Reviewerly/exHarmony huggingface dataset:
Eliciting Auxiliary Information for Cold Start User Recommendation: A Survey

Recommender systems suggest items of interest to users based on their preferences. These preferences are typically generated from user ratings of the items. If there are no ratings for a certain user or item, it is said that there is a cold start problem, which leads to unreliable recommendations. Existing studies that reviewed and examined cold start in recommender systems have not explained the process of deriving and obtaining the auxiliary information needed for cold start recommendation. This study surveys the existing literature in order to explain the various approaches and techniques employed by researchers and the challenges associated with deriving and obtaining the auxiliary information necessary for cold start recommendation. Results show that auxiliary information for cold start recommendation is obtained by adapting traditional filtering and matrix factorization algorithms typically with machine learning algorithms to build learning prediction models. The understanding of similar or connected user profiles can be used as auxiliary information for building cold start user profile to enable similar recommendations in social networks. Similar users are clustered into sub-groups so that a cold start user could be allocated and inferred to a sub-group having similar profiles for recommendations. The key challenges of the process for obtaining the auxiliary information involve: (1) two separate recommendation processes of conversion from pure cold start to warm start before eliciting the auxiliary information; (2) the obtained implicit auxiliary information is usually ranked and sieved in order to select the top rated and reliable auxiliary information for the recommendation. This study also found that cold start user recommendation has frequently been researched in the entertainment domain, typically using music and movie data, while little research has been carried out in educational institutions and academia, or with cold start for mobile applications.
Categories for Barriers to Digital Transformation in Higher Education: An Analysis Based on Literature

For higher education institutions that encourage digital transformation, understanding the barriers are necessary for the digital transformation accomplishment. The purpose of this paper is to present a review of the literature on barriers to digital transformation in higher education. To get a wide overview in identifying the barriers to the implementation of digital transformation, a structured literature review was used to select the relevant studies published. Nine categories were identified based on the literature reviewed: vision, strategy and policy, resources, leadership, digital skill and knowledge, technology, adaptability, resistance to change, and government and economic. Our findings provided a fish-bone diagram that outlines twenty-two barriers to digital transformation in higher education. The main contribution of this study is a synthesis of the state of the art of barriers to digital transformation in higher education. We contribute to provide a common basic understanding of barriers to digital transformation in higher education to overcome barriers for improving the possibility of success. Moreover, we give an insight into future research on barriers exploration
Reinforced Counterfactual Data Augmentation for Dual Sentiment Classification

Data augmentation and adversarial perturbation approaches have recently achieved promising results in solving the over-fitting problem in many natural language processing (NLP) tasks including sentiment classification.However, existing studies aimed to improve the generalization ability by augmenting the training data with synonymous examples or adding random noises to word embeddings, which cannot address the spurious association problem.In this work, we propose an end-toend reinforcement learning framework, which jointly performs counterfactual data generation and dual sentiment classification.Our approach has three characteristics: 1) the generator automatically generates massive and diverse antonymous sentences; 2) the discriminator contains a original-side sentiment predictor and an antonymous-side sentiment predictor, which jointly evaluate the quality of the generated sample and help the generator iteratively generate higher-quality antonymous samples; 3) the discriminator is directly used as the final sentiment classifier without the need to build an extra one.Extensive experiments show that our approach outperforms strong data augmentation baselines on several benchmark sentiment classification datasets.Further analysis confirms our approach's advantages in generating more diverse training samples and solving the spurious association problem in sentiment classification.
On the Robustness of Domain Constraints

Machine learning is vulnerable to adversarial examples--inputs designed to cause models to perform poorly. However, it is unclear if adversarial examples represent realistic inputs in the modeled domains. Diverse domains such as networks and phishing have domain constraints--complex relationships between features that an adversary must satisfy for an attack to be realized (in addition to any adversary-specific goals). In this paper, we explore how domain constraints limit adversarial capabilities and how adversaries can adapt their strategies to create realistic (constraint-compliant) examples. In this, we develop techniques to learn domain constraints from data, and show how the learned constraints can be integrated into the adversarial crafting process. We evaluate the efficacy of our approach in network intrusion and phishing datasets and find: (1) up to 82% of adversarial examples produced by state-of-the-art crafting algorithms violate domain constraints, (2) domain constraints are robust to adversarial examples; enforcing constraints yields an increase in model accuracy by up to 34%. We observe not only that adversaries must alter inputs to satisfy domain constraints, but that these constraints make the generation of valid adversarial examples far more challenging.
The Dual Codes of Several Classes of BCH Codes

As a special subclass of cyclic codes, BCH codes have wide applications in communication and storage systems. A BCH code of length <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> over <inline-formula> <tex-math notation="LaTeX">$\mathbb {F}_{q}$ </tex-math></inline-formula> is always relative to an <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula>-th primitive root of unity <inline-formula> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula> in an extension field of <inline-formula> <tex-math notation="LaTeX">$\mathbb {F}_{q}$ </tex-math></inline-formula>, and is called a dually-BCH code if its dual is also a BCH code relative to the same <inline-formula> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula>. The question as to whether a BCH code is a dually-BCH code is in general very hard to answer. In this paper, an answer to this question for primitive narrow-sense BCH codes and projective narrow-sense ternary BCH codes is given. Sufficient and necessary conditions in terms of the designed distances <inline-formula> <tex-math notation="LaTeX">$\delta $ </tex-math></inline-formula> will be presented to ensure that these BCH codes are dually-BCH codes. In addition, the parameters of the primitive narrow-sense BCH codes and their dual codes are investigated. Some lower bounds on minimum distances of the dual codes of primitive and projective narrow-sense BCH codes are developed. Especially for binary primitive narrow-sense BCH codes, the new bounds on the minimum distances of the dual codes improve the classical Sidel&#x2019;nikov bound, and are also better than the Carlitz and Uchiyama bound for large designed distances <inline-formula> <tex-math notation="LaTeX">$\delta $ </tex-math></inline-formula>. The question as to what subclasses of cyclic codes are BCH codes is also answered to some extent. As a byproduct, the parameters of some subclasses of cyclic codes are also investigated.
Rapid Bayesian Inference for Expensive Stochastic Models

Almost all fields of science rely upon statistical inference to estimate unknown parameters in theoretical and computational models.While the performance of modern computer hardware continues to grow, the computational requirements
Adversarial attacks on deep learning models in smart grids

A smart grid may employ various machine learning models for intelligent tasks, such as load forecasting, fault diagnosis and demand response. However, the research on adversarial machine learning has attracted broad interest recently with the rapid advancement of deep learning techniques, which poses an evident threat to those deep learning models deployed in smart grids. In the face of the emergent problem, we make a compact survey of the adversarial attacks against deep learning models in smart grids. The research status of deep learning applications in smart grids and adversarial machine learning is briefly summarized firstly. Adversarial evasion and poisoning attacks in smart grids are analyzed and exemplified respectively with focus. To mitigate the threat typical countermeasures against adversarial attacks are also presented. From the survey it can be concluded that the threat of adversarial attacks in smart grids will be a kind of long-term existence and need continuous attention.
Agile Methodology Vs. Traditional Waterfall SDLC: A case study on Quality Assurance process in Software Industry

We are very familiar with the phrase ‘change is the only constant’ and same thing applicable for software industry also. In this new world of software industry’ most of the Information technology companies are following a methodology’ named Agile where the development work moves quickly. Nowadays very few companies are still following Traditional Waterfall Model as software development life cycle method. In software development life cycle one of the most important phase is quality assurance phase or testing phase. In this context we will be discussing how the software testing has been implemented and how it's going to work with agile methodology. Also, we will do a comparative analysis between Traditional Waterfall model Testing approach and Agile testing approach.
The Role of Interactive Visualization in Fostering Trust in AI

The increasing use of artificial intelligence (AI) technologies across application domains has prompted our society to pay closer attention to AI's trustworthiness, fairness, interpretability, and accountability. In order to foster trust in AI, it is important to consider the potential of interactive visualization, and how such visualizations help build trust in AI systems. This manifesto discusses the relevance of interactive visualizations and makes the following four claims: i) trust is not a technical problem, ii) trust is dynamic, iii) visualization cannot address all aspects of trust, and iv) visualization is crucial for human agency in AI.
Racial Identity-Aware Facial Expression Recognition Using Deep Convolutional Neural Networks

Multi-culture facial expression recognition remains challenging due to cross cultural variations in facial expressions representation, caused by facial structure variations and culture specific facial characteristics. In this research, a joint deep learning approach called racial identity aware deep convolution neural network is developed to recognize the multicultural facial expressions. In the proposed model, a pre-trained racial identity network learns the racial features. Then, the racial identity aware network and racial identity network jointly learn the racial identity aware facial expressions. By enforcing the marginal independence of facial expression and racial identity, the proposed joint learning approach is expected to be purer for the expression and be robust to facial structure and culture specific facial characteristics variations. For the reliability of the proposed joint learning technique, extensive experiments were performed with racial identity features and without racial identity features. Moreover, culture wise facial expression recognition was performed to analyze the effect of inter-culture variations in facial expression representation. A large scale multi-culture dataset is developed by combining the four facial expression datasets including JAFFE, TFEID, CK+ and RaFD. It contains facial expression images of Japanese, Taiwanese, American, Caucasian and Moroccan cultures. We achieved 96% accuracy with racial identity features and 93% accuracy without racial identity features.
LCTR: On Awakening the Local Continuity of Transformer for Weakly Supervised Object Localization

Weakly supervised object localization (WSOL) aims to learn object localizer solely by using image-level labels. The convolution neural network (CNN) based techniques often result in highlighting the most discriminative part of objects while ignoring the entire object extent. Recently, the transformer architecture has been deployed to WSOL to capture the long-range feature dependencies with self-attention mechanism and multilayer perceptron structure. Nevertheless, transformers lack the locality inductive bias inherent to CNNs and therefore may deteriorate local feature details in WSOL. In this paper, we propose a novel framework built upon the transformer, termed LCTR (Local Continuity TRansformer), which targets at enhancing the local perception capability of global features among long-range feature dependencies. To this end, we propose a relational patch-attention module (RPAM), which considers cross-patch information on a global basis. We further design a cue digging module (CDM), which utilizes local features to guide the learning trend of the model for highlighting the weak local responses. Finally, comprehensive experiments are carried out on two widely used datasets, ie, CUB-200-2011 and ILSVRC, to verify the effectiveness of our method.
An Improved Automatic Image Annotation Approach Using Convolutional Neural Network-Slantlet Transform

Every day, websites and personal archives generate an increasing number of photographs. The extent of these archives is unfathomable. The ease of usage of these enormous digital image collections contributes to their popularity. However, not all of these databases provide appropriate indexing data. As a result, it&#x2019;s tough to find information that the user is interested in. Thus, in order to find information about an image, it is necessary to classify its content in a meaningful way. Image annotation is one of the most difficult issues in computer vision and multimedia research. The objective is to convert an image into a single or numerous labels. This necessitates a grasp of the visual content of an image. The necessity for unambiguous information to build semantic-level concepts from raw image pixels is one of the challenges of image annotation. Unlike text annotation, where a dictionary links words to their meaning, raw picture pixels are insufficient to construct semantic-level notions directly. A simple syntax, on the other hand, is well specified for combining letters to form words and words to form sentences. The automatic feature extraction for automatic annotation was the emphasis of this paper. And they employed a deep learning convolutional neural network to build and improve image coding and annotation capabilities. Performance of the suggested technique on the Corel-5K, ESP-Game, and IAPRTC-12 datasets. Finally, experimental findings on three data sets were used to demonstrate the usefulness of this model for image annotation.
Authenticated Encryption Schemes: A Systematic Review

Authenticated encryption (AE) is a cryptographic construction that simultaneously protects confidentiality and integrity. A considerable amount of research has been devoted to the area since its formal inception in 2000. Different lines of research have been proposed to enhance the available schemes in terms of security, efficiency, and design and to implement new ideas. However, a comprehensive systematic literature review (SLR) of the topic has not been provided to the best of the authors’ knowledge. This study fills this gap in the literature by proposing a framework for classifying AE schemes and highlighting past contributions to help researchers familiarize themselves with the current state and directions for future research in the area. This SLR covered AE schemes proposed from 2000 to 2020. A total of 217 articles, selected from eight sources, were categorized into independent schemes, CAESAR competition schemes, and NIST lightweight competition schemes. These schemes were then classified according to their design approaches, security-related properties, and functional features. Our analysis reveals that a significant outstanding challenge in AE is to balance security, efficiency, and the provision of desirable features.
DnRCNN: Deep Recurrent Convolutional Neural Network for HSI Destriping

In spite of achieving promising results in hyperspectral image (HSI) restoration, deep-learning-based methodologies still face the problem of spectral or spatial information loss due to neglecting the inner correlation of HSI. To address this issue, we propose an innovative deep recurrent convolution neural network (DnRCNN) model for HSI destriping. To the best of our knowledge, this is the first study on HSI destriping from the perspective of inner band and interband correlation explorations with the recurrent convolution neural network. In the novel DnRCNN, a selective recurrent memory unit (SRMU) is designed to respectively extract the correlative features involved in spectral and spatial domains. Moreover, an innovative recurrent fusion (RF) strategy incorporated with group concatenation is further proposed to remove strip noise and preserve scene details using the complementary features from SRMU. Experimental results on extensive HSI datasets validated that the proposed method achieves a new state-of-the-art (SOTA) HSI destriping performance.
Crime prediction using a hybrid sentiment analysis approach based on the bidirectional encoder representations from transformers

Sentiment analysis (SA) is widely used today in many areas such as crime detection (security intelligence) to detect potential security threats in realtime using social media platforms such as Twitter. The most promising techniques in sentiment analysis are those of deep learning (DL), particularly bidirectional encoder representations from transformers (BERT) in the field of natural language processing (NLP). However, employing the BERT algorithm to detect crimes requires a crime dataset labeled by the lexiconbased approach. In this paper, we used a hybrid approach that combines both lexicon-based and deep learning, with BERT as the DL model. We employed the lexicon-based approach to label our Twitter dataset with a set of normal and crime-related lexicons; then, we used the obtained labeled dataset to train our BERT model. The experimental results show that our hybrid technique outperforms existing approaches in several metrics, with 94.91% and 94.92% in accuracy and F1-score respectively.
Understanding accountability in blockchain systems

Purpose The purpose of this study is to examine how accountability is constructed for blockchain systems. With the aim of increasing knowledge on accountability across three different types of blockchains (public, private and consortium), the researchers ask: how do blockchain systems construct accountability? Design/methodology/approach This study draws on theorising in the accountability literature to study how blockchains relate to our construction and understanding of accountability. A qualitative field study of the Australian blockchain technology landscape is conducted, with insights garnered from 18 blockchain experts. Findings Findings reveal that different types of blockchains employ different forms and mechanisms of accountability and in novel ways previously less acknowledged in the literature. Importantly, this study finds that accountability does not require a principal–agent relation and can still manifest in less pure applications of blockchain technology across a wide range of stakeholders, contrary to that espoused in earlier exhortations of blockchain use in interdisciplinary literature. This study also finds that similar subtypes of accountability operate very differently across public, private and consortium blockchains and there exists an inverse relation between trust and consensus building through transparency as blockchains progress from public to private types. Overall, this study offers novel explanations for the relevance of greater accountability in blockchains, especially when the assumptions of public blockchains are softened and applied as private and consortium blockchains. Originality/value This study contributes to the accountability literature by addressing how different blockchain systems reshape the understanding of traditional accounting and accountability practices. This study questions the very need for a principal–agent relation to facilitate accountability and offers an additional perspective to how trust and transparency operate as key mechanisms of accountability.
COVID-19 vaccine mandates: what are the current European public perspectives?

The article aims to elaborate on European policy choices for the prevention of SARS‑CoV‑2 contagion, with a close focus on the rules and regulations enacted in Italy so far. European states have ruled out generalized vaccination mandates but have so far preferred to exert a form of "moral suasion", through the introduction of a digital certificate which can only be granted to those who are vaccinated, cured of COVID-19 or tested negative through an antigen test in the previous 48 hours. Italy has applied this tool, dubbed "Green Pass", very rigorously: many daily activities, including going to work, are only allowed for those who have the certificate. A one-year Green Pass is issued after vaccination, although data show that vaccine protection may subside gradually over about six months; the cost of the antigen tests every 48 hours is to be borne by the patient. Testing the unvaccinated is essential to contain the spread of the infection, but it would have been more logical to mandate that all the unvaccinated undergo regular testing (for example every ten days), instead of imposing a test every 48 hours only to be allowed to engage in some activities. The authors stress that in order to minimize the risk of future possible pandemics, prevention strategies are needed, and poor countries need to be enabled to vaccinate their populations in order to prevent new variants from developing. The pledges made by world leaders in that regard during the recent G20 summit must therefore be honored, for the sake of global health that never in our lifetime has been so threatened.