Reviewerly/exHarmony huggingface dataset:
Artificial Intelligence: the Liability Paradox

In this modern era, technology has evolved rapidly, and significant technological application of Artificial Intelligence (AI) has become an integral aspect of human nature. A significant shift has taken place in human life. The issue of ascertaining liability, civil and criminal, for damages or losses resulting from its activities becomes a matter of priority for AI, which exercises control over itself in various degrees. The critical concern is that either national or international law does not consider AI as a subject of law, implying that AI can not be held directly responsible for its actions and damage caused subsequently. Moreover, the issue of liability assessment also presents a complex question of whether or not AI should be granted legal personality. Given the preceding, a question naturally arises: who is responsible for the damage caused by the actions of AI?
Retail 4.0 adoption and firm performance among Malaysian retailers: the role of enterprise risk management as moderator

Purpose The purpose of this study was to ascertain the determinants of firm performance for Malaysian retailers. Design/methodology/approach An online survey was conducted to collect responses from members of the Malaysian Retailers' Chain Association. A total of 126 responses were obtained. Data analysis was done by using the PLS-SEM method. Findings The results of the study indicate that Retail 4.0 adoption is able to improve retailers' performance as-a-whole by improving the four perspectives of firm performance as given in the Balanced Scorecard, i.e. the finance perspective, the customer perspective, the internal processes' perspective and also learning and growth perspective. Further, enterprise risk management was found to have a positive moderating effect on retailers' performance as-a-whole and also on the finance and customer perspectives of performance. Research limitations/implications The study was conducted only in Malaysia and so, it might be geographically limited. Besides, it is cross-sectional in nature and therefore, the impact might be different if the study had been conducted over a longer period. Practical implications This study provides a useful framework for retailers who are seeking to improve firm performance. Originality/value This is one of the first studies to show the impact of Retail 4.0 adoption on firm performance. Besides, this is also the first time, enterprise risk management has been introduced as a positive moderator on the impact of technology adoption on retailers' performance.
Identity-Aware Multi-sentence Video Description

Standard video and movie description tasks abstract away from person identities, thus failing to link identities across sentences. We propose a multi-sentence Identity-Aware Video Description task, which overcomes this limitation and requires to re-identify persons locally within a set of consecutive clips. We introduce an auxiliary task of Fill-in the Identity , that aims to predict persons’ IDs consistently within a set of clips, when the video descriptions are given. Our proposed approach to this task leverages a Transformer architecture allowing for coherent joint prediction of multiple IDs. One of the key components is a gender-aware textual representation as well an additional gender prediction objective in the main model. This auxiliary task allows us to propose a two-stage approach to Identity-Aware Video Description . We first generate multi-sentence video descriptions, and then apply our Fill-in the Identity model to establish links between the predicted person entities. To be able to tackle both tasks, we augment the Large Scale Movie Description Challenge (LSMDC) benchmark with new annotations suited for our problem statement. Experiments show that our proposed Fill-in the Identity model is superior to several baselines and recent works, and allows us to generate descriptions with locally re-identified people.
Self-Illusion: A Study on Cognition of Role-Playing in Immersive Virtual Environments

We present the design and results of an experiment investigating the occurrence of self-illusion and its contribution to realistic behavior consistent with a virtual role in virtual environments. Self-illusion is a generalized illusion about one's self in cognition, eliciting a sense of being associated with a role in a virtual world, despite sure knowledge that this role is not the actual self in the real world. We validate and measure self-illusion through an experiment where each participant occupies a non-human perspective and plays a non-human role using this role's behavior patterns. 77 participants were enrolled for the user study according to the priori power analysis. In the mixed-design experiment with different levels of manipulations, we asked the participants to play a cat (a non-human role) within an immersive VE and captured their different kinds of responses, finding that the participants with higher self-illusion can connect themselves to the virtual role more easily. Based on statistical analysis of questionnaires and behavior data, there is some evidence that self-illusion can be considered a novel psychological component of presence because it is dissociated from sense of embodiment (SoE), plausibility illusion (Psi), and place illusion (PI). Moreover, self-illusion has the potential to be an effective evaluation metric for user experience in a virtual reality system for certain applications.
Why is China going to issue CBDC (Central Bank Digital Currency)?

The purpose of this paper is to discuss the importance of CBDC(Central Bank Digital Currency) and, based on this understanding, to identify the main characteristics of China’s CBDC and finally to analyze the possibility of China’s CBDC to challenge the dominance of the US dollars. The discussion of this paper can be summarized as follows. First, central bank’s interest in a CBDC is resulted from 1) the rise(and subsequent fall) of Bitcoin and various crytocurrencies, 2) the entry of big tech firms into financial services, 3) the unexpected intense debate concerning Libra and other stable coins. Second, the main characteristics of China’s CBDC are summarized as follows: 1) the PBoC(People’s Bank of China) would like to adopt a two-tier system, where commercial banks has the basic function to relate the PBoC with consumers and businesses.; 2) It is assumed that DLT (Disributed Ledger Technology) would be adopted in the relationship between commercial banks and consumers and businesses(1st tier). Meanwhile, centralized control, instead of DLT, would be employed in the relationship between PBoC and commercial banks(2nd tier).; 3) It is also assumed that the first tier is a kind of ‘DLT-based token CBDC’, while the second tier, ‘centralized CBDC accounts.’ Third, problems for internationalization of the currency can be categorized into technical and political challenges. From the technical perspective, the possibility of internationalization of RMB(and CBDC) would be positive. Meanwhile, it would not be from the political one. Although there might be a limited success within the region of “One Belt, One Road” projects, the expected opposition from the US and the corresponding conflict between them would be a great problem to the success of internationalization of RMB. Thus, the final conclusion regarding the possibility of China’s CBDC to challenge the US dollars would not be optimistic. However, it might be better to keep in mind that China’s digital money is not yet launched, so that there still exits some possibility for CBDC to challenge US dollars more than ever.
Effect of virtual learning on delivering the education as part of the sustainable development goals in Qatar

In the recently identified pandemic of COVID-19, most countries have stopped their work, industrial activities, businesses and education activities. This pandemic also had a great impact on energy and environment research, which is a vital component in the transition to sustainable development. In response to this pandemic, Qatar had to stop all kinds of on-site education activities and shifted to virtual learning techniques (or distance learning). Using distant learning has a potential impact on the quality of education and research activities, especially energy and environment research. So, to assess the effectiveness of virtual learning in this pandemic time in Qatar, a structured survey is conducted for university students and staff to assess their perception about distant learning. The survey revealed that virtual learning overall had a positive impact, but it can affect the quality and the progress of education in the country.
Corpse reviver: sound and efficient gradual typing via contract verification

Gradually typed programming languages permit the incremental addition of static types to untyped programs. To remain sound, languages insert run-time checks at the boundaries between typed and untyped code. Unfortunately, performance studies have shown that the overhead of these checks can be disastrously high, calling into question the viability of sound gradual typing. In this paper, we show that by building on existing work on soft contract verification, we can reduce or eliminate this overhead. Our key insight is that while untyped code cannot be trusted by a gradual type system, there is no need to consider only the worst case when optimizing a gradually typed program. Instead, we statically analyze the untyped portions of a gradually typed program to prove that almost all of the dynamic checks implied by gradual type boundaries cannot fail, and can be eliminated at compile time. Our analysis is modular, and can be applied to any portion of a program. We evaluate this approach on a dozen existing gradually typed programs previously shown to have prohibitive performance overhead—with a median overhead of 2.5× and up to 80.6× in the worst case—and eliminate all overhead in most cases, suffering only 1.5× overhead in the worst case.
A Traceable and Revocable Multiauthority Attribute-Based Encryption Scheme with Fast Access

Multiauthority ciphertext-policy attribute-based encryption (MA-CP-ABE) is a promising technique for secure data sharing in cloud storage. As multiple users with same attributes have same decryption privilege in MA-CP-ABE, the identity of the decryption key owner cannot be accurately traced by the exposed decryption key. This will lead to the key abuse problem, for example, the malicious users may sell their decryption keys to others. In this paper, we first present a traceable MA-CP-ABE scheme supporting fast access and malicious users’ accountability. Then, we prove that the proposed scheme is adaptively secure under the symmetric external Diffie–Hellman assumption and fully traceable under the <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>q</mi> </math> -Strong Diffie–Hellman assumption. Finally, we design a traceable and revocable MA-CP-ABE system for secure and efficient cloud storage from the proposed scheme. When a malicious user leaks his decryption key, our proposed system can not only confirm his identity but also revoke his decryption privilege. Extensive efficiency analysis results indicate that our system requires only constant number of pairing operations for ciphertext data access.
Image of Siberia in periodicals from the second half of the 19th to early 20th centuries

In periodicals from the late 19th to early 20th centuries, the image of Siberia was often revealed through publications about specific people or mythological characters coming from Siberia or living some important period of their life in the Siberian territory. The authors of the articles were in constant search for some ideal image, that of a public, state or spiritual figure who made a special contribution to improving the life and everyday routine of Siberians, highlighting regional issues and burning problems, romanticizing the image of Siberia as a “distant” land and at the same time the spiritual and material richest one as well as the most important part of the country.
Integrative analysis of histopathological images and chromatin accessibility data for estrogen receptor-positive breast cancer

Existing studies have demonstrated that the integrative analysis of histopathological images and genomic data can be used to better understand the onset and progression of many diseases, as well as identify new diagnostic and prognostic biomarkers. However, since the development of pathological phenotypes are influenced by a variety of complex biological processes, complete understanding of the underlying gene regulatory mechanisms for the cell and tissue morphology is still a challenge. In this study, we explored the relationship between the chromatin accessibility changes and the epithelial tissue proportion in histopathological images of estrogen receptor (ER) positive breast cancer.An established whole slide image processing pipeline based on deep learning was used to perform global segmentation of epithelial and stromal tissues. We then used canonical correlation analysis to detect the epithelial tissue proportion-associated regulatory regions. By integrating ATAC-seq data with matched RNA-seq data, we found the potential target genes that associated with these regulatory regions. Then we used these genes to perform the following pathway and survival analysis.Using canonical correlation analysis, we detected 436 potential regulatory regions that exhibited significant correlation between quantitative chromatin accessibility changes and the epithelial tissue proportion in tumors from 54 patients (FDR < 0.05). We then found that these 436 regulatory regions were associated with 74 potential target genes. After functional enrichment analysis, we observed that these potential target genes were enriched in cancer-associated pathways. We further demonstrated that using the gene expression signals and the epithelial tissue proportion extracted from this integration framework could stratify patient prognoses more accurately, outperforming predictions based on only omics or image features.This integrative analysis is a useful strategy for identifying potential regulatory regions in the human genome that are associated with tumor tissue quantification. This study will enable efficient prioritization of genomic regulatory regions identified by ATAC-seq data for further studies to validate their causal regulatory function. Ultimately, identifying epithelial tissue proportion-associated regulatory regions will further our understanding of the underlying molecular mechanisms of disease and inform the development of potential therapeutic targets.
Are Neural Nets Modular? Inspecting Functional Modularity Through Differentiable Weight Masks

Neural networks (NNs) whose subnetworks implement reusable functions are expected to offer numerous advantages, including compositionality through efficient recombination of functional building blocks, interpretability, preventing catastrophic interference, etc. Understanding if and how NNs are modular could provide insights into how to improve them. Current inspection methods, however, fail to link modules to their functionality. In this paper, we present a novel method based on learning binary weight masks to identify individual weights and subnets responsible for specific functions. Using this powerful tool, we contribute an extensive study of emerging modularity in NNs that covers several standard architectures and datasets. We demonstrate how common NNs fail to reuse submodules and offer new insights into the related issue of systematic generalization on language tasks.
Studies on the evaluation of college classroom teaching quality based on SVM multiclass classification algorithm

Abstract In order to improve the evaluation accuracy and efficiency of the quality of classroom teaching in colleges and universities, combined with the teaching process and evaluation system of colleges and universities, a multi-class classification algorithm is proposed, namely, the Distance binary tree support vector machine based on Euclidean distance (DBT-SVM) algorithm, and the algorithm is used to predict the quality evaluation of classroom teaching in colleges and universities. The algorithm uses the Euclidean distance of the center of the two nearest sample classes to influence the classification, so that the first isolated classes can be separated first. Experimentally, the algorithm can improve the efficiency and accuracy of classification and solve the problem of multi-class classification.
Planning flexible and smooth paths for lane‐changing manoeuvres of autonomous vehicles

Lane-changing manoeuvres are essential for daily driving tasks. Safety and comfort are the primary issues to be considered during the lane-changing manoeuvres of autonomous vehicles. However, because the traffic environment is complicated and changeable, executing fixed lane-changing manoeuvres cannot guarantee safety and comfort. Thus, it is important to find a flexible method that can plan and re-plan a lane-changing path according to the traffic environment in real time. But that remains smooth to ensure the vehicle occupants stay comfortable. Therefore, this paper proposes a method for planning a flexible and smooth lane-changing path in real time. The proposed method uses the bi-elementary paths to ensure comfort, and clothoid and arc curves are superimposed on the bi-elementary paths to add re-planning flexibility. Additionally, lookup tables and reasonable approximations are employed to quickly generate and evaluate lane-changing paths. The results of the simulations demonstrate that the proposed method is able to plan and re-plan a smooth lane-changing path. Then, the results of vehicle experiments performed based on the embedded controller platform show that the proposed method guarantees real-time performance, that the planned path is easy to follow, and that the planned paths make autonomous vehicle lane-changing manoeuvres safe and comfortable.
Application of Improved YOLO V3 Algorithm for Target Detection in Echo Image of Sonar under Reverb

Abstract aiming at the problems of low detection rate and high false alarm rate of small and medium targets in images, a YOLO V3 improvement method is proposed and applied to the detection of small targets. Because the small target occupies less pixels and the feature is not obvious, it is proposed to up-sample the 8 times downsampling feature map of the original network output, splice the 2 times upsampling feature map with the second residual block output feature map, and establish the feature fusion target detection layer with 4 times downsampling output. In order to gain More small target feature information is added to the second residual block DarkNe-t53 the YOLO V3 network structure. k-means clustering algorithm is used to cluster the number of target candidate boxes and the aspect ratio dimension. The results show that the improved YOLO V3 algorithm can effectively detect small targets and improve the recall rate and detection rate of small targets.
A Multi-Bit Fully Homomorphic Encryption With Shorter Public Key From LWE

There has been a great deal of work on improving the efficiency of fully homomorphic encryption (FHE) scheme. Our approach, in this regard, is to use the idea of packed ciphertexts to construct a multi-bit FHE with a short public key on the basis of the learning with errors (LWE) problem. More specifically, our FHE scheme builds on a basic encryption scheme that chooses LWE samples from the Gaussian distribution and adds Gaussian error to it. This results in decreasing the number of LWE samples from 2nlogq to n + 1. We prove that our FHE scheme is pragmatically feasible and its security relies on the hardness of the LWE problem. In addition, we form a new process of key switching for multi-bit FHE based on the ideas adopted by Brakerski et al. for optimizing the process of key switching. Finally, we analyze and compare the concrete parameters between our FHE scheme and BGH13 scheme. The result shows that compared with the BGH13 scheme, our scheme has a smaller public key by a factor about logq.
Uncertainty Sets for Image Classifiers using Conformal Prediction

Convolutional image classifiers can achieve high predictive accuracy, but quantifying their uncertainty remains an unresolved challenge, hindering their deployment in consequential settings. Existing uncertainty quantification techniques, such as Platt scaling, attempt to calibrate the network’s probability estimates, but they do not have formal guarantees. We present an algorithm that modifies any classifier to output a predictive set containing the true label with a user-specified probability, such as 90%. The algorithm is simple and fast like Platt scaling, but provides a formal finite-sample coverage guarantee for every model and dataset. Our method modifies an existing conformal prediction algorithm to give more stable predictive sets by regularizing the small scores of unlikely classes after Platt scaling. In experiments on both Imagenet and Imagenet-V2 with ResNet-152 and other classifiers, our scheme outperforms existing approaches, achieving coverage with sets that are often factors of 5 to 10 smaller.
Papaya Disease Detection Using Fuzzy Naïve Bayes Classifier

Papaya is one of the tropical fruits that is grown in Indonesia. The weather condition in Indonesia cause it to be attacked by pest and disease. The farmers have difficulty identifying them due to a lack of knowledge and obtaining information from experts. In this study, an expert system was developed to detect papaya disease. Expert knowledge is applied to the system so the farmer can use it to identify the condition without an expert. It is usually represented in the linguistic form, was converted into numbers using fuzzy reasoning, Triangular Fuzzy Number (TFN) membership function. Then the expert knowledge was processed using the Naïve Bayes Classifier to obtain the results of the disease classification. The test was also performed using forward chaining search methods. The accuracy was 88% for FNBC and 90% for forward chaining compared to expert knowledge.
A Probabilistic Ensemble Prediction Method for PV Power in the Nonstationary Period

Due to the large number of grid connection of distributed power supply, the existing scheduling methods can not meet the demand gradually. The proposed virtual power plant provides a new idea to solve this problem. The photovoltaic power prediction provides the data basis for the scheduling of the virtual power plant. Prediction intervals of photovoltaic power is a powerful statistical tool used for quantifying the uncertainty of photovoltaic power generation in power systems. To improve the interval prediction accuracy during the non-stationary periods of photovoltaic power, this paper proposes a probabilistic ensemble prediction model, which combines the modules of data preprocessing, non-stationary period discrimination, feature extraction, deterministic prediction, uncertainty prediction, and optimization integration into a general framework. More specifically, in the non-stationary period discrimination module, the method of discriminating the difference of the power ratio difference is introduced and applied for identifying the non-stationary period of the data of photovoltaic output; in the deterministic point prediction module, a stacking- long-short-term memory neural network model is used for point forecasts; in the uncertainty interval prediction module, a BAYES neural network is introduced for probabilistic forecasts; in the optimization integration module, an optimization algorithm named Non-dominated Sorting Genetic Algorithm-II is applied for integrating and optimizing the results of the point forecast and probabilistic forecast. The proposed model is tested using two photovoltaic outputs and weather data measured from a grid-connected photovoltaic system. The results show that the proposed model outperforms conventional forecast methods to predict short-term photovoltaic power outputs and associated uncertainties. The interval width is reduced by 10–20%, and the prediction accuracy is improved by at least 10%; this can be a useful tool for photovoltaic power forecasting.