Reviewerly/exHarmony huggingface dataset:
Unsupervised Machine Learning Approach for Anomaly Detection in E-coating Plant

The Internet of Things (IoT) revolutionized maintenance technology by allowing machines to communicate among themselves over the Internet in real-time. The very focus of Industry 4.0 towards automation and data exchange calls for the application of IoT in industry and for control, supervision, and maintenance of industrial equipment based on sensor data. The goal of this paper is to test several unsupervised machine learning algorithms for detecting anomalies in unlabeled (raw) data collected from a real e-coating plant that uses IoT technology. Unsupervised learning methods are advantageous because they do not require labeled data and benefit early stage anomaly detection in industry in general. The dataset used for training and testing is composed of real-time data gathered from the plant’s sensors over a period of 15 days. For the purpose of anomaly detection, we used the following unsupervised machine learning algorithms: Interquartile Range (IQR), Isolation Forest, and Elliptic Envelope. The algorithms were compared with each other by the time required for training and the total number of detected anomalies. According to the observed results, it was shown that IQR needs much less time for training and detects three times more anomalies compared to the other two algorithms.
An improved genetic algorithm based on explosion mechanism

For the phenomenon that traditional genetic algorithm have weak local search ability and are prone to premature in solving combinatorial optimization problems, the local search ability of the genetic algorithm is improved by introducing the explosion operator of the firework algorithm . An improved genetic algorithm, boom genetic algorithm, is proposed. Boom genetic algorithm not only has the advantages of traditional genetic algorithm with strong global search ability, but also has the characteristics of firework algorithm with strong local search ability, which achieves complementary advantages. By using the Traveling Salesman Problem simulation example, the superiority and validity of the improved genetic algorithm proposed in this paper compared with the traditional one are verified.
Novel Extended Kalman Filter Using Matrix-Based Levenberg-Marquardt Algorithm and Its Application for Variable Bit-Rate Video Frame-Size Prediction

Dynamic bandwidth allocation based on multimedia network-traffic prediction has been emerging as an important problem in multimedia networks. The well-known Kalman filter has been adopted for such network-traffic prediction but it is assumed that the state-transition model is linear and known a priori. Therefore, it is favorable to extend the conventional linear state-transition model to be nonlinear and dynamically estimate it. It is not trivial to estimate such a nonlinear model especially for a multimedia network supporting the 5G technology and operating in a highly mobile environment. In this work, we would like to address the aforementioned challenges by designing a new matrix-based Levenberg-Marquardt algorithm based extended Kalman filter (MLMA-EKF) to dynamically estimate the video frame-sizes in compiance with MPEG-4 specifications. Numerical results over MPEG-4 encoded movies demonstrate that our proposed novel MLMA-EKF frame-size predictor is effective for predicting the future bit rates, or video frame-sizes, in terms of normalized mean square error (NMSE).
Assessment of the effects of 5G MEC cache on DASH adaptation algorithms

Nowadays, video streaming services are the major sources of Internet traffic, and their usage is constantly growing. To cope with this increasing video traffic demand, 5G comes with a new Radio Access Network (RAN), having increased network data rates, and the Multi-access Edge Computing (MEC) architecture, enabling the deployment of services closer to the end devices. 5G RAN and MEC open to new opportunities to enforce and boost the video content delivery, as the content can be cached locally at the MEC host and served with improved RAN connectivity. However, the presence of a cache located at MEC may affect player adaptation algorithm when employing Adaptive Bitrate (ABR) streaming technologies, such as Dynamic Adaptive Streaming over HTTP (DASH). The cache influences the network measurements performed by the DASH player, which are later used, among other factors, by its adaptation algorithm to select the video representation bitrate. This paper investigates the interactions between MEC cache and DASH adaptation algorithms in a real testbed, including 5G RAN and MEC platform. The effects on different adaptation algorithms are analyzed while caching the DASH video content at the MEC.
Severity Prediction for Bug Reports using Tree-based Ensemble Models: A Comparative Study

Predicting the severity of software bug reports is an important research issue. In recent studies, ensemble approaches have been employed in various prediction tasks for bug reports to obtain performance improvements. In this study, we investigate the prediction performance of four tree-based ensemble models on the severity prediction task. The studied ensemble models include Random Forest, Extra Trees, XGBoost, and LightGBM. In addition, particle swarm optimization (PSO) is used to find the suitable weights of different features for severity prediction. The prediction performance is evaluated with datasets of three open- source projects, Eclipse, Mozilla, and Gentoo Linux. The experimental results show that LightGBM with PSO-optimized feature weights can achieve the best prediction performance in many performance metrics.
Understanding Trust Assumptions for Attestation in Confidential Computing

Despite its critical role, remote attestation in Intel Software Guard Extensions (SGX) and Trust Domain Extensions (TDX) is poorly specified by Intel with some obvious flaws. We believe that it is part of Intel’s strategic policy to create resistance to revealing trust assumptions of the process.
Fully Unsupervised Person Re-Identification via Centroids and Neighborhoods Joint Learning

This paper considers that the challenge of un-supervised person re-identification (re-ID) is generating high-quality pseudo labels. Recent label prediction methods can be mainly divided into Clustering-based Label Prediction (C-LP) and Similarity Measurements-based Label Prediction (SM-LP) methods. The existing researches only focus on improving the accuracy of one of the label generation method. In this letter, we first point out three complementarities between C- LP and SM-LP, including (1) interval of the pseudo label prediction (2) feature learning directions, and (3) inliers and outliers processing. Based on these three complementarities, we proposed a Joint Label Prediction (Joint-LP) method that can give full play to complementary advantages of C-LP and SM-LP. Moreover, we discover that standard Binary Cross Entropy (BCE) loss forces the unsupervised model to overfit the noisy labels, thereby leading the model training to fail. Therefore, we further proposed a Rectified Binary Cross Entropy (ReBCE) loss that is robust to label noise. The experimental results confirm the effectiveness of the proposed Joint-LP and ReBCE loss on two mainstream person re-ID datasets, Market-1501 and DukeMTMC-reID.
HDiff: A Semi-automatic Framework for Discovering Semantic Gap Attack in HTTP Implementations

The Internet has become a complex distributed network with numerous middle-boxes, where an end-to-end HTTP request is often processed by multiple intermediate servers before it reaches its destination. However, a general problem in this distributed network is the semantic gap attack, which is defined as inconsistent semantic interpretations in the processing chain. While some studies have found individual semantic gap attacks, most of them are based on ad-hoc manual analysis, which is inadequate for fundamentally enhancing the security assurance of a system as complex as the HTTP network.In this work, we propose HDiff, a novel semi-automatic detecting framework, systematically exploring semantic gap attacks in HTTP implementations. We designed a documentation analyzer that employs natural language processing techniques to extract rules from specifications, and utilized differential testing to discover semantic gap attacks. We implemented and evaluated it to find three kinds of semantic gap attacks in 10 popular HTTP implementations. In total, HDiff found 14 vulnerabilities and 29 affected server pairs covering all three types of attacks. In particular, HDiff also discovered three new types of attack vectors. We have already duly reported all identified vulnerabilities to the involved HTTP software vendors and obtained 7 new CVEs from well-known HTTP software, including Apache, Tomcat, Weblogic, and Microsoft IIS Server.
Treaty: Secure Distributed Transactions

Distributed transaction processing is a fundamental building block for large-scale data management in the cloud. Given the threats of security violations in untrusted cloud environments, our work focuses on: How to design a distributed transactional KV store that achieves high-performance serializable transactions, while providing strong security properties?We introduce Treaty, a secure distributed transactional KV storage system that supports serializable ACID transactions while guaranteeing strong security properties: confidentiality, integrity, and freshness. Treaty leverages trusted execution environments (TEEs) to bootstrap its security properties, but it extends the trust provided by the limited enclave (volatile) memory region within a single node to build a secure (stateful) distributed transactional KV store over the untrusted storage, network and machines. To achieve this, Treaty embodies a secure two-phase commit protocol co-designed with a high-performance network library for TEEs. Further, Treaty ensures secure and crash-consistent persistency of committed transactions using a stabilization protocol. Our evaluation on a real hardware testbed based on the YCSB and TPC-C benchmarks shows that Treaty incurs reasonable overheads, while achieving strong security properties.
How’d Security Benefit Reverse Engineers? : The Implication of Intel CET on Function Identification

As CPU vendors introduce various hardware-assisted security features, modern compilers have started to produce binaries containing security-related instructions. Interestingly, such instructions tend to alter the shape of resulting binaries, which can potentially affect the effectiveness of binary analysis. This paper presents the first systematic study on the implication of the Intel CET (Control-flow Enforcement Technology) instructions on function identification. Our study finds that CET-relevant instructions provide useful, although limited, hints for function entries. Therefore, we devise a novel function identification algorithm that utilizes the usage patterns of CET instructions, and demonstrate a tool named FunSeeker that implements the idea. Our evaluation shows that FunSeeker significantly outperforms current state-of-the-art function identification tools in terms of both correctness and speed.
Personalized Cryptography Algorithms – A Comparison Between Classic and Cognitive Methods

The main goal of this work is to present a method of personalized cryptography that differs from the biometrical ones by putting in elements of cognitive cryptography and would be useful to work in an IoT environment. Thus a state-of-the-art review of existing methods and their usefulness has already been made in the context of comparing them with a preliminary cognitive algorithm based on the author’s own work. The first half of the abstract would consist of various types of personalized cryptography, beginning with the oldest and most widespread and ending with the newest breakthroughs and inventions in the field. The other half would introduce a new algorithm based on cognitive computing and embedding context recognition into the data streams. Such an algorithm would be shortly presented compared to the other methods, and possible applications or drawbacks would be discussed.
Daric: A Storage Efficient Payment Channel With Penalization Mechanism

Lightning Network (LN), the most widely deployed payment channel for Bitcoin, requires channel parties to generate and store distinct revocation keys for all n payments of a channel to resolve fraudulent channel closures. To reduce the required storage in a payment channel, eltoo introduces a new signature type for Bitcoin to enable payment versioning. This allows a channel party to revoke all stale payments by using a payment with a higher version number, reducing the complexity of storage from $\mathcal{O}(n)$ to $\mathcal{O}(1)$. However, eltoo lacks a penalization mechanism, which may incentivise profit-driven channel parties to close a payment channel with a stale payment state, to their own advantage. This paper introduces Daric, a payment channel for Bitcoin that simultaneously disincentivize profit-driven attackers and achieves optimal storage. In addition, to achieve higher efficiency and robustness, Daric does not rely on any particular properties of the underlying digital signature to prevent state duplication.
Formal Foundations for SCONE Attestation

One of the essential features of confidential computing is the ability to attest to an application remotely. Our work focuses on ensuring correctness of attestation mechanisms and policies.
Reliability of Google’s Tensor Processing Units for Convolutional Neural Networks

This abstract presents the result of extensive reliability evaluation of Google’s Coral Tensor Processing Unit (TPU), which is one of the latest low power accelerators for CNNs. We report experimental data equivalent to more than 30 million years of natural irradiation and analyze the behavior of TPUs executing atomic operations (standard or depthwise convolutions) with increasing input sizes as well as eight CNN designs typical of embedded applications, including transfer learning and reduced data-set configurations. We found that, despite the high error rate, most neutrons-induced errors only slightly modify the convolution output and do not change the CNNs detection or classification. By reporting details about the fault model and error rate, we provide valuable information on how to evaluate and improve the reliability of CNNs executed on a TPU.
Named Entity Recognition in Emergency Domain based on BERT-BILSTM-CRF

Chinese named entity recognition is a key problem in natural language processing. The traditional language processing model can not effectively represent the context semantic information in the text, and can not deal with the different contexts of polysemy, which affects the effect of entity recognition. A BERT-BILSTM-CRF entity recognition model for the emergency domain is proposed. BERT pre-training language model generates word vectors which represent the semantic information of context, and the generated vectors are characterized by two-way long-term and short-term memory network. Self-attention mechanism can effectively obtain the long-distance dependence in text sentences. And finally, decoding is carried out through the CRF to generate an entity label sequence. The experimental results show that the model have achieved excellent results in the emergency domain corpus.
Convergence of Stochastic Gradient Decent Algorithm with Momentum

This paper aims to discuss the convergence of the stochastic gradient decent algorithm with momentum for feed-forward networks without hidden layers. Assuming that the required sample set is linearly independent, let all training samples be randomly grouped in each round of training, the number of samples in each group is fixed, and the weights are updated in the unit of group. The momentum and learning rate are selected in an adaptive manner, then we prove the convergence results of the learning process.
BERT-Based Automatic Scoring Model for Speech-Oriented Text Modality

Oral proficiency reflects L2 learners' ability to use a second language in the actual language environment and can reflect the learners' language proficiency in a more realistic way. In China, English speech and English oral test are the main ways to assess English speaking ability. In this scenario, the automatic assessment system for English speech and oral English test are of value. However, the current studies lack the consideration of language usage. With the continuous development of natural language processing(NLP), the research of automatic essay scoring(AES) is increasing. The direct application of AES to the scenarios mentioned above cannot achieve the best results yet. Therefore, in this paper, we analyze the demand the demand for text modality in this field in the context of the current development of automatic assessment of spoken English and English speech. First, we implement Base Model in light of the development of AES, which is slightly better than the baseline metrics on the ASAP dataset in AES domain. Second, on the basis of the characteristics of the demand of speech-oriented text modal automatic scoring, the feature engineering approach and the neural network approach are combined on the Base Model to implement an automatic scoring model for speech-oriented text modality based on Bert. Finally, the overall experiment is analyzed and summarized.
An Analysis of Artificial Intelligence Engineering Cost Information Management Platform

Based on the construction engineering cost information database and the whole process cost management of BIM, an artificial intelligence engineering cost information management platform is constructed. The platform is composed of the cost data collection and formation, the whole process cost calculation, the technical and economic analysis and the decision-making systems. It can carry out intelligent data processing and whole process cost calculation, economic evaluation and decision-making.
Augmented Intelligence Multilingual Conversational Service for Smart Enterprise Management Software

Conversational agents are gaining popularity in the corporate world as a way to increase customer experience and economic competitiveness. Additionally, developments in augmented intelligence systems employ natural language processing to provide the industry with natural and clear interaction experiences. Multilingual conversational bot or Chabot is important in every area of life, especially in a multicultural community recognized for its numerous accents and slang among many social groupings Moreover, most present Chabot systems only handle one language at a time, and the training session is cumbersome since it needs various dialects for different purposes. This research presents a multilingual chatbot that allows clients to converse in many languages as if they were conversing with a real person to achieve a smart enterprise management software. The proposed system was implemented using React.JS and python programming language on a Pentium III processor speed of 600 MHz minimum. The proposed multilingual service will deal with the limitation of the existing system by developing a system that will allow multiple languages on chatbot agents. The system will allow the users to converse in their languages, which will make communication easy between the system and the users. The proposed system will include a user-friendly interface that will assist in guiding each user on how to utilize it effectively without any specialized training.
Latent Group Dropout for Multilingual and Multidomain Machine Translation

Multidomain and multilingual machine translation often rely on parameter sharing strategies, where large portions of the network are meant to capture the commonalities of the tasks at hand, while smaller parts are reserved to model the peculiarities of a language or a domain.In adapter-based approaches, these strategies are hardcoded in the network architecture, independent of the similarities between tasks.In this work, we propose a new method to better take advantage of these similarities, using a latent-variable model.We also develop new techniques to train this model end-to-end and report experimental results showing that the learned patterns are both meaningful and yield improved translation performance without any increase of the model size.* Now Research Scientist at Zoom Video Communications 1 We will refer to these two situations as 'multi-task MT' and refer to individual domains and languages as 'tasks'.
PubHealthTab: A Public Health Table-based Dataset for Evidence-based Fact Checking

Inspired by human fact checkers, who use different types of evidence (e.g.tables, images, audio) in addition to text, several datasets with tabular evidence data have been released in recent years.Whilst the datasets encourage research on table fact-checking, they rely on information from restricted data sources, such as Wikipedia for creating claims and extracting evidence data, making the fact-checking process different from the real-world process used by fact checkers.In this paper, we introduce PubHealthTab, a table fact-checking dataset based on real-world public health claims and noisy evidence tables from sources similar to those used by real fact checkers.We outline our approach for collecting evidence data from various websites and present an in-depth analysis of our dataset.Finally, we evaluate state-of-theart table representation and pre-trained models fine-tuned on our dataset, achieving an overall F 1 score of 0.73.
Classification of Natural Language Descriptions for Bayesian Knowledge Tracing in Minecraft

Application of Bayesian Knowledge Tracing (BKT) has primarily occurred in formal learning settings. This paper presents an integration of BKT in an informal learning context to assess the structure and skill level of learner scientific observations. We compare different approaches to text classification in a Minecraft science simulation. Our models were trained on data collected from two separate middle schools with students of different backgrounds. Experimental results demonstrate the effectiveness of several machine learning models to automatically label observations.
Emerging Reform of Higher Education in Post Pandemic

Abstract The COVID-19 pandemic highlighted the reassessment of the potential transformation of digital education, thereby fueling the discussions on the need to rethink the future direction of higher education. Converting courses online was just one step toward a transformative paradigm. Further reexamination and drastic resetting of current education practice may be required, which can only be achieved with unprecedented levels of collaboration and open-minded innovations among stakeholders in education. Several practical recommendations were drawn from a case study based on the performing arts education (PAE) in the Korean higher education. They are, creating a platform for scalable innovations through the implementation of the right measure of technologies (learning technology innovation); advancing the effectiveness of technology integration in the non-traditional learning environment (pedagogical innovation); and promoting the cost efficiency of education (community knowledge construction innovations). This chapter explores and discusses the adaptation in educational / pedagogical technologies and suggests the building capacity for the future of PAE in the post-pandemic era.