Reviewerly/exHarmony huggingface dataset:
Enriching context descriptions for enhanced LA scalability: a case study

Abstract Learning analytics (LA) is a field that examines data about learners and their context, for understanding and optimizing learning and the environments in which it occurs. Integration of multiple data sources, an important dimension of scalability, has the potential to provide rich insights within LA. Using a common standard such as the Experience API (xAPI) to describe learning activity data across multiple sources can alleviate obstacles for data integration. Despite their potential, however, research indicates that standards are seldom used for integration of multiple sources in LA. Our research aims to understand and address the challenges of using current learning activity data standards for describing learning context with regard to interoperability and data integration. In this paper, we present the results of an exploratory case study involving in-depth interviews with stakeholders having used xAPI in a real-world project. Based on the subsequent thematic analysis of interviews, and examination of xAPI, we identified challenges and limitations in describing learning context data, and developed recommendations (provided in this paper in summarized form) for enriching context descriptions and enhancing the expressibility of xAPI. By situating the research in a real-world setting, our research also contributes to bridge the gap between the academic community and practitioners in learning activity data standards and scalability, focusing on description of learning context.
Learned Image Compression Using Adaptive Block-Wise Encoding and Reconstruction Network

In this paper, a flexible and adaptive block-wise image compression architecture with restoration network is proposed to improve the rate-distortion (R-D) performance of learned image compression. In contrast to existing learned compression algorithms which perform the image based transform coding, our approach splits the input image into non-overlapped blocks and compresses each block adaptively based on their local content characteristics. To reduce the artifacts caused by the block partitioning, block fusion network is subsequently proposed to overcome the drawbacks of block-wise independent coding. Further, quantization fine-tuning training process is proposed and utilized to reduce the difference of quantization operations between the training process and testing process. Experimental results show that the proposed network architecture has the ability to obtain significant coding gain compared with existing conventional compression standards including Versatile Video Coding (VVC) and achieves comparable R-D performance with state-of-the-art learned image codec using less parameters.
Anomaly Detection in Nonstationary Videos Using Time-Recursive Differencing Network-Based Prediction

Most videos, including those captured through aerial remote sensing, are usually nonstationary in nature having time-varying feature statistics. Although sophisticated reconstruction and prediction models exist for video anomaly detection (VAD), effective handling of nonstationarity has seldom been considered explicitly. In this letter, we propose to perform prediction using a time-recursive differencing network followed by autoregressive moving average estimation for VAD. The differencing network is employed to effectively handle nonstationarity in video data during the anomaly detection. Focusing on the prediction process, the effectiveness of the proposed approach is demonstrated considering a simple optical flow-based video feature, and by generating qualitative and quantitative results on three aerial video data sets and two standard anomaly detection video data sets. Equal error rate (EER), area under curve (AUC), and ROC curve-based comparison with several existing methods including the state-of-the-art reveal the superiority of the proposed approach.
Revisiting Graph Neural Networks: Graph Filtering Perspective

In this work, we develop quantitative results to the learnability of a two-layers Graph Convolutional Network (GCN). Instead of analyzing GCN under some classes of functions, our approach provides a quantitative gap between a two-layers GCN and a two-layers MLP model. From the graph signal processing perspective, we provide useful insights to some flaws of graph neural networks for vertex classification. We empirically demonstrate a few cases when GCN and other state-of-the-art models cannot learn even when true vertex features are extremely low-dimensional. To demonstrate our theoretical findings and propose a solution to the aforementioned adversarial cases, we build a proof of concept graph neural network model with different filters named Graph Filters Neural Network (gfNN) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
The Adoption of Blockchain-based Decentralized Exchanges: A Market Microstructure Analysis of the Automated Market Maker

We show that pricing curves implemented by automated market makers allow arbitrageurs to extract profits from liquidity providers. This raises the cost of liquidity provision, and may lead to a liquidity freeze if token pairs are too volatile. A more convex pricing curve results in higher price impact, which inhibits arbitrage but also reduces investors' surplus. The convexity of the socially optimal pricing curve is higher for more volatile token pairs. Our empirical analysis reveals that gas price volatility is 60% lower for stable-coin pairs, and that deposit inflows are negatively correlated with volatility, and positively correlated with trading volume.
Multi-View 3D Human Pose Estimation with Self-Supervised Learning

Modern 3D human pose estimation builds on a deep learning network, requiring expensive amounts of training data that contain pairs of 2D and 3D pose annotations. In this paper, we propose a self-supervised 3D human pose estimation without 3D annotations. Instead, we exploit multi-view images and camera parameters to make the network learn 3D human pose based on geometric consistency. The merit of the proposed method is validated via experiments.
Mobile application to track people in covid19 monitoring and patients under covid19 supervision

Abstract It is crucial for the community including the government and health workers to collaborate to halt the spread of Covid-19. The idea of developing the mobile application surfaced from the previous findings. Previous researches have implemented and developed different features to better the application. The development of a mobile application to provide a platform that will assist people with information regarding patients that are around the perimeter of users to help notify them. With the help of the notification, users will be able to avoid the chances of them being in contact with the people (ODP), patients that are under surveillance (PDP) confirmed patients. All patients including ODP and PDP are required to have the application therefore, the Minister of Health will be able to track the geolocation of patients. Moreover, people will be able to be aware of patients that are around their perimeter. Therefore, with the help of the application, it will be able to help assist the community for them to be aware of and to be able to avoid being in contact with the infected patients.
Improving convergence speed of the neural network model using Meta heuristic algorithms for weight initialization

Neural network is widely used nowadays as it shows great result in solving the data classification and regression problems. The neuron is a basic unit in the artificial neural networks, which are strongly connected to each other by means of synaptic weights. Convergence speed of the network model depends on various factors such as weight initialization, learning rate, learning algorithm, batch size etc. This article is about to reduce the training time of weight initialization during training process of the neural network has been done with the help of cuckoo search optimization algorithm which is one of the mostly used Meta heuristic algorithms. The performance of network with various dataset using the proposed method for weight initialization is compared with other initialization methods. The effect of synaptic weights initialization using the heuristic algorithm has shown better convergence over other techniques.
Oblivious TLS via Multi-party Computation

In this paper, we describe Oblivious TLS: an MPC protocol that we prove UC secure against a majority of actively corrupted parties. The protocol securely implements TLS 1.3. Thus, any party P who runs TLS can communicate securely with a set of servers running Oblivious TLS; P does not need to modify anything, or even be aware that MPC is used.
Towards Aggregated Features: A Novel Proxy Detection Method Using NetFlow Data

Proxies can provide privacy and anonymity protection for users, but can also be exploited by attackers to hide their malicious behaviors. In order to strengthen the monitoring and management of network, proxy detection has become an urgent and challenging task. Although a great deal of efforts has been made for proxy detection, existing methods mostly rely on the packet-level features of a single flow, such as packet inter-arrival time and payload size. In addition, handling the raw traffic throughout the communication process may lead to user privacy leakage to a certain extent. Considering the use of multi-flow statistics and reducing the invasion of user privacy, in this paper, we propose a machine learning based approach for proxy detection with NetFlow data, which only contains session-level statistical information. We extract features through NetFlow data aggregation to build machine learning model and verify the performances on different combinations of features to get the optimal feature sets. Furthermore, the importance of appropriate time windows and minimum flow numbers of NetFlow data aggregation are demonstrated by comprehensive experiments. Based on the real-world datasets, our detection method can distinguish proxy and normal traffic accurately, and achieve about 96% True Positive Rate(TPR) in our experiments with random forest classifier.
On the hybrid Davies like generator for quantum dissipation

We provide a class of quantum evolution beyond Markovian semigroup. This class is governed by a hybrid Davies like generator such that dissipation is controlled by a suitable memory kernel and decoherence by standard Gorini-Kossakowski-Lindblad-Sudarshan generator. These two processes commute and both of them commute with the unitary evolution controlled by the systems Hamiltonian. The corresponding memory kernel gives rise to semi-Markov evolution of the diagonal elements of the density matrix. However, the corresponding evolution needs not be completely positive. The role of decoherence generator is to restore complete positivity. Hence, to pose the dynamical problem, one needs two processes generated by classical semi-Markov memory kernel and purely quantum decoherence generator. This scheme is illustrated for a qubit evolution.
LODENet: A Holistic Approach to Offline Handwritten Chinese and Japanese Text Line Recognition

One of the biggest obstacles in Chinese and Japanese text line recognition is how to present their enormous character sets. The most common solution is to merely choose and represent a small subset of characters using one-hot encoding. However, such an approach is costly to describe huge character sets, and ignores their semantic relationships. Recent studies have attempted to utilize different encoding methods, but they struggle to build a bijection mapping. In this work, we propose a novel encoding method, called LOgographic DEComposition encoding (LODEC), that can efficiently perform a 1-to-1 mapping for all Chinese and Japanese characters. As such, LODEC enables to encode over 21,000 Chinese and Japanese characters by 520 fundamental elements. Moreover, to handle the vast style variety of handwritten texts in the two languages, we propose a novel deep learning (DL) architecture, called LODENet, together with an end-to-end training scheme, that leverages auxiliary ground truths generated by LODEC or other radical-based encoding methods. We systematically performed experiments on both Chinese and Japanese datasets, and found that our approach surpassed the performance of state-of-the-art baselines. Furthermore, empirical evidence shows that our method can gain significantly improvement using synthesized text line images without the need for domain knowledge.
Information structures in sociology research papers: Modeling cause–effect and comparison relations in research objective and result statements<sup>1</sup>

Abstract When writing a research paper, the author has to select information to include in the paper to support various arguments. The information has to be organized and synthesized into a coherent whole through relationships and information structures. There is hardly any research on the information structure of research papers, and how information structure supports rhetorical and argument structures. Thus, this study is focused on information organization in the Abstract and Introduction sections of sociology research papers, analyzing the information structure of research objective, question, hypothesis, and result statements. The study is limited to research papers reporting research that investigated cause–effect relations between two concepts. Two semantic frames were developed to specify the types of information associated with cause–effect and comparison relations, and used as coding schemes to annotate the text for different information types. Six link patterns between the two frames were identified—showing how comparisons are used to support the claim that the cause‐effect relation is valid. This study demonstrated how semantic frames can be incorporated in discourse analysis to identify deep structures underlying the argument structure. The results carry implications for the knowledge representation of academic research in knowledge graphs, for semantic relation extraction, and teaching of academic writing.
Learning to Recognize Masked Faces by Data Synthesis

Face coverings have become the new normal for people living through the global COVID-19 pandemic crisis. While wearing a mask is a necessary public health measure, the social phenomenon raises new challenges to existing face recognition models. In this work, we evaluate deep neural network approaches for the masked face recognition task. We find that current deep networks can not generalize successfully to recognizing faces with masks. To address this issue, we investigate the use of images of faces with simulated masks to train a deep neural network model for face recognition. We train our model using a collection of two face recognition datasets: the Labeled Faces in the Wild (LFW) dataset, the Real-world Masked Face Recognition (RMFR) dataset and the Simulated Masked Face Recognition (SMFR) dataset. We find that the data sampling strategy during training plays a significant role when the number of simulated examples is much greater than that of available real instances. We show that the model trained using a combination of real and simulated data accurately classifies masked faces with an accuracy of 99%.
D3Net: Joint Demosaicking, Deblurring and Deringing

Images acquired with standard digital cameras have Bayer patterns and suffer from lens blur. A demosaicking step is implemented in every digital camera, yet blur often remains unattended due to computational cost and instability of deblur-ring algorithms. Linear methods, which are computationally less demanding, produce ringing artifacts in deblurred images. Complex non-linear deblurring methods avoid artifacts, however their complexity imply offline application after camera demosaicking, which leads to sub-optimal performance. In this work, we propose a joint demosaicking deblurring and deringing network with a light-weight architecture inspired by the alternating direction method of multipliers. The proposed network has a transparent and clear interpretation compared to other black-box data driven approaches. We experimentally validate its superiority over state-of-the-art demosaicking methods with offline deblurring.
Iterative Paraphrastic Augmentation with Discriminative Span Alignment

Abstract We introduce a novel paraphrastic augmentation strategy based on sentence-level lexically constrained paraphrasing and discriminative span alignment. Our approach allows for the large-scale expansion of existing datasets or the rapid creation of new datasets using a small, manually produced seed corpus. We demonstrate our approach with experiments on the Berkeley FrameNet Project, a large-scale language understanding effort spanning more than two decades of human labor. With four days of training data collection for a span alignment model and one day of parallel compute, we automatically generate and release to the community 495,300 unique (Frame,Trigger) pairs in diverse sentential contexts, a roughly 50-fold expansion atop FrameNet v1.7. The resulting dataset is intrinsically and extrinsically evaluated in detail, showing positive results on a downstream task.
A Study on Attack Pattern Generation and Hybrid MR-IDS for In-Vehicle Network

The CAN (Controller Area Network) bus, which transmits and receives ECU control information in vehicle, has a critical risk of external intrusion because there is no standardized security system. Recently, the need for IDS (Intrusion Detection System) to detect external intrusion of CAN bus is increasing, and high accuracy and real-time processing for intrusion detection are required. In this paper, we propose Hybrid MR (Machine learning and Ruleset) -IDS based on machine learning and ruleset to improve IDS performance. For high accuracy and detection rate, feature engineering was conducted based on the characteristics of the CAN bus, and the generated features were used in detection step. The proposed Hybrid MR-IDS can cope to various attack patterns that have not been learned in previous, as well as the learned attack patterns by using both advantages of rule set and machine learning. In addition, by collecting CAN data from an actual vehicle in driving and stop state, five attack scenarios including physical effects during all driving cycle are generated. Finally, the Hybrid MR-IDS proposed in this paper shows an average of 99% performance based on F1-score.
Genetic algorithm-optimized BP neural network model for prediction of soil heavy metal content in XRF

In the quantitative analysis of XRF (X-ray fluorescence spectroscopy) for predicting trace heavy metal elements in soil, there are large errors in data analysis due to background interference and signal interference of neighboring elements. This paper proposed a hybrid algorithm, which optimizes the weights and thresholds of the BP neural network by using Genetic Algorithm, so as to study the intrinsic relationship between the element's component information and content. The presented hybrid algorithm is used for quantitative analysis of four heavy metal elements Pb, Mn, Cr, and Cu in soil. Firstly, the X fluorescence spectra of 57 standard soil samples were obtained using a portable X fluorescence analyzer, and the discrete wavelet was used for denoising and background deduction to improve the signal-to-noise ratio. Then, the component information of four heavy metals and some other elements that might interfere with them are obtained by using Compton's normalization method, which are respectively taken as the inputs of the model, and the actual contents of Pb, Mn, Cr, and Cu are respectively taken as the outputs, so as to establish a three-layer BP neural network with one implicit layer. Finally, the training results of the model show that there is a good overlap between the predicted data and the expected one which verifies that the method is effective for the quantitative analysis of trace heavy metal elements in the soil.
A Framework for Automatic Monitoring of Norms that Regulate Time Constrained Actions

This paper addresses the problem of proposing a model of norms and a framework for automatically computing their violation or fulfilment. The proposed model can be used to express abstract norms able to regulate classes of actions that should or should not be performed in a temporal interval. We show how the model can be used to formalize obligations and prohibitions and for inhibiting them by introducing permissions and exemptions. The basic building blocks for norm specification consists of rules with suitably nested components. The activation condition and the regulated actions together with their time constrains are specified using the W3C Web Ontology Language (OWL 2). Thanks to this choice, it is possible to use OWL reasoning for computing the effects that the logical implication between actions has on the fulfilment or violation of the norms. The operational semantics of the model is specified by providing an unambiguous procedure for translating every norm and every exception into production rules.
Continuous Learning of Face Attribute Synthesis

The generative adversarial network (GAN) exhibits great superiority in the face attribute synthesis task. However, existing methods have very limited effects on the expansion of new attributes. To overcome the limitations of a single network in new attribute synthesis, a continuous learning method for face attribute synthesis is proposed in this work. First, the feature vector of the input image is extracted and attribute direction regression is performed in the feature space to obtain the axes of different attributes. The feature vector is then linearly guided along the axis so that images with target attributes can be synthesized by the decoder. Finally, to make the network capable of continuous learning, the orthogonal direction modification module is used to extend the newly-added attributes. Experimental results show that the proposed method can endow a single network with the ability to learn attributes continuously, and, as compared to those produced by the current state-of-the-art methods, the synthetic attributes have higher accuracy.
Efficient DPA side channel countermeasure with MIM capacitors-based current equalizer

Side-channel attacks are one of the security threats to the chip-level devices of the communication systems. To strengthen chip-level cryptographic devices against side-channel attacks, a current equalizer that weakens the current difference is proposed, which barely changes the original design flow and is resistant to physical teardown. This method overlays (metal-insulator-metal) MIM capacitors on top of the critical modules in chip-level components, thus weakening the differences in supply current at different inputs. Meanwhile, the proposed countermeasure is an anti-physical teardown, due to the way the MIM capacitors are connected longitudinally to the power network of the key modules. Experimental results show that this approach is highly resistant to DPA, which has the lowest security evaluation metrics value and area overhead than three existing DPA countermeasures.
TONIC

Privacy-preserving deep learning computing becomes popular these days as it helps protect, for example, both user data and deep neural network (DNN) model parameters at the same time with cryptographic techniques. In particular, significant efforts have been made to leverage secure two-party computation schemes for preventing user/model data from disclosure during DNN inference. Nevertheless, the existing works require manual intervention while converting trained models into secure computation programs, which is not scalable to modern deep networks efficiently. In this work, we propose a compiler framework, TONIC, to do the conversion automatically with scalability. Given a pre-trained DNN model, TONIC converts it into one of two secure two-party computation languages, i.e., ObliVM and ABY. Based on tailored backends built on top of a DNN compiler, TVM, our case studies show that TONIC is able to automatically convert popular DNN models, such as CryptoNets and MobileNetV2, into the corresponding programs for secure computations.