Reviewerly/exHarmony huggingface dataset:
Design and implementation of intelligent teaching attendance system based on image recognition

This The form and location of teaching at colleges and universities are varied. However, there is a lack of efficient and intelligent assessment methods for attendance of teachers in teaching. This paper designs and implements an intelligent teaching attendance system based on image recognition, which is a novel attendance assessment mode by turning active attendance into passive attendance. Based on the existing monitoring system, the development of the system gets the most out of the monitoring camera as the data acquisition terminal. Combining two types of people counting algorithms, the usage of the teaching locations, analysis of the teaching plan and evaluation of the teaching attendance are realized without any perception. The application of the system is then extended to the statistical analysis of the utilization of scientific research laboratories, achieving a full coverage of the management of teaching and scientific research locations. The results of this study provide an efficient, convenient, and intelligent means for the teaching attendance and rational use of locations.
Tightness Subtleties for Multi-user PKE Notions

Public key encryption schemes are increasingly being studied concretely, with an emphasis on tight bounds even in a multi-user setting. Here, two types of formalization have emerged, one with a single challenge bit and one with multiple challenge bits. Another modelling choice is whether to allow key corruptions or not. How tightly the various notions relate to each other has hitherto not been studied in detail. We show that in the absence of corruptions, single-bit left-or-right indistinguishability is the preferred notion, as it tightly implies the other (corruption-less) notions. However, in the presence of corruptions, this implication no longer holds; we suggest the use of a more general notion that tightly implies both existing options. Furthermore, for completeness we study how the relationship between left-or-right versus real-or-random evolves in the multi-user PKE setting.
Pixdoor: A Pixel-space Backdoor Attack on Deep Learning Models

Deep learning algorithms outperform the machine learning techniques in various fields and are widely deployed for recognition and classification tasks. However, recent research focuses on exploring these deep learning models' weaknesses as these can be vulnerable due to outsourced training data and transfer learning. This paper proposed a rudimentary, stealthy Pixel-space based Backdoor attack (Pixdoor) during the training phase of deep learning models. For generating the poisoned dataset, the bit-inversion technique is used for injecting errors in the pixel bits of training images. Then 3% of the poisoned dataset is mixed with the clean dataset to corrupt the complete training images dataset. The experimental results show that the minimal percent of data poisoning can effectively fool a deep learning model with a high degree of accuracy. Likewise, in experiments, we witness a marginal degradation of the model accuracy by 0.02%.
The Statistical Nature of Causation

Abstract Causation is a macroscopic phenomenon. The temporal asymmetry displayed by causation has no counterpart in the fundamental dynamics of the microscopic world. So causation must somehow emerge from the underlying dynamics, along with other macroscopic phenomena like entropy increase and the arrow of radiation. In this paper I shall offer an account of causation that accounts for this emergence. I shall show how macroscopic events fall into lawlike patterns that are subject to the random influences of microscopic processes. It is the probabilistic independence of these random influences that constitutes these laws as temporally asymmetric and causal. I shall approach these issues by considering ‘causal inference’ techniques that allow causal relations to be inferred from sets of observed correlations. I shall show that these techniques are best explained by a reduction of causation to structures of equations with probabilistically independent exogenous terms. This exogenous probabilistic independence imposes a recursive order on these equations and a consequent distinction between dependent and independent variables that lines up with the temporal asymmetry of causation.
Requirements Engineering: Conflict Detection Automation Using Machine Learning

The research community has well recognized the importance of requirement elicitation.Recent research has shown the continuous decreasing success rate of IS projects in the last five years due to the complexity of the requirement conflict refinement process.Requirement conflict is at the heart of requirement elicitation.It is also considered the prime reason for deciding the success or failure of the intended Information System (IS) project.This paper introduces the requirements conflict detection automation model based on the Mean shift clustering unsupervised machine learning model.It utilizes the advantages of Artificial Intelligence in detecting and classifying the requirement conflicts occurring in the requirement elicitation phase.An experiment of the proposed model was conducted, composed of 207 observations and 11 parameters.The results show that the correct detection accuracy for the (Conflicted Requirements, Partial Conflicted Requirements & Conflict Free Requirements).The proposed model findings provide a promising and effective detection process regarding requirements classification.The model validation process provides a performance comparison between the model output vs. the output produced by the requirement conflict verification phase, detailing the Standard Error (SE) measure of accuracy values and the detected clusters.The implications of this study could be used to promote the automatization of the requirement elicitation process.Thus, increasing the potentiality of enhancing the produced systems designs.
Spatial Domain Block Based Blind Image Watermarking for Hardware Applications

Internet revolution led to a vast growth in multimedia applications. So, it has turned to be a common practice to copy, transmit and distribute digital data and this, in turn, resulted in using the resources of data illegally and users who are not authorized hack by using such applications.Digital watermarking is regarded as one of the proposed solutions, which is suggested for copyright protection. This research accentuates the digital image watermarking, which protects the image by means of hiding a watermark image in an original image to demonstrate legal ownership.This paper suggests a new method of embedding and retrieving a watermark image besides the works within the (spatial domain) of digital images in which an invisible watermark image is embedded. By using a secret key to encrypt the watermark image for the sake of more security and implementing the block based spatial watermarking method embeds to the watermark to achieve a good robustness against many attacks. MATLAB has been used to perform and implement this work.”
Extractive Dialogue Summarization Without Annotation based on Distantly Supervised Machine Reading Comprehension in Customer Service

Given a long dialogue, the dialogue summarization system aims to obtain a shorter highlight which retains the important information in the original text. For the customer service scenarios, the summaries of most dialogues between an agent and a user focus on several fixed key points, such as users' question, users' purpose, the agent's solution, and so on. Traditional extractive methods are difficult to extract all predefined key points exactly. Furthermore, there is a lack of large-scale and high-quality extractive summarization datasets containing the annotation for key points. Moreover, the speaker's role information is ignored or not fully utilized in previous work. In order to solve the above challenges, we propose a Distant Supervision based Machine Reading Comprehension model for extractive Summarization (DSMRC-S). DSMRC-S transforms the summarization task into the machine reading comprehension problem, to fetch key points from the original text exactly according to the predefined questions. In addition, a distant supervision method is proposed to alleviate the lack of eligible extractive summarization datasets. What's more, a speaker's role token and the solver classification task are proposed to make full use of speaker's role information. We conduct experiments on a real-world summarization dataset collected in customer service scenarios, and the results show that the proposed method outperforms the strong baseline methods by 6 percentage points on ROUGE_L.
Hyper-Laplacian regularized multi-view subspace clustering with jointing representation learning and weighted tensor nuclear norm constraint

In recent years, tensor-Singular Value Decomposition (t-SVD) based tensor nuclear norm has achieved remarkable progress in multi-view subspace clustering. However, most existing clustering methods still have the following shortcomings: (a) It has no meaning in practical applications for singular values to be treated equally. (b) They often ignore that data samples in the real world usually exist in multiple nonlinear subspaces. In order to solve the above shortcomings, we propose a hyper-Laplacian regularized multi-view subspace clustering model that joints representation learning and weighted tensor nuclear norm constraint, namely JWHMSC. Specifically, in the JWHMSC model, firstly, in order to capture the global structure between different views, the subspace representation matrices of all views are stacked into a low-rank constrained tensor. Secondly, hyper-Laplace graph regularization is adopted to preserve the local geometric structure embedded in the high-dimensional ambient space. Thirdly, considering the prior information of singular values, the weighted tensor nuclear norm (WTNN) based on t-SVD is introduced to treat singular values differently, which makes the JWHMSC more accurately obtain the sample distribution of classification information. Finally, representation learning, WTNN constraint and hyper-Laplacian graph regularization constraint are integrated into a framework to obtain the overall optimal solution of the algorithm. Compared with the state-of-the-art method, the experimental results on eight benchmark datasets show the good performance of the proposed method JWHMSC in multi-view clustering.
See SIFT in a Rain: Divide-and-conquer SIFT Key Point Recovery from a Single Rainy Image

Scale-Invariant Feature Transform (SIFT) is one of the most well-known image matching methods, which has been widely applied in various visual fields. Because of the adoption of a difference of Gaussian (DoG) pyramid and Gaussian gradient information for extrema detection and description, respectively, SIFT achieves accurate key points and thus has shown excellent matching results but except under adverse weather conditions like rain. To address the issue, in the paper we propose a divide-and-conquer SIFT key points recovery algorithm from a single rainy image. In the proposed algorithm, we do not aim to improve quality for a derained image, but divide the key point recovery problem from a rainy image into two sub-problems, one being how to recover the DoG pyramid for the derained image and the other being how to recover the gradients of derained Gaussian images at multiple scales. We also propose two separate deep learning networks with different losses and structures to recover them, respectively. This divide-and-conquer scheme to set different objectives for SIFT extrema detection and description leads to very robust performance. Experimental results show that our proposed algorithm achieves state-of-the-art performances on widely used image datasets in both quantitative and qualitative tests.
GLOBAL ENVIRONMENTAL MONITORING SYSTEM

This article reveals the problems of creating a monitoring system to assess the ecological state of the environment of the selected area. An information model of the system has been developed, which takes into account the parameters of air, surface water, and soil pollution. The main components of the system, including the logical model of the database, have been designed and implemented. To assess the state of the environment according to the selected pollution parameters, the fuzzy logic model is constructed.
Few-Shot Class-Incremental Learning with Meta-Learned Class Structures

Learning continually from few-shot examples is a hallmark of human intelligence but it poses a great challenge for deep neural networks since they commonly suffer from catastrophic forgetting and overfitting. In this paper, we tackle this challenge in the few-shot class-incremental learning (FSCIL) setting, where a sequence of few-shot learning sessions containing disjoint sets of classes is created for a model to incrementally learn new classes, and the model should avoid forgetting information of old classes. Simply accumulating information of all learned classes will severely degrade the performance of the model since new classes are not learned to be discriminative across different sessions and they tend to be biased with limited training examples. To address this problem, we introduce class structures to regularize the learned classes on how they should distribute in the embedding space such that they are distinctive with each other within and across different learning sessions. Concretely, these class structures are encoded in a subspace where an alignment kernel aligns a learned class with class structures by moving it along the base vectors of the subspace. We sample incremental tasks in the training to simulate incremental learning and formulate the training as a meta-learning process to learn generalizable class structures across many incremental tasks. Experimental results on the CIFAR100, miniImageNet, and CUB200 datasets demonstrate the effectiveness of our method in combating overfitting and catastrophic forgetting.
WCDGAN: Weakly Connected Dense Generative Adversarial Network for Artifact Removal of Highly Compressed Images

In highly compressed images, i.e. quality factor q ≤ 10, JPEG compression causes severe compression artifacts including blocking, banding, ringing and color distortion. The compression artifacts seriously degrade image quality, which is not conducive to subsequent tasks, such as object detection and semantic segmentation. In this paper, we propose a weakly connected dense generative adversarial network for artifacts removal of highly compressed images, named WCDGAN. WCDGAN has three main ingredients of mixed convolution, weakly connected dense block (WCDB), and mixed attention. In the loss function, we add a perceptual loss to generate photo-realistic images with compression artifact removal. Experimental results show that WCDGAN successfully removes compression artifacts and produces sharp edges, clear textures and vivid colors even in highly compressed images. Moreover, WCDGAN outperforms state-of-the-art methods for compression artifact removal in terms of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM).
A Hybrid Approach to Resolve Data Sparsity and Cold Start Hassle in Recommender Systems

There is a notable growth of RS and utilizations in a different area. The real aim of RS is yielding suitable options to users. In the process of implementation, there are different methodologies, namely CF, CBF and HRS. For this article, we will try to resolve it by using the hybrid approach. The benefits of these approaches are their Style, operation and productivity. CS and Data Sparsity is a beautiful area which has been troubling RS. CS hassle occurs when the RS cannot recommend new users items since there is data sparsity. In this article we will look into the hybrid approach solve the above said issues with hybrid approach and finding the similarities with computational equations and steps to resolve the hassles and experimental design of the approach constructed and comparing the hybrid approach with the Staple CF algorithm and finding out which gives the good performance and solves our Research problem.
Fine Grained Access Control Based on Smart Contract for Edge Computing

Traditional centralized access control faces data security and privacy problems. The core server is the main target to attack. Single point of failure risk and load bottleneck are difficult to solve effectively. And the third-party data center cannot protect data owners. Traditional distributed access control faces the problem of how to effectively solve the scalability and diversified requirements of IoT (Internet of Things) applications. SCAC (Smart Contract-based Access Control) is based on ABAC (Attributes Based Access Control) and RBAC (Role Based Access Control). It can be applied to various types of nodes in different application scenarios that attributes are used as basic decision elements and authorized by role. The research objective is to combine the efficiency of service orchestration in edge computing with the security of consensus mechanism in blockchain, making full use of smart contract programmability to explore fine grained access control mode on the basis of traditional access control paradigm. By designing SSH-based interface for edge computing and blockchain access, SCAC parameters can be found and set to adjust ACLs (Access Control List) and their policies. The blockchain-edge computing combination is powerful in causing significant transformations across several industries, paving the way for new business models and novel decentralized applications. The rationality on typical process behavior of management services and data access control be verified through CPN (Color Petri Net) tools 4.0, and then data statistics on fine grained access control, decentralized scalability, and lightweight deployment can be obtained by instance running in this study. The results show that authorization takes into account both security and efficiency with the “blockchain-edge computing” combination.
Efficient Hardware Implementations of Legendre Symbol Suitable for MPC Applications

Multi-party computation (MPC) allows each peer to take part in the execution of a common function with their private share of data without the need to expose it to other participants. The Legendre symbol is a pseudo-random function (PRF) that is suitable for MPC protocols due to their efficient evaluation process compared to other symmetric primitives. Recently, Legendre-based PRFs have also been employed in the construction of a post-quantum signature scheme, namely LegRoast. In this paper, we propose, to the best of our knowledge, the first hardware implementations for the Legendre symbol by three approaches: 1) low-area, 2) high-speed, and 3) high-frequency. The high-speed architecture outperforms state-of-the-art software implementations, which run on Intel’s Core-i5. Our evaluation results on FPGA show that this architecture reduces the Legendre calculation time by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.56\times $ </tex-math></inline-formula> compared to software implementations on Core-i5. On the other hand, the low-area architecture consumes only 5489 slices on the Artix-7 FPGA and is suitable for resource-constrained devices. Moreover, our ASIC implementation results indicate that the low-area architecture consumes 97.56K gates to implement and requires <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4.01~mW$ </tex-math></inline-formula> to operate on 50 MHz. The high-frequency architecture increases the frequency by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.72\times $ </tex-math></inline-formula> over the high-speed architecture and achieves 200 MHz frequency on FPGA.
Designing Benchmark Generator for Dynamic Optimization Algorithm

Real-world problems always change over time. In the past two decades, many researchers have become increasingly interested in studying evolutionary optimization algorithms for dynamic problems owing to their great importance in real-world applications. Currently, there are no unified and mature test functions for dynamic problems, and the existing dynamic test functions are incapable of simulating a real dynamic environment. This study constructs a new benchmark test function generator for simulating dynamic environments that combine existing static benchmark functions and their related properties to form new dynamic test functions. It is simple to construct and conveniently control the dynamic properties by adjusting the corresponding parameters. Through a series of comparisons with the dynamic benchmark of the generalized dynamic benchmark generator (GDBG), the simulation results show that the dynamic test functions constructed by this generator can reflect different types of changing intensity.
Competition Between Cryptocurrency and Fiat Currency: Control Over the Future of Global Economy

Cryptocurrency has become a buzzword globally. The number of stakeholders in the cryptocurrency takeover has increased at an unprecedented level and what was once mocked and speculated upon only as a theoretical construct may now very well be implemented globally. The ethos guiding the cryptocurrency revolution is in stark contrast with the existing economic structure and thus appears to be in direct competition with the existing fiat currencies. The authors ponder upon the regulatory challenges related to cryptocurrencies specifically with the lens of competition law in this article and suggest some probable remedies which might help tame this volatile resource.
Approximating a deep reinforcement learning docking agent using linear model trees

Deep reinforcement learning has led to numerous notable results in robotics. However, deep neural networks (DNNs) are unintuitive, which makes it difficult to understand their predictions and strongly limits their potential for real-world applications due to economic, safety, and assurance reasons. To remedy this problem, a number of explainable AI methods have been presented, such as SHAP and LIME, but these can be either be too costly to be used in real-time robotic applications or provide only local explanations. In this paper, the main contribution is the use of a linear model tree (LMT) to approximate a DNN policy, originally trained via proximal policy optimization(PPO), for an autonomous surface vehicle with five control inputs performing a docking operation. The two main benefits of the proposed approach are: a) LMTs are transparent which makes it possible to associate directly the outputs (control actions, in our case) with specific values of the input features, b) LMTs are computationally efficient and can provide information in real-time. In our simulations, the opaque DNN policy controls the vehicle and the LMT runs in parallel to provide explanations in the form of feature attributions. Our results indicate that LMTs can be a useful component within digital assurance frameworks for autonomous ships.
Inter-annotator Agreement Using the Conversation Analysis Modelling Schema, for Dialogue

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