Reviewerly/exHarmony huggingface dataset:
Semi-supervised visual anomaly detection based on convolutional autoencoder and transfer learning

Recent advances in deep neural networks have shown that reconstruction-based methods using autoencoders have potential for anomaly detection in visual inspection tasks. However, there are challenges when applying these methods to high-resolution images, such as the need for large network training and computation of anomaly scores. Autoencoder-based methods detect anomalies by comparing an input image to its reconstruction in pixel space, which can result in poor performance due to imperfect reconstruction. In this paper, we propose a method to address these challenges by using a conditional patch-based convolutional autoencoder and one-class deep feature classification. We train an autoencoder using only normal images and compute anomaly maps as the difference between the input and output of the autoencoder. We then embed these anomaly maps using a pretrained convolutional neural network feature extractor. Using the deep feature embeddings from the anomaly maps of training samples, we train a one-class classifier to compute an anomaly score for an unseen sample. A simple threshold-based criterion is used to determine if the unseen sample is anomalous or not. We compare our proposed algorithm to state-of-the-art methods on multiple challenging datasets, including a dataset of zipper cursors and eight datasets from the MVTec dataset collection. We find that our approach outperforms alternatives in all cases, achieving an average precision score of 94.77% for zipper cursors and 96.51% for MVTec datasets.
Confirm Not Command: Examining Fraudsters’ Use of Language to Compel Victim Compliance in Their Own Exploitation

Abstract Using discourse analysis to examine exchanges between fraudsters and victims in telephone-mediated frauds, this research examines the interactional techniques used by perpetrators of fraud to gain and maintain compliance from their victims, without causing them alarm. It reveals how compliance is secured and maintained in a process of establishing the relationship, grooming the victim and setting expectations of follow-through. Reimagining traditional understandings of fraud victimization and vulnerability, this work exposes how social and interactional norms are replicated and manipulated by fraudsters in order to compel individuals to be drawn into participating in an alternate, exploitative reality that is indistinguishable from safety; quashing a victim’s ability to recognize the situation as harmful and rendering any motivation to escape as nonsensical. In doing so, this paper questions the efficacy of public fraud protection guidance strategies and delivers evidence for the need to change the present approach to understanding and tackling fraud victimization and complicity.
SNACKs: Leveraging Proofs of Sequential Work for Blockchain Light Clients

The success of blockchains has led to ever-growing ledgers that are stored by all participating full nodes. In contrast, light clients only store small amounts of blockchain-related data and rely on the mediation of full nodes when interacting with the ledger. A broader adoption of blockchains calls for protocols that make this interaction trustless. We revisit the design of light-client blockchain protocols from the perspective of classical proof-system theory, and explain the role that proofs of sequential work (PoSWs) can play in it. To this end, we define a new primitive called succinct non-interactive argument of chain knowledge (SNACK), a non-interactive proof system that provides clear security guarantees to a verifier (a light client) even when interacting only with a single dishonest prover (a full node). We show how augmenting any blockchain with any graph-labeling PoSW (GL-PoSW) enables SNACK proofs for this blockchain. We also provide a unified and extended definition of GL-PoSWs covering all existing constructions, and describe two new variants. We then show how SNACKs can be used to construct light-client protocols, and highlight some deficiencies of existing designs, along with mitigations. Finally, we introduce incremental SNACKs which could potentially provide a new approach to light mining.
General Properties of Quantum Bit Commitments (Extended Abstract)

While unconditionally-secure quantum bit commitment (allowing both quantum computation and communication) is impossible, researchers turn to study the complexity-based one, a.k.a. computational quantum bit commitment. A computational canonical (non-interactive) quantum bit commitment scheme refers to a kind of schemes such that the commitment consists of just a single (quantum) message from the sender to the receiver that later can be opened by uncomputing the commit stage. In this work, we study general properties of computational quantum bit commitments through the lens of canonical quantum bit commitments. Among other results, we in particular obtain the following two: Canonical quantum bit commitments can be based on quantum-secure one-way functions or pseudorandom quantum states. But in our opinion, the formulation of canonical quantum bit commitment is so clean and simple that itself can be viewed as a plausible complexity assumption as well. We propose to explore canonical quantum bit commitment from perspectives of both quantum cryptography and quantum complexity theory in the future.
Compatible-domain Transfer Learning for Breast Cancer Classification with Limited Annotated Data

Microscopic analysis of breast cancer images is the primary task in diagnosing cancer malignancy. Recent attempts to automate this task have employed deep learning models whose success has depended on large volumes of data, while acquiring annotated data in biomedical domains is time-consuming and may not always be feasible. A typical strategy to address this is to apply transfer learning using pre-trained models on a large natural image database (e.g., ImageNet) instead of training a model from scratch. This approach, however, has not been effective in several previous studies due to fundamental differences between natural and medical images. In this study, for the first time we proposed the idea of using a compatible data set of histopathological images to classify breast cancer cytological biopsy specimens. Despite intrinsic differences between histopathological and cytological images, we demonstrate that the features learned by deep networks during the pre-training procedure are compatible with those obtained throughout fine-tuning process. To thoroughly investigate this assertion, we explore three different strategies for training as well as two different approaches for fine-tuning deep learning models. By comparing the obtained results with those of previous state-of-the-art research conducted on the same data set, we demonstrate that the proposed method boasts of improved classification accuracy by 6% to 17% compared to the studies which were based on traditional machine learning techniques, and also enhanced accuracy by roughly 7% compared to those who utilized deep learning methods, eventually achieving 98.73% validation accuracy and 94.55% test accuracy. Exploring different training scenarios also revealed that using a compatible dataset has helped to elevate the classification accuracy by 3.0% compared to the typical approach of using ImageNet. Experimental results show that our approach, despite using a very small number of training images, has achieved performance comparable to that of experienced pathologists and has the potential to be applied in clinical settings.
Assessment of the Gross, Technical and Economic Potential of Region’s Solar Energy for Photovoltaic Energetics

A great number of factors determining the development of photovoltaics are associated with the assessment of possible volumes of solar energy use in correlation with the technical and economic characteristics of photovoltaic equipment. An appropriate assessment of solar energy potential that applies universally to any subsequent use option still remains a crucial task. This work is devoted to the assessment and analysis of the gross, technical and economic potentials of solar energy for photovoltaics. The smart analysis includes the basic program working in the context of connection to databases and to the programs used for determining required initial data or, as a limited option, in the context of full or partial initial data input by the user. Therefore, optimally, a smart network is formed, which for the purposes of obtaining the values of potentials, uses the most up-to-date values of initial data and other required information. This work sets out the tried and tested assessment program for the potentials of solar energy available in large and medium areas. The proposed approach to the analysis of solar energy potential in a region makes it possible to secure a high degree of assessment reliability which can be used for more detailed calculations, including the potentials analysis for a specific point on the ground or a specific type of PV system.
A novel approach for Arabic business email classification based on deep learning machines

During the last decades, the reliance on email communication, especially in business, has increased significantly. Companies receive a massive amount of emails daily, that include business inquiries, customers' feedback, and other types of emails. This inspired many researchers to propose different algorithms to classify and redistribute the numerous emails according to their content. Nowadays, emails containing Arabic text, especially in the Arab world, have raised an increasing concern since they became widely used in official correspondence. Nevertheless, just a small amount of literature focuses on Arabic text classification. Therefore, this work addresses Arabic business emails classification based on natural language processing (NLP). A dataset of 63,257 emails was used and the emails were classified as: urgency, sentiment, and topic classification. The proposed models are based on machine learning techniques and a lexicon of words on which the emails are identified. The models are composed of different settings of convolutional neural networks (CNN). A separate model was built, trained, and tested for each category. The results were promising and gave an accuracy of about 92% and a loss of less than 8%. They also proved the correctness and robustness of this work.
Mediating role of employee empowerment for transformational leadership and job satisfaction

The study aimed to investigate the relationship between transformational leadership and job satisfaction. In addition, it analyzes whether employee empowerment mediates the mentioned relationship. Data were collected from 228 employees of pharmaceutical companies in Jordan (Al Hayat Pharmaceutical Industries, Al Hikma Pharmaceutical Company, and Jordan Swedish Medical Products). The study employed SPSS for data analysis and hypotheses testing. The findings reveal a statistically significant impact of transformational leadership on job satisfaction, empowerment on job satisfaction, transformational leadership on empowerment, and transformational leadership on job satisfaction through empowerment in Jordanian pharmaceutical firms. Furthermore, the results showed that the beta value represents the relative importance, where the intellectual stimulation factor reached the highest level in the size of the impact on the dependent variable, and its value was 0.37 of the total impact. In contrast, inspirational stimulation was the second factor, then the idealized influence factor with 0.30, while the smallest impact size was shown by the dimension of individualized consideration with approximately 0.151. This result indicates the leader’s weak interest and respect for subordinates. The study recommended adopting transformational leadership, supporting employees by empowering them, and conducting more studies on transformational leadership and empowerment in the future.
Automated labeling of training data for improved object detection in traffic videos by fine-tuned deep convolutional neural networks

The exponential increase in the use of technology in road management systems has led to real-time visual information in thousands of locations on road networks. A previous step in preventing or detecting accidents involves identifying vehicles on the road. The application of convolutional neural networks in object detection has significantly improved this field, enhancing classical computer vision techniques. Although, there are deficiencies due to the low detection rate provided by the available pre-trained models, especially for small objects. The main drawback is that they require manual labeling of the vehicles that appear in the images from each IP camera located on the road network to retrain the model. This task is not feasible if we have thousands of cameras distributed across the extensive road network of each nation or state. Our proposal presented a new automatic procedure for detecting small-scale objects in traffic sequences. In the first stage, vehicle patterns detected from a set of frames are generated automatically through an offline process, using super-resolution techniques and pre-trained object detection networks. Subsequently, the object detection model is retrained with the previously obtained data, adapting it to the analyzed scene. Finally, already online and in real-time, the retrained model is used in the rest of the traffic sequence or the video stream generated by the camera. This framework has been successfully tested on the NGSIM and the GRAM datasets.
Long-Range attack detection on permissionless blockchains using Deep Learning

Blockchain has been viewed as a breakthrough and an innovative technology due to its privacy, security, immutability, and data integrity characteristics. The consensus layer of the blockchain is the backbone and the most important layer of the blockchain architecture because it acts as the performance and security manager of the blockchain. The detection of Long-Range Attacks (LRA) on the Proof-of-Stake (PoS) blockchain is a complex task. Earlier studies have shown various challenges in detecting long-range attacks and monitoring the activities of validator nodes on the blockchain network. Thus, this paper proposes a novel dataset for node classification on a proof-of-stake permissionless blockchain and proposes a Deep Learning method that can be used to classify nodes into malicious or non-malicious nodes to mitigate long-range attacks with high accuracy. The performance metrics for the model are compared and measured which suggest the developed performance of the proposed model. The proposed solution can serve as a guide on how future researchers and blockchain developers can simulate and curate proof-of-stake datasets and goes further to demonstrate that artificial intelligence models can be used as a mitigating checkpoint for long-range attacks. The dataset in the paper is publicly available and can be used by other researchers to detect other activities and behaviors on a permissionless blockchain. These techniques can further enhance security, performance and create fairness on the proof-of-stake consensus.
Multi-source data driven cryptocurrency price movement prediction and portfolio optimization

The existing cryptocurrency portfolio studies have relied heavily on historical asset returns and ignored the importance of the prediction information of asset returns, which leads to poor out-of-sample performance of the resulting portfolio strategies. To this end, we first crawl the tweets related to cryptocurrencies on Twitter, analyze tweets' sentiment, and construct sentiment indicators. Second, we use the historical trading data, daily Google Trends, and sentiment indicators to forecast the movement of cryptocurrency prices using Support Vector Machine (SVM). Third, we propose a portfolio optimization model by considering both the forecasting information and the global minimum variance model, and then derive the corresponding portfolio strategy. Finally, we compare the out-of-sample performance of the proposed strategy with classic portfolio strategies and the Cryptocurrency Index. The empirical results show that: on the one hand, the proposed multi-source data can effectively help forecast the cryptocurrency price movements; on the other hand, the proposed portfolio strategy outperforms traditional portfolio strategies regarding the out-of-sample Sharpe ratio, Sortino ratio, and certainty equivalent return, this proves that the proposed strategy can sufficiently combine information between history and future. More importantly, the above conclusions are well verified in the robustness test.
Detecting collusive spammers with heterogeneous graph attention network

Detecting collusive spammers who collaboratively post fake reviews is extremely important to guarantee the reliability of review information on e-commerce platforms. In this research, we formulate the collusive spammer detection as an anomaly detection problem and propose a novel detection approach based on heterogeneous graph attention network. First, we analyze the review dataset from different perspectives and use the statistical distribution to model each user's review behavior. By introducing the Bhattacharyya distance, we calculate the user-user and product-product correlation degrees to construct a multi-relation heterogeneous graph. Second, we combine the biased random walk strategy and multi-head self-attention mechanism to propose a model of heterogeneous graph attention network to learn the node embeddings from the multi-relation heterogeneous graph. Finally, we propose an improved community detection algorithm to acquire candidate spamming groups and employ an anomaly detection model based on the autoencoder to identify collusive spammers. Experiments show that the average improvements of precision@k and recall@k of the proposed approach over the best baseline method on the Amazon, Yelp_Miami, Yelp_New York, Yelp_San Francisco, and YelpChi datasets are [13%, 3%], [32%, 12%], [37%, 7%], [42%, 10%], and [18%, 1%], respectively.
Cross-domain few-shot learning based on pseudo-Siamese neural network

Cross-domain few-shot learning is one of the research highlights in machine learning. The difficulty lies in the accuracy drop of cross-domain network learning on a single domain due to the differences between the domains. To alleviate the problem, according to the idea of contour cognition and the process of human recognition, we propose a few-shot learning method based on pseudo-Siamese convolution neural network. The original image and the sketch map are respectively sent to the branch network in the pre-training and meta-learning process. While maintaining the original image features, the contour features are separately extracted as branch for training at the same time to improve the accuracy and generalization of learning. We conduct cross-domain few-shot learning experiments and good results have been achieved using mini-ImageNet as source domain, EuroSAT and ChestX as the target domains. Also, the results are qualitatively analyzed using a heatmap to verify the feasibility of our method.
The World of Graph Databases from An Industry Perspective

Rapidly growing social networks and other graph data have created a high demand for graph technologies in the market. A plethora of graph databases, systems, and solutions have emerged, as a result. On the other hand, graph has long been a well studied area in the database research community. Despite the numerous surveys on various graph research topics, there is a lack of survey on graph technologies from an industry perspective. The purpose of this paper is to provide the research community with an industrial perspective on the graph database landscape, so that graph researcher can better understand the industry trend and the challenges that the industry is facing, and work on solutions to help address these problems.
Debating stereotypes: Online reactions to the vice-presidential debate of 2020

The 2020 Vice-Presidential debate afforded the opportunity to examine online reactions toward a woman of color, Kamala Harris, and a white man, Mike Pence, as they vied for the same position. We collected tweets from the Twitter API related to Harris and Pence, mainly using neutral hashtags. We examined keywords for gender and race slurs and conducted a multivariate analysis of tweet sentiment. Gender and racial slurs surface in both Harris and Pence datasets, showcasing the insidious nature of sexist and racist stereotypes that seep into online conversations regarding a high-status job debate. As anticipated, tweets regarding Harris contained a higher proportion of racist and sexist curse words, highlighting greater levels of harassment and "intersectional," multi-ethnic/gender attacks. Racial insults targeting Blacks or Asians were more negative than those associated with Whites. Unexpectedly, tweets related to Harris were more positive in average sentiment than those regarding Pence. Yet, there were significantly more retweets, and more negativity of retweets, relating to Harris than to Pence, underscoring the relatively widespread broadcasting of derogatory messages about Harris. Overall, we found that harassing messages toward the candidates reinforced traditional race and gender stereotypes and bolstered the status of those who posted negative content by attaining more retweets. Harassers routinely invoked well-worn, stereotypical insults in their attacks, especially when targeting a multiracial woman.
A Prototype of Blockchain/Distributed File System Platform

The purpose of this paper is to present a prototype of a platform for data exchange that integrates blockchain and distributed file system technologies. The development phases are defined, an architecture and topology design is suggested, and the main functionalities of the prototype are implemented. Also presented are experiments and results on prototype’s performance and resource usage evaluation. The presented prototype’s functionalities enable upload/download files operations, a monetization protocol, digital rights management for some file formats, testing the performance with different file sizes, testing the decentralized operation mode, performance and resource usage evaluation and activity log reporting. Presented are results of 800 test experiments for the blockchain and distributed file system networks performance, the prototype functionality verification and the estimation of resource usage.