Reviewerly/exHarmony huggingface dataset:
Review of Object Detection Techniques

As the front-end technology of artificial intelligence, computer vision has been widely studied in recent years, and the introduction of deep learning methods has accelerated this process. This paper shows the progress made in object detection in the last 5 years, followed by the mainstream model topology including Convolutional Neural Network and Transformer. We further compared the accuracy and model complexity of different backbones, analyzed the differences and the inner link between Convolutional Neural Network and Transformer, at the end of the thesis, the prospect of future development is presented.
Event Neural Networks

Video data is often repetitive; for example, the contents of adjacent frames are usually strongly correlated. Such redundancy occurs at multiple levels of complexity, from low-level pixel values to textures and high-level semantics. We propose Event Neural Networks (EvNets), which leverage this redundancy to achieve considerable computation savings during video inference. A defining characteristic of EvNets is that each neuron has state variables that provide it with long-term memory, which allows low-cost, high-accuracy inference even in the presence of significant camera motion. We show that it is possible to transform a wide range of neural networks into EvNets without re-training. We demonstrate our method on state-of-the-art architectures for both high- and low-level visual processing, including pose recognition, object detection, optical flow, and image enhancement. We observe roughly an order-of-magnitude reduction in computational costs compared to conventional networks, with minimal reductions in model accuracy.
Learning Skills to Navigate without a Master: A Sequential Multi-Policy Reinforcement Learning Algorithm

Solving complex problems using reinforcement learning necessitates breaking down the problem into manageable tasks, and learning policies to solve these tasks. These policies, in turn, have to be controlled by a master policy that takes high-level decisions. Hence learning policies involves hierarchical decision structures. However, training such methods in practice may lead to poor generalization, with either sub-policies executing actions for too few time steps or devolving into a single policy altogether. In our work, we introduce an alternative approach to learn such skills sequentially without using an overarching hierarchical policy. We propose this method in the context of environments where a major component of the objective of a learning agent is to prolong the episode for as long as possible. We refer to our proposed method as Sequential Soft Option Critic. We demonstrate the utility of our approach on navigation and goal-based tasks in a flexible simulated 3D navigation environment that we have developed. We also show that our method outperforms prior methods such as Soft Actor-Critic and Soft Option Critic on various environments, including the Atari River Raid environment and the Gym-Duckietown self-driving car simulator.
Footprint Logic for Object-Oriented Components

We introduce a new way of reasoning about invariance in terms of footprints in a program logic for object-oriented components. A footprint of an object-oriented component is formalized as a monadic predicate that describes which objects on the heap can be affected by the execution of the component. Assuming encapsulation, this amounts to specifying which objects of the component can be called. Adaptation of local specifications into global specifications amounts to showing invariance of assertions, which is ensured by means of a form of bounded quantification which excludes references to a given footprint.
Search and Seizure of Digital Evidence: Human Rights Concerns and New Safeguards

In the last decades, the use of technological tools for investigative purpose has considerably increased, due to the digitalisation of daily activities in any field of private and social life. Search and seizure of digital evidence stand out among the most used means of investigation, being able to find and collect communicative as well as non-communicative data from an electronic device in a short period of time. Since modern technologies allow people to be permanently in contact, recourse to such measures entails significative implications on fundamental rights, affecting the individuals against whom the investigation is addressed as well as third parties. Online searches, in particular, can severely interfere with the right to privacy, affecting the most intimate sphere of a person’s life. Like traditional searches, moreover, online searches are aimed at seizing information stored in the targeted device. But the data collection, which is generally carried out through a bit-by-bit copy, entails the need to strike a balance between privacy and the aims of the criminal investigation. The present study will try to point out the worrisome lack of an adequate legal basis of the measures of digital search and seizure, from different viewpoints: first, the domestic law level; next, at the European level, both EU law and ECtHR case law; and finally, from a comparative law perspective, with particular regard to the United States.
Chinese Medical Named Entity Recognition Using External Knowledge

Chinese medical named entity recognition (NER) task usually lacks sufficient annotation data, and it contains many medical professional terms and abbreviations, making the NER task more difficult. In addition, compared with English NER, Chinese NER is more challenging because it lacks standard feature symbols to determine named entity boundaries. Therefore, Chinese NER needs to perform word segmentation. In this paper, we are inspired by lexicon-based BERT and propose a novel method for Chinese medical NER task. Besides, We design a template-based strategy to enrich the words’ information and improve the model’s ability to distinguish medical professional terms and abbreviations. Our method enhances the word segmentation accuracy by introducing the external medical lexicon. To verify the effectiveness of our method, we carry out experiments on three medical datasets and our method improves them by 0.92%, 1.18% and 1.55% F1-score compared to baseline.
Comparison of Naïve Bayes, C4.5 and K-Nearest Neighbor for Covid-19 Data Classification

Covid-19 is a new virus that appeared in the city of Wuhan in 2019. This virus spreads very quickly even to Indonesia. One effort that can be done to detect the presence of this virus is the PCR and antigen test. Increasing this case resulted in a medical team having difficulty detecting suspects exposed to viruses. This research was conducted to find the best classification algorithm in predicting and classifying status on the suspected Covid-19 both exposed or not exposed. The method used in this study is Naïve Bayes, C4.5 and K-Nearest Neighbor which have very high accuracy using secondary data from the Dumai City Health Agency. From this study it was found that the algorithm C4.5 as the best algorithm in predicting the status of COVID-19 patients, especially in the city of Dumai with an accuracy of 86.54%, recall 71.51%and precision 85.14%. This study has implications for further researchers in choosing an algorithm to predict the COVID-19 case.
Text Classification of Facebook Messages Using Multiclass Support Vector Machine

Text classification is assigning classes or categories to a specific set of words. Different text classification techniques are used in classifying academic-related messages with acceptable accuracy. However, using datasets about academic-related messages from previous studies, which are formal in structure, will provide inaccurate results when used for training since the messages from Facebook pages are informal. This study focused on creating a classifier that can categorize informal academic-related messages from the Facebook pages of schools of Mapua University. The dataset collected undergoes Data Pre-processing to clean the messages. Then, the cleaned messages are converted to vectors using the TF-IDF Text vectorizer. Four SVM kernels were tested for text classification: Linear, Sigmoid, RBF, and Polynomial. Sigmoid achieved the highest accuracy of 81.6% and F1-score of 81.4 in classifying informal academic-related messages.
Field Trials of Discrepancy Evaluation Application Based on Tat Twam Asi-TOPSIS

Field trials are very important to do to determine the effectiveness of an application that is applied in the field. This study aimed to show the percentage of effectiveness level of the Tat Twam Asi-TOPSIS-based Discrepancy Evaluation Application through field trials. This study approach was the development research by a development model referring to Borg & Gall. The study focus was only on field trials and revisions. The location of this study was carried out at several IT vocational schools in Bali. The subjects involved in the field trials were 54 respondents. Data collection tool in the form of a questionnaire consisting of 14 questions. The technique used to analyze the study data was descriptive quantitative, by comparing the field trials results with the effectiveness standard which refers to a scale of eleven. The study results showed that the average percentage of the effectiveness level of the evaluation application was 87.487, so it was categorized as very good. The impact of the study results on the fields of computer science and education is to raise awareness of the importance of field trials being conducted to determine the effectiveness of an evaluation application.
Influence of Sound Direction and Distance on Immersive Experience in VR Gaming

Audio is an indispensable and emotion-guiding factor in horror-themed virtual reality games as it enhances the sense of immersion in the gameplay. We establish an enclosed environment in the shape of a corridor and modules such as windows, door frames, and broken parts in the upper, lower, left, and right sides of the space. All the externally visible environments are placed into black shadows to create a virtual environment within the room except the enclosed corridor. The game plot includes flashing lights and grisly characters that appear intermittently to induce the horror experience. Herein, under the same visual and plot settings, game audio processing (for the test group) is enhanced by the sound direction, distance of character actions, and distance to the player. The system is compared with the game audio processing (for the control group) that does not consider sound direction and distance to explore whether these factors can effectively guide and enhance players’ immersion in the horror game environment.
Preface

Extract Disruptive Dreams When I first started taking Bitcoin seriously in 2012, I thought it was just a currency. That was my first exposure to it and I was really excited for what it was going to do to the financial system. It was really going to give it some liberty and freedom. . . . As I’ve grown in my understanding of what Bitcoin is, and how it is just the first app on the blockchain, I’ve realised how impactful and exciting the blockchain actually is. I think you will all agree with me: this is the single most exciting, most revolutionary idea that has hit in the past, probably, one hundred years. . . . This is going to change everything. And you being here as part of this program—all the things that each and every one of you are building . . . with the tools that many of the companies here have helped build previously—you’re building the future. . . . We’re really excited to see what people have built and where the future is going. You being here in these seats tonight means you are some of the earliest adopters in the world in what will change every single industry on this planet.(Developer Evangelist, BitPay)
SafeTAC: Safe Tsallis Actor-Critic Reinforcement Learning for Safer Exploration

Satisfying safety constraints is the top priority in safe reinforcement learning (RL). However, without proper exploration, an overly conservative policy such as freezing at the same position can be generated. To this end, we utilize maximum entropy RL methods for exploration. In particular, an RL method with Tsallis entropy maximization, called Tsallis actor-critic (TAC), is used to synthesize policies which can explore with more promising actions. In this paper, we propose a Tsallis entropy-regularized safe RL method for safer exploration, called SafeTAC. For more expressiveness, we extend the TAC to use a Gaussian mixture model policy, which improves the safety performance. To stabilize the training process, the retrace estimators for safety critics are formulated, and a safe policy update rule using a trust region method is proposed.
City of Clones

Code Cities are used to visualize various aspects of software in 3D including clone information. Software entities such as methods or classes are often depicted by simple regular shapes, for instance, rectangular cuboids or cylinders, where different code metrics can be used to determine their height, width, and depth. Clone information is often represented by coloring or by edges connecting similar code entities. Because Code Cities may be used for purposes other than showing clone relations, the metrics for height, width, depth, and color of a shape may already be used for information not related to cloning and if so, cannot simply be re-assigned differently because that would lead to drastic changes in the visualization causing disorientation of human beholders.As an alternative, we propose to use the shape of the code entities to express their similarity. In this paper, we will discuss four ways to generate similar shapes for similar code entities and investigate whether dissimilar code entities have dissimilar shapes, too, to prevent false visual impressions. These approaches might even enable visual clone detection where clones are immediately recognizable by a human beholder not requiring a clone detector.
Quantization-Aware Federated Learning with Coarsely Quantized Measurements

In this work, we present an energy-efficient federated learning framework using coarsely quantized measured data for Internet of Things (IoT) networks. In particular, we develop a quantization-aware Federated Averaging Least Mean Square (QA-FedAvg-LMS) algorithm that can learn parameters in an energy-efficient fashion using measurements quantized with few bits and develop a bias compensation strategy to further improve the performance of the QA-FedAvg-LMS algorithm. We carry out a statistical analysis of the proposed QA-FedAvg-LMS algorithm. Numerical results assess the QA-FedAvg-LMS algorithm against existing techniques for a parameter estimation task in a scenario where IoT devices operate in federated learning mode.
Manufacturing Data Privacy Protection System for Secure Predictive Maintenance

In Industry 4.0 technology of industrial equipment is becoming more and more complex as physical-digital systems are combined to form a new industrial phase. The complexity of the equipment affects the frequency of equipment failures, and to avoid failures we need to predict the health of the equipment. We found that PdM is the most effective health prediction, but it has the challenges of data security and data availability. To address this aspect, we design and develop a manufacturing data privacy protection system (MDPPS). Data are collected by smart sensors and then encrypted. After that, machine learning models are used to extract features and predict the remaining useful life of the industrial equipment. Finally, the predicted results will help workers to carry out maintenance activities. The performances of the MDPPS achieve an average of 87.40% Accuracy, 95.57% Precision, 77.74% Recall, and 83.86% F1-Score.
Supply Chain Risk Management: Data Structuring

Supply chain risk management (SCRM) is an area of research that addresses both logistics concepts to maximize efficiency, reliability, and revenue as well as risk features, such as potential weak points, break points, and vulnerabilities within the supply chain. SCRM is used to find risks introduced at each node in a supply chain and how these risks can impact a company’s products, individuals, customers, and reputation. SCRM is a relatively new field, so standardized processes including data structuring are not fully documented. This paper explains the importance of a standard data structuring methodology and how it can enhance current SCRM efforts. Data ingest, structuring, and analysis are predominantly managed by humans. Automating some of the less complex steps can positively impact SCRM by allowing human analysts to focus on more strategic analyses. Types of data to be collected and structured are collected via publicly available information related to hardware, software, and corporate entities. After the data has been collected, the information is formatted in a specific manner, conforming to a schema, to allow for more effective and efficient ingest for further analysis. This paper outlines data structures used by Pacific Northwest National Laboratory for SCRM research and analysis purposes. These structures have been used for hundreds of analyses and have been successful in developing a common baseline. Data structuring is one of the first steps in data standardization, which will further mature and enhance the SCRM research area.
Mitigating the Bias of Centered Objects in Common Datasets

Convolutional networks are considered shift invariant, but it was demonstrated that their response may vary according to the exact location of the objects. In this paper we will demonstrate that most commonly investigated datasets have a bias, where objects are over-represented at the center of the image during training. This bias and the boundary condition of these networks can have a significant effect on the performance of these architectures and their accuracy drops significantly as an object approaches the boundary. We will also demonstrate how this effect can be mitigated with data augmentation techniques and architectural changes.
Self-Sovereign Digital Agents for a Grassroots Digital Society

Mainstream cryptocurrencies, based on proof of work or stake, require paying miners for the capital-intensive execution of a consensus protocol, and hence are unsuitable as a foundation for capital-free digital communities and for the bootstrap of a grassroots digital society. We aim to adapt and adjust the concepts, tools and technologies developed by the cryptocurrencies ecosystem, together with related networking technologies, into a foundation for a healthy grassroots digital economy and society. In this context we present the design and proof-of-concept implementation of a self-sovereign digital agent (ssDA), as an essential building block for a grassroots digital economy and society. The ssDA serves as a party, on behalf of its sovereign—a person—in digital social contracts, which are smart contracts among vetted participants, who are its sovereign in that they jointly execute the contract with an egalitarian consensus protocol. Digital social contracts may realize social networks, sharing economy applications, social governance of a digital community, and more. The ssDA is a software application that allows a person to partake in multiple digital social contracts simultaneously. Participation in a contract can be realized by initiating it or by being invited to it. Extra confidence in the integrity of the data is achieved by each person maintaining a blockchain containing all the person’s transactions in all contracts.
A Fuzzy Logic-Based System of Abnormal Behavior Detection Using PoseNet for Smart Security System

This paper aims to contribute towards creating ambient abnormal behavior detection for smart security system from real-time human pose estimation using fuzzy-based systems. Human poses from keypoint detected by pose estimation model are transformed to as angle positions of the axis between human bodies joints comparing to reference point in the axis x to deal with problem of the position change occurred when an individual move in the image. Also, the article attempts to resolve the problem of the ambiguity interpreting the poses with triangular fuzzy logic-based system that determines the detected individual behavior and compares to the poses previously learnt, trained, and recorded by the system. The experiment reveals that the accuracy of the system ranges between 90.75% (maximum) and 84% (minimum). This means that if the accuracy of the system at 85%. The system can be applied to guide future research for designing automatic visual human behavior detection systems.
MF-TagRec: Multi-feature Fused Tag Recommendation for GitHub

GitHub is one of the most popular hosting platforms for open-source projects, where tags are widely used to facilitate software organization and retrieval. However, the existences of inadequate and low-quality tags on GitHub hinder users from searching and retrieving their desired projects. In this paper, we propose MF-TagRec, an automatic tag recommendation method for projects by extracting multiple features from Readme documents, programming languages and dependency package tags of projects. We capture topics and global semantics of Readme documents as text features, along with programming languages and dependency package tags as word vector features. We construct a convolutional neural network and feed text and word vector features to predict the most relevant tags for untagged or few tag-assigned projects. We evaluate our proposed MF-TagRec on a real dataset GitHubDepDataSet compared with five baselines. The results show that MF-TagRec achieves Recall@5 and Recall@10 to 0.756 and 0.864 respectively, which outperforms the previous baselines.
Customizing FFT with FMA for China Accelerator

Recently, Fast Fourier Transform (FFT) was introduced to study pattern characterization in textiles. Accelerators are a promising platform to accelerate large-scale FFT computation. However, current accelerating FFT only uses the accelerator to compute, but uses a CPU as a mere task controller. Additionally, a specified interface for FFT is built into the China accelerator (CA), which severely restricts FFT parallelization and performance. Hence, we transform four multiplications and six additions into six Fused Multiply Add (FMA) operations, then reduce total float operations by 40%, assisted by FMAs equipped with a Vector Processing Unit (VPE). Moreover, we propose an Interface Adapter (IA) to cater to a specified interface and a Fused Algorithm for Interface Adapter (FAIA) to fully use both the CA and CPU to compute large-scale FFT with coordination. Experimental results validated successful performance.
Design and Implementation of a Prototype System of Professional Resources for Primary Education in Universities Based on Intelligent Personalised Recommendations

With the rapid development of the Internet industry, hundreds of millions of online educational resources are also growing vigorously. In the information space of huge and complicated educational resources, it is necessary to help students and teachers of primary education in colleges and universities find the educational resources they are interested in quickly and save the learning time of teachers and students. At present, personalized recommendation technology is widely used in the field of e-commerce, but less in the field of education and teaching, especially in the application of primary education professional resources. In this paper, we design and implement a prototype system based on intelligent personalized recommendation of resources for primary education majors in universities, with the aim of better serving teachers and students of primary education majors in universities.