Reviewerly/exHarmony huggingface dataset:
Flexible data anonymization using ARX—Current status and challenges ahead

Summary The race for innovation has turned into a race for data. Rapid developments of new technologies, especially in the field of artificial intelligence, are accompanied by new ways of accessing, integrating, and analyzing sensitive personal data. Examples include financial transactions, social network activities, location traces, and medical records. As a consequence, adequate and careful privacy management has become a significant challenge. New data protection regulations, for example in the EU and China, are direct responses to these developments. Data anonymization is an important building block of data protection concepts, as it allows to reduce privacy risks by altering data. The development of anonymization tools involves significant challenges, however. For instance, the effectiveness of different anonymization techniques depends on context, and thus tools need to support a large set of methods to ensure that the usefulness of data is not overly affected by risk‐reducing transformations. In spite of these requirements, existing solutions typically only support a small set of methods. In this work, we describe how we have extended an open source data anonymization tool to support almost arbitrary combinations of a wide range of techniques in a scalable manner. We then review the spectrum of methods supported and discuss their compatibility within the novel framework. The results of an extensive experimental comparison show that our approach outperforms related solutions in terms of scalability and output data quality—while supporting a much broader range of techniques. Finally, we discuss practical experiences with ARX and present remaining issues and challenges ahead.
Discriminative Feature Selection via A Structured Sparse Subspace Learning Module

In this paper, we first propose a novel Structured Sparse Subspace Learning S^3L module to address the long-standing subspace sparsity issue. Elicited by proposed module, we design a new discriminative feature selection method, named Subspace Sparsity Discriminant Feature Selection S^2DFS which enables the following new functionalities: 1) Proposed S^2DFS method directly joints trace ratio objective and structured sparse subspace constraint via L2,0-norm to learn a row-sparsity subspace, which improves the discriminability of model and overcomes the parameter-tuning trouble with comparison to the methods used L2,1-norm regularization; 2) An alternative iterative optimization algorithm based on the proposed S^3L module is presented to explicitly solve the proposed problem with a closed-form solution and strict convergence proof. To our best knowledge, such objective function and solver are first proposed in this paper, which provides a new though for the development of feature selection methods. Extensive experiments conducted on several high-dimensional datasets demonstrate the discriminability of selected features via S^2DFS with comparison to several related SOTA feature selection methods. Source matlab code: https://github.com/StevenWangNPU/L20-FS.
Improving Object Detection with Selective Self-supervised Self-training

We study how to leverage Web images to augment human-curated object detection datasets. Our approach is two-pronged. On the one hand, we retrieve Web images by image-to-image search, which incurs less domain shift from the curated data than other search methods. The Web images are diverse, supplying a wide variety of object poses, appearances, their interactions with the context, etc. On the other hand, we propose a novel learning method motivated by two parallel lines of work that explore unlabeled data for image classification: self-training and self-supervised learning. They fail to improve object detectors in their vanilla forms due to the domain gap between the Web images and curated datasets. To tackle this challenge, we propose a selective net to rectify the supervision signals in Web images. It not only identifies positive bounding boxes but also creates a safe zone for mining hard negative boxes. We report state-of-the-art results on detecting backpacks and chairs from everyday scenes, along with other challenging object classes.
Cryptocurrency adoption in travel and tourism – an exploratory study of Asia Pacific travellers

Blockchain technologies are predicted to substantially transform the tourism industry. At present, cryptocurrencies are the most advanced application of public blockchains that promise benefits such as a universal means of payment and minimal fees through the removal of intermediaries. In the tourism industry, though many tourism vendors have been accepting cryptocurrencies and the potential of using cryptocurrencies in travel-related consumption has been intensively documented, existing knowledge about travellers’ intention to use cryptocurrencies for payment purposes is limited. Traditional models do not account for the idiosyncrasies of cryptocurrencies and are therefore less appropriate to foster the understanding of travellers’ adoption of travel-related payments. To fill this knowledge gap, an exploratory study was conducted with 161 travellers from the Asia-Pacific region who have previously consumed travel-related services with cryptocurrencies. Their previous usage experiences are analysed and reported. Through harnessing the correspondence analysis, several technological contingency factors were identified, as well as positive and negative perceptual antecedents. Additionally, their levels of satisfaction and intention to re-use the technology in future trips were investigated. Based on these findings, several propositions are suggested for guiding future research on travellers’ cryptocurrency adoption in the travel and tourism contexts.
SoK: The Faults in our ASRs: An Overview of Attacks against Automatic Speech Recognition and Speaker Identification Systems

Speech and speaker recognition systems are employed in a variety of applications, from personal assistants to telephony surveillance and biometric authentication. The wide deployment of these systems has been made possible by the improved accuracy in neural networks. Like other systems based on neural networks, recent research has demonstrated that speech and speaker recognition systems are vulnerable to attacks using manipulated inputs. However, as we demonstrate in this paper, the end-to-end architecture of speech and speaker systems and the nature of their inputs make attacks and defenses against them substantially different than those in the image space. We demonstrate this first by systematizing existing research in this space and providing a taxonomy through which the community can evaluate future work. We then demonstrate experimentally that attacks against these models almost universally fail to transfer. In so doing, we argue that substantial additional work is required to provide adequate mitigations in this space.
Investigating and counteracting popularity bias in group recommendations

Popularity bias is an undesirable phenomenon associated with recommendation algorithms where popular items tend to be suggested over long-tail ones, even if the latter would be of reasonable interest for individuals. Such intrinsic tendencies of the recommenders may lead to producing ranked lists, in which items are not equally covered along the popularity tail. Although some recent studies aim to detect such biases of traditional algorithms and treat their effects on recommendations, the concept of popularity bias remains elusive for group recommender systems. Therefore, in this study, we focus on investigating popularity bias from the view of group recommender systems, which aggregate individual preferences to achieve recommendations for groups of users. We analyze various state-of-the-art aggregation techniques utilized in group recommender systems regarding their bias towards popular items. To counteract possible popularity issues in group recommendations, we adapt a traditional re-ranking approach that weighs items inversely proportional to their popularity within a group. Also, we propose a novel popularity bias mitigation procedure that re-ranks items by incorporating their popularity level and estimated group ratings in two distinct strategies. The first one aims to penalize popular items during the aggregation process highly and avoids bias better, while the second one puts more emphasis on group ratings than popularity and achieves a more balanced performance regarding conflicting goals of mitigating bias and boosting accuracy. Experiments performed on four real-world benchmark datasets demonstrate that both strategies are more efficient than the adapted approach, and empowering aggregation techniques with one of these strategies significantly decreases their bias towards popular items while maintaining reasonable ranking accuracy.
College English Teaching Quality Evaluation System Based on Information Fusion and Optimized RBF Neural Network Decision Algorithm

In the process of deepening and developing the current higher education reform, people pay more and more attention to the research of college English education. The key to improve the college English education is to improve the quality of education, and learning evaluation is the key measure to improve the quality of education and training. This paper mainly studies the college English teaching quality evaluation system based on information fusion and optimized RBF neural network decision algorithm. This paper analyzes the main problems and complexity of creating an ideal learning quality evaluation system. On the basis of analyzing the advantages and disadvantages of the previous learning quality evaluation methods, this paper summarizes the existing learning quality evaluation methods and puts forward some suggestions according to the existing evaluation methods. A learning quality evaluation model based on RBF algorithm of neural network is proposed. RBF regularization network method, RBF neural network decision algorithm, and experimental investigation method are used to study the college English teaching quality evaluation system based on information fusion and optimization of RBF neural network decision algorithm. By innovating teaching methods and enriching teaching means, college students’ thirst for English knowledge can be aroused, and teachers’ teaching level can be improved. The results show that 50% of college students think that the level of college English teaching is average and needs to be improved. In the performance evaluation system of college English teaching quality based on information fusion and optimized RBF neural network decision algorithm, it is necessary to establish a learning evaluation system, monitor the learning quality in real time, find problems and improve them in time, and recognize the current situation of education.
A Survey on Silicon Photonics for Deep Learning

Deep learning has led to unprecedented successes in solving some very difficult problems in domains such as computer vision, natural language processing, and general pattern recognition. These achievements are the culmination of decades-long research into better training techniques and deeper neural network models, as well as improvements in hardware platforms that are used to train and execute the deep neural network models. Many application-specific integrated circuit (ASIC) hardware accelerators for deep learning have garnered interest in recent years due to their improved performance and energy-efficiency over conventional CPU and GPU architectures. However, these accelerators are constrained by fundamental bottlenecks due to (1) the slowdown in CMOS scaling, which has limited computational and performance-per-watt capabilities of emerging electronic processors; and (2) the use of metallic interconnects for data movement, which do not scale well and are a major cause of bandwidth, latency, and energy inefficiencies in almost every contemporary processor. Silicon photonics has emerged as a promising CMOS-compatible alternative to realize a new generation of deep learning accelerators that can use light for both communication and computation. This article surveys the landscape of silicon photonics to accelerate deep learning, with a coverage of developments across design abstractions in a bottom-up manner, to convey both the capabilities and limitations of the silicon photonics paradigm in the context of deep learning acceleration.
Improving security of medical big data by using Blockchain technology

Big data refers to a very large and diverse set of data that grow at exponential rates. In the modern healthcare system, medical big data face many security issues due to the presence of hackers and malicious users. Nowadays, medical big data are tremendously benefitted by Blockchain technology due to its several features, namely decentralization, confidentiality, security, privacy, etc. Nevertheless, the conventional cloud and client-server-based information storage models in healthcare are suffering from single-point failure, centralized control of data resources and privacy leakage. This paper explores the usages of Blockchain technology to manage the healthcare system by providing a solution to these problems. Here, a distributed scheme is proposed for data management, which is used to implement Blockchain technology in the healthcare sector. The proposed scheme ensures security by specifying rules with a smart contract. Results and discussions show that the proposed scheme is more efficient than the existing schemes.
The perils of hotel technology: The robot usage resistance model

The COVID-19 outbreak has accelerated the development of service robots. However, service robots in some hotels have been put aside despite successful adoption. This study thus focuses on hotel employees' inhibited continuous usage intention by examining the challenges of benefiting from service robots. A robot usage resistance model (RURM) has been proposed based on the results. In this model, lack of authentic anthropomorphous features and low usability as technological characteristics could influence employees' cognitions toward service robots, while robot-related excessive workloads, techno-insecurity, and techno-uncertainty as psychological stimuli could trigger negative emotional arousal, which in turn fosters employee resistance to service robot continuous usage. This study offers a more solid conceptual investigation into employee resistance to service robot continuous usage, thus allowing the development of strategies to better reap the rewards of hotel service robot usage.
Interpretability application of the Just-in-Time software defect prediction model

Software defect prediction is one of the most active fields in software engineering. Recently, some experts have proposed the Just-in-time Defect Prediction Technology. Just-in-time Defect prediction technology has become a hot topic in defect prediction due to its directness and fine granularity. This technique can predict whether a software defect exists in every code change submitted by a developer. In addition, the method has the advantages of high speed and easy tracking. However, the biggest challenge is that the prediction accuracy of Just-in-Time software is affected by the data set category imbalance. In most cases, 20% of defects in software engineering may be in 80% of modules, and code changes that do not cause defects account for a large proportion. Therefore, there is an imbalance in the data set, that is, the imbalance between a few classes and a majority of classes, which will affect the classification prediction effect of the model. Furthermore, because most features do not result in code changes that cause defects, it is not easy to achieve the desired results in practice even though the model is highly predictive. In addition, the features of the data set contain many irrelevant features and redundant features, which are invalid data, which will increase the complexity of the prediction model and reduce the prediction efficiency. To improve the prediction efficiency of Just-in-Time defect prediction technology. We trained a just-in-time defect prediction model using six open source projects from different fields based on random forest classification. LIME Interpretability technique is used to explain the model to a certain extent. By using explicable methods to extract meaningful, relevant features, the experiment can only need 45% of the original work to explain the prediction results of the prediction model and identify critical features through explicable techniques, and only need 96% of the original work to achieve this goal, under the premise of ensuring specific prediction effects. Therefore, the application of interpretable techniques can significantly reduce the workload of developers and improve work efficiency.
ASTREAM: Data-Stream-Driven Scalable Anomaly Detection With Accuracy Guarantee in IIoT Environment

Intrusion detection exerts a crucial influence on securing the IIoT driven by anomaly detection approaches. Dissimilar with the static data, the intrusion detection data is in the form of a dynamic data stream possessing the properties of infiniteness, correlations, and data distribution change. However, these properties cause some issues for current anomaly detection approaches. Firstly, it is impractical to save the whole dataset due to the infiniteness. Secondly, the correlations are hardly considered. Thirdly, the data distribution change can’t be appropriately handled due to a lack of model update and change detection strategy. Thus, we propose ASTREAM ( <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</u> nomaly detection in data <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">stream</u> s), a novel anomaly detection approach that merges sliding window, model update, and change detection strategies into LSHiForest to achieve accurate and efficient anomaly detection with better scalability. ASTREAM has the following characteristics: (a) the sliding window can be utilized to handle the infiniteness of data streams; (b) the introduced PCA can consider the correlations between different attributes; (c) the change detection and model update can detect data distribution change in time and train the new model. Comprehensive experiments are implemented on the KDDCUP99 dataset to validate ASTREAM performance. Experiment results reveal that ASTREAM outperforms baselines in aspects of accuracy and efficiency and has better scalability.
Salient Object Detection via Fuzzy Theory and Object-Level Enhancement

This paper proposes a bottom-up saliency detection method via effective integration of regional saliency measure and object-level information using fuzzy theory. First, we generate an initial saliency map by fusing multiple prior maps. Second, to emphasize the object-level concept of saliency, we further generate many object proposals of the input image. A fuzzy set theory is then applied to measure the objectness score of the object proposals and integrate them into an objectness map. Third, an optimization framework is proposed to effectively fuse various prior saliency cues and object-level information to produce a clean and uniform saliency map as well as to maintain the salient object completeness. Experimental studies in several benchmark datasets confirmed the superiority of the proposed method over state-of-the-art saliency detection methods.
Sentiment Analysis of Lithuanian Texts Using Traditional and Deep Learning Approaches

We describe the sentiment analysis experiments that were performed on the Lithuanian Internet comment dataset using traditional machine learning (Naïve Bayes Multinomial—NBM and Support Vector Machine—SVM) and deep learning (Long Short-Term Memory—LSTM and Convolutional Neural Network—CNN) approaches. The traditional machine learning techniques were used with the features based on the lexical, morphological, and character information. The deep learning approaches were applied on the top of two types of word embeddings (Vord2Vec continuous bag-of-words with negative sampling and FastText). Both traditional and deep learning approaches had to solve the positive/negative/neutral sentiment classification task on the balanced and full dataset versions. The best deep learning results (reaching 0.706 of accuracy) were achieved on the full dataset with CNN applied on top of the FastText embeddings, replaced emoticons, and eliminated diacritics. The traditional machine learning approaches demonstrated the best performance (0.735 of accuracy) on the full dataset with the NBM method, replaced emoticons, restored diacritics, and lemma unigrams as features. Although traditional machine learning approaches were superior when compared to the deep learning methods; deep learning demonstrated good results when applied on the small datasets.
Online Hate Ratings Vary by Extremes

Analyzing 5,665 crowd ratings on 1,133 social media comments, we find that individuals tend to agree on the extremes of a hate rating scale more than in the middle when evaluating the hatefulness of online comments. The agreement is higher for less hateful comments and lowest on moderately hateful comments. The results have implications for researchers developing machine learning models for online hate processing, as the extreme classes are likely to require fewer annotations for reaching statistical stability. Our findings suggest that the models developed in this domain should consider the distributions of hate ratings rather than average hate scores.
HEMIGEN: Human Embryo Image Generator Based on Generative Adversarial Networks

We propose a method for generating the synthetic images of human embryo cells that could later be used for classification, analysis, and training, thus resulting in the creation of new synthetic image datasets for research areas lacking real-world data. Our focus was not only to generate the generic image of a cell such, but to make sure that it has all necessary attributes of a real cell image to provide a fully realistic synthetic version. We use human embryo images obtained during cell development processes for training a deep neural network (DNN). The proposed algorithm used generative adversarial network (GAN) to generate one-, two-, and four-cell stage images. We achieved a misclassification rate of 12.3% for the generated images, while the expert evaluation showed the true recognition rate (TRR) of 80.00% (for four-cell images), 86.8% (for two-cell images), and 96.2% (for one-cell images). Texture-based comparison using the Haralick features showed that there is no statistically (using the Student’s t-test) significant (p &lt; 0.01) differences between the real and synthetic embryo images except for the sum of variance (for one-cell and four-cell images), and variance and sum of average (for two-cell images) features. The obtained synthetic images can be later adapted to facilitate the development, training, and evaluation of new algorithms for embryo image processing tasks.
Blockchain in Healthcare: A Patient-Centered Model

In the world of healthcare today, there are two major focuses that must be addressed: data security and data ownership. Sensitive medical records currently lack a secure structure, leading to data breaches with severe consequences