Reviewerly/exHarmony huggingface dataset:
Label Consistent Flexible Matrix Factorization Hashing for Efficient Cross-modal Retrieval

Hashing methods have sparked a great revolution on large-scale cross-media search due to its effectiveness and efficiency. Most existing approaches learn unified hash representation in a common Hamming space to represent all multimodal data. However, the unified hash codes may not characterize the cross-modal data discriminatively, because the data may vary greatly due to its different dimensionalities, physical properties, and statistical information. In addition, most existing supervised cross-modal algorithms preserve the similarity relationship by constructing an n × n pairwise similarity matrix, which requires a large amount of calculation and loses the category information. To mitigate these issues, a novel cross-media hashing approach is proposed in this article, dubbed label flexible matrix factorization hashing (LFMH). Specifically, LFMH jointly learns the modality-specific latent subspace with similar semantic by the flexible matrix factorization. In addition, LFMH guides the hash learning by utilizing the semantic labels directly instead of the large n × n pairwise similarity matrix. LFMH transforms the heterogeneous data into modality-specific latent semantic representation. Therefore, we can obtain the hash codes by quantifying the representations, and the learned hash codes are consistent with the supervised labels of multimodal data. Then, we can obtain the similar binary codes of the corresponding modality, and the binary codes can characterize such samples flexibly. Accordingly, the derived hash codes have more discriminative power for single-modal and cross-modal retrieval tasks. Extensive experiments on eight different databases demonstrate that our model outperforms some competitive approaches.
What Makes Agile Software Development Agile?

Together with many success stories, promises such as the increase in production speed and the improvement in stakeholders' collaboration have contributed to making agile a transformation in the software industry in which many companies want to take part. However, driven either by a natural and expected evolution or by contextual factors that challenge the adoption of agile methods as prescribed by their creator(s), software processes in practice mutate into hybrids over time. Are these still agile? In this article, we investigate the question: what makes a software development method agile? We present an empirical study grounded in a large-scale international survey that aims to identify software development methods and practices that improve or tame agility. Based on 556 data points, we analyze the perceived degree of agility in the implementation of standard project disciplines and its relation to used development methods and practices. Our findings suggest that only a small number of participants operate their projects in a purely traditional or agile manner (under 15%). That said, most project disciplines and most practices show a clear trend towards increasing degrees of agility. Compared to the methods used to develop software, the selection of practices has a stronger effect on the degree of agility of a given discipline. Finally, there are no methods or practices that explicitly guarantee or prevent agility. We conclude that agility cannot be defined solely at the process level. Additional factors need to be taken into account when trying to implement or improve agility in a software company. Finally, we discuss the field of software process-related research in the light of our findings and present a roadmap for future research.
Understanding the challenges of rapid digital transformation: the case of COVID-19 pandemic in higher education

Rapid digital transformation is taking place due to the COVID-19 pandemic, forcing organizations and higher educational institutions to change their working and learning culture.This study explores the challenges of rapid digital transformation arising during the pandemic in the higher education context.This research used the Q-methodology to understand the nine challenges that higher education encountered, perceived differently as four main patterns: 1) Digital-nomad enterprise; 2) Corporate-collectivism; 3) Well-being-oriented; 4) Pluralistic.This study broadens the current understanding of digital transformation, especially in higher education.The nine challenges and four patterns of transformation actors serve as a starting point for organizations in supporting technological choice and strategic interventions, based on individual, group, and organizational behavioral levels.Moreover, five propositions, based on the competing concerns of these challenges, establish a framework for comprehending the ecosystem that enables rapid digital transformation.Strategies, prerequisites, and key factors during the (digital) technology development process benefit the cyber-society ecosystem.As a practical contribution, Q-methodology was used to investigate perspectives on digitalization challenges during the pandemic.
PSO-X: A Component-Based Framework for the Automatic Design of Particle Swarm Optimization Algorithms

The particle swarm optimization (PSO) algorithm has been the object of many studies and modifications for more than 25 years. Ranging from small refinements to the incorporation of sophisticated novel ideas, the majority of modifications proposed to this algorithm have been the result of a manual process in which developers try new designs based on their own knowledge and expertise. However, manually introducing changes is very time consuming and makes the systematic exploration of all the possible algorithm configurations a difficult process. In this article, we propose to use automatic design to overcome the limitations of having to manually find performing PSO algorithms. We develop a flexible software framework for PSO, called PSO-X, which is specifically designed to integrate the use of automatic configuration tools into the process of generating PSO algorithms. Our framework embodies a large number of algorithm components developed over more than 25 years of research that have allowed PSO to deal with a large variety of problems, and uses <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">irace</monospace> , a state-of-the-art configuration tool, to automatize the task of selecting and configuring PSO algorithms starting from these components. We show that <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">irace</monospace> is capable of finding high-performing instances of PSO algorithms never proposed before.
The effects of blockchain technology adoption on business ethics and social sustainability: evidence from the Middle East

Sustainable development is not just possible with regard to the environmental and economic dimensions, and social issues are also important in achieving sustainable development. Social sustainability, as one of the dimensions of sustainable development, has been considered by policy makers and managers. Social sustainability and business ethics in the organization are affected by social and behavioral interactions of individuals. Blockchain technology, as a disruptive technology, leads to a peer-to-peer and decentralized network management using distributed architecture. Blockchain technology can affect the way information is exchanged and creates transparency in the organization. Therefore, the purpose of this study is to evaluate the impact of blockchain technology on business ethics and social sustainability in the organization. In the first part of the research, a conceptual model is extracted using previous studies. In the second part, the relationship between model variables among 411 managers of small and medium enterprises (SMEs) active in the Middle East region is evaluated according to the importance of sustainability in this region. Structural equation model and SmartPLS software have been used for data analysis. The research findings show that the use of blockchain technology has a direct impact on business ethics, corporate governance and social sustainability due to the creation of a decentralized system, information transparency and traceability. Also, business ethics and corporate governance have an impact on achieving social sustainability.
Monocular 3D Multi-Person Pose Estimation by Integrating Top-Down and Bottom-Up Networks

In monocular video 3D multi-person pose estimation, inter-person occlusion and close interactions can cause human detection to be erroneous and human-joints grouping to be unreliable. Existing top-down methods rely on human detection and thus suffer from these problems. Existing bottom-up methods do not use human detection, but they process all persons at once at the same scale, causing them to be sensitive to multiple-persons scale variations. To address these challenges, we propose the integration of top-down and bottom-up approaches to exploit their strengths. Our top-down network estimates human joints from all persons instead of one in an image patch, making it robust to possible erroneous bounding boxes. Our bottom-up network incorporates human-detection based normalized heatmaps, allowing the network to be more robust in handling scale variations. Finally, the estimated 3D poses from the top-down and bottom-up networks are fed into our integration network for final 3D poses. Besides the integration of top-down and bottom-up networks, unlike existing pose discriminators that are designed solely for a single person, and consequently cannot assess natural inter-person interactions, we propose a two-person pose discriminator that enforces natural two-person interactions. Lastly, we also apply a semi-supervised method to overcome the 3D ground-truth data scarcity. Quantitative and qualitative evaluations show the effectiveness of the proposed method. Our code is available publicly. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
OpenForensics: Large-Scale Challenging Dataset For Multi-Face Forgery Detection And Segmentation In-The-Wild

The proliferation of deepfake media is raising concerns among the public and relevant authorities. It has become essential to develop countermeasures against forged faces in social media. This paper presents a comprehensive study on two new countermeasure tasks: multi-face forgery detection and segmentation in-the-wild. Localizing forged faces among multiple human faces in unrestricted natural scenes is far more challenging than the traditional deepfake recognition task. To promote these new tasks, we have created the first large-scale dataset posing a high level of challenges that is designed with face-wise rich annotations explicitly for face forgery detection and segmentation, namely Open-Forensics. With its rich annotations, our OpenForensics dataset has great potentials for research in both deepfake prevention and general human face detection. We have also developed a suite of benchmarks for these tasks by conducting an extensive evaluation of state-of-the-art instance detection and segmentation methods on our newly constructed dataset in various scenarios.
Algorithmic Hiring in Practice

The use of AI-enabled hiring software raises questions about the practice of Human Resource (HR) professionals' use of the software and its consequences. We interviewed 15 recruiters and HR professionals about their experiences around two decision-making processes during hiring: sourcing and assessment. For both, AI-enabled software allowed the efficient processing of candidate data, thus providing the ability to introduce or advance candidates from broader and more diverse pools. For sourcing, it can serve as a useful learning resource to find candidates. Though, a lack of trust in data accuracy and an inadequate level of control over algorithmic candidate matches can create reluctance to embrace it. For assessment, its implementation varied across companies depending on the industry and the hiring scenario. Its inclusion may redefine HR professionals' job content as it automates or augments pieces of the existing hiring process. Finally, we discuss how candidate roles that recruiters and HR professionals support drive the use of algorithmic hiring software.
Sentiment classification using attention mechanism and bidirectional long short-term memory network

We propose a sentiment classification method for large scale microblog text based on the attention mechanism and the bidirectional long short-term memory network (SC-ABiLSTM). We use an experimental study to compare our proposed method with baseline methods using real world large-scale microblog data. Comparing the accuracy of the baseline methods to the accuracy of our model, we demonstrate the efficacy of our proposed method. While sentiment classification of social media data has been extensively studied, the main novelty of our study is the implementation of the attention mechanism in a deep learning network for analyzing large scale social media data.
HANP-Miner: High average utility nonoverlapping sequential pattern mining

Nonoverlapping sequential pattern mining (SPM) is a data analysis task, which aims at identifying repetitive sequential patterns with gap constraint in a set of discrete sequences. Nonoverlapping means that any character in the sequence can be rematched by characters at different positions in the pattern, while overlapping means that any character can be reused at the same position, which can be regarded as no condition. Compared with overlapping SPM, nonoverlapping SPM has more strict constraint on the occurrences of pattern, and meets the Apriori property. However, current algorithms mine frequent or closed patterns, resulting in some low-frequency but extremely important patterns being ignored. To tackle this issue, this paper proposes to mine high average utility nonoverlapping sequential patterns (HANP). An efficient algorithm called HANP-Miner is proposed, which involves two key steps: support calculation and candidate pattern reduction. To calculate the support (the occurrence frequency of a pattern), depth-first search and backtracking strategies based on the simplified Nettree structure are adopted, an approach that effectively reduces the time and space complexities of the algorithm. To effectively reduce the number of candidate patterns, a pattern join strategy based on an upper bound on the average utility is proposed. Experiments on biological sequences and real sales sequences are carried out to verify the efficiency of HANP-Miner and the superiority of HANPs. The results demonstrate that HANP-Miner is not only more efficient, but that the HANPs mined in this way are also more valuable than existing frequent patterns. The algorithms and datasets can be downloaded from https://github.com/wuc567/Pattern-Mining/tree/master/HANP-Miner.
Colon Tissues Classification and Localization in Whole Slide Images Using Deep Learning

Colorectal cancer is one of the leading causes of cancer-related death worldwide. The early diagnosis of colon cancer not only reduces mortality but also reduces the burden related to the treatment strategies such as chemotherapy and/or radiotherapy. However, when the microscopic examination of the suspected colon tissue sample is carried out, it becomes a tedious and time-consuming job for the pathologists to find the abnormality in the tissue. In addition, there may be interobserver variability that might lead to conflict in the final diagnosis. As a result, there is a crucial need of developing an intelligent automated method that can learn from the patterns themselves and assist the pathologist in making a faster, accurate, and consistent decision for determining the normal and abnormal region in the colorectal tissues. Moreover, the intelligent method should be able to localize the abnormal region in the whole slide image (WSI), which will make it easier for the pathologists to focus on only the region of interest making the task of tissue examination faster and lesser time-consuming. As a result, artificial intelligence (AI)-based classification and localization models are proposed for determining and localizing the abnormal regions in WSI. The proposed models achieved F-score of 0.97, area under curve (AUC) 0.97 with pretrained Inception-v3 model, and F-score of 0.99 and AUC 0.99 with customized Inception-ResNet-v2 Type 5 (IR-v2 Type 5) model.
SWSR: A Chinese dataset and lexicon for online sexism detection

Online sexism has become an increasing concern in social media platforms as it has affected the healthy development of the Internet and can have negative effects in society. While research in the sexism detection domain is growing, most of this research focuses on English as the language and on Twitter as the platform. Our objective here is to broaden the scope of this research by considering the Chinese language on Sina Weibo. We propose the first Chinese sexism dataset – Sina Weibo Sexism Review (SWSR) dataset –, as well as a large Chinese lexicon SexHateLex made of abusive and gender-related terms. We introduce our data collection and annotation process, and provide an exploratory analysis of the dataset characteristics to validate its quality and to show how sexism is manifested in Chinese. The SWSR dataset provides labels at different levels of granularity including (i) sexism or non-sexism, (ii) sexism category and (iii) target type, which can be exploited, among others, for building computational methods to identify and investigate finer-grained gender-related abusive language. We conduct experiments for the three sexism classification tasks making use of state-of-the-art machine learning models. Our results show competitive performance, providing a benchmark for sexism detection in the Chinese language, as well as an error analysis highlighting open challenges needing more research in Chinese NLP. The SWSR dataset and SexHateLex lexicon are publicly available.1
Toxic language in online incel communities

Abstract The internet-based incel subculture has evolved over the past decade on a number of different platforms. The subculture is known to be toxic and has become associated with several high-profile cases of lethal violence. In this paper, we study the level of toxic language and its targets on three large incel forums: incels.co , lookism.net and looksmax.me . These three forums are the most well-known and active online platforms where incels meet and discuss. Our results show that even though usage of toxic language is pervasive on all three forums, they exhibit significant differences in the composition of their toxicity. These differences correspond to different groups or philosophies within the incel communites.
Detection and analysis of real-time anomalies in large-scale complex system

For data-driven anomaly detection, it is difficult to model a prediction model with high accuracy and sensitivity to anomalous states. In order to solve the above problems, this paper proposes an improved multivariate Transfer Entropy method to judge the physical causality between high-dimensional time series. According to the causality, combined with the LSTM (Long Short-Term Memory) model, the CF-LSTM (causality features-LSTM) model is proposed. The CF-LSTM model uses the causality features of the parameter to make predictions. Compared with other models, the CF-LSTM model improves the prediction accuracy and it is sensitive to anomalies. Based on this, an anomaly detection algorithm is proposed. Case analysis based on the actual data shows that the detection precision, recall, and F1-score (a measure of classification problem) of this method are improved compared with other anomaly detection models, which illustrates the effectiveness of this method.
Moving peak drone search problem: An online multi-swarm intelligence approach for UAV search operations

Many practical, real-world applications have dynamic features. This paper introduces a novel dynamic optimization problem applied to Unmanned Aerial Vehicle (UAV) search and rescue scenarios, named the Moving Peak Drone Search Problem (MPDSP). It utilizes the dynamic environment generator of the well-known Moving Peak Benchmark (MPB) and it is formulated as a maximization problem, with additional constraints imposed by the use of UAVs. For solving the MPDSP, a multi-swarm framework is proposed and seven optimization algorithms are tested. Five well known swarm intelligence algorithms and two algorithms effectively used in continuous and dynamic optimization problems. The implemented methods are evaluated and compared on 105 scenarios with 4 different UAV fleet configurations. Among the tested swarm intelligence variants, the Particle Swarm Optimization implementations proved to be the most effective for solving the MPDSP.
Distributed additive encryption and quantization for privacy preserving federated deep learning

Homomorphic encryption is a very useful gradient protection technique used in privacy preserving federated learning. However, existing encrypted federated learning systems need a trusted third party to generate and distribute key pairs to connected participants, making them unsuited for federated learning and vulnerable to security risks. Moreover, encrypting all model parameters is computationally intensive, especially for large machine learning models such as deep neural networks. In order to mitigate these issues, we develop a practical, computationally efficient encryption based protocol for federated deep learning, where the key pairs are collaboratively generated without the help of a trusted third party. By quantization of the model parameters on the clients and an approximated aggregation on the server, the proposed method avoids encryption and decryption of the entire model. In addition, a threshold based secret sharing technique is designed so that no one can hold the global private key for decryption, while aggregated ciphertexts can be successfully decrypted by a threshold number of clients even if some clients are offline. Our experimental results confirm that the proposed method significantly reduces the communication costs and computational complexity compared to existing encrypted federated learning without compromising the performance and security.
Towards applicability of blockchain in agriculture sector

Food is an important part of the lives of people and of human cultures across the world. The agricultural and food sector is regarded as a major global employment. Agricultural supply chain management is made one of the most complicated and demanding processes by the huge number and heterogeneity of the stakeholders involved, such as farmers, distributors and retailers, as well as customers. The blockchain technology can monitor the origin of food and so assist construct reliable chains of food supply and increase customer confidence. It makes it easier to employ data based technology to make farming smarter as a confidential means of storing data. It also permits timely payment between stakeholders, utilized in conjunction with smart contracts, which may be triggered by changes in data on the blockchain. This article discusses blockchain technology applications in food supply chains, agricultural insurance, smart agriculture and agricultural goods transactions.