Reviewerly/exHarmony huggingface dataset:
Model-Agnostic Causal Principle for Unbiased KPI Anomaly Detection

KPI anomaly detection plays an important role in operation and maintenance. Due to incomplete or missing labels are common, methods based on VAE (i.e., Variational Auto-Encoder) is widely used. These methods assume that the normal patterns, which is in majority, will be learned, but this assumption is not easy to satisfy since abnormal patterns are inevitably embedded. Existing debias methods merely utilize anomalous labels to eliminate bias in the decoding process, but latent representation generated by the encoder could still be biased and even ill-defined when input KPIs are too abnormal. We propose a model-agnostic causal principle to make the above VAE-based models unbiased. When modifying ELBO (i.e., evidence of lower bound) to utilize anomalous labels, our causal principle indicates that the anomalous labels are confounders between training data and learned representations, leading to the aforementioned bias. Our principle also implements a do-operation to cut off the causal path from anomaly labels to training data. Through do-operation, we can eliminate the anomaly bias in the encoder and reconstruct normal patterns more frequently in the decoder. Our proposed causal improvement on existing VAE-based models, CausalDonut and CausalBagel, improve F1-score up to 5% compared to Donut and Bagel as well as surpassing state-of-the-art supervised and unsupervised models. To empirically prove the debias capability of our method, we also provide a comparison of anomaly scores between the baselines and our models. In addition, the learning process of our principle is interpreted from an entropy perspective.
Novel Nearest-Neighbor Data Association Algorithm with an Adaptive Threshold

In the overlapping detection area of multiple millimeter-wave radars, the echo of a single target contains multiple radar measurement points. In the process of data fusion, these multiple homologous echo points need to be associated. In this paper, an adaptive threshold nearest neighbor data association (AT-NNDA) approach is proposed, which adds an adaptive association threshold judgment to the nearest neighbor algorithm, dedicated to solving the problem of multi-homolog echo point association. The proposed AT-NNDA is validated by collecting traffic scene data using smart vehicles equipped with multiple millimeter-wave radars. The experimental results show that, compared with the traditional foxed-threshold nearest-neighbor association algorithm, the AT-NNDA algorithm can effectively correlate multiple echo points, with a low false correlation rate and of high practical value.
Sentiment Analysis of Cooking Oil using Bidirectional Encoder Representations from Transformers

Recently, cooking oil has been widely discussed on social media. Many people ask how to analyze this phenomenon, one of which is using a sentiment analysis application on Twitter that performs a scientific analysis of the tweets of this media user. This study aims to test the performance of the sentiment analysis algorithm utilizing the Bidirectional Encoder Representations from Transformers (BERT) algorithm from Twitter, which is used to classify the types of tweets processed in 2000 tweets. The study shows the results of the confusion matrix, accuracy, and precision. The study obtained a fairly good accuracy and precision performance of 84%, which occurred at a learning rate of 1e-6 and epoch 5. The author also discusses the factors that affect the performance of BERT, such as; pre-processing, data quantity, and data variation.
Viruses, Exploits, Malware and Security Issues on IoT Devices

The necessity of using secure Internet-of-Things (IoT) devices in various use cases has increased over years. According with various analysis in the first half of the year 2021, there were 1.5 billion attacks on smart devices for stealing data, mining cryptocurrency or building botnets. Therefore, the security of the IoT devices is mandatory for any solution in the field – e.g., from Smart Cities to Healthcare. The main challenge for having reasonable security for IoT devices is the fragmentation of the market landscape and protocols, as well as poor penetration of the device attestation and embedded/integrated secure elements for the IoT nodes. First section of this paper is an overview of the IoT certification schemes and in the second section the authors present a proof-of-concept solution for direct and reverse shell in an IoT gateway. The last section offers conclusions regarding the cybersecurity for the IoT gateways and nodes.
Augmented Reality Games

Augmented reality strengthens its ties with the gaming world every day. The fact that smartphones can be used as an augmented reality tool, in particular, shows this interest as a remarkable phenomenon for both gamers and game producers. The development of augmented reality applications is of great importance for the future of the gaming world, as it is not only limited to mobile phones but also covers more sophisticated devices. This research intends to evaluate how augmented reality games interpret gaming concepts and principles, through field research methods, new applications, and studies that deal with gamification, presence, immersion, and game transfer phenomena. It is also aimed to make inferences about how our daily life can be gamified in the near future thanks to augmented reality.
The Environmental Problem in Anime <i>Weathering With You</i> Directed By Makoto Shinkai

Environmental issues have become a major issue that concerns all parties, including the attention of directors in the anime they make. Many anime takes the theme of environmental issues which contain a moral message about the importance of protecting and preserving the environment from damage. This study aims to examine what environmental problems are described in the anime Weathering with You by director Makoto Shinkai, what negative impacts arise from these environmental problems, and what kind of environmental criticism the director wants to convey through the anime. This study uses the descriptive analysis method, namely the method used to analyze the data by describing the data that has been collected as it is without intending to make conclusions that apply to the public. Because the material object in this study is anime, the theory used in this study is the literary ecocritic theory. From the results of the study, it was found that the environmental problem described in this anime are problems of extreme climate change which result in various kinds of negative impacts such as falling air temperatures, erratic seasonal changes, losses in economic problems, and others. Through this anime, the director wants to invite the audience to be wise in protecting and maintaining the environment so that the negative impacts resulting from environmental damage can be avoided.
Videosar Image Speckle Reduction with Space-Time Context Information and Similarity Test

VideoSAR realizes continuously scene observation and expands SAR's range-azimuth two-dimensional scattering information to range-azimuth-time three-dimensional scattering information. Considering the effects of speckle noise, a novel speckle filter for VideoSAR image was proposed in this paper using the three-dimensional information of VideoSAR image. The proposed method mainly contains three steps. Firstly, a context covariance matrix that contains spatial and temporal context information is constructed for each pixel. Then, the similar samples are selected for each pixel according to the similarity test of covariance matrices. Finally, a sample averaging estimator is applied based on the similar samples. Experiments on real VideoSAR data show that the proposed algorithm achieves effective speckle filtering performance while well preserving the edge texture.
Conditional Patch-Based Domain Randomization: Improving Texture Domain Randomization Using Natural Image Patches

Using Domain Randomized synthetic data for training deep learning systems is a promising approach for addressing the data and the labeling requirements for supervised techniques to bridge the gap between simulation and the real world. We propose a novel approach for generating and applying class-specific Domain Randomization textures by using randomly cropped image patches from real-world data. In evaluation against the current Domain Randomization texture application techniques, our approach outperforms the highest performing technique by 4.94 AP and 6.71 AP when solving object detection and semantic segmentation tasks on the YCB-M [1] real-world robotics dataset. Our approach is a fast and inexpensive way of generating Domain Randomized textures while avoiding the need to handcraft texture distributions currently being used.
Practical Single-Pass Oblivious Aggregation and Billing Computation Protocols for Smart Meters

In this paper, we first propose the notion of authenticated aggregator-oblivious encryption (AAOE) and then develop a privacy-preserving smart metering protocol based on it. Our protocol enjoys computing aggregated metering data periodically and the time-of-use tariff-based billing of customers simultaneously, without revealing actual metering data to the utility provider. Our protocol construction is based on computationally cheap primitives, namely aggregator-oblivious encryption and message authentication code. We analyze the security of our protocol, and evaluate their performance on a real-world dataset.
Finding the Optimal Ratio of Summaries to NEWS Articles and Establishing a Hybrid NEWS Summary System

Today, people can easily connect to the Internet anytime, anywhere. They can check the weather, find nearby restaurants, locate places on maps, and easily find various venues for reading the news. Due to the spread of the Internet and the development of speed, the number of news outlets has increased exponentially. As a result, people can access news articles through various methods. People primarily use smartphones, computers, or tablets to access the news. There are many ways people read the news. Some people may want to read the entire content of an article, while others may want to read only the title and a simple summary. For this reason, many studies have been conducted on news summaries. Some media outlets provide their own news texts and summaries, but there are many places that do not. Summarizing the news is time-consuming, and shorter news articles are also difficult to summarize. Current news summary methods require labeled data created by humans. To address these limitations, we propose a hybrid summarization system that combines extractive and abstractive methods. The proposed method extracts the news text to create label data and uses this data to perform abstractive summarization. The purpose is to compare the summary obtained through this process with the results of the existing general summary and to make as similar results as possible without label data.
Using Swarm Intelligence Algorithms for Optimization in IoT Applications

Internet of Things (IoT) is considered as the next industrial revolution in the years ahead. Many devices will have been connected to the Internet and more and more devices were added to them. The number of these types of devices is increased rapidly. So, management of them needs advanced technologies. Optimization of different problems in IoT is an essential subject of many researches and Swarm Intelligence algorithms (SI) are one of the most successful methods in this field. They were inspired by the collective behavior of simple creatures which communicate directly and indirectly with each other. Although they are considered as simple agents, their collective behavior is smart. They can overcome most problems in their life using collective behavior. In this research, we investigated some of the most common optimization problems in IoT and the SI algorithms which are used to solve them.
Ontology-Based Domain Knowledge Construction for Location Based Social Network

With the development of mobile applications and location technology, people can share their location and conduct various social activities by mobile phones, which promotes the formation of location-based social network(LBSN). Data from different sources with different formats are generated in LBSN. How to extract knowledge from these data and fuse them together is of great significance to analyze the characteristics of user in LBSN. In this paper, an ontology-based LBSN domain knowledge graph construction method is proposed. Firstly, the data model and ontology of the graph is defined, which includes 8 entities, 11 relations and their related attributes. Then, the construction process of the knowledge graph and the process to merge knowledge extracted from check-in data and the label data is defined. Finally, a case study is conducted to verify the effectiveness of the method. Result shows that the method can construct knowledge graph for LBSN and merge knowledge extracted from multi-source effectively, and the knowledge graph constructed can promote the storage of LBSN domain knowledge and user feature analysis.
Blockchain Security: A Systematic Review

Blockchain is a technology that is decentralized. It has the ability to tackle a wide range of industrial issues. A blockchain transaction’s records are secured by cryptography, and each transaction is linked to previous transactions or records. Algorithms on the nodes validate blockchain transactions. As a final point, blockchains enable transparency, allowing each participant to keep track of transactions at any point in time. Smart contracts provide for safe transactions, reducing the risk of third-party interference. Readers will have a better understanding of how blockchain technology helps protect and manage today’s users. There is a thorough report on diverse blockchain studies and security proposed by the research community, and their distinct implications on blockchain, in the review. This article concludes with a detailed description of the blockchain security followed by a discussion of the many varieties of security enhancements.
Side Keyboard – the New Approach for Eye-typing

A novel approach for eye-typing using an on-screen keyboard was proposed in the paper. It was compared with the two applied in the previous studies. All utilized the dwell-time selection for writing letters, set to 1000 ms. This value was chosen due to the planned group of participants. Among 14 engaged people, there were four over the age of 50 and one aged 47. The text utilized in the research was prepared in two languages – Polish and English. The obtained results showed that the language used had no influence on typing efficiency. Additionally, they revealed the new keyboard layout was more convenient than one of the two chosen for comparison and similar to the second. Such conclusions were based on the experiment duration and the number of errors made. They were confirmed by the users’ opinions.
Ensemble2: Anomaly Detection in Communication Network

KPI anomaly detection is one important function of network management system. Traditional methods either require prior knowledge or manually set thresholds. To overcome these shortcomings, we propose the Ensemble2 algorithm, which applies 1D CNN structure to improve effectiveness and robustness. The model is assessed on production datasets to verify its effectiveness and achieves promising result in the end.
Few-Shot Correlation Estimation for Cross-Camera Video Analytics: A Mean-Field Game Approach

Cross-camera video analytics is a major video analytic task that associates and analyzes information across multiple cameras. Existing studies exploit static temporal and spatial correlation among different cameras to reduce the searching space and thus accelerate searching. However, in practice, correlation among cameras changes over time, making the previous static approach inefficient. In this paper, we present the first work to support dynamic correlation estimation based on a few samples. Specifically, we formulate the correlation estimation problem as a mean-field game and interpret the cross-camera correlation as a novel mean-field term to approximate the target cameras' density field. Despite the significant complexity to solve the coupled partial differential equations in the game, we propose a G-prox primal-dual hybrid gradient algorithm to solve it efficiently. Our derived correlation models can guide searching with controllable granularity and solely rely on the knowledge of the initial correlation and destination correlation information. Extensive experiments on a real-world dataset demonstrate that, with the help of our dynamic correlation models, the overall workload can be reduced by 36% in general.
Destructive Resistance Analysis of UAV Cluster Network Based on Cascade Failure Model

The complex network theory is used to construct the Unmanned Aerial Vehicle (UAV) cluster network and its characteristic indicators are analyzed. The cascade failure theory is used to study the destructive resistance of the UAV cluster network under different node capacities and attack information, etc. The results show that increasing the node capacity can effectively improve the network destructibility, and the cost and network performance requirements can be balanced by setting appropriate node capacity parameters; the UAV cluster network has higher destructibility under random attacks and shows vulnerability under deliberate attacks, and obtaining some important node information can also significantly reduce the network destructibility.
The Decline of Surface Global Solar Radiation at Palembang City of Indonesia Correlated to the Smoke Events in 2019

The decline of surface solar global radiation has been analyzed and reduced atmospheric visibility at Palembang, South Sumatra Province, Indonesia, during the smoke events in September-December 2019. The surface global solar radiation was measured at the campus of Bina Darma University at Palembang City using AWS integrated radiometer, and the visibility was observed in Talang Betutu, Palembang Airport. The smoke produced by forest fire surrounding Palembang caused declining in surface global solar intensity significantly. It also reduced atmospheric visibility in Palembang Airport down to 1.6 km, while in the atmospheric condition without smoke, the visibility was around 8.2 km. This condition made the sunrise in the morning as if delayed for some ten minutes. Among 86 days of data in this period, 46 days (53.3%) have lower surface solar radiation intensity. This study aims to get values of surface global solar radiation decline and the decrease in atmospheric visibility due to smoke events.
Research on Blockchain User Privacy Protection Technology Based on PBFT Algorithm

This paper studies one of the consensus algorithms in blockchain Technology: pbft algorithm (Practical Byzantine algorithm), and mainly discusses the role of this algorithm in user privacy protection and information security. Starting from the blockchain mechanism and technical principles, on the basis of Byzantine algorithm, comparing the application scenarios of traditional practical Byzantine algorithm, this paper deeply analyzes the consensus mechanism of Byzantine algorithm in the blockchain practical mechanism. Through the comparative analysis of blockchain technology development stage and some typical applications, it is found that Ethereum is selected as the intelligent contract consensus algorithm operation platform, It provides a reference for the research and development based on blockchain technology, and proves that the practical Byzantine algorithm has broad prospects for the application of privacy protection in blockchain technology.
Newly-Published Paper Recommendation With a Joint Multi-Relational Model

Reading newly-published papers in time is important for researchers since these papers provide the latest research findings. However, it is challenging to retrieve newly-published papers through common query-based search engines because the papers lacking sufficient citations and links are usually ranked too low in the search list. To this end, we design a time-aware joint model to infer users’ preference for the newly-published papers with the help of subsidiary relations of social and article linkages. The temporal preference of researchers for articles is jointly modeled with social and article relations by a group of matrices sharing common dimensions of researchers and articles. A joint multi-relational factorization algorithm is devised to approximate the latent factor matrices along with a temporal recommendation algorithm to predict the personalized new referential papers based on these factor matrices. The experimental results on real-world datasets show that the proposed model outperforms the state-of-the-art methods.
Deadlock-Free Method for Multi-Agent Pickup and Delivery Problem Using Priority Inheritance with Temporary Priority

This paper proposes a control method for the multi-agent pickup and delivery problem (MAPD problem) by extending the priority inheritance with backtracking (PIBT) method to make it applicable to more general environments. PIBT is an effective algorithm that introduces a priority to each agent, and at each timestep, the agents, in descending order of priority, decide their next neighboring locations in the next timestep through communications only with the local agents. Unfortunately, PIBT is only applicable to environments that are modeled as a bi-connected area, and if it contains dead-ends, such as tree-shaped paths, PIBT may cause deadlocks. However, in the real-world environment, there are many dead-end paths to locations such as the shelves where materials are stored as well as loading/unloading locations to transportation trucks. Our proposed method enables MAPD tasks to be performed in environments with some tree-shaped paths without deadlock while preserving the PIBT feature; it does this by allowing the agents to have temporary priorities and restricting agents’ movements in the trees. First, we demonstrate that agents can always reach their delivery location without deadlock. Our experiments indicate that the proposed method is very efficient, even in environments where PIBT is not applicable, by comparing them with those obtained using the well-known token passing method as a baseline.
Aspect Term Extraction via Contrastive Learning over Self-augmented Data

Aspect Term Extraction (ATE) is a natural language processing task, which identifies the languages describing product attributes. Such languages (words) are referred to aspect terms in this field. The current neural ATE models suffer from sparsity of available training data. As a result, they fail to be robust in real applications due to overfitting. More seriously, the distinguishable underlying features cannot be learned sufficiently by neural networks, which causes high misjudgement rates. Deliberate data expansion by human annotation undoubtedly helps to alleviate the problem. However, it is time-consuming. In order to overcome the bottleneck, we utilize the Regularized Dropout (R-Drop) approach to implement self data augmentation, creating variant distributed representations for learning in the real-time computation process of neural networks. More importantly, we propose to conduct contrastive learning over the self-augmented data, which sufficiently leverages the variant distributed representations to explore the distinguishable features. We experiment on four widely-used benchmark datasets (R14-16 and L14) in the shared tasks of Semantic Evaluation (SemEval). Experimental results show that contrastive learning over self-augmented data yields significant performance gains, where the improvement is up to 1.9% F1-score at best. In addition, it is demonstrated in the experiments that our method outperforms the state of the art on two test sets (R14 and R16), and meanwhile achieves competitive performance on the rest test sets.