Reviewerly/exHarmony huggingface dataset:
Automated Expected Value Analysis of Recursive Programs

In this work, we study the fully automated inference of expected result values of probabilistic programs in the presence of natural programming constructs such as procedures, local variables and recursion. While crucial, capturing these constructs becomes highly non-trivial. The key contribution is the definition of a term representation, denoted as infer[.], translating a pre-expectation semantics into first-order constraints, susceptible to automation via standard methods. A crucial step is the use of logical variables, inspired by previous work on Hoare logics for recursive programs. Noteworthy, our methodology is not restricted to tail-recursion, which could unarguably be replaced by iteration and wouldn't need additional insights. We have implemented this analysis in our prototype ev-imp. We provide ample experimental evidence of the prototype's algorithmic expressibility.
Applying English Idiomatic Expressions to Classify Deep Sentiments in COVID-19 Tweets

Millions of people are connecting and exchanging information on social media platforms, where interpersonal interactions are constantly being shared. However, due to inaccurate or misleading information about the COVID-19 pandemic, social media platforms became the scene of tense debates between believers and doubters. Healthcare professionals and public health agencies also use social media to inform the public about COVID-19 news and updates. However, they occasionally have trouble managing massive pandemic-related rumors and frauds. One reason is that people share and engage, regardless of the information source, by assuming the content is unquestionably true. On Twitter, users use words and phrases literally to convey their views or opinion. However, other users choose to utilize idioms or proverbs that are implicit and indirect to make a stronger impression on the audience or perhaps to catch their attention. Idioms and proverbs are figurative expressions with a thematically coherent totality that cannot understand literally. Despite more than 10% of tweets containing idioms or slang, most sentiment analysis research focuses on the accuracy enhancement of various classification algorithms. However, little attention would decipher the hidden sentiments of the expressed idioms in tweets. This paper proposes a novel data expansion strategy for categorizing tweets concerning COVID-19. The following are the benefits of the suggested method: 1) no transformer fine-tuning is necessary, 2) the technique solves the fundamental challenge of the manual data labeling process by automating the construction and annotation of the sentiment lexicon, 3) the method minimizes the error rate in annotating the lexicon, and drastically improves the tweet sentiment classification’s accuracy performance.
From a Visual Scene to a Virtual Representation: A Cross-Domain Review

The widespread use of smartphones and other low-cost equipment as recording devices, the massive growth in bandwidth, and the ever-growing demand for new applications with enhanced capabilities, made visual data a must in several scenarios, including surveillance, sports, retail, entertainment, and intelligent vehicles. Despite significant advances in analyzing and extracting data from images and video, there is a lack of solutions able to analyze and semantically describe the information in the visual scene so that it can be efficiently used and repurposed. Scientific contributions have focused on individual aspects or addressing specific problems and application areas, and no cross-domain solution is available to implement a complete system that enables information passing between cross-cutting algorithms. This paper analyses the problem from an end-to-end perspective, i.e., from the visual scene analysis to the representation of information in a virtual environment, including how the extracted data can be described and stored. A simple processing pipeline is introduced to set up a structure for discussing challenges and opportunities in different steps of the entire process, allowing to identify current gaps in the literature. The work reviews various technologies specifically from the perspective of their applicability to an end-to-end pipeline for scene analysis and synthesis, along with an extensive analysis of datasets for relevant tasks.
Multimedia Signal Processing: A history of the Multimedia Signal Processing Technical Committee

Multimedia signal processing (MMSP) refers to processing of signals from multiple media—speech, audio, images, text, graphics, point clouds, etc.—often jointly. This article reviews the history of MMSP and, in parallel, the history of the MMSP Technical Committee (TC), with a focus on the last three decades ( <xref ref-type="fig" rid="fig1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Figure 1</xref> ).
John von Neumann’s Time-Frequency Orthogonal Transforms

John von Neumann (JvN) was one of the greatest scientists and minds of the 20th century. His research encompassed a large variety of topics (especially from mathematics), and the results he obtained essentially contributed to the progress of science and technology. Within this article, one function that JvN defined long time ago, namely the cardinal sinus (sinc), was employed to define transforms to be applied on 1D signals, either in continuous or discrete time. The main characteristics of JvN Transforms (JvNTs) are founded on a theory described at length in the article. Two properties are of particular interest: orthogonality and invertibility. Both are important in the context of data compression. After building the theoretical foundation of JvNTs, the corresponding numerical algorithms were designed, implemented and tested on artificial and real signals. The last part of the article is devoted to simulations with such algorithms by using 1D signals. An extensive analysis on JvNTs effectiveness is performed as well, based on simulation results. In conclusion, JvNTs prove to be useful tools in signal processing.
Anomalous Situations Recognition in Surveillance Images Using Deep Learning

Anomalous situations in surveillance videos or images that may result in security issues, such as disasters, accidents, crime, violence, or terrorism, can be identified through video anomaly detection. However, differentiating anomalous situations from normal can be challenging due to variations in human activity in complex environments such as train stations, busy sporting fields, airports, shopping areas, military bases, care centers, etc. Deep learning models’ learning capability is leveraged to identify abnormal situations with improved accuracy. This work proposes a deep learning architecture called Anomalous Situation Recognition Network (ASRNet) for deep feature extraction to improve the detection accuracy of various anomalous image situations. The proposed framework has five steps. In the first step, pretraining of the proposed architecture is performed on the CIFAR-100 dataset. In the second step, the proposed pre-trained model and Inception V3 architecture are used for feature extraction by utilizing the suspicious activity recognition dataset. In the third step, serial feature fusion is performed, and then the Dragonfly algorithm is utilized for feature optimization in the fourth step. Finally, using optimized features, various Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) based classification models are utilized to detect anomalous situations. The proposed framework is validated on the suspicious activity dataset by varying the number of optimized features from 100 to 1000. The results show that the proposed method is effective in detecting anomalous situations and achieves the highest accuracy of 99.24% using cubic SVM.
Assessing Key performance indicators in Blockchain-Based Supply Chain Financing: Case Study of Chain Stores

In recent years, due to the growth and development of financing tools and methods, financial institutions are always looking for new, efficient, and low-cost methods. In carrying out their daily production operations, companies are faced with different and diverse financial input and output flows from purchasing orders and inventory to receiving the price of sold goods, which are not the same in terms of time. Therefore, they will inevitably look for the financing of these processes, which is referred to as working capital financing. However, in the conditions of recession and lack of bank loans and considering the problems caused by the unilateral management of this issue, there is a need for innovative financial tools such as supply chain financing (SCF). In recent years, with the introduction and evolution of blockchain technology, this opportunity has also arisen in financial fields to make the most of this new technology. Considering the growth of this technology and the importance of supply chain financing, in this research, it was first tried to fully investigate the dimensions and key components of blockchain-based supply chain financing. chain stores were considered as a case study and the importance of supply chain financing was analyzed in them and the SCF framework was presented in these applied enterprises. In addition, the most important key performance indicators in a blockchain-based supply chain financing system are identified and evaluated with an emphasis on chain store processes. To evaluate the data, a non-linear hierarchical analysis method was used. The results show that transferring and reducing risks in different types is one of the most important performance indicators of the blockchain-based supply chain financing system.
Robust Watermarking Techniques for the Authentication and Copyright Protection of Digital Images: A Survey

Digital image authentication and security are crucial concerns for the digital revolution since any image may be readily tampered with. Digital watermarking schemes were since been utilized to tackle a range of problems over time and involving the verification of digital images and copyright protection. In a real-world setting, watermarked contents are frequently subject to a succession of assaults which makes it necessary to strike a balance between resilience and imperceptibility in the face of attacks. There are numerous hazards to this, and numerous watermarking techniques have been created to counter them. Data with less perceptual distortion should be able to be included in a real watermarking technique in addition to restoring the original cover content. As a result, robustness, imperceptibility, and security are three crucial criteria for digital content'sauthentication and copyrightpreservation. This study therefore presents a careful review of some cutting-edge watermarking technologies used for copyright protection and authentication so as to identify their strengths and limitations. For clarity, this study shall explain some basic concepts of digital watermarking as well as the various forms of attacks on watermarks. The contributions from this work will be useful to researchers aim at developing efficient watermarking techniques
Forecasting Student Academic Performance Using Neuro-Fuzzy Model

To achieve objective and precise forecasting of students’ academic performance while keeping up with technological improvements and changes, this paper discusses a neuro-fuzzy technique for the prediction of student academic performance. The technique comprises a Neural Network (NN), Fuzzy Logic (FL), Knowledge Base (KB) and Rule Base (RB) subcomponents. The NN subcomponent uses a three-layered feed-forward architecture to acquire a large collection of interconnected units in some pattern for communications. The FL subcomponent is an extension of Boolean logic and it was used for establishing accurate selection processes and precise solutions to multivariable problems. The KB component forms the database of multi-level information while the RB comprises a set of if-then statements for decision-making. The inference engine subcomponent applies a pre-defined procedure on input from the rule base and fuzzy logic interfaces for final prediction. The proposed technique performs predefined procedures that are based on some input sets that store multi-level information derived from several pre-specified scores. Results from the implementation of the technique established its practical function.
A new Chinese text clustering algorithm based on WRD and improved K-means

Text clustering has been widely used in data mining, document management, search engines, and other fields. The K-means algorithm is a representative algorithm of text clustering. However, traditional K-means algorithm often uses Euclidean distance or cosine distance to measure the similarity between texts, which is not effective in face of high-dimensional data and cannot retain enough semantic information. In response to the above problems, we combine word rotator’s distance with the K-means algorithm, and propose the WRDK-means algorithm, which use word rotator’s distance to calculate the similarity between texts and preserve more text features. Furthermore, we define a new cluster center initialization method that improves cluster instability during random initial cluster center selection. And, to solve the problem of inconsistent length between texts, we propose a new iterative approximation method of cluster centers. We selected three suitable datasets and five evaluation indicators to verify the feasibility of the proposed algorithm. Among them, the RI value of our algorithm exceeds 90%. And for Marco_F1, our scheme was about 37.77%, 23.2%, 13.06% and 20.12% better than other four methods, respectively.
Efficient federated learning on resource-constrained edge devices based on model pruning

Abstract Federated learning is an effective solution for edge training, but the limited bandwidth and insufficient computing resources of edge devices restrict its deployment. Different from existing methods that only consider communication efficiency such as quantization and sparsification, this paper proposes an efficient federated training framework based on model pruning to simultaneously address the problem of insufficient computing and communication resources. First, the framework dynamically selects neurons or convolution kernels before the global model release, pruning a current optimal subnet and then issues the compressed model to each client for training. Then, we develop a new parameter aggregation update scheme, which provides training opportunities for global model parameters and maintains the complete model structure through model reconstruction and parameter reuse, reducing the error caused by pruning. Finally, extensive experiments show that our proposed framework achieves superior performance on both IID and non-IID datasets, which reduces upstream and downstream communication while maintaining the accuracy of the global model and reducing client computing costs. For example, with accuracy exceeding the baseline, computation is reduced by 72.27% and memory usage is reduced by 72.17% for MNIST/FC; and computation is reduced by 63.39% and memory usage is reduced by 59.78% for CIFAR10/VGG16.
The Emergence of Chat GPT and Exploration of the Direction of Liberal Arts Education

이 연구의 목적은 Chat GPT 등장으로 인한 대학 교양교육의 방향을 제시하는 것이다. 연구목적 달성을 위한 연구내용은 첫째, Chat GPT를 이해하고, 둘째, Chat GPT 등장과 대학 교양교육의 방향 제시이다. Chat GPT는 사용자와 주고받는 대화에서 질문에 답하도록 설계된 언어 모델이다. Chat GPT는 교수 및 학습측면에서 교육에 여러 가지 영향을 미칠 수 있다. 첫째, 자료를 찾는 데 도움을 준다. 둘째, 독학이 가능하다. 셋째, 언어 학습이 가능하다. 넷째, 창작이 가능하다. 다섯째, 코딩도 가능하다. 여섯째, 플래너 역할도 가능하다. 일곱째, 다양한 정보를 얻을 수 있다. 여덟째, 교사 지원이 가능하다. 아홉째, 채팅 GPT는 장애가 있거나 기타 특별한 도움이 필요한 학생이 교육에 더 쉽게 접근할 수 있도록 도와준다. Chat GPT 등장으로 인한 대학 교양교육의 방향을 제시하면 다음과 같다. 첫째, 소프트 스킬 교육을 강화할 필요가 있다. 둘째, 인간의 정체성 확립을 위해 과학철학 교육을 강화할 필요가 있다. 셋째, 교양교육에서 융복합 교육을 강화해야 한다. 넷째, 교양교육에서 윤리 및 사회적 책임 강조 교육이 필요하다. 다섯째, Chat GPT 등장 시대에 교양교육에서 바람직한 교수학습 방법 혁신이 필요하다. The purpose of this study is to suggest the direction of liberal arts education in universities due to the emergence of Chat GPT. The contents of the study to achieve the purpose of the study are, first, to understand Chat GPT, and second, the emergence of Chat GPT and the direction of liberal arts education in universities. Chat GPT is a language model designed to answer questions in conversations with users. Chat GPT can have multiple impacts on education in terms of teaching and learning. First, it helps to find the material. Second, self-study is possible. Third, language learning is possible. Fourth, creativity is possible. Fifth, coding is possible. Sixth, it is possible to act as a planner. Seventh, various information can be obtained. Eighth, teacher support is possible. Ninth, Chat GPT helps students with disabilities or other special needs more easily access education. The following is the direction of liberal arts education at the university due to the emergence of Chat GPT. First, it is necessary to strengthen soft skill education. Second, it is necessary to strengthen science and philosophy education to establish human identity. Third, convergence education should be strengthened in liberal arts education. Fourth, education emphasizing ethics and social responsibility is needed in liberal arts education. Fifth, in the era of Chat GPT, it is necessary to innovate desirable teaching and learning methods in liberal arts education.
A Perceptually Evaluated Signal Model: Collisions Between a Vibrating Object and an Obstacle

The collision interaction mechanism between a vibrating string and a non-resonant obstacle is at the heart of many musical instruments. This paper focuses on the identification of perceptually salient auditory features related to this phenomenon. The objective is to design a signal-based synthesis process, with an eye towards developing intuitive control strategies. To this end, a database of synthesized sounds is assembled through physics-based emulation of a string/obstacle collision, in order to characterize the effect of collisions on time-frequency content. The investigation of this database reveals characteristic time-frequency patterns related to the position of the obstacle during the interaction. In particular, a frequency shift of certain modes is apparent for strong interactions, which, alongside the generation of new frequency components, leads to increased perceived roughness and inharmonicity. These observations enable the design of a real-time compatible signal-based sound synthesis process, with a mapping of synthesis parameters linked to the perceived location of the obstacle. The accuracy of the signal model with respect to the physical model sound output and recorded sounds was evaluated through listening tests: time-frequency patterns reproduced by the signal model enabled listeners to precisely recognize the transverse location of the obstacle.
High speed human action recognition using a photonic reservoir computer

The recognition of human actions in videos is one of the most active research fields in computer vision. The canonical approach consists in a more or less complex preprocessing stages of the raw video data, followed by a relatively simple classification algorithm. Here we address recognition of human actions using the reservoir computing algorithm, which allows us to focus on the classifier stage. We introduce a new training method for the reservoir computer, based on “Timesteps Of Interest”, which combines in a simple way short and long time scales. We study the performance of this algorithm using both numerical simulations and a photonic implementation based on a single non-linear node and a delay line on the well known KTH dataset. We solve the task with high accuracy and speed, to the point of allowing for processing multiple video streams in real time. The present work is thus an important step towards developing efficient dedicated hardware for video processing.
MSGSA: Multi-Scale Guided Self-Attention Network for Crowd Counting

The use of convolutional neural networks (CNN) for crowd counting has made significant progress in recent years; however, effectively addressing the scale variation and complex backgrounds remain challenging tasks. To address these challenges, we propose a novel Multi-Scale Guided Self-Attention (MSGSA) network that utilizes self-attention mechanisms to capture multi-scale contextual information for crowd counting. The MSGSA network consists of three key modules: a Feature Pyramid Module (FPM), a Scale Self-Attention Module (SSAM), and a Scale-aware Feature Fusion (SFA). By integrating self-attention mechanisms at multiple scales, our proposed method captures both global and local contextual information, leading to an improvement in the accuracy of crowd counting. We conducted extensive experiments on multiple benchmark datasets, and the results demonstrate that our method outperforms most existing methods in terms of counting accuracy and the quality of the generated density map. Our proposed MSGSA network provides a promising direction for efficient and accurate crowd counting in complex backgrounds.
Microsoft Power Platform

This chapter discusses the basic architecture of Microsoft Power Platform and some of the primary products that are available therein. While the focus of this book is on Dataverse, it is important to be aware of the other products that are available in Power Platform and how they work together. From application development in Power Apps to process automation in Power Automate, Power Platform provides the tools to build end-to-end solutions.
Plataformas educativas y la educación a distancia en la virtualidad

Information and communication technologies (ICT) have intervened in all human activities and education has not been the exception. In higher education, educational platforms have proven to be very useful in virtual education, forced by the pandemic caused by covid-19. To be effective, these educational platforms must combine ICT, teaching strategies and an institutional IT support team. Therefore, both students and teachers must modify the roles to which they were accustomed and become active and participatory, self-reflective and self-regulating actors. The purpose of this work is to propose teaching strategies to generate meaningful learning in virtual distance higher education, in times of the pandemic caused by covid-19. It is research with a qualitative, phenomenological and descriptive approach. A group of teachers from the Meritorious Autonomous University of Puebla (BUAP) were interviewed about the teaching strategies applied in their academic development in this time of pandemic. Selecting those that best adapt to the virtual model and that can be developed through educational platforms in higher education. Reaching the conclusion that it is necessary to reflect on the teaching practice and question the student and the teacher of the digital age.
Differentiable Logic Policy for Interpretable Deep Reinforcement Learning: A Study from an Optimization Perspective

The interpretability of policies remains an important challenge in Deep Reinforcement Learning (DRL). This paper explores interpretable DRL via representing policy by Differentiable Inductive Logic Programming (DILP) and provides a theoretical and empirical study of DILP-based policy learning from an optimization perspective. We first identified a fundamental fact that DILP-based policy learning should be solved as a constrained policy optimization problem. We then proposed to use Mirror Descent for policy optimization (MDPO) to deal with the constraints of DILP-based policies. We derived the closed-form regret bound of MDPO with function approximation, which is helpful to the design of DRL frameworks. Moreover, we studied the convexity of DILP-based policy to further verify the benefits gained from MDPO. Empirically, we experimented MDPO, its on-policy variant, and 3 mainstream policy learning methods, and the results verified our theoretical analysis.