Reviewerly/exHarmony huggingface dataset:
Foundation Models for Text Generation

Abstract This chapter discusses Foundation Models for Text Generation. This includes systems for Document Retrieval, which accept a query and return an ordered list of text documents from a document collection, often evaluating the similarity of embeddings to retrieve relevant text passages. Question Answering systems are given a natural language question and must provide an answer, usually in natural language. Machine Translation models take a text in one language and translate it into another language. Text Summarization systems receive a long document and generate a short summary covering the most important contents of the document. Text Generation models use an autoregressive Language Model to generate a longer story, usually starting from an initial text input. Dialog systems have the task of conducting a dialog with a human partner, typically not limited to a specific topic.
Introduction

Abstract With the development of efficient Deep Learning models about a decade ago, many Deep Neural Networks have been used to solve pattern recognition tasks such as natural language processing and image recognition. An advantage of these models is that they automatically create features arranged in layers which represent the content and do not require manually constructed features. These models rely on Machine Learning employing statistical techniques to give machines the capability to ‘learn’ from data without being given explicit instructions on what to do. Deep Learning models transform the input in layers step by step in such a way that complex patterns in the data can be recognized. This chapter first describes how a text is pre-processed and partitioned into tokens, which form the basis for natural language processing. Then we outline a number of classical Machine Learning models, which are often used as modules in advanced models. Examples include the logistic classifier model, fully connected layers, recurrent neural networks and convolutional neural networks.
Summary and Outlook

Abstract Foundation Models emerged as a new paradigm in sequence interpretation that can be used for a large number of tasks to understand our environment. They offer the remarkable property of combining sensory input (sound, images, video) with symbolic interpretation of text and may even include action and DNA sequences. We briefly recap the process of pre-training, fine-tuning or prompting of Foundation Models and summarize their main properties. For the different application areas presented in the book, we summarize the performance levels of the models and delineate different promising economic applications. A section is devoted to discussing the potential harm that can be caused by Foundation Models, including bias, fake news, but also possible economic monopolies and unemployment. There is an urgent need for a legal regulation of the construction and deployment of these models. The last section considers advanced artificial intelligence systems and the shortcomings of current systems. Foundation Models have significantly improved performance in recent years and have the potential to reduce the gap to a truly general AI.
In‐the‐wild Material Appearance Editing using Perceptual Attributes

Abstract Intuitively editing the appearance of materials from a single image is a challenging task given the complexity of the interactions between light and matter, and the ambivalence of human perception. This problem has been traditionally addressed by estimating additional factors of the scene like geometry or illumination, thus solving an inverse rendering problem and subduing the final quality of the results to the quality of these estimations. We present a single‐image appearance editing framework that allows us to intuitively modify the material appearance of an object by increasing or decreasing high‐level perceptual attributes describing such appearance (e.g., glossy or metallic). Our framework takes as input an in‐the‐wild image of a single object, where geometry, material, and illumination are not controlled, and inverse rendering is not required. We rely on generative models and devise a novel architecture with Selective Transfer Unit (STU) cells that allow to preserve the high‐frequency details from the input image in the edited one. To train our framework we leverage a dataset with pairs of synthetic images rendered with physically‐based algorithms, and the corresponding crowd‐sourced ratings of high‐level perceptual attributes. We show that our material editing framework outperforms the state of the art, and showcase its applicability on synthetic images, in‐the‐wild real‐world photographs, and video sequences.
Efficent Gradient Propagation for Robot Control and Learning

The recent wealth of discoveries in deep learning has coincided with the development of specialized automatic differentiation frameworks which can efficiently propagate gradients through repeating structures in artificial neural networks. For model-based approaches, automatic differentiation still performs relatively poorly and it is common to formulate gradients manually or to focus on low-dimensional problems. To accelerate research into model-based control of high-DOF robots such as humanoids with articulated hands and to enable hybrid approaches that combine model-based methods with deep learning, we develop a novel automatic differentiation framework that can evaluate gradients of robot models around previous candidate solutions multiple times faster than state-of-the-art methods.
Cognitive Computing and Systems Analysis of Alumni Economic Theory and Practice

It aims to inspire domestic and foreign alumni to participate in the construction of the Second Centenary National Demonstration Zone of the Communist Party of China by reviewing the cognitive computing and system analysis of the latest progress in the establishment of alumni economic theory and its practice. The method is as follows: First, review the historical process that alumni economic theory and practice have gone through, and then, through cognitive computing and systematic analysis of alumni economic theory and practice, provide more specific examples for further development and expansion of its application scenarios, and finally, through the establishment of the China Alumni Economic Theory and Practice Research Institute, especially in combination with the characteristics of the times, it highlights the basic topics of cognitive computing and systematic analysis of alumni economic theory and practice. The results are beneficial for the economic practice of domestic and foreign alumni to further rise to the height of theoretical research, and to achieve sufficient interpersonal communication and high interpersonal collaboration. Its significance lies in: Chinese and foreign alumni economic talents, with the help of cognitive computing and systematic analysis of alumni economic theory and practice, not only better participate in the construction of the national demonstration zone, but also an international and domestic demonstration base for the practice of alumni economic theory in the Hengqin Guangdong-Macao Deep Cooperation Zone, and extended to at home and abroad. Taking advantage of the location advantage, the International Research Institute of Alumni Economics was established.
Cognitive Computing and System Analysis of Seven Times Pass Method Applications and Its Significance

The aim is to further explore the cognitive computing and system design of the Seven Times Pass method application by combining the learning method with the eight-person group management method through years of accumulated teaching practice and management practice. The method is: first, systematically review and summarize multiple series of Seven Times Pass Method application, and then extract the cognitive content of interpersonal communication and further development, finally, focus on the characteristics of the era of human-computer interaction and even collaboration, which are characterized by both personalization and standardization. The results not only summarize and review the various cognitive methods of the seven-pass learning method and the eight-person group training management method, but also further explore the cognitive computing and system design optimization methods of the seven-pass application, and further enrich the teaching and learning practice. The way in which the management practice is active. The significance is that this not only has a positive effect on the teaching, review and examination quality of national education system at all levels, but also has a significant effect on the quality education, vocational and lifelong education have positive and ongoing effects.
GK-Means SOM Algorithm Used to Plan the Paths for Multiple Agents Exploring Multiple Target Points

Multi-agent technology is widely used in many fields such as intelligent manufacturing, logistics and environment exploration. In this paper, we propose a greedy K-means self-organizing map algorithm to balance the tasks and plan the paths for multiple agents exploring multiple target points, where greedy k-means adopts greedy strategy to ensure the tasks allocated for agents tending equally, and the self-organizing map networks are used for parallel path planning to speed up the problem solved.
Non-Commutative Logic for Collective Decision-Making with Perception Bias

In this paper, we suggest an implementation of non-commutative logic and apply its operators for decision-making in a group of autonomous agents. The suggested operators extend the uninorm and absorbing norm aggregators and use an additional asymmetry parameter that defines the “level of non-commutativity”. The value of this parameter is specified using the perception bias of humans measured in the experiments. The suggested operators and decision-making method are illustrated by the simulated behavior of mobile robots in the group, which verified the possibility of processing systematic sensing errors, as well as of distinguishing and mimicking the biased decisions.
The Survey of Self-play Method in Computer Games

In recent years, the achievements of computer games in Go, Chess, Shogi and other chess games are amazing, which is inseparable from the development of reinforcement learning and deep reinforcement learning. DeepMind pioneered the use of deep reinforcement learning and self-play to achieve a breakthrough in the Atari arcade game. Since then, AlphaGo has also reached the level of beating the top human players. AlphaGo Zero quickly surpassed the former by just playing against itself without using human chess data. These all reflect the excellent performance of the self-play method in deep reinforcement learning. Therefore, this paper analyzes the application of self-play and deep reinforcement learning methods applied in computer games, summarizes its research status in the field of complete information games and incomplete information games and pointed out its existing problems and possible development directions.
Application of expression recognition based on genetic algorithm in English distance teaching platform

In Distance English teaching, teachers cannot take care of every student because of their own limitations. Therefore, more advanced technology is needed to assist teachers in completing teaching. Based on this point, this paper proposes a distance English teaching assistant method based on facial expression recognition technology. First, the distance learning platform collects the image data of students through the camera, preprocesses the captured image, detects and recognizes the skin color, and finally determines the position of the face. Then, based on the theory of genetic algorithm, a model to improve the characteristics of face images is established, and the recognized facial expression images are classified by random forest algorithm, and the facial expression characteristics of students are analyzed according to the five kinds of information of the target, such as attention, suspicion, distraction, excitement and anxiety. This paper adds the design of expression recognition application to the English learning distance platform, collects and summarizes the changes in students' expressions through the mobile terminal, and reports them to the teacher as reference information in the teaching process. The results show that the facial expression recognition module carried on the distance English teaching platform has a relatively accurate recognition rate, can provide some help for English teachers, and is well received by teachers, so it is worth promoting. By studying genetic algorithm and facial expression recognition technology, this paper applies them to the design of English distance teaching platform, which has a certain auxiliary effect on English teachers' teaching behavior.
Digital Newspaper Using Augmented Reality

Augmented reality (AR) is a recent technology that provides the users with an enhanced and interactive experience with the digital world, by combining real-time objects and computer-generated images and videos. This project aims to focus on the integration of AR with the newspaper. Since the daily newspapers provide only limited content about an article, this application will allow the users to have detailed information about an event with the help of video clips and interactive images. The application provides a feature where the users will be redirected to the online website as soon as they click on the media on their screen, where they can learn more about the respective commercial and the user can bookmark an article. The process of linking media files with the article’s image will be implemented using Unity and Vuforia. The application will be developed using Android Studio.
Detection of Consumption of Alcohol Using Artificial Intelligence

The habit of overdrinking alcohol is a major problem for the behavior of people in public places the behaviour of such people is annoying, and hence opens the door for severe security issues, especially for the surroundings. In this paper, we are proposing an accurate method to detect a drunken person without informing the person. First, the intelligent camera will detect the person by providing an alert signal, and in the second stage, the face recognition system will not allow him/her to use metros, airplanes or public transport. The system is 87% accurate and tested on over 50 people.
Research on mining software repositories to facilitate refactoring

Abstract Software refactoring focuses on improving software quality by applying changes to the internal structure that do not alter the observable behavior. Determining which refactorings should be applied and presented to developers the most relevant and optimal refactorings is often challenging. Existing literature suggests that one of the potential sources to identify and recommend required refactorings is the past software development and evolution histories which are often archived in software repositories. In this article, we review a selection of existing literature that has attempted to propose approaches that facilitate refactoring by exploiting information mined from software repositories. Based on the reviewed papers, existing works leverage software history mining to support analysis of code smells, refactoring, and guiding software changes. First, past history information is used to detect design flaws in source code commonly referred to as code smells. Moreover, other studies analyze the evolution of code smells to establish how and when they are introduced into the code base and get resolved. Second, software repositories mining provides useful insights that can be used in predicting the need for refactoring and what specific refactoring operations are required. In addition, past history can be used in detecting and analyzing previously applied refactorings to establish software change facts, for instance, how developers refactor code and the motivation behind it. Finally, change patterns are used to predict further changes that might be required and recommend a set of files for change during a given modification task. The paper further suggests other exciting possibilities that can be pursued in the future in this research direction. This article is categorized under: Algorithmic Development &gt; Text Mining Application Areas &gt; Data Mining Software Tools
Student's Perception of Learning Media Based on Virtual Laboratory on Straight Motion Kinematics Materials

This study aims to determine the feasibility of laboratory virtual mediaon straight motion kinematics using modelsproblem based learning as well as knowing how students perceive when using the productthat have been developed are reviewed based on the feasibility of the content, graphics, appearance and language.The method used in this study is a quantitative research methodobtained from the results of assessments by media expert validators, material expert validators, and the results of student perception questionnaires regarding the product being developedwith a sample of 28 students. The sampling technique used was purposive sampling. The instruments used were media validation questionnaires, materials, and perception questionnaires. The data analysis used is descriptive statistics, in which the average result obtained from the five assessment indicators is 79.76% which indicates that the media is suitable for use and is greeted very positively by students who use it.&#x0D;
Residual vulnerabilities to power side channel attacks of lightweight ciphers cryptography competition finalists

Abstract The protection of communications between Internet of Things (IoT) devices is of great concern because the information exchanged contains vital sensitive data. Malicious agents seek to exploit those data to extract secret information about the owners or the system. Power side channel attacks are of great concern on these devices because their power consumption unintentionally leaks information correlatable to the device's secret data. Several studies have demonstrated the effectiveness of authenticated encryption with advanced data, in protecting communications with these devices. A comprehensive evaluation of the seven (out of 10) algorithm finalists of the National Institute of Standards and Technology (NIST) IoT lightweight cipher competition that do not integrate built‐in countermeasures is proposed. The study shows that, nonetheless, they still present some residual vulnerabilities to power side channel attacks (SCA). For five ciphers, an attack methodology as well as the leakage function needed to perform correlation power analysis (CPA) is proposed. The authors assert that Ascon, Sparkle, and PHOTON‐Beetle security vulnerability can generally be assessed with the security assumptions “Chosen ciphertext attack and leakage in encryption only, with nonce‐misuse resilience adversary (CCAmL1)” and “Chosen ciphertext attack and leakage in encryption only with nonce‐respecting adversary (CCAL1)”, respectively. However, the security vulnerability of GIFT‐COFB, Grain, Romulus, and TinyJambu can be evaluated more straightforwardly with publicly available leakage models and solvers. They can also be assessed simply by increasing the number of traces collected to launch the attack.
Between street and digital capital? A qualitative study of judicial sentencing of persons convicted of online drug dealing in Sweden

Purpose Drug sales facilitated through digital communication on the surface web and on darknet cryptomarkets have increased during the past two decades. This has resulted in an increase in drug law enforcement efforts to combat these markets and a subsequent increase in judicial sentencing of people selling drugs online. The aim of this study was to analyze how Swedish courts describe sentenced sellers and how the courts apply case law. Design/methodology/approach The empirical material consists of 71 sentencing documents produced by Swedish courts in cases of online drug selling between January 1, 2010 and January 1, 2020. In total, 99 sentenced persons occur in the documents. Using a qualitative research design, the authors analyzed the material through thematic text analysis. Findings Overall, in their descriptions of online drug sale operations, the courts’ characterizations of the concepts of street capital and digital capital show a dichotomy. These forms of capital are situationally described as both aggravating and mitigating aspects in the application of case law, indicating that it may be fruitful to view both street and digital capital as resources used on contemporary drug markets in general. Originality/value Very little research exists into how judicial systems describe and perceive the developing phenomenon of online drug sales. Using a relatively large sample from a decade of sentencing, the authors provide an analysis of how Swedish courts view and valuate capital forms in the online drugs trade.
A hybrid agent-based model integrated with a multi-stage learning-based fuzzy cognitive map

Purpose This article proposes a novel hybrid simulation model for understanding the complex tobacco use behavior. Design/methodology/approach The model is developed by embedding the concept of the multistage learning-based fuzzy cognitive map (FCM) into the agent-based model (ABM) in order to benefit from advantageous of each methodology. The ABM is used to represent individual level behaviors while the FCM is used as a decision support mechanism for individuals. In this study, socio-demographic characteristics of individuals, tobacco control policies, and social network effect are taken into account to reflect the current tobacco use system of Turkey. The effects of plain package and COVID-19 on tobacco use behaviors of individuals are also searched under different scenarios. Findings The findings indicate that the proposed model provides promising results for representing the mental models of agents. Besides, the scenario analyses help to observe the possible reactions of people to new conditions according to characteristics. Originality/value The proposed method combined ABM and FCM with a multi-stage learning phases for modeling a complex and dynamic social problem as close as real life. It is expected to contribute for both ABM and tobacco use literature.
Understanding employees' perceptions of SETA events: the role of pedagogical and communication approaches

Purpose Security education, training and awareness (SETA) programs are the key to addressing “people problems” in information systems (IS) security. Contrary to studies using conventional methods, the present study leveraged an “event” lens and dimensionalized employees' perceptions into three sub-dimensions: perceived novelty, perceived disruption and perceived criticality. Moreover, this research went a step further by examining how pedagogical and communication approaches to a SETA program affect employees' perceptions of the program. This study then investigated whether – and if so, how – these approaches impact employees' perceptions of the SETA program and their subsequent commitment to it. Design/methodology/approach Utilizing a factorial-based scenario survey, this study empirically tested a model of the above relationships via covariance-based structural equation modeling. Findings The results of this research showed that pedagogical approaches were more effective than communication approaches and that employees' perceptions of the SETA program accounted for a large variance in their commitment to SETA. Originality/value First, this research deepens understanding of the protection of information assets by elaborating on the different approaches that organizations can take to encourage employees' commitment to SETA. Second, the study enriches the SETA literature by theorizing a SETA program as an organizational “event”, which represents a major shift from the conventional approach. Third, the study adds to the theoretical knowledge of the event lens by extending it to the SETA context and investigating the relationship among three event strength components.