Reviewerly/exHarmony huggingface dataset:
Edge-aware object pixel-level representation tracking

Recently, there has been a trend in tracking to use more refined segmentation mask instead of coarse bounding box to represent the target object. Some trackers proposed segmentation branches based on the tracking framework and maintain real-time speed. However, those trackers use a simple FCNs structure and lack of the edge information modeling. This makes performance quite unsatisfactory. In this paper, we propose an edge-aware segmentation network, which uses the complementarity between target information and edge information to provide a more refined representation of the target. Firstly, We use the high-level features of the tracking backbone network and the correlation features of the classification branch of the tracking framework to fuse, and use the target edge and target segmentation mask for simultaneous supervision to obtain an optimized high-level feature with rough edge information and target information. Secondly, we use the optimized high-level features to guide the low-level features of the tracking backbone network to generate more refined edge features. Finally, we use the refined edge features to fuse with the target features of each layer to generate the final mask. Our approach has achieved leading performance on recent pixel-wise object tracking benchmark VOT2020 and segmentation datasets DAVIS2016 and DAVIS2017 while running on 47 fps. Code is available at https://github.com/TJUMMG/EATtracker.
Unique identification of gold and banknotes through the use of antipode elements: Material selection, laboratory verification steps, and industrial software–hardware implementation

The falsification problems associated with golden coins, banknotes, and legal documents could be solved through "antipode elements," microfiber materials selected to have the exact opposite (antipode) properties when incorporated into gold, paper, or any other tangible manufactured object. Any discrepancies in gold or banknotes could be found using various non-destructive testing machinery. This research focus was given to help a student or junior lab technician replicate all steps and conclude that a fully functional product is ready for the alpha test. It requires minimal interdisciplinary knowledge in statistics, programming, and metrology, along with chemical, material, and digital electronics engineering. The research methodology can be categorized into four tracks: material selection and method validation. The two validation steps were kept short and low level, that is, minimal and only to guide reproducibility, due to limitations in presentation, procedural specification, pricing, consumable options, and software modules. A last-minute development occurred by describing the procedures in the current submission, not initially granted in the invention patents. This technique is an innovative design to capture raw sensor data straight from the photodiode pad. These Big-Data are manipulated using the presented and future data analytics methodologies.
PENGGALIAN KEBUTUHAN MENGGUNAKAN METODE PERSONA PADA SISTEM PENILAIAN LMS

The development of a software is very necessary to understand the users/users of the system. The aspect of user convenience in using the system is a very important aspect so that the system that has been created is in accordance with the purpose of its manufacture and according to user needs. Persona is a technique of Human Computer Interaction by collecting information from users as a reference in understanding the needs of users. The purpose of this study is to apply the persona technique by adjusting the validation process to the user personas of our system users. The method used in this research is the Human Computer Interaction technique. User persona is used as a reference so that a system is made based on user needs so that the purpose of making the system can be met. The result of this research is to use the user persona technique in the elicitation of needs which will facilitate understanding related to the development of a system.Keywords: Human Computer Interaction, Persona, Elicitation of Needs
Towards an Intelligent Electric Wheelchair: Computer Vision Module

Handicapped people represent fifteen percent of the world population. Autonomy is one of the key things that disabled people seek to have. Intelligent wheelchairs could contribute to people’s autonomy by removing assistance needs and performing tasks such as autonomous navigation, real-time object detection and obstacle avoidance. The present work proposes a new intelligent electric wheelchair architecture, and then focuses only on the computer vision module. To test this module, a new dataset was created with twenty different classes, using three different vendor cameras at the same location with the same illumination conditions. We employ YOLOv4, a real-time object detector based on CNNs, installed into an embedded system, the LattePanda Alpha 864s minicomputer. Mean average precision was the metric used to evaluate the performance, in terms of localization and classification of objects. The iDS camera, an RGB color format and high-resolution images, demonstrated to have the best performance.
Parallel Clustering Method Based on Density Peaks

This work develops a clustering method which is based on the identification of density peaks in the available data. The realization is characterized by its goal of carrying in parallel as many operations as it is possible and to exploit current commodity many/multi-core machines with shared memory. The algorithm is prototyped in Java using parallel streams and lambda expressions. The paper first describes the rationale underlying the design and implementation of the clustering method. Then, the tool is practically applied to several and challenging benchmark datasets, for example, admitting general not spherical clusters. The experimental results confirm efficiency and reliability of the proposed method. Finally, conclusions are drawn with an indication of on-going and future work.
Earthquake Monitoring with MEMS Sensors

Earthquake monitoring is a crucial process for human safety. Existing monitoring instruments present a high level of accuracy, along with increased size and cost because of the mechanical sensing elements they rely on. This work proposes an implementation of an earthquake monitoring device either for continuous or for non-continuous monitoring, which combines the recent advances in portable instrumentation systems along with the latest MEMS sensors. The focus is on creating a modern measuring node with improved characteristics, like high accuracy, smart and adaptive functionality, reduced energy consumption, as well as small size and cost. This system is used in a case study, targeting the evaluation and safety of school buildings, demonstrating high-quality characteristics on the acquired earthquake data.
Posture Control of Collaborative Robotic Manipulator Based on Gesture Sensors

As one of the common ways of human–computer interaction, gesture control has attracted wide attention of researchers. In this article, we design a posture control system that control a six-joint collaborative robotic manipulator-xArm by the gesture detected by Leap motion gesture sensor. To accomplish this, the kinematics properties of xArm is analyzed, the transformation matrix is derivated by coordinate transformation, in order to avoid the duplicate data of moving target points that caused by Leap motion’s typical working frequency being lower than xArm’s maximum receiving frequency, a constraint criterion is designed. Experiments show that this method can track the palm posture detected by Leap motion gesture sensor in real time, and then control xArm accurately.
An Overview of Security Issues in Smart Contracts on the Blockchain

Blockchain technology is a factor of disruption for the current state of the internet, and it has the potential to solve many of its security, centralization, and trust issues. The second generation of blockchain appeared in 2013, with the launch of Ethereum and introduced smart contracts, as a way of building applications on top of the blockchain. Nevertheless, smart contracts raise particular security challenges due to their immutability, attack surface, and economic implications. This paper aims to present the most common security vulnerabilities and possible exploits in smart contracts and the best practices for combating them.
Showing the Impact of Data Augmentation on Model’s Decisions Using Integrated Gradients

Deep neural network-based systems are increasingly performing tasks that can significantly impact various aspects of human life, such as medical diagnosis or financial risk assessment. Therefore, it is important to understand their decision-making process in order to determine whether those decisions may have been biased. Among the many factors that can influence a model’s decision is that of a data augmentation strategy. In this paper, we analyze how various augmentation techniques affect the image area that the model takes into account when classifying images. We use Integrated Gradients as a method to calculate the attribution of model’s output to its input features. Integrated Gradients results are clustered to determine the strategies the model uses to decide on the label, and to uncover possible changes in decision-making strategy due to the application of a particular augmentation. The results show that even when the accuracy is fairly uniform among the models, there may be a significant difference between the areas of the image to which they give more importance, as a consequence of the applied augmentation.
Theoretical Study of Security for a Software Product

In the beginning, some of the most fundamental aspects of security, like confidentiality, integrity, and availability are presented. Then, the attacker’s key motivators are mentioned. In the following parts, the focus is on the ways of designing and developing a secure software product. So, from a design point of view, the secure software development life cycle and how a security management process should take place are described in multiple phases starting from security threat and risk analysis phase to security testing phase and malware scanning. To keep track of vulnerabilities that might appear in the future, especially for 3rd party products, a security vulnerability management process should be used. Finally, some of the most common vulnerabilities and ways to assure secure code are presented in the secure development chapter. Making source code security analysis by using static/dynamic/interactive application security testing tools with manual code reviews is some important factors in assuring secure software development.
Blockchain and Smart Contracts for Voting in a University

In this article, we will begin by presenting a comparison between various platforms for the development of smart contracts. The goal is to find that technology that offers various facilities to the developer and multiple functionalities and performances in the development of smart contracts. We will analyze the 3 top platforms and highlight the advantages and disadvantages encountered, which architecture is used by each model, and which is more reliable for developing smart contracts. We will propose a voting solution that can be used in a university based on an ERC20 token.
Pulse-Based Smart Electricity Meter Using Raspberry Pi and MEFN

As the computation power of microcomputers increases over time, we need to put them to proper use. Green energy generation and consumption are evident problems that need to be prioritized. We know that green energy is not produced at a scale for us to rely on but if we know how electricity is consumed, we might be able to hack our way. In order to know that we are using light-dependant resistor (LDR) to count pulses from the electricity meter and update it in the database using a Raspberry Pi connected to the Internet which hits the server, the end user can see the utilization in real time.
New Hybrid Monitoring Approach for Distributed Installations in Rural Areas

In rural areas that are often difficult to access, we have different types of installations (sanitary, hydraulic, solar, school, etc.). The remote monitoring of these installations poses a real problem. Local authorities often do not have enough resources to remotely monitor all installations. To facilitate the monitoring of these equipment, we have proposed hybrid monitoring in our works. However, if equipement are of the same type and have the same characteristics, we can easily classify from failed equipment the failure of other equipment pairs. We used the similarity calculation method to sort and classify similar failures of equipment pairs. An approach where each type of failure is represented by an attribute has been proposed. Algorithms were then used to represent the proposed models.
Web-Based Village Bridge and Road Project Monitoring Information System

In this era of globalization, developments in an area have begun to increase over time both in terms of technology and development, and this is indicated by the large number of large-scale projects built by the government and private sectors such as road and bridge construction. Based on data from the Ministry of PUPR in the 2018 Statistical Information Book regarding the allocation of APBN funds to the Ministry of PUPR shows that the largest APBN allocation is allocated to Directorate General of Highways at 40.04%. The data shows that the highest allocation of funds is allocated to build village road and bridge infrastructure, and this indicates that the government is taking priority in carrying out village infrastructure development; therefore, a Project Management is needed. In the process of implementing Project Management, there is often a problem both at the time of planning and when the project is running, things that are commonly experienced by the developer include financial problems such as budgeting, estimation, resources, costs that are not in accordance with the plan and the embezzlement of funds by unscrupulous persons. These problems can occur due to several important factors such as communication and transparency, hence the Project Management Information System is developed, which aims to facilitate stakeholders in monitoring and ensuring that funds budgeted by the government can be channeled and absorbed optimally, and reports absorption of funds are carried out transparently, to minimize fraud and errors in the village infrastructure development process, by using the Rational Unified Process (RUP) method. The system is designed in the cloud thus all the parties involved in the project can control the entire process and absorption of funds that occur directly so that the infrastructure development process can be right on the target according to the predetermined plan.
Classification Algorithm for Predicting the Graduation Level: Case Study of e-Learning in Telkom University

E-learning is one of the technologies used to support the learning process in university level. Telkom University with the university base on technology, Telkom University so intensively apply the learning using e-learning in order to become effective and efficient method. E-learning is used to reduce the cost and time spent learning in an offline class. With e-learning a review system can be done anywhere, because students do not need to come to the classroom to study. Simply by passing the Internet network can already attend the class. With the existing data in the e-learning system can be obtained some information by processing the existing data. This research will be predictions of student data contained in e-learning to determine the performance of algorithms C4.5, Naïve Bayes and Bayesian network, which will be used to determine what factors affect the level of graduation of students to a course. The process will go through several stages of data retrieval, preprocessing and classification, and the process will be done gradually to get the results of all the process.
An Enhanced K-Means Clustering Algorithm to Improve the Accuracy of Clustering Using Centroid Identification Based on Compactness Factor

The researchers find it difficult to extract information from a large data set through a standard function. It is found insufficient of standard functions to extract needed information. It has been considered that the k-means algorithm in the situation where the data is too enormous to be stored in main memory and must be retrieved sequentially, such as from a disk, and where it must be used as slight memory as possible. The k-means clustering also converges very quickly when it is employed to obtain data from huge data collections. It is also on other hand, k-means has some disadvantages too, and it includes affluent computation by getting cluster centers which are randomly selected at initial. It influences the two factors, such as performance of the algorithm and number of clusters initialization. In this paper, an improved k-means algorithm in terms of data clash strainer mechanism is given. The data clash strainer mechanism is implemented through a function regional centroid component (RCC) mechanism which is added to the standard k-means algorithm. This density-based recognition mechanism is built on the properties of clash data. The clustering result is effectively enhanced by ignoring the clash data prior to the process of data clustering. Hence, the improved algorithm offers a great accuracy when compared to other existing cluster algorithms.
Classification of Interaction Participants in the Formation of the Trajectory of Diagnosis and Treatment of Bronchopulmonary Diseases to Design Agents of a Multi-agent System

The article deals with the formation of a rational trajectory of diagnosis and treatment of bronchopulmonary diseases of patients in hospital conditions. The description of the process of treatment taking into account various factors is given. The task of forming effective trajectory of diagnostics and treatment of bronchopulmonary diseases is considered in the context of clinical guidelines describing the process of treatment, individual characteristics of patient’s health, and available diagnostic equipment and medicines. To solve this problem it is proposed to use a multi-agent approach. This approach will take into account clinical guidelines describing the treatment process, the individual characteristics of the patient’s health, and the available diagnostic equipment. The interaction of real participants in the process can be modeled as the interaction of corresponding prototypes—the agents of a multi-agent system. To design the agents of a multi-agent system, a classification of the participants of such interaction for forming a diagnostic and treatment trajectory is proposed. At the first stage digital platform AnyLogic is proposed to be used as a tool for modeling the interaction processes. A formal statement of the problem of forming a trajectory for diagnosing and treating a patient, the results of primary data processing necessary for subsequent modeling, state diagrams of the main agents of the future model are presented, and directions for further research are determined #COMESYSO1120.
Research and Development Corrective Automorphism of Repeated Code for Quantum Error Correction

One of the main problems to be overcome in the near future for the practical creation of quantum computer to make quantum computing resistant to interaction with the external environment and to eliminate inaccuracy of operations. In this paper we study quantum codes correcting errors, proposed as a way to protect against the occurrence errors due to the influence of the external environment. Results of work are considered software implemented model of the simplest quantum code. This work is a study of the influence of the environment on a quantum system of qubits. The article is supposed to describe the foundations of quantum information theory, as well as the place in it of the concept of quantum entanglement. A technique for correcting two main types of quantum errors has been developed, based on the implementation and execution of certain quantum circuits. The main difficulties of ensuring the protection of a quantum channel from various types of errors are analyzed and considered. Demonstrated are the dependences of data distortion on the noise level and the measure of decoherence on the noise level in one qubit.
Learningchain. A Novel Blockchain-Based Meritocratic Marketplace for Training Distributed Machine Learning Models

Beez blockchain is a decentralized AI model network that encourages peer collaboration to improve machine learning accuracy. It's a peer-to-peer network where peers can submit models with improved precision. The system maintains a decentralized, append-only ledger to record critical information, such as who trained and improved the model, when and by how much, and where to find the most recent model. Beez gives cryptographic tokens to contributors. To demonstrate it, in this paper, we designed a simple Wallet API that isolates low-level node communication across the temporal cluster of compute nodes and enables fine-grained parameter-sharing behavior customization. We created a low-overhead communication layer using a custom blockchain to help developers build computational clusters. This API connects model training and parameter pooling. We want that Beez operations to be stateless and independent of ML. Medicine, reconnaissance, and resource-limited situations benefit from decentralized learning. Decentralization lets nodes learn independently and cooperatively without sharing data. To capitalize on this developing technology, decentralized learning algorithms may differ in clustering connection and individual node sharing. A high-level learning framework can improve codebase uniformity and readability. This open-source library accelerates decentralized learning research. Decentralized learning is still young, so many issues must be addressed before cooperative learning technologies become mainstream.
The Mysterious User of Research Data: Knitting Together Science and Technology Studies with Information and Computer Science

Open, accessible, and standardized research data are seen as essential scaffolding for open science. To support this vision, data repositories and scientific publishers have developed new tools to facilitate data discovery while funders and policy makers have implemented open science and data management policies. Users are often invoked as central to these efforts. Despite this stated focus, the concept of ‘user’ often remains an abstraction, visible only via anonymous ensembles of click behavior or data management plans. This chapter reports and reflects on a project which draws on science and technology studies (STS) to open up the black box of research data use, bridging the gap between designers of data search systems and researchers who (re-)use both data and these systems in their actual practices. Quantitative and qualitative studies conducted in the course of this project will be drawn upon to demonstrate the insights gained from an interdisciplinary approach.