Reviewerly/exHarmony huggingface dataset:
Multiple Uses of Global and Local Features for Person Re-identification

Person re-identification has been extensively studied in recent years and has made great progress. Many papers propose a lot of effective methods to improve the accuracy of the person re-identification. However, there are still many problems that remain unsolved. For example, persons are often occluded by obstacles or other persons, leading to loss of the complete person information, and changes in person behaviors or postures make it difficult to identify. In this paper, we propose a person re-identification algorithm that repeatedly uses global feature information and local feature information for mutual supervised learning. The algorithm consists of two parts, person alignment branch and spatial channel feature branch. First, for person alignment branch, we use global feature information and local feature information to correct misaligned person pictures, and calculate the shortest distance to match the right part of the images. For the spatial channel feature branch, the spatial features are segmented to obtain the local feature information of the person image. At the same time, the spatial feature information is extended using the convolution layer and divided to obtain global feature information of the person image. The global feature information and local feature information are used to calculate the spatial channel feature loss. So that the network can learn better discriminative features through the global information and local information repeatedly. The experimental results show that, on the market-1501 and Duke datasets, the algorithm in this paper obtains good experimental results, has strong robustness, and has greatly improved the rate compared with the existing person re-identification algorithms.
Interactive Method to Elicit Local Causal Knowledge for Creating a Huge Causal Network

Effective disaster drills and exercises require appropriate scenarios reflecting concrete disaster situations. It is however not easy to manually create such a scenario with enough details and validity, because it is fundamental difficult to comprehensively predict and assume disaster situations that may occur in various phases through a chain of causality from the primary damage. In order to make a scenario creation easier and more efficient, some support tools are necessary, in particular for predicting what kind of situations will happen through a causal chain from a base disaster assumption. In this paper, we proposed a simple and practical causal model consisting of three elements: cause, precondition, and effect, which can capture indirect causal relationships between two events by introducing the concept of preconditions. We also developed an interactive method with a GUI to elicit causal knowledge about disaster situations based on the model. Users can enter possible events that can occur in a disaster as well as countermeasures against those events by answering the questions presented on the GUI. Then the entered sentences are processed to identify causal elements automatically by a newly developed NLP techniques, and finally those elements are integrated into the database. The proposed method still has a room for improvement, however its performance is satisfying and can be expected to be utilized as a technical base for the creation of effective disaster scenarios.
Hybrid Compression and Lightweight Encryption of Color Image

Based on generalized Arnold transformation and RC4 stream cipher, this paper proposes a method to encrypt images in the transform-domain. The RGB color image is transformed into YUV form first, then the Arnold transformation is carried out according to 8x8 size blocks as the basic unit to scramble the pixel position, and then each block is DCT transformed and quantified. Finally, the quantization results are encoded by run-length and differential coding to achieve data compression, and the final result is obtained by encrypting the coded data with RC4 stream cipher. The encryption method has the characteristics of large key space, mixing the YUV three color gamuts, and combining with the transform domain of the image. Accordingly, the security is high. In addition, the results of the experiments show that the algorithm runs fast and the pixel value distribution of cipher text image is more uniform, that is, the encryption effect is good.
Knowledge Data Discovery (Frequent Pattern Growth): The Association Rules for Evergreen Activities on Computer Monitoring

The aim of this research paper is to construct a set of guidelines that can improve the quality and efficiency of knowledge data discovery process by utilizing various types of knowledge domains. In addition, this paper offered the way of how the knowledge domain could be adopted for helping the system developer. The methodologies contain various scenarios of data exploring and the authors used data mining approach. The paper shows evidence of important to adopt data mining methods in the industry sector as well as the advantages and disadvantages. Evergreen human machine interface (HMI) at PT. Chevron Pacific Indonesia (CPI) is kind of activities to maintenance computer equipment. Nowadays, the errors were frequently happened in the accuracy of computer maintenance which has a profound effect on production results. Therefore, this study focuses on the rules of association using the frequent pattern growth algorithm (FP-growth) which is producing knowledge with trust value of 100% and a support value is 95%. The value results of support and confidence are the new approach and knowledge for the management level to decide decisions in the evergreen activities process.
Patterns in language: Text analysis of government reports on the Irish industrial school system with word embedding

Abstract Industrial Memories is a digital humanities initiative to supplement close readings of a government report with new distant readings, using text analytics techniques. The Ryan Report (2009), the official report of the Commission to Inquire into Child Abuse (CICA), details the systematic abuse of thousands of children from 1936 to 1999 in residential institutions run by religious orders and funded and overseen by the Irish State. Arguably, the sheer size of the Ryan Report—over 1 million words—warrants a new approach that blends close readings to witness its findings, with distant readings that help surface system-wide findings embedded in the Report. Although CICA has been lauded internationally for its work, many have critiqued the narrative form of the Ryan Report, for obfuscating key findings and providing poor systemic, statistical summaries that are crucial to evaluating the political and cultural context in which the abuse took place (Keenan, 2013, Child Sexual Abuse and the Catholic Church: Gender, Power, and Organizational Culture. Oxford University Press). In this article, we concentrate on describing the distant reading methodology we adopted, using machine learning and text-analytic methods and report on what they surfaced from the Report. The contribution of this work is threefold: (i) it shows how text analytics can be used to surface new patterns, summaries and results that were not apparent via close reading, (ii) it demonstrates how machine learning can be used to annotate text by using word embedding to compile domain-specific semantic lexicons for feature extraction and (iii) it demonstrates how digital humanities methods can be applied to an official state inquiry with social justice impact.
Recent Advances in Artificial Intelligence

The paper focuses on several recent applications of deep learning approaches to a broad range of tasks, including a personal view on current trends, critical issues, and limitations of the technology.
Time-Lapse Detection for Evolution of Trustworthy Network User Operation Behavior Using Bayesian Network

In the environment of human-computer interaction of information systems, people are paying more attention to user identity authentication based on operation behaviors. Behavior science research shows that each user has a his/her own behavioral pattern that reflects the unique habits, and maintains stability over a period. As known, most of the previous research have explored the user's behavior using static authentication models. However, the user's behavior is evolutionary, even the same user will develop different behavioral tendencies under various times and conditions (job position change or promotion, business content change, increase in age, etc.), causing the difficulty of user authentication under the evolution of user's behavior. This paper proposes a method named time-lapse detection attempting to establish the authentication model based on the evolution of user's behavior. We obtained the log data of several years period of the information system of a publishing house. Firstly, we extracted the data of employees' early operation behaviors and the Bayesian network is used to identify a detection model. Next, the behavior data are divided into multiple test sets according to the time series, and multiple authentication models are carried out to observe the change of authentication accuracy over time. The result shows that, for employees with stable positions and business content, the characteristics of their behavior patterns will change when the number of interactions increases. Moreover, the consequences of the initial detection model fluctuate to different degrees, reducing the accuracy of authentication. Therefore, in future we need to grasp the rules of user behavior and continue to optimize the existing authentication methods of information systems.
Fault Attack Detection in AES by Monitoring Power Side-Channel Statistics

Differential Fault Analysis (DFA) is a cryptoanalysis technique to extract internal state of crypto-algorithms by inducing and propagating the faults during encryption. In this work, we present a low-power CMOS based mixed-signal framework for on-line DFA-based clock-glitch attack detection by monitoring power side-channel statistics. We discuss non-parametric kernel density estimation (KDE)-based technique to develop statistical model of power side-channel leakage. Clock-glitch attack is detected by identifying the low-likelihood samples using the developed statistical model. We have implemented KDE using CMOS current-mode Gilbert Gaussian Circuit-based Gaussian kernels. AES-128 was implemented on ARM Microcontroller by ST Microelectronics and Chip Whisperer-lite board was used to launch clock-glitch attack as well as capture power side-channel traces. We have evaluated the performance of our approach using power side-channel trace with clock-glitch attacks. We have adopted sliding window approach to update the statistical model in real-time. Discussed CMOS-based mixed-signal framework was designed at 45nm technology node and proposed design on an average consumes <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\sim\mathbf{210}\mu\mathbf{W}$</tex> at 2 MHz sampling frequency while utilizing 10 recently validated samples for PDF estimation. Moreover, discussed approach allows programming of parameters such as kernel standard deviation <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(Kernel_{SD})$</tex> and likelihood-threshold <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(LH_{Thres})$</tex> for high efficiency detection.
Evolutionary Algorithm for Fine-Tuning Perceptual Computer for Grading Mathematics Tests Within Bilingual Education Program

Bilingual education implies simultaneous mastery of two major competences, linguistic one and subject one. This fact complicates the task of evaluating skills and knowledge of students, especially when it comes to grading mathematical tests. A solution to the problem can be found in using linguistic grades instead of numbers and performing grading of mathematical tests in hierarchical fashion, first by assigning grades to single tasks and then by aggregating these linguistic grades taking into consideration the relative weights of each task. This can be done within the framework of perceptual computing. However, assigning these weights is in itself a challenging and tedious problem, not free of high level of subjectivity. In this paper, we propose an evolutionary algorithm for selecting the weights for the perceptual computer based on grades already assigned by a teacher when grading works numerically.
Wherein is the Necessity and Importance of Changing Human-Computer Interaction Well-Known Design Methods?

In positive psychology, aspects of everyday human life that extend beyond the zero-state play a central role. Positive user experience (UX) based on positive psychology can potentially offer improvement in the design of user interfaces at a fundamental level. The parallel whereby positive psychology developed from classical psychology has also taken place in human-computer interaction (HCI) research. Software ergonomics in the world of work examines the how and the why regarding the negative effects on users. Usability has simplified the use of the application. With the increase in complex problem situations approached from a problem-solving perspective, we must move away from the concept of solution. The results and procedures from human-centered methods and models are not sufficient for the design process of fulfilling needs. On the other hand, the findings from positive UX research have not yet incorporated into the human-centric user interface/user experience design. These statements reflect why there are efforts to create new opportunities and possibilities to integrate positive user experience into the design process or introduce a completely different approach to HCI design.
Developing Persona for the Chinese Learning Application for Foreigners in China on Mobile Devices

This study presented how to develop persona for the Chinese learning application on mobile devices. Two personas were developed according to the understanding of current products and previous studies. Target market analysis was used in developing the two personas for learning Chinese characters on mobile devices. Consequently, the two personas were designed for two target groups: the short-term visitors (e.g. exchanging students) and the long-term Cosmopolitans (e.g. professions in China). Other translation application and learning applications on mobile devices were also discussed and evaluated in this study. An interview was used to revise and improve the persona. Future research directions were discussed at the end, including location-oriented mode, rotation mode, and dark mode.
Indexing Data Based on the CW-tree Algorithm Using Parallel Data Reading

The research objective is to develop indexing data algorithm to reduce database searching time by using multithreaded frameworks. In this article the indexing data algorithm in a tree form which is called CW-tree is supposed. Traversing through CW-tree occurs asynchronously with help additional start nodes. Compared to existing analogs of tree-based algorithms, the CW-tree has some advantages, the main of which is full parallelization both on the levels of branches and on the levels of leaves. To confirm benefits from using of CW-tree algorithm, comparison test between reading data with help B-tree and reading data with CW-tree was conducted. Result of test described in chapter 4 and shows that reading data time with CW-tree less than reading with B-tree.
How to Design the Expression Ways of Conversational Agents Based on Affective Experience

With the rapid development of artificial intelligence, the technology of human-computer interaction is becoming more and more mature. The variety of terminal products equipped with conversational agents are more diverse, and the product penetration rate is also getting higher and higher. This study focused on the problems of the conversational agent in response. In this paper, we presented a study with 20 participants to explore how to design the expression ways of conversational agents’ feedback with considerations of users’ affective experience. We explored the performance of three different expression ways (general way, implicit way, and explicit way) in different time and different functions. And we examined whether users of different genders have different preferences for these three expression ways. Therefore, we used the “Wizard of Oz techniques” to simulate a real environment for communication between the user and the conversational agent. In this study, we combined quantitative scoring (five aspects: affection, confidence, naturalness, social distance, and satisfaction) with qualitative interviews. The results showed that: (1) the user’s affective experience should be considered in expression ways’ design; (2) different expression ways had different performances in different functions, and the explicit way performed better in most situations; (3) male users seemed to rate the agent’s expression performance higher than female users.
Towards a Wearable Augmented Reality Visor for High-Precision Manual Tasks

Augmented Reality (AR) systems based on head-mounted displays intrinsically provide the user with an egocentric and natural viewpoint, and for this reason they are deemed as the most efficient solution for those tasks manually performed under user’s direct vision. They are commonly classified according to the AR paradigm implemented: Video See-Through (VST) and Optical See-Through (OST). A current research goal for these systems is to determine either of which of the two modalities is to be considered the best solution for the guidance of high-precision manual tasks. To answer this question, in this paper we report the outcomes of two user studies performed by our research team that involved a commercially available OST (Microsoft HoloLens) and a customized new-concept VST head-mounted display. The average error in tracing a line under AR aid is considerably inferior in VST user study than the one performed with OST device (1 mm and 5.8 mm, respectively). However, some of the advantages offered by OST device are not negligible, thus a hybrid solution implementing the two paradigms can overcome the limitations of both technologies.
Theory and practice of population diversity in evolutionary computation

tutorial Theory and practice of population diversity in evolutionary computation Share on Authors: Dirk Sudholt University of Sheffield, Sheffield, United Kingdom University of Sheffield, Sheffield, United KingdomView Profile , Giovanni Squillero Politecnico di Torino, Torino, Italy Politecnico di Torino, Torino, ItalyView Profile Authors Info & Claims GECCO '20: Proceedings of the 2020 Genetic and Evolutionary Computation Conference CompanionJuly 2020 Pages 975–992https://doi.org/10.1145/3377929.3389892Published:08 July 2020 0citation69DownloadsMetricsTotal Citations0Total Downloads69Last 12 Months27Last 6 weeks3 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
A New Hybrid Algorithm Based on ABC and PSO for Function Optimization

Abstract Artificial bee colony algorithm (ABC) and particle swarm optimization (PSO) are both famous optimization algorithms that have been successfully applied to various optimization problems, especially in function optimization. Those two algorithms have been attracting more and more research interest because of their efficiency and simplicity. However, PSO has poor exploration capabilities and thus is easy to fall into the local optimum; Likewise, ABC has low convergence speed. To address these shortcomings, firstly, we improved the ABC with the combination of greedy selection and crossover, secondly, a sine-cosine method will be used to help PSO jump into local optimal. Finally, a new hybrid algorithm based on improved ABC and PSO are proposed. Moreover, four functions are used to verify the effectiveness of the proposed algorithm, and the results show that, compared with other well-known algorithms, ABC-PSO is more efficient, faster and more robust in function optimization.
Comparing the User Preferences Towards Emotional Voice Interaction Applied on Different Devices: An Empirical Study

Voice interaction has been widely used. Designers hope to use artificial intelligence technology to create humanlike voice assistants, which not only help users handle the daily chores, but also provide emotional support and communication. Voice interaction has been utilized for both mobile and home automation, and many researchers have studied the emotional voice interaction of various devices. As different voice interaction products have diversified orientations and the functions integrated in them varies, users may prefer to use them under different operating conditions or interaction modes. This paper chooses mobile phone voice assistant and smart home voice assistant as study subjects, representing mobile and stand-alone voice assistant respectively. A 2 (voice assistant type: mobile phone assistant V.S. smart home assistant) × 3 (interaction level: low V.S. medium, V.S. high) between-subjects experiment was conducted. Then the influences of different devices and voice interaction modes on user emotional experience were discussed. Thus it provides reference and guidance for the voice interaction design of corresponding products.
Predicting Employee Performance

The research aims to perform an intergenerational analysis regarding the impact of counterproductive behavior and contextual performance on employees' task performance. The analysis is performed on a convenience sample of 165 employees from three different generations who work in the banking system. The results show that (1) 33.50% of task performance variance is determined by the variance of contextual individual performance and counterproductive behavior, (2) 13% of the variance of contextual individual performance can be explained by the variance in counterproductive behavior, (3) 33.70% of the variance of contextual organizational performance can be explained by the variance of task performance and counterproductive behavior. In addition, significant differences appear regarding the influence of (1) the counterproductive behavior on the contextual performance (Generation X vs. Y), (2) the contextual individual performance on task performance (Generation X vs. Z), and (3) the counterproductive behavior on the contextual individual performance (Generation Y vs. Z).