Reviewerly/exHarmony huggingface dataset:
Light-weight AI and IoT collaboration for surveillance video pre-processing

As one of the internet of things (IoT) use cases, wireless surveillance systems are rapidly gaining popularity due to their easier deployability and improved performance. Videos captured by surveillance cameras are required to be uploaded for further storage and analysis, while the large amount of its raw data brings great challenges to the transmission through resource-constraint wireless networks. Observing that most collected consecutive frames are redundant with few objects of interest (OoIs), the filtering of these frames before uploading can dramatically relieve the transmission pressure. Additionally, real-world monitoring environment may bring shielding or blind areas in videos, which notoriously affects the accuracy on frame filtering. The collaboration between neighbouring cameras can compensate for such accuracy loss. Under the computational constraint of edge cameras, we present an efficient video pre-processing strategy for wireless surveillance systems using light-weight AI and IoT collaboration. Two main modules are designed for either fixed or rotated cameras: (i) frame filtering module by dynamic background modelling and light-weight deep learning analysis; and (ii) collaborative validation module for error compensation among neighbouring cameras. Evaluations based on real-collected videos show the efficiency of this strategy. It achieves 64.4% bandwidth saving for the static scenario and 61.1% for the dynamic scenario, compared with the raw video transmission. Remarkably, the relatively high balance ratio between frame filtering accuracy and latency overhead outperforms than state-of-the-art light-weight AI structures and other surveillance video processing methods, implying the feasibility of this strategy.
MEET-LM: A method for embeddings evaluation for taxonomic data in the labour market

Taxonomies are the mainstay of the semantic web as they aim at organising knowledge in concepts linked by IS-A relationships. However, keeping such hierarchies updated and able to represent the domain from which they have been drawn is still a time-consuming, costly and error prone activity. Here, word embeddings have proven to be effective in catching lexicon and semantic similarities to enrich taxonomies from text data. This, in turn, would require to evaluate the generated embeddings to estimate the extent to which they encode the semantic similarity derived from the hierarchy itself. In this paper, we propose and implement MEET-LM, a methodology that aims at generating and evaluating embeddings from a text corpus preserving the co-hyponymy relations synthesised from a domain-specific taxonomy. We apply MEET-LM to a real-life dataset of 2M+ vacancies related to ICT-jobs, framed within the research activities of an EU project that collects millions of Online Job Vacancies and classifies them within the European standard hierarchy ESCO. To show MEET-LM is useful in practice, we also trained a neural network to classify co-hyponym relations using the selected embeddings as features. Our experiments reach 99.4% of accuracy and 86.5% of f1-score.
Effort-aware just-in-time defect identification in practice: a case study at Alibaba

Effort-aware Just-in-Time (JIT) defect identification aims at identifying defect-introducing changes just-in-time with limited code inspection effort. Such identification has two benefits compared with traditional module-level defect identification, i.e., identifying defects in a more cost-effective and efficient manner. Recently, researchers have proposed various effort-aware JIT defect identification approaches, including supervised (e.g., CBS+, OneWay) and unsupervised approaches (e.g., LT and Code Churn). The comparison of the effectiveness between such supervised and unsupervised approaches has attracted a large amount of research interest. However, the effectiveness of the recently proposed approaches and the comparison among them have never been investigated in an industrial setting.
What Accountants Need to Know about Blockchain*

ABSTRACT Reports by professional organizations (e.g., AICPA, CPA Canada, and ICAEW) argue that blockchain adoption is likely to reduce the need for record‐keeping tasks and shift the accounting focus to higher‐level activities. To evaluate such expectations and form their own opinions, accounting professionals need to understand the way blockchain works in a business setting. The primary objective of this article is to provide the reader with the foundational knowledge needed to better understand reports such as those suggesting that blockchain will change the way auditors execute their engagements and generate new assurance opportunities for configuring policies in blockchain networks. Building on evidence indicating that the most promising use cases are from the logistics industry, we describe a blockchain implementation in a supply chain setting. Using a scaffolding approach, we start with a supply chain and describe how blockchain is implemented in one of its segments, before we show how a complete transaction is recorded on the ledger.
Radio Frequency Fingerprint Identification Based on Deep Complex Residual Network

Radio frequency fingerprint identification is a non-password authentication method based on the physical layer hardware of the communication device.Deep learning methods provide new ideas and techniques for radio frequency fingerprint identification.As a bridge between electromagnetic signal recognition and deep learning, the electromagnetic signal recognition method based on statistical constellation still needs to go through data preprocessing and feature engineering, which is contrary to the end-to-end learning method emphasized by deep learning.Moreover, in the process of converting electromagnetic signal waveform data into images, there is inevitably information loss.Establishing a universal radio frequency fingerprint recognition model suitable for wireless communication scenarios is not only conducive to optimizing the communication system, but also can reduce the cost and time of model selection.Therefore, how to design a deep learning radio frequency fingerprint recognition model suitable for wireless communication is an important problem for researchers.Aiming at the problem that the existing radio frequency fingerprint extraction and identification methods have low recognition rate of communication radiation source individuals, a radio frequency fingerprint identification method based on deep complex residual network is proposed.Through the deep complex residual network, the radio frequency fingerprint feature extraction of the communication radiation source individual is integrated with the recognition process, and an end-to-end deep learning model suitable for wireless communication is established, which greatly improves the identification accuracy of the communication radiation source individuals compared with typical constellation based methods.
Optimal Universal Programming of Unitary Gates

A universal quantum processor is a device that takes as input a (quantum) program, containing an encoding of an arbitrary unitary gate, and a (quantum) data register, on which the encoded gate is applied. While no perfect universal quantum processor can exist, approximate processors have been proposed in the past two decades. A fundamental open question is how the size of the smallest quantum program scales with the approximation error. Here we answer the question, by proving a bound on the size of the program and designing a concrete protocol that attains the bound in the asymptotic limit. Our result is based on a connection between optimal programming and the Heisenberg limit of quantum metrology, and establishes an asymptotic equivalence between the tasks of programming, learning, and estimating unitary gates.Received 4 August 2020Accepted 17 September 2020DOI:https://doi.org/10.1103/PhysRevLett.125.210501© 2020 American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum algorithmsQuantum computationQuantum protocolsResource theoriesQuantum Information
Multi-Class classification of vulnerabilities in smart contracts using AWD-LSTM, with pre-trained encoder inspired from natural language processing

Abstract Vulnerability detection and safety of smart contracts are of paramount importance because of their immutable nature. Symbolic tools like OYENTE and MAIAN are typically used for vulnerability prediction in smart contracts. As these tools are computationally expensive, they are typically used to detect vulnerabilities until some predefined invocation depth. These tools require more search time as the invocation depth increases. Since the use of smart contracts increases rapidly, their analysis becomes difficult using these traditional tools. Recently, a machine learning technique called Long Short Term Memory (LSTM) has been used to predict the vulnerability of a smart contract. In the present article, we present how to classify smart contracts into Suicidal, Prodigal, Greedy, or Normal categories using Average Stochastic Gradient Descent Weight-Dropped LSTM (AWD-LSTM), a variant of LSTM. We reduced the class imbalance by considering only distinct opcode combinations for normal contracts and achieved a weighted average F1 score of 90.0%. Such techniques can be utilized in real-time to analyze a large number of smart contracts and to improve their security.
Improved Adversarial Training via Learned Optimizer

Adversarial attack has recently become a tremendous threat to deep learning models. To improve the robustness of machine learning models, adversarial training, formulated as a minimax optimization problem, has been recognized as one of the most effective defense mechanisms. However, the non-convex and non-concave property poses a great challenge to the minimax training. In this paper, we empirically demonstrate that the commonly used PGD attack may not be optimal for inner maximization, and improved inner optimizer can lead to a more robust model. Then we leverage a learning-to-learn (L2L) framework to train an optimizer with recurrent neural networks, providing update directions and steps adaptively for the inner problem. By co-training optimizer's parameters and model's weights, the proposed framework consistently improves over PGD-based adversarial training and TRADES.
Updated map of the mafic dike swarms of Brazil based on airborne geophysical data

Identification of mafic dike swarms and LIPs (Large Igneous Provinces) are of vital importance in geologic history because they provide information on geodynamics, mantle geochemistry, and paleomagnetism. These data provide key information for paleogeographic reconstructions with the aid of barcode matches and precise radiometric ages. Considering such issues, the Brazilian Precambrian shield can be used as a case for refining the cartography of the relevant intraplate activity (e.g., dikes, sills, flood basalts) in space and time. This work presents an updated map of Brazilian mafic dike swarms produced from airborne geophysical maps (Series 1000 – Geological Survey of Brazil). Linear and strong anomalies found on aeromagnetic maps using First Vertical Derivative of the Magnetic Field and Amplitude of the Analytic Signal were mapped on a GIS platform. The obtained data were compared to ternary radiometric maps and geological maps in order to exclude those that do not correspond to mafic dikes. The remaining structures - those believed to represent mafic dikes - were classified based on data compiled from the literature. The updated map exhibits more than 5000 elements, including dikes and magmatic suites, in which about 75% were geologically identified and divided into 60 dike swarms and 10 igneous suites and/or units. The dikes were grouped into sixteen extensional episodes from the Archean to the Cenozoic, although some are related to extension/transtension domains within regional compressive zones akin to orogenic settings. The most frequent records refer to the Proterozoic, representing intraplate episodes, some of them consistent with LIPs. The dataset also includes a large record of the Mesozoic age, which corresponds to major LIP events related to the opening of the Atlantic Ocean and the fragmentation of Gondwana.
PONE

Open-domain generative dialogue systems have attracted considerable attention over the past few years. Currently, how to automatically evaluate them is still a big challenge. As far as we know, there are three kinds of automatic evaluations for open-domain generative dialogue systems: (1) Word-overlap-based metrics; (2) Embedding-based metrics; (3) Learning-based metrics. Due to the lack of systematic comparison, it is not clear which kind of metrics is more effective. In this article, we first measure systematically all kinds of metrics to check which kind is best. Extensive experiments demonstrate that learning-based metrics are the most effective evaluation metrics for open-domain generative dialogue systems. Moreover, we observe that nearly all learning-based metrics depend on the negative sampling mechanism, which obtains extremely imbalanced and low-quality samples to train a score model. To address this issue, we propose a novel learning-based metric that significantly improves the correlation with human judgments by using augmented PO sitive samples and valuable NE gative samples, called PONE. Extensive experiments demonstrate that PONE significantly outperforms the state-of-the-art learning-based evaluation method. Besides, we have publicly released the codes of our proposed metric and state-of-the-art baselines. 1
Rethinking the roles and skills of information professionals in the 4th Industrial Revolution

With the development of the 4th Industrial Revolution (4th IR), its emerging technologies and skills; there is a mismatch between 4th IR, and the skills needed by information professionals to survive. This paper bridges the gap based on the skills needed to survive and provide possible solutions to challenges faced by information professionals, which will in turn help to reduce the number of unemployed, semi-employed, non-employed, and provide economic empowerment among information professionals in this new revolution. Information professionals should adopt the missing middle model/techniques in organization which asserts that robots, by and large, will not be taking our jobs; instead, human Machine collaboration will reconfigure some of our work, making and make human skills more unique and important than ever.
Anonymous Message Authentication Scheme for Semitrusted Edge-Enabled IIoT

As internet of things and other technologies are widely used in industrial manufacturing, automation and intelligence have witnessed rapid developments, resulting in the proposal of the industrial internet of things (IIoT). However, the IIoT still faces various network security threats; hence, data integrity, confidentiality, and anonymity need to be ensured. The use of cloud and edge servers as semitrusted third parties often results in the leaking of privacy sensitive user data. Meanwhile, existing security schemes treat the cloud and edge as fully trusted entities, which is not always valid. Considering edge servers as semitrusted entities, we propose a novel message authentication scheme that leverages group signature technology and proxy reencryption technology to ensure data integrity, confidentiality, and anonymity. Through theoretical analysis and performance comparison, we prove the security of our scheme. In addition, we implement our scheme on a real publish/subscribe system, and the experimental results show the feasibility of our scheme.
A Survey On Image Semantic Segmentation Methods With Convolutional Neural Network

Image semantic segmentation is an important branch in the field of AI. Traditional semantic segmentation algorithms are mostly specific to the problem, and there is no universal segmentation algorithm suitable for all images. But deep learning approaches can solve this problem. Recently convolutional neural network is a widely used model in image segmentation, target recognition and scene classification, and has achieved great success. This survey introduces what image semantic segmentation is, what the semantic segmentation approaches are, and the methods of image segmentation with CNN. Next, we present several data sets that are often used in image segmentation experiments. Finally, these deep learning algorithms of image segmentation are analyzed and compared appropriately.
RETRACTED: Swimming target detection and tracking technology in video image processing

The new system is presented for target detection swimming, tracking multiple targets in a video sequence. Since the output of the system is based on target motion, to use background subtraction to move the area within each video frame of the first segment foreground and background. A new method proposed for detecting drowning in a person swimming in a data using video footage. The proposed Swimming target detection and tracking technology based on Discrete Cosine Transform Algorithm, Target Detection Algorithm, and Traditional Cam-Shift Algorithm. For background extraction and updating, use the frame-by-frame differential Discrete Cosine Transform Algorithm used to the exact motion region of the entire video section in image processing technology based analysis. Static and dynamic feature detection identification normal swimmers and drowning swimmers. Swimming-based data are collected in real time using a camera on the surface of the water, including a video image sequence with the main observation swimming and behavior analysis based system immersion and three steps to detect swimming. By combining frame subtraction and background subtraction, the advanced moving Target Detection Algorithm can detect the full area of interest. Then, with average particle filtering, analysis of Traditional Cam Shift Algorithm, it can automatically select the target for the resulting detection results and introduce a new person tracking method. Swim target detection and track results to analyze motion parameter estimation algorithms. Finally, the purpose of the set of human diagnostics and follow-up tests is to test algorithmic efficiency.
Exploring the Enumeration Accuracy of Cross-Validation Indices in Latent Class Analysis

A crucial issue when estimating mixture models is selecting the model with the correct number of latent classes underlying the data, which is commonly referred to as class enumeration. Although cross-validation methods have been suggested with mixture models to help augment the class enumeration process (Masyn, 2013), they have been seldom used. The purpose of this simulation study was to compare the performance of traditionally used single sample enumeration indices with the performance of cross-validation indices when selecting the correct latent class model with binary indicators. Various conditions were manipulated, including the number of indicators, sample size, class separation, mixing proportions, and number of latent classes. The enumeration accuracy of sixteen indices (traditional, cross-validated, and double cross-validated) were documented in the manipulated conditions. The traditional sample-size adjusted BIC index was the most accurate among the indices. The performance of the double cross-validated -2LL was also promising. Recommendations are provided.
Digital Assets and Blockchain: Hackable, Fraudulent, or Just Misunderstood?<sup>*</sup>

ABSTRACT Unhackable. Immutable. Fraud‐proof. These terms are frequently used to describe cryptocurrencies and the blockchain technology that underpins them. Together, they imply that a high degree of safety accompanies cryptocurrencies and blockchain ledgers. But is this understanding supported by the facts, or is it more based on the promise and theoretical construction of blockchain and cryptocurrencies? To better answer this question, we have compiled and analyzed existing research on initial coin offerings, security offerings, blockchain hacks and thefts, and data breaches of blockchain‐based platforms and digital wallets. In contrast to the popular press, we find that in practice, blockchain and cryptocurrencies are more prone to malfeasance, fraud, and manipulation than is commonly understood. The security and trust provided by blockchain as a technology tool are only as secure as the underlying code that establishes the blockchain, and the value derived from cryptocurrencies is only as trustworthy as the entity developing the cryptocurrency. Neither are without their vulnerabilities. Skepticism and proper due diligence should be maintained for any entity looking to utilize blockchain technology or invest in cryptocurrencies.
Sentiment Analysis of the Covid-19 Virus Infection in Indonesian Public Transportation on Twitter Data: A Case Study of Commuter Line Passengers

The appearance of the Covid-19 virus in early 2020 became a frightening pandemic for the world, including Indonesia.The infection of the Covid-19 virus was rapid because of its transmission can be through human contact.This condition causes worrying in society.Besides, these worrying also occurs in the passenger of public transportation, especially the commuter line.Passengers in large numbers and push each other will cause worry if commuter line passengers will transmit the Covid-19 virus to the commuter line.Many passengers write their opinions about the transmission of the Covid-19 pandemic on social media Twitter.This causes various opinions that can be positive, negative, or even neutral.Therefore, to see the opinions on commuter line passengers, a research was made to analyze the sentiment of the Covid-19 transmission to commuter line passengers.This research was implemented using a comparison of 2 methods, Naïve Bayes outperformed the Decision Tree with an accuracy of 73.59%.Furthermore, the result of sentiment analysis was a positive classification compared to the other 2 classes.
CNN Convolutional layer optimisation based on quantum evolutionary algorithm

In this paper, a quantum convolutional neural network (CNN) architecture is proposed to find the optimal number of convolutional layers. Since quantum bits use probability to represent binary information, the quantum CNN does not represent the actual network, but the probability of existence of each convolutional layer, thus achieving the aim of training weights and optimising the number of convolutional layers at the same time. In the simulation part, CIFAR-10 (including 50k training images and 10k test images in 10 classes) is used to train VGG-19 and 20-layer, 32-layer, 44-layer and 56-layer CNN networks, and compare the difference between the optimal and non-optimal convolutional layer networks. The simulation results show that without optimisation, the accuracy of the test data drops from approximately 90% to about 80% as the number of network layers increases to 56 layers. However, the CNN with optimisation made it possible to maintain the test accuracy at more than 90%, and the number of network parameters could be reduced by nearly half or more. This shows that the proposed method can not only improve the network performance degradation caused by too many hidden convolutional layers, but also greatly reduce the use of the network’s computing resources.