highlight detector on target video category by transferring the highlight knowledge acquired from source video category to the target one. We call this problem cross-category video highlight detection, which has been rarely studied in previous works. For tackling such practical problem, we propose a Dual-Learner-based Video Highlight Detection (DL-VHD) framework. Under this framework, we first design a Set-based Learning module (SL-module) to improve the conventional pair-based learning by assessing the highlight extent of a video segment under a broader context. Based on such learning manner, we introduce two different learners to acquire the basic distinction of target category videos and the characteristics of highlight moments on source video category, respectively. These two types of highlight knowledge are further consolidated via knowledge distillation. Extensive experiments on three benchmark datasets demonstrate the superiority of the proposed SL-module, and the DL-VHD method outperforms five typical Unsupervised Domain Adaptation (UDA) algorithms on various cross-category highlight detection tasks. Our code is available at https://github.com/ChrisAllenMing/Cross_Category_Video_Highlight.
Federated FCM: Clustering Under Privacy Requirements

Federated learning addresses the issue of machine learning realized under constraints of privacy and security. While there have been intensive studies on building and analyzing federated regression models, this topic has not been analyzed so far in the area of fuzzy systems. To narrow down this gap, in this study, we formulate and solve a problem of unsupervised federated learning by designing an original federated FCM (F-FCM) clustering which could serve as a basis toward building a spectrum of fuzzy set constructs including rule-based models. Following a general client–server structure, where the local data residing with each client are not available globally and cannot be centralized (as commonly encountered in learning scenarios), the aim is to discover an overall structure across all data. The federated gradient-based optimization realized in the horizontal mode is developed. An overall learning process is derived, which is composed of communicating gradients coming from clients and providing updates of the prototypes at the server side and passing them on to the clients. It is also shown that the relevance of the globally constructed structure is conveniently assessed in terms of granular footprints of the prototypes constructed by the F-FCM. Some illustrative examples are covered to illustrate the efficiency of the developed federated algorithm.