Reviewerly/exHarmony huggingface dataset:
ClaimsKG: A Knowledge Graph of Fact-Checked Claims

Various research areas at the intersection of computer and social sciences require a ground truth of contextualized claims labelled with their truth values in order to facilitate supervision, validation or reproducibility of approaches dealing, for example, with fact-checking or analysis of societal debates. So far, no reasonably large, up-to-date and queryable corpus of structured information about claims and related metadata is publicly available. In an attempt to fill this gap, we introduce ClaimsKG, a knowledge graph of fact-checked claims, which facilitates structured queries about their truth values, authors, dates, journalistic reviews and other kinds of metadata. ClaimsKG is generated through a semi-automated pipeline, which harvests data from popular fact-checking websites on a regular basis, annotates claims with related entities from DBpedia, and lifts the data to RDF using an RDF/S model that makes use of established vocabularies. In order to harmonise data originating from diverse fact-checking sites, we introduce normalised ratings as well as a simple claims coreference resolution strategy. The current knowledge graph, extensible to new information, consists of 28,383 claims published since 1996, amounting to 6,606,032 triples.
Differential Privacy in Practice: Expose your Epsilons!

Differential privacy is at a turning point. Implementations have been successfully leveraged in private industry, the public sector, and academia in a wide variety of applications, allowing scientists, engineers, and researchers the ability to learn about populations of interest without specifically learning about these individuals. Because differential privacy allows us to quantify cumulative privacy loss, these differentially private systems will, for the first time, allow us to measure and compare the total privacy loss due to these personal data-intensive activities. Appropriately leveraged, this could be a watershed moment for privacy.&#x0D; Like other technologies and techniques that allow for a range of instantiations, implementation details matter. When meaningfully implemented, differential privacy supports deep data-driven insights with minimal worst-case privacy loss. When not meaningfully implemented, differential privacy delivers privacy mostly in name. Using differential privacy to maximize learning while providing a meaningful degree of privacy requires judicious choices with respect to the privacy parameter epsilon, among other factors. However, there is little understanding of what is the optimal value of epsilon for a given system or classes of systems/purposes/data etc. or how to go about figuring it out.&#x0D; To understand current differential privacy implementations and how organizations make these key choices in practice, we conducted interviews with practitioners to learn from their experiences of implementing differential privacy. We found no clear consensus on how to choose epsilon, nor is there agreement on how to approach this and other key implementation decisions. Given the importance of these implementation details there is a need for shared learning amongst the differential privacy community. To serve these purposes, we propose the creation of the Epsilon Registry—a publicly available communal body of knowledge about differential privacy implementations that can be used by various stakeholders to drive the identification and adoption of judicious differentially private implementations.
Flash

Offchain networks emerge as a promising solution to address the scalability challenge of blockchain. Participants make payments through offchain networks instead of committing transactions on-chain. Routing is critical to the performance of offchain networks. Existing solutions use either static routing with poor performance or dynamic routing with high overhead to obtain the dynamic channel balance information. In this paper, we propose Flash, a new dynamic routing solution that leverages the unique transactions characteristics in offchain networks to strike a better tradeoff between path optimality and probing overhead. By studying the traces of real offchain networks, we find that the payment sizes are heavy-tailed, and most payments are highly recurrent. Flash thus differentiates the treatment of elephant payments from that of mice payments. It uses a modified max-flow algorithm for elephant payments to find paths with sufficient capacity, and strategically routes the payment across paths to minimize the transaction fees. Mice payments are sent directly by looking up a routing table with a few precomputed paths to reduce probing overhead. Testbed experiments and trace-driven simulations show that Flash improves the success volume of payments by up to 2.3x compared to the state-of-the-art routing algorithm.
Over-sampling algorithm for imbalanced data classification

For imbalanced datasets, the focus of classification is to identify samples of the minority class.The performance of current data mining algorithms is not good enough for processing imbalanced datasets.The synthetic minority over-sampling technique (SMOTE) is specifically designed for learning from imbalanced datasets, generating synthetic minority class examples by interpolating between minority class examples nearby.However, the SMOTE encounters the overgeneralization problem.The densitybased spatial clustering of applications with noise (DBSCAN) is not rigorous when dealing with the samples near the borderline.We optimize the DBSCAN algorithm for this problem to make clustering more reasonable.This paper integrates the optimized DBSCAN and SMOTE, and proposes a density-based synthetic minority over-sampling technique (DSMOTE).First, the optimized DBSCAN is used to divide the samples of the minority class into three groups, including core samples, borderline samples and noise samples, and then the noise samples of minority class is removed to synthesize more effective samples.In order to make full use of the information of core samples and borderline samples, different strategies are used to over-sample core samples and borderline samples.Experiments show that DSMOTE can achieve better results compared with SMOTE and Borderline-SMOTE in terms of precision, recall and F-value.
Accurate Fault Location using Deep Neural Evolution Network in Cloud Data Center Interconnection

Due to the threat of failure and the discrete distribution of data center users, the research of distributed cloud data center provides real-time cloud services with robustness, reliability and security. Faced with data center interconnection, network failures cause mass services delay and interruption, which do a great damage to cloud computing. Many researchers have studied fault location methods in data center interconnection, which are easy to trap in local optimum limited by search capability and reduce the accuracy of location, especially when confronted with large-scale alarm information. In this article, the deep neural evolution network is introduced to extract deep-hidden fault features from massive collected alarm information in cloud data center interconnection. It has the prominent capacity of global search without the constraint of gradient to realize the breakthrough of fault location accuracy. The fault location method based on deep neural evolution network (FL-DNEN) is applied which uses the alarm set and suspicious scope of fault getting from fault propagation model as input and export deterministic faults accurately. The emulations demonstrate that the proposed method dramatically improves the accuracy of fault location to 92 percent with large-scale alarm information, which improves the resilience of cloud data center interconnection dramatically.
Fully-Hierarchical Fine-Grained Prosody Modeling For Interpretable Speech Synthesis

This paper proposes a hierarchical, fine-grained and interpretable latent variable model for prosody based on the Tacotron 2 text-to-speech model. It achieves multi-resolution modeling of prosody by conditioning finer level representations on coarser level ones. Additionally, it imposes hierarchical conditioning across all latent dimensions using a conditional variational auto-encoder (VAE) with an auto-regressive structure. Evaluation of reconstruction performance illustrates that the new structure does not degrade the model while allowing better interpretability. Interpretations of prosody attributes are provided together with the comparison between word-level and phone-level prosody representations. Moreover, both qualitative and quantitative evaluations are used to demonstrate the improvement in the disentanglement of the latent dimensions.
SCANet

Continuous authentication monitors the security of a system throughout the login session on mobile devices. In this article, we present SCANet, a two-stream convolutional neural network--based continuous authentication system that leverages the accelerometer and gyroscope on smartphones to monitor users’ behavioral patterns. We are among the first to use two streams of data—frequency domain data and temporal difference domain data—from the two sensors as the inputs of the convolutional neural network (CNN). SCANet utilizes the two-stream CNN to learn and extract representative features and then performs the principal component analysis to select the top 25 features with high discriminability. With the CNN-extracted features, SCANet exploits the one-class support vector machine to train the classifier in the enrollment phase. Based on the trained CNN and classifier, SCANet identifies the current user as a legitimate user or an impostor in the continuous authentication phase. We evaluate the effectiveness of the two-stream CNN and the performance of SCANet on our dataset and BrainRun dataset, and the experimental results demonstrate that CNN achieves 90.04% accuracy, and SCANet reaches an average of 5.14% equal error rate on two datasets and takes approximately 3 s for user authentication.
A Critical look at Educational Technology from a Distance Education Perspective

This article focuses on educational technology as applied in the context of programs and institutions that offer completely distance education courses. All education in the 21st century is digital education in that the use of networking, text and image creation and editing and search and retrieval of information punctuates the life of almost every teacher and student. However, the context of distance education – where all the interactions are mediated, creates a unique and heightened context of digitalization. This paper focuses on two questions: (1) What aspects have not been completely satisfactory in the transit and transformation that education has undergone, from its more traditional, campus-based conception, towards its new configuration marked by the continuous use of digital technologies and environments? (2) In order what are the future challenges that distance education must deal with to support sustainability of this teaching model?From a theoretical and interpretative analysis, based on the review of relevant articles and documents on distance education, some critical dimensions (limitations, unkept promises and future challenges) the use of digital technologies in distance education is identified and subsequently analyzed. These dimensions evidence how the initial (sometimes excessive) enthusiasm about the insertion of digital technologies in distance education has not (yet?) been fully reflected in reality.
Anomaly Detection for Industrial Control System Based on Autoencoder Neural Network

As the Industrial Internet of Things (IIoT) develops rapidly, cloud computing and fog computing become effective measures to solve some problems, e.g., limited computing resources and increased network latency. The Industrial Control Systems (ICS) play a key factor within the development of IIoT, whose security affects the whole IIoT. ICS involves many aspects, like water supply systems and electric utilities, which are closely related to people’s lives. ICS is connected to the Internet and exposed in the cyberspace instead of isolating with the outside recent years. The risk of being attacked increases as a result. In order to protect these assets, intrusion detection systems (IDS) have drawn much attention. As one kind of intrusion detection, anomaly detection provides the ability to detect unknown attacks compared with signature-based techniques, which are another kind of IDS. In this paper, an anomaly detection method with a composite autoencoder model learning the normal pattern is proposed. Unlike the common autoencoder neural network that predicts or reconstructs data separately, our model makes prediction and reconstruction on input data at the same time, which overcomes the shortcoming of using each one alone. With the error obtained by the model, a change ratio is put forward to locate the most suspicious devices that may be under attack. In the last part, we verify the performance of our method by conducting experiments on the SWaT dataset. The results show that the proposed method exhibits improved performance with 88.5% recall and 87.0% F1-score.
A survey of machine learning techniques in adversarial image forensics

Abstract Image forensic plays a crucial role in both criminal investigations (e.g., dissemination of fake images to spread racial hate or false narratives about specific ethnicity groups or political campaigns) and civil litigation (e.g., defamation). Increasingly, machine learning approaches are also utilized in image forensics. However, there are also a number of limitations and vulnerabilities associated with machine learning-based approaches (e.g., how to detect adversarial (image) examples), and there are associated real-world consequences (e.g., inadmissible evidence, or wrongful conviction). Therefore, with a focus on image forensics, this paper surveys techniques that can be used to enhance the robustness of machine learning-based binary manipulation detectors in various adversarial scenarios.
WNUT-2020 Task 2: Identification of Informative COVID-19 English Tweets

In this paper, we provide an overview of the WNUT-2020 shared task on the identification of informative COVID-19 English Tweets.We describe how we construct a corpus of 10K Tweets and organize the development and evaluation phases for this task.In addition, we also present a brief summary of results obtained from the final system evaluation submissions of 55 teams, finding that (i) many systems obtain very high performance, up to 0.91 F 1 score, (ii) the majority of the submissions achieve substantially higher results than the baseline fastText (Joulin et al., 2017), and (iii) fine-tuning pre-trained language models on relevant language data followed by supervised training performs well in this task.
Cold-start Active Learning through Self-supervised Language Modeling

Active learning strives to reduce annotation costs by choosing the most critical examples to label.Typically, the active learning strategy is contingent on the classification model.For instance, uncertainty sampling depends on poorly calibrated model confidence scores.In the cold-start setting, active learning is impractical because of model instability and data scarcity.Fortunately, modern NLP provides an additional source of information: pretrained language models.The pre-training loss can find examples that surprise the model and should be labeled for efficient fine-tuning.Therefore, we treat the language modeling loss as a proxy for classification uncertainty.With BERT, we develop a simple strategy based on the masked language modeling loss that minimizes labeling costs for text classification.Compared to other baselines, our approach reaches higher accuracy within less sampling iterations and computation time.
Quantum Dynamics of a Few-Photon Parametric Oscillator

Modulating the frequency of a harmonic oscillator at nearly twice its natural frequency leads to amplification and self-oscillation. Above the oscillation threshold, the field settles into a coherent oscillating state with a well-defined phase of either $0$ or $\pi$. We demonstrate a quantum parametric oscillator operating at microwave frequencies and drive it into oscillating states containing only a few photons. The small number of photons present in the system and the coherent nature of the nonlinearity prevents the environment from learning the randomly chosen phase of the oscillator. This allows the system to oscillate briefly in a quantum superposition of both phases at once - effectively generating a nonclassical Schr\"{o}dinger's cat state. We characterize the dynamics and states of the system by analyzing the output field emitted by the oscillator and implementing quantum state tomography suited for nonlinear resonators. By demonstrating a quantum parametric oscillator and the requisite techniques for characterizing its quantum state, we set the groundwork for new schemes of quantum and classical information processing and extend the reach of these ubiquitous devices deep into the quantum regime.
A Framework for Fall Detection Based on OpenPose Skeleton and LSTM/GRU Models

Falling is one of the causes of accidental death of elderly people over 65 years old in Taiwan. If the fall incidents are not detected in a timely manner, it could lead to serious injury or even death of those who fell. General fall detection approaches require the users to wear sensors, which could be cumbersome for the users to put on, and misalignment of sensors could lead to erroneous readings. In this paper, we propose using computer vision and applied machine-learning algorithms to detect fall without any sensors. We applied OpenPose real-time multi-person 2D pose estimation to detect movement of a subject using two datasets of 570 × 30 frames recorded in five different rooms from eight different viewing angles. The system retrieves the locations of 25 joint points of the human body and detects human movement through detecting the joint point location changes. The system is able to effectively identify the joints of the human body as well as filtering ambient environmental noise for an improved accuracy. The use of joint points instead of images improves the training time effectively as well as eliminating the effects of traditional image-based approaches such as blurriness, light, and shadows. This paper uses single-view images to reduce equipment costs. We experimented with time series recurrent neural network, long- and short-term memory, and gated recurrent unit models to learn the changes in human joint points in continuous time. The experimental results show that the fall detection accuracy of the proposed model is 98.2%, which outperforms the baseline 88.9% with 9.3% improvement.
A Bidirectional LSTM Language Model for Code Evaluation and Repair

Programming is a vital skill in computer science and engineering-related disciplines. However, developing source code is an error-prone task. Logical errors in code are particularly hard to identify for both students and professionals, and a single error is unexpected to end-users. At present, conventional compilers have difficulty identifying many of the errors (especially logical errors) that can occur in code. To mitigate this problem, we propose a language model for evaluating source codes using a bidirectional long short-term memory (BiLSTM) neural network. We trained the BiLSTM model with a large number of source codes with tuning various hyperparameters. We then used the model to evaluate incorrect code and assessed the model’s performance in three principal areas: source code error detection, suggestions for incorrect code repair, and erroneous code classification. Experimental results showed that the proposed BiLSTM model achieved 50.88% correctness in identifying errors and providing suggestions. Moreover, the model achieved an F-score of approximately 97%, outperforming other state-of-the-art models (recurrent neural networks (RNNs) and long short-term memory (LSTM)).
Semi-supervised anomaly detection algorithms: A comparative summary and future research directions

While anomaly detection is relatively well-studied, it remains a topic of ongoing interest and challenge, as our society becomes increasingly interconnected and digitalized. In this paper, we focus on existing anomaly detection approaches, by empirically studying the performance of 29 semi-supervised anomaly detection algorithms on 95 benchmark imbalanced databases from the KEEL repository. These include well-established and commonly used classifiers (e.g., One-Class Support Vector Machine (ocSVM) and Isolation Forest) and recent proposals (e.g., BRM and XGBOD). Findings from our in-depth empirical study show that BRM is a robust classifier, in terms of achieving better classification results than the other 28 state-of-the-art techniques on diverse anomaly detection problems. We also observe that OCKRA, Isolation Forest, and ocSVM achieve good performance overall AUC, but poor classification results on databases where the number of objects is equal or greater than 1,460, all features are nominal, or the imbalance ratio is equal or greater than 39.14.
A Multidimensional Information Fusion-Based Matching Decision Method for Manufacturing Service Resource

With the development of specialization, coordination and intelligence in the manufacturing service process, the issue of how to quickly extract potential resources or capabilities for distributed manufacturing service requirements, and how to carry out resource matching for manufacturing service requirements with correlated mapping characteristics, have become the critical issues to be addressed in the cloud manufacturing environment.Through the combination of the characteristics of relevance, synergy and diversity of manufacturing service tasks on the intelligent cloud platform, a matching decision method for manufacturing service resources is proposed in this paper based on multidimensional information fusion.On the basis of integrating multidimensional information data in cloud manufacturing resource, the information entropy and rough set theory are applied to classify the importance of manufacturing service tasks, while the matching capability are analyzed by using a hybrid collaborative filtering (HCF) algorithm.Then, the information of function attribute, reliability and preference is employed to match and push manufacturing service resources or capabilities actively, so as to realize the matching decision of manufacturing service resources with precise quality, stable service and maximum efficiency.At last, a case study of resources matching decision for body & chassis manufacturing service in a new energy automobile enterprise is presented, in which the experimental results show that the proposed approach is more accuracy and effective compared with other different recommendation algorithms.
Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection

We present a human-and-model-in-the-loop process for dynamically generating datasets and training better performing and more robust hate detection models.We provide a new dataset of ∼40, 000 entries, generated and labelled by trained annotators over four rounds of dynamic data creation.It includes ∼15, 000 challenging perturbations and each hateful entry has fine-grained labels for the type and target of hate.Hateful entries make up 54% of the dataset, which is substantially higher than comparable datasets.We show that model performance is substantially improved using this approach.Models trained on later rounds of data collection perform better on test sets and are harder for annotators to trick.They also have better performance on HATECHECK, a suite of functional tests for online hate detection.We provide the code, dataset and annotation guidelines for other researchers to use.