Reviewerly/exHarmony huggingface dataset:
Dataset Properties and Degradation of Machine Learning Accuracy with an Anonymized Training Dataset

With the widespread use of machine learning, we are seeing many use cases where personal data are used for training. There are two privacy threats in such use cases: leakage of training data to a model constructor and inference of personal information from a model by a model user. One countermeasure against these threats is to anonymize training data to preserve privacy. However, it is difficult to find a better anonymization method with less degradation in machine learning performance, such as accuracy, for a given dataset, since anonymization causes inconsistent degradation among datasets. This paper proposes a dataset-property-based method of comparing degradation of machine learning accuracy caused by anonymization. Experiments show that the proposed method can capture the relationship between dataset properties and anonymization methods. They also show the possibility that some dataset properties are related to anonymization and some are not. The proposed method provides a good starting point to help practitioners to choose an anonymization method, that will cause less degradation of machine learning accuracy, for a certain dataset.
A Conceptual Framework for Top- N Recommendation Using Attributed Bipartite Graph Embedding

Recommender systems play a major role in improving users' experience by giving the list of items most relevant to users among large available resources. By representing the user-item relations of a Recommender System as an Attributed Bipartite Graph, we can explore other relations such as user-user and item-item relations which can help in solving the sparsity problem of a Recommender System. In this work, we propose a conceptual framework based on Graph Representation Learning, that can be used to represent the user-item relations for both explicit rating/implicit feedback and other user-user/item-item relations of the Attributed Bipartite Graph into a low dimensional space. The learned low dimensional space is such that users and items having similar attributes or interaction behaviour can be close together making it possible to use nearest neighbour search algorithm for Top- N item recommendations. This framework when used is expected to provide more accurate recommendation results.
Systematic assessment of food security by recurrent addition of fuzzy cognitive maps

Abstract The results of an analytical review of the problems of modeling the state and evolution of socio-economic systems solved using the mathematical apparatus of matrix analysis of cognitive maps are presented. The difference between classical and fuzzy cognitive maps (FCM) is shown. The problems, strengths and weaknesses of algorithms and tools for assessing the level of food security (FS) based on a fuzzy cognitive approach are considered. The basic groups of concepts and methods for constructing the FCM that provide a fuzzy integral assessment of the PB level are analyzed. A method of recurrent construction of the NCC by successive addition of a set of concepts is proposed. In the process of adding concepts, experts make step-by-step recurrent adjustments to the values of the mutual influence of concepts. Recommendations for justification are presented, using the example of assessing the level of FS, the choice of tools for building the NCC. The solution of the problem of assessing the level of regional FS is illustrated for a set of economic, environmental and organizational concepts. It is shown that for adjusting the para-meters generated by the FCM, including to take account of their changes over time require the creation of software tools for fuzzy cognitive modeling, taking into account specific tasks and retrospective information together with simulated system factors.
Application research on application performance management system in big data of power grid

In order to solve the challenges brought by the operation and maintenance of power system in the era of big data, APM (Application Performance Management) system is introduced, which can monitor the operation of software and hardware system, show the health of system operation, and find the performance bottleneck. On the Hadoop platform, a big data deep mining and analysis platform based on map / reduce mode is built, integrating regression analysis, association analysis, data classification, data clustering, text mining, web mining and other data mining algorithms. It can complete 100TB level data retrieval in 30s, and then analyze; the system monitoring server can run stably in a cluster of 256 nodes. The use of APM system can prevent performance bottlenecks, greatly reduce the response time of performance problem processing, and quickly locate the location of performance problems, so as to ensure higher availability and stability of information system.
Expanded Boundaries

There is an important gap in Assistive Technology related to the multiple capabilities of exploratory creativity, as with multi-aesthetic and inter-mediatic surfaces, especially in the design and development of child prosthetics. This in-progress research explores creative interactions and combinations of art and digital fabrication to design playful prosthetics for children's upper limbs. In order to do so, we are using feline animal life as inspiration and as a ludic premise to engagement through playfulness. With this artistic exploration in the scope of tangible and embodied interfaces, we seek to contribute to improve and incorporate engagement and fantasy through playful interfaces, ultimately transforming a perceived difference into empowerment of a child's self-esteem, awareness and imagination.
Noise Robust Training of Segmentation Model Using Knowledge Distillation

Deep Neural Networks are susceptible to label noise, which can lead to poor generalization. Degradation of labels in a Histopathology segmentation dataset can be especially caused due to the large inter-observer variability between expert annotators. Thus, obtaining a clean dataset may not be feasible. We address this by using Knowledge Distillation as a learned Label Smoothening Regularizer which has a denoising effect when training on a noisy dataset. To show the effectiveness of our approach, an evaluation is performed on the Gleason Challenge dataset which has high discordance between expert pathologists. Based on the reported experiments, we show that the distilled model achieves significant performance gain when training on the noisy dataset.
On Gradient Coding With Partial Recovery

We consider a generalization of the gradient coding framework where a dataset is divided across <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> workers and each worker transmits to a master node one or more linear combinations of the gradients over its assigned data subsets. Unlike the conventional framework which requires the master node to recover the sum of the gradients over all the data subsets in the presence of straggler workers, we relax the goal to computing the sum of at least some <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> fraction of the gradients. We begin by deriving a lower bound on the computation load of any scheme and also propose two strategies which achieve this lower bound, albeit at the cost of high communication load and a number of data partitions which can be polynomial in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> . We then propose schemes based on cyclic assignment which utilize <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> data partitions and have a lower communication load. When each worker transmits a single linear combination, we prove lower bounds on the computation load of any scheme using <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> data partitions. Finally, we describe a class of schemes which achieve different intermediate operating points for the computation and communication load and provide simulation results to demonstrate the empirical performance of our schemes.
Structured Expert Judgement Issues in a Supply Chain Cyber Risk Management System

The escalation of cyberthreats is a major problem for supply chain managers with potentially enormous impacts affecting service availability and reputation, among other performance indicators. We sketch a framework and system to support supply chain cyber risk management. As data regarding impacts of cyberattacks are scarce and difficult to obtain, we describe how we acquire the required operational parameters through structured expert judgement techniques. We then describe how the whole framework is set up and implemented.
Effective Big Data Caching through Reinforcement Learning

In the era of big data, data volumes continue to grow in several different domains, from business to scientific fields. Sensors, edge devices, scientific applications and detectors generate huge amounts of data that are distributed for their nature. In order to extract value from such data requires a typical pipeline made of two main steps: first, the processing and then the data access. One of the main features for data access is fast response time, whose order of magnitude can vary a lot depending on the specific type of processing as well as processing patterns. The optimization of the access layer becomes more and more important while dealing with a geographically distributed environment where data must be retrieved from remote servers of a data lake. From the infrastructural perspectives, caching systems are used to mitigate latency and to serve better popular data. Thus, the role of the cache becomes a key to have an effective and efficient data access. In this article, we propose a Reinforcement Learning approach, using the Q-Learning technique, to improve the performances of a cache system in terms of data management. The proposed method uses two agents with different objectives and actions to control the addition and the eviction of files in the cache. The aim of this system is to increase the throughput reducing, at the same time, the cache costs, such as the amount of data written, and network utilization. Moreover, we tested our method in a context of data analysis, with information taken from High Energy Physics (HEP) workflow.
Towards Understanding and Inferring the Crowd: Guided Second Order Attention Networks and Re-identification for Multi-object Tracking

Multi-human tracking in the crowded environment is a challenging problem due to occlusions, pose change, viewpoint variation and cluttered background. In this work, we propose a robust feature learning for tracking-by-detection methods based on second-order attention network that can capture higher-order relationships between salient features at the early stages of Convolutional Neural Network (CNN). Guided Second-Order Attention Network (GSAN) that, unlike the existing attention learning methods which are weakly-supervised, uses a supervisory signal based on the quality of the self-learned attention maps. More specifically, GSAN looks into the attended maps of a person having the highest confidence and supervise itself to look into the correct regions in the images of the person. Attention maps learned this way are spatially aligned and thus robust to camera-view changes and body pose variations. We verify the effectiveness of our approach by comparing with the state-of-the-art methods on challenging person re-identification and multi object tracking (MOT) datasets.
TEACHERS’ PERCEPTION OF STUDENTS’ READINESS IN THE IMPLEMENTATION OF THE 21ST-CENTURY SKILLS IN PRIVATE PRIMARY SCHOOLS

This research was examined the teachers’ readiness for the implementation of 21st century skills in teaching English in private primary schools in Buleleng. The research was done in North Bali Bilingual School and SD Mutiara. The respondents involved in this research were 15 respondents. There were English teachers, teachers who use English as the medium of instruction, and the headmaster of the schools was becoming the research subject. The teachers’ readiness were analyzed from perceived readiness and observed readiness. However, this article focused on the teachers’ perception of students’ readiness in the implementation of 21st century skills. The results of this research represented how the teachers understand the usage skills in implementing the 21st century skills. The results of this present research indicated that the students from private primary schools were ready to apply to 21st century skills.
Achieving Privacy-Preserving Vehicle Selection for Effective Content Dissemination in Smart Cities

By integrating various connected devices, it is possible for smart cities to optimize the efficiency of various aspects of city operations. In particular, connected vehicles in smart cities, which are coordinated by Intelligent Transportation Systems (ITS), can not only enjoy enhanced safety and efficiency, but also offer content dissemination services through smart cities. In order to achieve effective content dissemination, a vehicle selection approach usually needs to be involved to select a limited number of vehicles while disseminating content to a city as wide as possible. However, such an approach inevitably requires the trajectories of vehicles, which are private to the vehicles. In this paper, to preserve the trajectory privacy of the vehicles during the vehicle selection, we propose a privacy-preserving vehicle selection scheme for effective content dissemination. Specifically, in the proposed scheme, given encrypted trajectories of n vehicles, a cloud with two non-collusive servers can select k vehicles that jointly cover an approximately optimal area of the city. Detailed security analysis and performance evaluation show that our proposed scheme can not only preserve the privacy of vehicles' trajectories, but also achieve efficient vehicle selection with an approximately optimal coverage.
Development Trend of the Integration of Artificial Intelligence and Sports Industry

Abstract Artificial intelligence has slowly become the focus of research in various countries, and the application of artificial intelligence has become more and more extensive, and it can be seen in more and more industries. However, the application of artificial intelligence technology to the sports industry is still an attempt with unpredictable results, but it will be a bold attempt. In addition, in the process of the integration and development of artificial intelligence and the sports industry, there must be some problems. This requires researchers and staff to have enough patience to solve these problems, and through joint efforts to continuously improve, make artificial intelligence and sports the integrated development of the industry is more stable. This paper adopts a combination of empirical analysis and theoretical analysis, systematically researches the current status of the development of the sports industry with artificial intelligence, and analyzes the development trend of this new type of sports industry. The results of the experiment show that the market size of China’s artificial intelligence sports education and the growth rate of the number of users have both remained above 20%, which has doubled in just four years, showing the rapid development of it. It proves from the side that the unique environmental advantages and sufficient talent advantages of the artificial intelligence sports industry have laid a strong human resource foundation for the sports industry in the central plains urban agglomeration; the artificial intelligence sports industry has a wealth of traditional characteristic sports industry projects, and is the development of the artificial intelligence sports industry provides a rich resource base for the sports industry.
Compact Data Structures for Dedekind Groups and Finite Rings

A group with n elements can be stored using \(\mathcal {O}(n^2)\) space via its Cayley table which can answer a group multiplication query in \(\mathcal {O}(1)\) time. Information theoretically it needs \(\varOmega (n\log n)\) bits or \(\varOmega (n)\) words in word-RAM model just to store a group (Farzan and Munro, ISSAC 2006).For functions \(s,t:\mathbb {N}\longrightarrow \mathbb {R}_{\ge 0}\), we say that a data structure is an \((\mathcal {O}(s),\mathcal {O}(t))\)-data structure if it uses \(\mathcal {O}(s)\) space and answers a query in \(\mathcal {O}(t)\) time. Except for cyclic groups it was not known if we can design \((\mathcal {O}(n),\mathcal {O}(1))\)-data structure for interesting classes of groups.In this paper, we show that there exist \((\mathcal {O}(n),\mathcal {O}(1))\)-data structures for several classes of groups and for any ring and thus achieve information theoretic lower bound asymptotically. More precisely, we show that there exist \((\mathcal {O}(n),\mathcal {O}(1))\)-data structures for the following algebraic structures with n elements: Dedekind groups: This class contains abelian groups, Hamiltonian groups. Groups whose indecomposable factors admit \((\mathcal {O}(n),\mathcal {O}(1))\)-data structures. Groups whose indecomposable factors are strongly indecomposable. Groups defined as a semidirect product of groups that admit \((\mathcal {O}(n),\mathcal {O}(1))\)-data structures. Finite rings.
Private Edge Computing for Linear Inference Based on Secret Sharing

We consider an edge computing scenario where users want to perform a linear computation on local, private data and a network-wide, public matrix. Users offload computations to edge servers located at the edge of the network, but do not want the servers, or any other party with access to the wireless links, to gain any information about their data. We provide a scheme that guarantees information-theoretic user data privacy against an eavesdropper with access to a number of edge servers or their corresponding communication links. The novelty of the proposed scheme lies in the utilization of secret sharing and partial replication to provide privacy, mitigate the effect of straggling servers, and to allow for joint beamforming opportunities in the download phase, to minimize the overall latency, consisting of upload, computation, and download latencies.
Optimization of Route Selection and Carbon Emission Release for Waste Collection Systems

In general, transportation is important to any industry in the world that involves the movement of goods from one destination to another. In waste collection systems, the movement of waste collectors using transportation is one of the main activities in handling waste management today. The activity of collecting waste involves the waste generated by the residential area, industrial area, and port area that consists of many types of waste such as domestic waste, food waste, plastics, industrials waste, metals, and waste that is generated by the manufacturing processes. Then by choosing the best route for collecting waste which is to define the shortest way that merges with the time of operation will reduce the operational cost of the company and at same time reducing the quantity of carbon emission release that impacts the environment. Thus, this research is a preliminary study to analyze the actual route that is usually selected and used by truck drivers by using optimization method called the traveling salesman problem (TSP) method. This method is used as a tool to decide the optional route for collecting waste by Majlis Perbandaran Manjung (Sitiawan district) to collect the waste generated by the Sitiawan residential area and Kg Acheh industrial area to the final destination which is located at Segari Landfill. The final result shows that by optimizing the routes selection, the suggested routes will show a lower distance of about 28.3% (29.18 km) and release only 15.95 kg Co2 of carbon emission. The result shows a slightly significance and benefit to the company in reducing the cost of operations and time-consuming without underlook on the environmental aspect. However, this result depends on several variables that need to be considered such as the rest time, road closure, number of traffic lights, congestion, accident, peak time, and current situation during the process of collecting the data. All of these variables are different for any location and number of vehicle in one time.
Deep Learning-Based Wireless Module Identification (WMI) Methods for Cognitive Wireless Communication Network

Nowadays, the internet of things (IoT) enabled event monitoring, controlling and managing systems employ different kinds of short range, medium-range and long-range wireless technologies. Wireless communication modules of most of the commercial IoT enabled automation systems operates in industrial, scientific and medical (ISM) frequency band. Therefore, identification of active wireless modules has become most essential to timely detect and track unauthorized users within the restricted zones. Further, it is also useful for finding density of wireless modules operating in the same and/or different frequencies with different and/or same communication protocols. In this paper, we attempt to present an automated wireless module identification (WMI) system based on the communication protocols of the three wireless modules, such as ZigBee, Bluetooth and Wi-Fi which are operated in the ISM frequency band. In this study, three WMI systems are developed based on deep learning networks (DLN), such as two-dimensional convolutional neural network (2D-CNN), long short-term memory (LSTM), and convolutional long short-term deep neural network (CLDNN). We evaluated three DLN based WMI methods on real-time RF signals recorded by using Blade RF, a software defined radio (SDR).
R. Kurzweil’s Worldview: A Critical Analysis of the Technocratic Projectto Achieve Immortality

The article is devoted to understanding the prospects of possible application of advanced technological developments in order to improve the biological nature of man. It analyses the ideas of Ray Kurzweil, who is a supporter of such transformations of human nature. It is shown that Kurzweil’s conclusions are of a worldview nature: in the near future, through the cyborgization of people, it will be possible to achieve a state close to immortality and, thus, solve the world-view issues of self-knowledge and create the conditions for unlimited self-development. It is concluded that such optimistic forecasts are insufficiently justified due to the limited possibility of modern technologies to “improve” a person’s life and help with the most difficult technical and ethical problems.
Visual and Textual Information Fusion Method for Chart Recognition

In this report, we present our method in the ICPR 2020 Competition on Harvesting Raw Tables from Infographics, which is composed of Chart Classification, Text Detection/Recognition, Text Role Classification, Axis Analysis, Legend Analysis, Plot Element Detection/Classification and CSV Extraction. The image classification models of ResNet are adopt in Chart Classification. We adopted a two-stage based pipeline for end-to-end recognition, considering detection and recognition as two modules in Text Detection/Recognition. An ensemble model with LayoutLM and object detection model is adopted in Text Role Classification. A two-stage pipeline with two detection model is adopt in Legend Analysis. The final results are discussed.