Reviewerly/exHarmony huggingface dataset:
Boundaries of Single-Class Regions in the Input Space of Piece-Wise Linear Neural Networks

An input space is a set of all the possible inputs for a neural network. An element or a group of elements in the input space can easily be understood by projecting them on their original forms. Even though Piece-wise Linear Neural Networks (PLNNs) are a nonlinear system in general, a PLNN can also be expressed in terms of linear constraints because the Rectified Linear Units (ReLU) function is a piece-wise linear function. A PLNN divides the input space into disjoint linear regions. We proved that all components of the outputs are continuous at the boundary between two different adjacent regions. This continuity implies that the boundary corresponding to a unit itself should be continuous regardless of the regions. Furthermore, we also obtained the boundaries of a single-class region, which has the same predicted classes in the interior of the region. Finally, we suggested that the point-wise robustness of a neural network can be calculated by investigating the boundaries of linear regions and the single-class regions. We obtained adversarial examples in which Euclidean distances from original inputs are less than 0.01 pixels.
A Dense Tensor Accelerator with Data Exchange Mesh for DNN and Vision Workloads

We propose a dense tensor accelerator called VectorMesh, a scalable, memory-efficient architecture that can support a wide variety of DNN and computer vision workloads. Its building block is a tile execution unit (TEU), which includes dozens of processing elements (PEs) and SRAM buffers connected through a butterfly network. A mesh of FIFOs between the TEUs facilitates data exchange between tiles and promote local data to global visibility. Our design performs better according to the roofline model for CNN, GEMM, and spatial matching algorithms compared to state-of-the-art architectures. It can reduce global buffer and DRAM fetches by 2-22 times and up to 5 times, respectively.
Understanding Narrative-Driven Intergroup Conflict Dynamics Using Computational Cognitive Coherence Maps

Humans make sense of the world through narratives. Therefore, adversaries often use conflict-sustaining narratives to maintain dominance and delegitimize the actions of the rivals. To better understand narratives’ role and influence in such intractable conflicts, a computational framework and methodology are introduced. The computational cognitive model and its underlying inference mechanism allow analysts to simulate and analyze narratives in relation to opposing narratives. The ability to simulate the interaction of adversarial stories with a set of micronarratives shared by members of a group opens new avenues to counter conflict-sustaining narratives. The methodology and its application to a concrete conflict scenario demonstrate how to conduct simulation-driven exploratory analysis over a complex adaptive narrative space to discern how narratives are matched to unfolding events and how they can be used to facilitate favorable change.
Applying the Un Guiding Principles on Business and Human Rights to Online Content Moderation

What do Rembrandt and social media platforms have in common? Light. Both judiciously use it to emphasize certain aspects and relegate others to obscurity, leveraging darkness to highlight flattering features. This article assesses the accountability of social media platforms with regards to content moderation. It probes voluntary measures like the Facebook Oversight Board and transparency reports for similarities with the chiaroscuro painting technique. These two self-governance initiatives shed light on fairly uncontroversial aspects of content moderation, obscuring more problematic areas in the process. In that sense, chiaroscuro and self-governance actually travel in opposite directions; chiaroscuro uses darkness to create light while self-governance uses light to create darkness.The United Nations Guiding Principles on Business and Human Rights (UNGP) could fill self-governance gaps by activating a clearer link between companies and user well-being. The notions of access to remedy and due diligence support the case for harmonizing accountability measures across moderation practices. External oversight should cover a broader array of moderation decisions, beyond individual content takedowns. It should also approach harms holistically, integrating privacy, equality and other human rights dimensions to the analysis. Transparency reports should provide more granular information about platforms’ informal collaboration with states.
The Scheme of Security Requirement Acquisition Based on Knowledge Graph

Specifying security requirements (SR) during the requirement analysis phase is essential for enhancing system quality, especially for security-critical software systems. However, it is difficult and complex to analyze SRs in detail according to ISO/IEC 15408 (known as Common Criteria). In this paper, we propose a SR acquisition scheme based on knowledge graph (KG). In this scheme, some security concepts such as Security Threat, Organizational Security Policy (OSP), Security Objective, Evaluation Assurance Level (EAL), SR and their relationships are all represented in the KG. SRs can be acquired through knowledge reasoning after embedding KG into low dimensional space and training by using TransE model. To evaluate the KG-based scheme, our experiments are performed on certified Security Target (ST) documents provided by Common Criteria (CC). Experimental results show that more accurate SRs can be acquired through KG reasoning.
Pinpointing the Memory Behaviors of DNN Training

The training of deep neural networks (DNNs) is usually memory-hungry due to the limited device memory capacity of DNN accelerators. Characterizing the memory behaviors of DNN training is critical to optimize the device memory pressures. In this work, we pinpoint the memory behaviors of each device memory block of GPU during training by instrumenting the memory allocators of the runtime system. Our results show that the memory access patterns of device memory blocks are stable and follow an iterative fashion. These observations are useful for the future optimization of memory-efficient training from the perspective of raw memory access patterns.
Destylization of text with decorative elements

Style text with decorative elements has a strong visual sense, and enriches our daily work, study and life. However, it introduces new challenges to text detection and recognition. In this study, we propose a text destylized framework, that can transform the stylized texts with decorative elements into a type that is easily distinguishable by a detection or recognition model. We arranged and integrate an existing stylistic text data set to train the destylized network. The new destylized data set contains English letters and Chinese characters. The proposed approach enables a framework to handle both Chinese characters and English letters without the need for additional networks. Experiments show that the method is superior to the state-of-the-art style-related models.
The Comparative Study of Segmentation Strategies for Bio-inspired Models of Mammography Images

Breast cancer is an intense medical issue everywhere throughout the world and one of the most well-known tumors that cause demise among ladies. To build the recuperation and to diminish breast cancer specialists like to utilize the imaging modalities such as a mammogram. To detect cancer, ROI representing the tumorto be extracted from the input image. Segmentation is one of the underlying principles and mostly used in the classification of breast cancer. However, the segmentation is quite tricky in the presence of noise or blur, poor contrast. In the early phase, pre-processing helps to erase unnecessary data from an image or to increase image contrast. Segmentation likewise impacts the classification into generous and complicated classes. Automated, semi-automated segmentation strategies have been proposed in recent studies to extract the ROI, masses, and lesions in breast cancer confirmation. This paper gives a brief gist of segmentation mechanisms, especially to mammogram pictures of current research. Among these techniques, the bio-inspired algorithms such as novel bat algorithm give profound results. Besides, the comparative analysis of the conventional methods with the heuristic and optimized bat algorithm described. Furthermore, we described the accessible datasets used along with the challenges in the segmentation process for breast cancer analysis.
Decision Support System for an Art University

A prototype of an automated decision support system for creative universities has been developed, which will allow assessing the achievements particularly talented students and identifying the needs in the learning process in order to help organize the educational process in accordance with identified capabilities. Use of a decision support system based on the Bayesian classifier is suggested which will allow to evaluate factors contributing to the progress in teaching students particular techniques, and in perspective to assess the possible resources that will be required to make changes to the learning. The list of specific performance indicators is given. The system should contribute to the formation of the learning plan, taking into account the capabilities of both a group art workshop as a whole, and special needs of an individual to develop, if necessary an individual approach.
Accelerating Robot Reinforcement Learning with Accumulation of Knowledge

Reinforcement Learning (RL) can be applied in robotics to refine robot skills, but it may take several tens or even hundreds of attempts, which are typically just forgotten. In this paper, we show how all learning attempts, even those that do not fulfill the intended task, can be used to accelerate RL on the long-run. In a manner of non-continual lifelong learning we re-train a deep neural network that maps between the desired target/action outcome and the action in order to provide an initial estimate. Furthermore, for even faster learning, it is performed in reduced – latent space of a deep autoencoder. Robot throwing experiments clearly indicate, that the proposed methodology accelerates RL.
Analysis and Application of Bayesian Network and Qt View Framework in Network Fault Location

With the continuous development of science and technology, artificial intelligence algorithms have gradually been applied to various industries, especially in the industrial field have achieved significant development. The petroleum industry is an important pillar of my country's energy development, meeting the energy needs of all walks of life, and as the power system that supplies power to the petroleum industry. It plays a pivotal role in the entire industrial chain. This article will explain the Bayesian network algorithm and the mainstream view framework, and study the related algorithms and technical issues of the fault location system.
“Smart” nuclear cities and training

Abstract New innovative approaches to educational activities in “smart” nuclear cities are described. Examples of using the developed system of online control over student perception of materials of a particular discipline are given. Processing the results of monitoring the educational process allows you to draw preliminary conclusions about the abilities (giftedness) of students for certain types of activities, described, including in the course materials. The software and hardware complex can be used for other educational programs, which, depending on the subject area of study, allows you to form a kind of “digital portrait” of each student.
MinViME/Minimum Viable Model Estimator

MinViME is a python package and web application that allows scientists and engineers to estimate a lower bound on the performance characteristics of a machine learning model for a problem with well quantified characteristics.It is used to evaluate the feasibility of machine learning projects, prioritize among multiple competing projects, and run simulations to explore the structure of the difficulty landscape in a multi-dimensional space of potential problems.
Binary Cuckoo Optimisation Algorithm and Information Theory for Filter-Based Feature Selection

Dimensionality reduction is among the data mining process that is used to reduce the noise and complexity of the features. Feature selection (FS) is a typical dimensionality reduction that is used to reduce the unwanted features from the datasets. FS can be either filter or wrapper. Filters lack interaction among selected subsets of features which in turns affect the classification performance of the chosen subsets of features. This study proposes two ideas of information theory entropy (E) as well as mutual information (MI). Both of them were used together with binary cuckoo optimisation algorithm BCOA (BCOA-E and BCOA-MI) to reduce both the error rate and computational complexity on four different datasets. A support vector machine classifier was used to measure the error rates. The results are in favour of BCOA-E in terms of accuracy. In contrast, BCOA-MI is computationally faster than BCOA-E. Comparison with other approaches found in the literature shows that the proposed methods performed better in terms of accuracy, number of selected features and execution time.
Variational Capsule Encoder

We propose a novel capsule network based variational encoder architecture, called Bayesian capsules (B-Caps), to modulate the mean and standard deviation of the sampling distribution in the latent space. We hypothesized that this approach can learn a better representation of features in the latent space than traditional approaches. Our hypothesis was tested by using the learned latent variables for image reconstruction task, where for MNIST and Fashion-MNIST datasets, different classes were separated successfully in the latent space using our proposed model. Our experimental results have shown improved reconstruction and classification performances for both datasets adding credence to our hypothesis. We also showed that by increasing the latent space dimension, the proposed B-Caps was able to learn a better representation when compared to the traditional variational auto-encoders (VAE). Hence our results indicate the strength of capsule networks in representation learning which has never been examined under the VAE settings before.
Application and Future Development of Computer Aided Writing Software in Chinese Language and Literature

Abstract In recent years, the use of computer technology to study Chinese language and literature has become a new trend. There are many resources for the introduction and research of Chinese multimedia textbooks, multimedia courseware, and Chinese language and literature websites. But at present, there are few researches on the application and practice of computer-aided writing software. In this regard, the purpose of this article is to study the application and future development of computer-aided writing software in Chinese language and literature. This article first discusses the status quo of my country’s Chinese language and literature construction and resources, as well as the main problems currently existing. Then, through the method of questionnaire survey, to understand the Chinese language and literature researchers’ understanding, like and cognition of computer-assisted writing software Chinese language, so as to more truly understand the help of computer-aided writing software to Chinese language and literature. After that, this article combines the survey results to compare traditional manual writing and computer-assisted writing software, and explores the effectiveness and necessity of computer-assisted writing in teaching Chinese as a foreign language. Through the comparison of real Chinese language writing and the test results, the problem is explored, and corresponding countermeasures are proposed to judge its future development trend. The experimental results show that computer-aided writing software is more recognized and loved by Chinese language and literature researchers. Computer-aided writing software can effectively search for the content of writing, and then integrate resources with reference value, and then perform auxiliary writing based on it, which greatly improves the writing efficiency of Chinese language and literature researchers has increased by about 20%. Computer-assisted writing software is of great significance to the development of Chinese language and literature, and it will be more perfect in the future to achieve more functions.
Classifier Pool Generation based on a Two-level Diversity Approach

This paper describes a classifier pool generation method guided by the diversity estimated on the data complexity and classifier decisions. First, the behavior of complexity measures is assessed by considering several subsamples of the dataset. The complexity measures with high variability across the subsamples are selected for posterior pool adaptation, where an evolutionary algorithm optimizes diversity in both complexity and decision spaces. A robust experimental protocol with 28 datasets and 20 replications is used to evaluate the proposed method. Results show significant accuracy improvements in 69.4% of the experiments when Dynamic Classifier Selection and Dynamic Ensemble Selection methods are applied.
Application of Natural Language Processing Technology in Educational Resources Retrieval

Abstract According to the data structure characteristics of educational resources, natural language processing technology is used to process the original educational resources data from the aspects of word segmentation processing, named entity recognition, part of speech tagging, synonym analysis, word vector analysis, etc. For complex image data, OCR preprocessing and coding format conversion are used to reduce the complexity of the original data; according to the difficulty requirements of the user to retrieve data, key words or ontology are selected to further expand and enrich the semantics and improve the accuracy of the retrieval results.
Analysis of Elderly Inventory of Electricity Meters Based on Association Mining

At present, China's economy is developing rapidly and the scale of power grid is constantly expanding. With the continuous deepening of the intensive operation mode of human, financial and physical assets of the power grid companies, intensive management of electricity meters as metering assets has been paid more and more attention. This paper considers properties of electricity meter, which including management unit, status, specification and so on, to carry out association rule mining for the elderly electricity meters. Based on the results of association rules mining, the internal relationship between elderly inventory of electricity meters and management unit, status, specification, warehouse level and other factors was obtained. The analysis result can provide support for the inventory management of electricity meters, reduce inventory age of electricity meters and improve the utilization rate of electricity meters, further reduce losses of human, financial and physical assets.
An Adaptive Ant Colony System Memorizing Better Solutions (aACS-MBS) For Traveling Salesman Problem

Choosing the optimal among the many alternatives that meet the criteria is one of the problems that occupy life. This kind of problems frequently encountered by commercial companies in daily life is one of the issues that operators focus on with care. Many techniques have been developed that can provide acceptable solutions in a reasonable time. However, one of the biggest problems for these techniques is that the appropriate values can be assigned to the algorithm parameters. Because one of the most important issues determining algorithm performance is the values to be assigned to its parameters. The Ant Colony System (ACS) is a metaheuristic method that produces successful solutions, especially in combinatorial optimization problems. However, it is very difficult to be able to direct the algorithm to different areas of the search space and, on the other hand, to maintain its local search capability. In this study, a solution proposal is presented that updates the q0 parameter dynamically, which balances the exploitation and exploration activities of the ACS. The method has been tested on the traveling salesman problem (TSP) of different sizes, and the obtained results are evaluated together with the change in the q0 parameter, and the solution search strategy of the algorithm is analyzed. With the pheromone maps formed as a result of the search, the effect of transfer functions was evaluated. Results obtained with aACS-MBS were compared with different ant colony optimization (ACO) algorithms. The aACS-MBS fell behind the most successful solution found in the literature, by up to 4%, in large TSP benchmarks. As a result, it has been seen that the method can be successfully applied to combinatorial optimization problems.
Innovation and Practice of Training Management of Major Directors under the Dual Backgrounds of Intelligent Society and Epidemic Prevention and Control

The emergence of intelligent society has proposed new missions and requirements for development of colleges and universities, whereas the outbreak of COVID-19 epidemic brings a lot of adverse impacts on them in different aspects. Under the dual backgrounds of intelligent society and epidemic prevention and control, the overriding task in development of colleges and universities is still to cultivate talents. Major is the basic unit for talent cultivation, of which the quality depends on abilities of major directors, so it is of crucial significance to enhance their cultivation. Based on case study, this paper explores innovation and practice of training management of major directors under the dual backgrounds of intelligent society and epidemic prevention and control, so as to let major directors play a full role in organizing and guiding major construction and talent cultivation and further improve the major construction level and talent cultivation quality in colleges and universities.
Innovative Blockchain-Based Applications - State of the Art and Future Directions

Recently, blockchain technology has increasingly being used to provide a secure environment that is immutable, consensus-based and transparent in the finance technology world. However, significant efforts have been made to use blockchain in other fields where trust and transparency are required. The distributed power and embedded security of blockchain leverage the operational efficiency of other domains to be immutable, transparent, and trustworthy. The trust of the published literature in blockchain technology is centered on crypto-currencies. Therefore, this paper addresses this gap and presents to the user several applications in many fields, including education, health, carbon credits, robotics, energy, pharmaceutical supply chains, identity management, and crypto-currency wallets. This paper overviews the knowledge on blockchain technology, discusses the innovation of blockchain technology based on the number of applications which have been introduced, describes the challenges associated with blockchain technology, and makes suggestions for future work.