Reviewerly/exHarmony huggingface dataset:
Statistically Consistent Saliency Estimation

The growing use of deep learning for a wide range of data problems has highlighted the need to understand and diagnose these models appropriately, making deep learning interpretation techniques an essential tool for data analysts. The numerous model interpretation methods proposed in recent years are generally based on heuristics, with little or no theoretical guarantees. Here, we present a statistical framework for saliency estimation for black-box computer vision models. Our proposed model-agnostic estimation procedure, which is statistically consistent and capable of passing sanity checks, has polynomial-time computational efficiency since it only requires solving a linear program. An upper bound is established on the number of model evaluations needed to recover regions of importance with high probability through our theoretical analysis. Furthermore, a new perturbation scheme is presented for the estimation of local gradients that is more efficient than commonly used random perturbation schemes. The validity and excellence of our new method are demonstrated experimentally via sensitivity analyses.
Providing Energy-efficient and High-performance Infrastructure for Smart Network

Energy efficiency, performance and service availability are the key performance indicators of modern Smart Networks. These networks use large-scale distributed computing systems to provide high-performance data processing. In such systems, it is always difficult to find a balance between performance and power consumption, given the possible heterogeneity of nodes in a distributed system. Existing approaches propose using horizontal scaling, workload consolidation, and energy-efficient scheduling to improve the energy efficiency of large-scale distributed systems while maintaining sufficient processing performance. We propose to combine the benefits of horizontal scaling and energy efficient scheduling and model the system as a queuing system to achieve the optimal balance of performance, power consumption, and service availability in our PCPB-2 approach. Experimental analysis of the approach proves its efficiency up to 8.9% compared to the state-of-the-art Backfill energy-aware scheduling approach.
A Study of Identity Threat using CyberThreat Defense Network

Many commercial organizations that run their business on web interface, with zero down time is questionable due to the vulnerability in their entire commercialization process. The reason behind such unsuccessful event or disrupt in the process depends on the conditions such as variations of traffic, authentication failure, difficulty in detection of botnets, violations of rules in accessing information and other breeches on security. Cognitive Intelligence is a specific topic to alleviate such abnormalities on data related to secure transaction for resolving contemporary issues occurring in due process of various web-based applications. Cyber Threat Defense (CTD) is an approach to deal with the mentioned issues to protect threats on advance, doubtful client progress and rule violations, on organization information. Confrontation reaction deals with the real time notification of ongoing application and the same can be controlled by CTD Incident Response. Python Library map are used to simulate the introspection of security concern on organization's information.
Cyberlaw: Content Moderation &amp;amp; Freedom of Expression @ Twitter, Instagram &amp;amp; TikTok (Field Project Report)

This report documents the activities and findings of the Field Project on Content Moderation and Freedom of Expression conducted by a group of students at FGV law school in Rio de Janeiro between February and May 2021. The twofold objective of the Field Project was (1) to scrutinize the content moderation practices of Twitter, Instagram and Tiktok; and (2) to examine the extent to which those practices align with key international principles governing freedom of expression, in particular those of legality, necessity and proportionality
A Grad-CAM and YOLO based approach to extracting a human passageway ROI

We propose a computer vision based solution for the use of public video feeds to monitor crowd congestion with a focus on full automation as a potential scalable solution to address crowd statistics extraction needs amplified by the COVID-19 pandemic. The novelty is the provision of a fully autonomous solution that is able to generate a region of interest (ROI) upon initial feed registration with a self-refinement algorithm that perfects the ROI over time. Five classes were used from the Places 2 dataset. The root model of the hierarchy was used to classify between a beach, fast-food restaurant, train station, lawn and market with an overall accuracy of 95.58% and F1-Score of 88.94%. The market and beach class were then split into two sub-classes each. The “beach” model was further explored using a Grad-CAM based post-processing technique to better understand what the model bases the classification on. The novelty is the use of the same technique to generate a human passageway region of interest based on the localisation of the Grad-CAM in several live beach footages. These were also inferred using a YOLOv5 based human tracking approach. The Grad-CAM based ROI was then evaluated for each footage on the YOLOv5 generated ROI.
Analysis of a Joint Data Security Architecture Integrating Artificial Intelligence and Cloud Computing in the Era of Big Data

This article analyzes the analysis of the joint data security architecture that integrates artificial intelligence and cloud computing in the era of big data. The article discusses and analyzes the integrated applications of big data, artificial intelligence and cloud computing. As an important part of big data security protection, joint data security Protecting the technical architecture is not only related to the security of joint data in the big data era, but also has an important impact on the overall development of the data era. Based on this, the thesis takes the big data security and joint data security protection technical architecture as the research content, and through a simple explanation of big data security, it then conducts detailed research on the big data security and joint data security protection technical architecture from five aspects and thinking.
Exploring Reinforcement Learning: A Case Study Applied to the Popular Snake Game

Bonnici, Russell Sammut Saliba, Chantelle Caligari, Giulia Elena Bugeja, MarkReinforcement Learning is a machine learning approach in which an agent interacts with their environment to gather information, and make an informed decision based on the accumulated information. In this research, we investigate the applicability of various reinforcement learning techniques for Snake, a video game popular on the Nokia 3310 mobile phone. Q-Learning (Quality-Learning), SARSA (State-Action Reward State-Action) and PPO (Proximal Policy Optimization), were implemented and evaluated for Snake. Q-Learning and SARSA did not generate optimal results due to the large environment of the game. Meanwhile, PPO was implemented with three varying approaches for input; a vector, CNN and raycasting based approach. PPO, in conjunction with raycasting, resulted in the best performance, with the snake agent learning for both collecting food and avoiding obstacles. Furthermore, A* Pathfinding was tested and it achieved a performance better than Q-Learning and SARSA but not better than PPO as it was less adaptable to large environments. In the future, agents in large dynamic game environments, may benefit further from utilizing PPO.
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Let R=Fpm[u,θ]〈u2〉 where θ is an automorphism of Fpm but it is not the identity. The list of (left and right) α-constacyclic codes of length ps over R is provided where θ(α)=α. The self-dual α-constacyclic codes are given. In addition, we derive the condition of LCD codes for those codes. For the remaining result, the condition of self-orthogonal left α-constacyclic codes is also obtained.
Application of Smart Image Processing Technology in Feature Extraction of Glass Artistic Style Patterns

With the emerging development of the digital signal processing technology, the image information can be analyzed from different perspectives. Hence, this paper studies the application of smart image processing technology in feature extraction of glass artistic style patterns. Information entropy reflects the amount of information contained in the image. The larger the information entropy, the more information the image contains. Therefore, the vector transform model is considered for the image feature analysis and adjustment work. The glass style is considered as the pattern to further analyze the image feature. The model is tested on Matlab and compared with the other latest models. The results have reflected that the proposed model can extract the features effectively.
A New Generative Adversarial Network Based Imbalanced Fault Diagnosis Method

In the field of mechanical fault diagnosis, most of the collected signals are normal signals, leading to data imbalance and reduction of fault diagnosis performance. To address the issue, a conditional Wasserstein generative adversarial network with gradient penalty (CWGAN-GP) model-based data augmentation method is proposed to improve the classification accuracy. Firstly, the CWGAN-GP is used to generate data. Specially, a Pearson correlation coefficient screening criterion (PCCSC) is proposed to ensure the generation of high-quality data. Then, the generated data are added to the original data. Finally, the method based on the gated recurrent unit recurrent neural network for fault diagnosis (FDGRU) is applied for fault recognition. Extensive experiments are conducted, such as the influence of the number of health states, and the imbalance ratio, etc., to prove the merits of the proposed approach. Experimental results illustrate that the proposed approach can effectively enhance the classification accuracy of imbalanced fault diagnosis.
Tibetan-Chinese Cross-Lingual Sentiment Classification Based on Co-Training

As a low-resource language, Tibetan lacks high- quality corpus resources, leading to a certain extent hinders the development of text sentiment analysis. Through the task of cross-language text sentiment classification, using a small number of labeled samples of Tibetan to dig out the sentiment information of a large number of unlabeled samples, it can solve the problem of lack of corpus to a certain extent. This paper introduces a typical semi-supervised algorithm-collaborative training algorithm in the Tibetan-Chinese cross-language sentiment classification task, constructs a cross-language sentiment classification model based on semi-supervised collaborative training, and treats a balanced Tibetan-Chinese bilingual data set as two different views Conduct bilingual collaborative training and use abundant Chinese annotation data to solve the problem of lack of emotional resources in Tibetan and lack of sufficient labeled samples. Experimental results show that the use of collaborative training algorithms can enhance the learning ability of Tibetan sentiment classifiers for unlabeled samples and improve the accuracy of Tibetan sentiment classification.
Simple to complex dictionary learning for action recognition

Complex human action recognition has gained extensive attention in recent years. However, Own to view point change and other factors, recognizing complex actions is greatly challenging. Considering each complex action can be decompose into a sequence of simple actions, and dictionary learning has gained promising performance. Thus, a simple to complex action dictionary learning model (SCA-DLM) is proposed for complex action recognition. The proposed model used a sequence of simple actions to help complex action learning. More importantly, we unify the simple action and complex action into an objective function for learning a sparse dictionary for complex actions. The objective function learns simple action dictionary shared by different actions to model action-shared features. This approach represents the complex actions using complex action dictionary and the simple action dictionary. The extensive experiments on the complex action datasets demonstrate that SCA-DLM outperforms state-of-the-art approaches.
Medical Diagnosis for Incomplete and Imbalanced Data

The process of identifying a disease that a patient is affected by with the help of signs, test reports, and symptoms is known as diagnosis. Deep learning plays a major role in automated diagnosis in the medical field. The efficiency of the automated diagnosis system depends on how well the data provided for training is and how it is used to train the system. The data is subject to data quality concerns like its accuracy, completeness, consistency, and data balance. Additionally, significantly, in reality, clinical data is created solely from many useful and important attributes, rather than the complete patient data. But, in the real world, data is of poor quality due to various reasons, for example, data validity, respectability, fulfillment, exactness, and so on. Specifically, in the medical domain also, the data is imbalanced and incomplete. So, in this project, we propose a multi-instance neural network to predict the disease based on the patients' existing and reliable data. The proposed approach is planned to be tested with the imbalanced dataset named the Western Medicine (WM) and Disease Symptom Prediction. The proposed multi-instance neural network architecture predicts the disease with high accuracy.
Ets-3d: An Efficient Two-Stage Framework for Stereo 3d Object Detection

We propose an efficient two-stage framework (ETS-3D) for stereo 3D object detection. Contrary to many recent approaches that rely on predicted depth maps using time-consuming stereo matching models, our approach utilizes the well-designed features to generate high-quality 3D proposals in stage-1, without predicting the depth map explicitly. Specifically, we leverage pixel-wise correlation to produce the normalized cost volumes to weight the left image features, and fuse multi-scale weighted features to obtain the weighted and fused features for 3D proposal generation. To maintain fast computation, only the filtered positive 3D proposals are fed into stage-2 sub-network for further proposal refinement and quality prediction. Furthermore, we reconstruct the 3D proposal features in stage-2 to make use of different feature representations, allowing more accurate detection results. Experiments results on the KITTI 3D object detection benchmark shows that our method not only achieves state-of-the-art performance and also can run at approximately 10 fps.
Bibliographic Information on Psychology on the Sites of the Russian State Library and Universities

In modern conditions of unstable socio-economic situation caused by the threat to human health due to the pandemic of coronavirus infection, the need for psychological information is significantly increasing. The need to assist modern readers in finding and choosing meaningful, scientifically reliable and accessible psychological information among a huge number of books and online resources has determined the relevance of the topic of the article. The article discusses popular bibliographic resources on psychology, prepared by the Research Department of Bibliography in the Russian State Library (RSL) and posted on the RSL website in the “Bibliographic publications” section: “Psychology for everyone. Index of the best books”, “Psychology. Internet Resources”, including “Psychology on the Websites of Russian Universities”. A scientific and methodological analysis of these resources is given: the criteria for their selection are shown, a variety of forms of presentation of materials is noted, accessibility for a wide range of readers is emphasized. The potential of popular bibliographic resources on psychology in the formation of a holistic view of psychological science among users and providing easy access to high-quality information on psychological issues is considered.
Crowd counting and density estimation method based on multi-column CNN and adaptive projections onto convex sets

Aiming at the low accuracy of crowd counting and density estimation algorithm in the environment of dense crowd with poor characteristics and complex background, a Crowd counting and density estimation method based on Multi-column CNN and adaptive projections onto convex sets was proposed. Firstly, a series of images translated from a single image by a specific value are put into the Multi-column CNN to obtain initial density estimation maps with information difference by sub-pixel displacement. Secondly, the crowd density recognition process is regarded as the sampling and mapping form the real space to density map, and the recognition ambiguity is simulated by the point spread function. In the training of convolutional neural network, the adaptive parameters of projections onto convex sets are trained synchronously to optimize the constraints of convex sets and the degree of image fusion correction. Finally, several density estimation maps with information difference by sub-pixel displacement are fused by adaptive projections onto convex sets to obtain the final density estimation map. The accuracy and robustness of the proposed model are proved by experiments
Design of Rock Climbing Data Acquisition System Based on LoRa

As a new sport, rock climbing is deeply loved by people, but it is difficult to intuitively understand the exercise intensity of rock climbers during training. Through the combination of Internet of Things technology and sports, it is possible to monitor rock climbing strength data in real time, and through subsequent analysis of the data, it can help coaches and rock climbers to improve the scientific nature of sports competitive strategies. The main control module of the LoRa-based sports data acquisition system is STM32. The ratio method can be used to calculate the digital signal value of the pressure sensor. The corresponding algorithm is written by establishing the corresponding relationship between the digital signal and the actual strength. Finally, the obtained data is transmitted to the LoRa module store in the computer. Experiments show that the system can provide coaches and rock climbers with accurate training data, provide technical support for coaches to analyze rock climbing technical movements, and improve sports training efficiency.
Efficient Verifiable Partially-Decryptable Commitments from Lattices and Applications

We introduce verifiable partially-decryptable commitments (VPDC), as a building block for constructing efficient privacy-preserving protocols supporting auditability by a trusted party. A VPDC is an extension of a commitment along with an accompanying proof, convincing a verifier that (i) the given commitment is well-formed and (ii) a certain part of the committed message can be decrypted using a (secret) trapdoor known to a trusted party. We first formalize VPDCs and then introduce a general decryption feasibility result that overcomes the challenges in relaxed proofs arising in the lattice setting. Our general result can be applied to a wide class of Fiat-Shamir based protocols and may be of independent interest. Next, we show how to extend the commonly used lattice-based ‘Hashed-Message Commitment’ (HMC) scheme into a succinct and efficient VPDC. In particular, we devise a novel ‘gadget’-based Regev-style (partial) decryption method, compatible with efficient relaxed lattice-based zero-knowledge proofs. We prove the soundness of our VPDC in the setting of adversarial proofs, where a prover tries to create a valid VPDC output that fails in decryption. To demonstrate the effectiveness of our results, we extend a private blockchain payment protocol, MatRiCT, by Esgin et al. (ACM CCS ’19) into a formally auditable construction, which we call MatRiCT-Au, with very low communication and computation overheads over MatRiCT.
An Integer Programming Model for the Optimal Qubit Allocation

A qubit allocation problem is to find an assignment of logical qubits in a quantum algorithm to the physical qubits of a quantum computing device. The physical qubits are often configured in a coupling map, which is a directed graph that represents communication relationship between adjacent physical qubits. A proper qubit allocation satisfies all logical qubit communication relationship in a quantum circuit by conforming to the coupling map restriction. To find the proper qubit allocation, we often need to change the quantum circuit by using a set of circuit transformations, which include reversal, bridge, and swap. Such a transformation results in the quantum circuit with newly added quantum gates and subsequenlty causes extra quantum errors. In this study, we propose an integer programming model that minimizes the sum of the quantum costs of the circuit transformation. In our model, all three circuit transformations are considered. Moreover, we present the results of the computational experiment in which our model is compared with heuristic procedures from previous studies.
Quantifying Students’ Involvement during Virtual Classrooms: A Meeting Wrapper for the Teachers

Educational systems have witnessed a rapid transformation from the traditional physical classroom to the online teaching mode due to the COVID-19 pandemic. This sudden shift throws two significant questions to the educational policymakers and system designers. (1) How can an instructor improve the teaching performance over an online teaching platform? (2) How does the instructor understand the students’ learning pace? For answering these questions, we propose a platform in this paper that analyzes the real-time presentation video and the facial video feeds from the instructor as well as the students to explore the visual engagement of the student towards the lecture contents. However, this is challenging as the students may get involved in various multitasking instances, such as taking notes or browsing relevant reading materials. The crux of this paper is to understand a few real-time opportunistic moments when the students should visually focus on the presentation content if they are engaged and been able to follow the lecture. We investigate these instances and analyze the visual engagement of the students from their eye gaze and gaze gestures in real-time during those instances to assess their engagement during an online class. Our system achieves 71% (standard deviation accuracy for all over the scenarios in students’ involvement detection during the virtual classroom.