Reviewerly/exHarmony huggingface dataset:
Use of vision-based augmented reality to improve student learning of the spine and spinal deformities. An exploratory study

Knowledge of anatomy and pathology of the spine together with spinal deformities is integral to several healthcare disciplines. This knowledge is crucial for graduates for assessment and management of patients with spinal problems. Physiotherapy students generally find it difficult to conceptualise the integrity of the structure and function of the spine that affects their acquisition of related physiotherapy skills.Our first objective was to introduce and evaluate the use of a Vision-Based Augmented Reality (VBAR) mobile application to teach students the anatomy and accessory movements of the spine. A further objective was to explore student experiences of and engagement with VBAR by conducting a post-lecture survey comparing VBAR to traditional teaching.This post-intervention crossover design study included two groups: final year physiotherapy students (n = 74) and mean age of 23 (±1.8). The computing department at Teesside University developed the VBAR mobile application. Moreover, a survey adapted from a previously published article was disseminated to students to evaluate their level of understanding following the use of the VBAR application.The results demonstrated that the median questionnaire scores in students' perceived level of understanding for the VBAR group were significantly higher than for the traditional teaching group (p < 0.05).The results of this post-intervention survey suggest that the integration of VBAR learning activities results in gains relating to students' understanding of spinal anatomy, function, pathology and deformities. These findings suggest that VBAR could be an additional teaching tool to support student learning.Greater understanding is expected to increase the quality of clinical practice.
WebFed: Cross-platform Federated Learning Framework Based on Web Browser with Local Differential Privacy

For data isolated islands and privacy issues, federated learning has been extensively invoking much interest since it allows clients to collaborate on training a global model using their local data without sharing any with a third party. However, the existing federated learning frameworks always need sophisticated condition configurations (e.g., sophisticated driver configuration of standalone graphics card like NVIDIA, compile environment) that bring much inconvenience for large-scale development and deployment. To facilitate the deployment of federated learning and the implementation of related applications, we innovatively propose WebFed, a novel browser-based federated learning framework that takes advantage of the browser’s features (e.g., Cross-platform, JavaScript Programming Features) and enhances the privacy protection by applying local differential privacy. Finally, We conduct experiments on heterogeneous devices to evaluate the performance of the proposed WebFed framework.
The Supply Chain Financial Supervision Mechanism of the Internet of Things Based on the Integration of RFID and Wireless Sensor Network

Based on the fusion of wireless sensor modules and UHF RFID readers, this paper develops a UHF RFID reading and writing system based on wireless sensing technology and designs a communication interface circuit that integrates wireless sensing and RFID technology. The financial supervision mechanism of the Internet of Things compatible with these two technologies improves the communication distance limitation of the RFID system and improves the flexibility of the RFID reading and writing system. Secondly, in view of the security issues of WSN small and medium data distribution, this paper proposes a lightweight small data supply chain financial supervision program. The solution adopts two-way authentication technology based on the improvement of the Internet of Things supply chain, the negotiation of the Internet of Things supply chain, and a lightweight encryption algorithm, which realizes the secure communication between ordinary nodes and gateway nodes, and can more effectively solve the problem of small and medium data distribution in WSN to resist the deficiencies of the financial supervision mechanism; compared with existing solutions of the same magnitude, it has higher security and efficiency. Experimental simulation shows that an important problem in the converged network is the imbalance of node energy consumption. In order to reduce the negative impact of the imbalance of energy consumption on the network, this article compares the two for direct transmission protocol and minimum energy multihop routing protocol. From the perspective of supply chain process performance, based on the proposed technical impact framework, it analyzes how companies actually carry out ID application work, making the research from general to individual, from theory to practice. The network energy consumption calculation method under this protocol improves an improved network architecture model that increases the communication scale. The test results show that the technical impact analysis framework established in this article based on the theory of the Internet of Things and the advantages of RFID technology can analyze the automation benefits, information benefits, and transformative benefits brought by automatic identification technology to the supply chain and introduce complementary resources and potential factors. It provides a theoretical basis and operation method for measuring the impact of RFID on the performance of the financial supervision process based on the Internet of Things supply chain in practical applications.
An Abductive Question-Answer System for the Minimal Logic of Formal Inconsistency $$\mathsf {mbC}$$

Abstract The aim in this paper is to define an Abductive Question-Answer System for the minimal logic of formal inconsistency $$\mathsf {mbC}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>mbC</mml:mi></mml:math> . As a proof-theoretical basis we employ the Socratic proofs method. The system produces abductive hypotheses; these are answers to abductive questions concerning derivability of formulas from sets of formulas. We integrated the generation of and the evaluation of hypotheses via constraints of consistency and significance being imposed on the system rules.
Automated Support for Searching and Selecting Evidence in Software Engineering: A Cross-domain Systematic Mapping

Context: Searching and selecting relevant evidence is crucial to answer research questions from secondary studies in Software Engineering (SE). The activities of search and selection of studies are labour-intensive, time-consuming and demand automation support. Objective: Our goal is to identify and summarize the state-of-the-art on automation support for searching and selecting evidence for secondary studies in SE. Method: We performed a systematic mapping on existing automating support to search and select evidence for secondary studies in SE, expanding our investigation in a cross-domain study addressing advancements from the medical field. Results: Our results show that the SE field has a variety of tools and Text Classification (TC) approaches to automate the search and selection activities. However, medicine has more well-established tools with a larger adoption than SE. Cross-validation and experiment are the most adopted methods to assess TC approaches. Furthermore, recall and precision are the most adopted assessment metrics. Conclusion: Automated approaches for searching and selecting studies in SE have not been applied in practice by SE researchers. Integrated and easy-to-use automated approaches addressing consolidated TC techniques can bring relevant advantages on workload and time saving for SE researchers who conduct secondary studies.
Equivalent Laplacian and adjacency quantum walks on irregular graphs

The continuous-time quantum walk is a particle evolving by Schr\"odinger's equation in discrete space. Encoding the space as a graph of vertices and edges, the Hamiltonian is proportional to the discrete Laplacian. In some physical systems, however, the Hamiltonian is proportional to the adjacency matrix instead. It is well known that these quantum walks are equivalent when the graph is regular, i.e., when each vertex has the same number of neighbors. If the graph is irregular, however, the quantum walks evolve differently. In this paper, we show that for some irregular graphs, if the particle is initially localized at a certain vertex, the probability distributions of the two quantum walks are identical, even though the amplitudes differ. We analytically prove this for a graph with five vertices and a graph with six vertices. By simulating the walks on all 1 018 689 568 simple, connected, irregular graphs with 11 vertices or less, we found 64 graphs with this notion of equivalence. We also give eight infinite families of graphs supporting these equivalent walks.
Environmental load from use of blockchain technology and cryptocurrency mining in Russia

With the increasing use of information technologies (IT) their opportunities to ensure environmental sustainability and the risks of their widespread adoption are growing. And if the possibilities have been studied well enough, then the risks have been paid attention to relatively recently. However, awareness of these risks is becoming increasingly important with the spread of technologies. Currently, there are already hundreds of cryptocurrencies, and the technological basis for many of these currencies is the blockchain—a digital ledger of transactions. This article has assessed the environmental burden of mining and supporting transactions in the cryptocurrency market in Russia using CO2-equivalent. For this, for the first time, the amount of electricity consumed to support cryptocurrency transactions in Russia was calculated, data on the largest cryptocurrency mining centres were collected and systematized, and the main factors for the placement of both large and small private farms were determined. Based on the collected data a map of the spread of mining centres in Russia was created. Our analysis showed that on average, 2.977 million tons of CO2 equivalent are emitted in Bitcoin production in Russia, and the total emissions from cryptocurrency mining in Russia are 4.466 million tons of CO2 equivalent. Based on our data on environmental damage, we believe that when deciding on the use of blockchain technology, not only its capabilities should be taken into account, but also an assessment of the ratio of potential benefits and impact on the environment. A systematic understanding of interrelated direct and indirect impacts is needed to make decisions on the use of blockchain, since the technology shows itself as potentially one of the most energy and resource intensive.
It takes a Flywheel to Fly: Kickstarting and Growing the A/B testing Momentum at Scale

Companies run A/B tests to accelerate innovation and make informed data-driven decisions. At Microsoft alone, over twenty thousand A/B tests are ran each year helping decide which features maximize user value. Not all teams and companies succeed in establishing and growing their A/B testing programs. In this paper, we explore multiple-case studies at Microsoft, Outreach, Booking.com, and empirical data collected, and share our learnings for iteratively adopting and growing A/B testing. The main contribution of this paper is the A/B Testing Flywheel. This conceptual model illustrates iteratively navigating the value-investment cycle with the goal to scale A/B testing. In every turn of the flywheel, teams need to invest in order to increase the A/B testing momentum. We describe the investments in software development processes that have been advantageous in getting the flywheel to turn. We also share example metrics that track the progress towards sustainable A/B testing momentum.
Instance-Aware Deep Graph Learning for Multi-Label Classification

Graph convolutional neural network (GCN) has effectively boosted the multi-label image recognition task by modeling correlation among labels. In previous methods, label correlation is computed based on statistical information through label diffusion, and therefore the same for all samples. This, however, makes graph inference on labels insufficient to handle huge variations among numerous image instances. In this paper, we propose an instance-aware graph convolutional neural network (IA_GCN) framework for the multi-label classification. As a whole, two fused branches of sub-networks are involved in the framework: a global branch modeling the whole image and a local branch exploring dependencies among regions of interests (ROIs). For both the branches, an image-dependent label correlation matrix (ID_LCM), fusing both the statistical label correlation matrix (LCM) and an individual one of each image instance, is constructed to inject adaptive information of label-awareness into the learned features of the model through graph convolution. Specifically, the individual LCM of each image is obtained by mining the label dependencies based on the predicted label scores of those detected ROIs. In this process, considering the contribution differences of ROIs to multi-label classification, variational inference is introduced to learn adaptive scaling factors for those ROIs by considering their complex distribution. Finally, extensive experiments on MS-COCO and VOC datasets show that our proposed approach outperforms existing state-of-the-art methods.
Towards non-data-hungry and fully-automated diagnosis of breast cancer from mammographic images

Analysing local texture and generating features are two key issues for automatic cancer detection in mammographic images. Recent researches have shown that deep neural networks provide a promising alternative to hand-driven features which suffer from curse of dimensionality and low accuracy rates. However, large and balanced training data are foremost requirements for deep learning-based models and these data are not always available publicly. In this work, we propose a fully-automated method for breast cancer diagnosis that performs training using small sets of data. Feature extraction from mammographic images is performed using a genetic-programming-based descriptor that exploits statistics on a local binary pattern-like local distribution defined in each pixel. The effectiveness of the suggested method is demonstrated on two challenging datasets, (1) the digital database for screening mammography and (2) the mammographic image analysis society digital mammogram database, for content-based image retrieval as well as for abnormality/malignancy classification. The experimental results show that the proposed method outperforms or achieves comparable results with deep learning-based methods even those with transfer learning and/or data-augmentation.
Reinforcement learning using expectation maximization based guided policy search for stochastic dynamics

Guided policy search algorithms have been proven to work with incredible accuracy not only for controlling complicated dynamical systems, but also in learning optimal policies from exploration of various unseen instances. This paper deals with a trajectory optimization problem for an unknown dynamical system subject to measurement noise using expectation maximization and extends it to learning (optimal) policies which have less stochasticity in trajectories because of the higher exploitation efficiency. Theoretical and empirical evidence of learned optimal policies for the new approach is depicted in comparison to some well known baselines which are evaluated on an autonomous system with widely used performance metrics.
First Full-Fledged Side Channel Attack on HMAC-SHA-2

Side-channel attacks pose a threat to cryptographic algorithms. Hash functions, in particular those from the SHA-2 family, can also be an interesting target if some of their inputs are secret. HMAC is an important use case of a hash function, in which the input is partially secret and thus unknown to the attacker. Despite a few publications that discuss applications of power analysis techniques to attack HMAC-SHA-2, no generic method that shows a full attack on its hardware implementation has been proposed so far. In this article, we present a novel practical template attack on HMAC-SHA-2 intended primarily against its implementations in hardware. To the best of our knowledge, it is the first practical attack on a true hardware implementation. We detail all the stages of the attack and validate it experimentally. Our experiments are based on an open-source hardware SHA-256 implementation that was implemented on two targets: (1) a pre-silicon side-channel leakage simulator and (2) an FPGA. In both cases, we show a full attack implementation up to the discovery of the key derivatives that allow for forging HMAC signatures. The setup used to attack the FPGA implementation cost less than $3K. The entire attack (the trace acquisition and the analysis) on the FPGA took about two hours including the profiling stage, and about half an hour excluding the profiling stage.
JacSim*: An Effective and Efficient Solution to the Pairwise Normalization Problem in SimRank

Despite the fact that SimRank has been successfully applied to various applications as a link-based similarity measure, it suffers from a counter-intuitive property called a pairwise normalization problem; JacSim is a powerful variant of SimRank that effectively solves this problem.In this paper, we first point out three existing drawbacks of JacSim and then propose JacSim* to effectively solve them; JacSim* exploits those paths neglected by JacSim in similarity computation, its matrix form provides the exact similarity scores while not being sensitive to the number of node-pairs with common neighbors, and it has simpler, easier to understand, and easier to implement formulas in both iterative and matrix forms than those of JacSim.We conduct extensive experiments with eight real-world datasets to evaluate both the accuracy and performance of JacSim* in comparison with those of JacSim.Our experimental results demonstrate that JacSim* shows better accuracy than JacSim and the JacSim* matrix form is dramatically faster than its iterative form and also than the two forms of JacSim with all datasets.
RobustProphet: Time Series Anomaly Detection Method

Many real-world time series data exhibit outliers and noise like abnormal machine operation, emergency monitoring etc. Anomaly detection is the most important way to deal with it. During all the solutions, Prophet is an effective model for large-scale time series with complicated patterns in anomaly detection and forecasting scenarios. However, Prophet model is easy to fall into overfitting at special time points, which leads to large errors in some behaviors. In this paper we propose a robust complex univariate time series anomaly detection framework RobustProphet based on forecasting. Specifically, in the step of forecasting, linear regression function is adopted to fuse Prophet and XGBoost model which use L2 regularization to resolve the overfitting problem and optimize the prediction results. Once the final forecasting results are generated, we offer a nonparametric dynamic thresholding approach to evaluate residuals to detect anomalies in original time series. We made a comparative study between our proposed model and other classical anomaly detection methods such as Local Outlier Factor (LOF), Isolation Forest on datasets from Yahoo benchmark datasets. The experimental results show that our proposed anomaly detection model has better precision and higher AUC.
A Deep Dive into Blockchain Consensus Protocols

Blockchain is the backbone of digital cryptocurrency systems, and it eliminates the need for a central authority in a decentralized network. Blockchain is a system that allows the sharing of information securely and transparently in a peer-to-peer connected decentralized network consisting of untrustable users. Blockchain technology ensures security, equality, and fairness of the system by following a well-defined set of rules known as a consensus protocol. These consensus protocols are at the core of blockchain technology and secure the network from various attacks and frauds. It is nearly impossible to breach a system following these protocols. There are various consensus protocols, each with its advantages and disadvantages. In this paper, we have discussed various consensus protocols in terms of performance, fairness, resource requirements, and security breaches. We have discussed the working of typical blockchain technology by describing major concepts in the implementation of Bitcoin technology. We also discussed the most common security breaches in decentralized systems.
Detecting Anomaly in IoT Devices using Multi-Threaded Autonomous Anomaly Detection

In this paper, we proposed a multi-threaded anomaly detection algorithm that works autonomously and without any prior assumption in streaming data. The proposed algorithm was upgraded from the autonomous anomaly detection (AAD) algorithm. The difference is we change the AAD algorithm from working offline to online by using IoT devices. Furthermore, to make it fast, we change the algorithm to process asynchronously. Before this, the AAD algorithm works in offline and worked in synchronous processing. Although AAD was altered to receive online data from IoT devices, the algorithm performance is still slow. Hence, this paper aims to prove that multi-threaded or asynchronous processing is much more efficient and faster in handling dynamic streaming data from IoT devices than single-threaded AAD.
EAR: Efficient action recognition with local-global temporal aggregation

Temporal modeling in videos is crucial for action recognition. Traditionally, it involves feature aggregation for both local motion and global semantic. In this paper, we propose an Efficient Action Recognition network (EAR), which includes a Persistence of Appearance (PA) module and a Various-timescale Aggregation (VA) module for local and global temporal aggregations respectively. For local motion aggregation, instead of using the previous time-consuming optical flow, our PA calculates pixel-wise differences in feature space as the motion representation, which is much more efficient (8196 fps vs. 8 fps in optical flow). Besides, to capture global semantic hints, we propose VA module which adaptively emphasizes expressive features and suppresses less informative ones across various timescales. Empowered by the local-global temporal aggregation, our EAR achieves competitive results on six challenging action recognition benchmarks at low FLOPs.
Facilitating 3D Object Tracking in Point Clouds with Image Semantics and Geometry

Recent works have shown remarkable success in 3D object tracking in point clouds. However, these methods may fail when tracking distant objects or objects interfered by similar geometries in point clouds. We aim to use high-resolution images with rich textures to help point cloud based tracking to deal with the above-mentioned failures. In this paper, we propose an end-to-end framework, which effectively uses both image semantic features and geometric features to facilitate tracking in point clouds. Specifically, we design a fusion module to establish the correspondence between image and point cloud features in a point-to-point manner and use attention-weighted image features to enhance point features. In addition, we utilize geometric transformation to convert 2D image geometric features inferred by deep layer aggregation network (DLA) to 3D as extra tracking clues for 3D voting. Quantitative and qualitative comparisons on the KITTI tracking dataset demonstrate the advantages of our model.