Reviewerly/exHarmony huggingface dataset:
Knowledge exchange under asymmetric information in emerging economies: impact of signals from Indian universities

Abstract Emerging and developing countries are characterized by severe information asymmetries in knowledge markets, which when combined with other institutional weaknesses, lead to very low levels of interactions between universities and industry. Using data from a sample of Indian universities, we identify university-specific publicly available organizational characteristics which, acting as “signals,” may reduce the information asymmetry problem and catalyze knowledge exchange (KE) partnerships. We find that strength of passive signals such as university ownership structure and reputation, and active ones such as volume of patents filed, impacts a university’s KE performance positively. The impact of each signal is very KE channel specific, and the magnitude of this impact is large. We also find that signal strength negatively moderates the direct linkage between research and KE, implying that signaling is more relevant for universities where there is greater separation of its research outputs and KE performance.
Reasoning Heuristics for the Theorem-Proving Platform Rodin/Event-B

Developments in formal- and mathematical logic; and computing the past couple of decades have paved the way for the automation of deductive reasoning. However, despite theoretical and technological advances in computing, the rapid growth in the search space for complex proofs where the reasoner explores the consequences of irrelevant information, remains problematic. The challenge of a combinatorial explosion of the search space can in many cases be addressed by heuristics. Consequently, in this paper we investigate the extent to which heuristics may usefully be applied in discharging complex set-theoretic proof obligations using the hybrid reasoning environment, Rodin/Event-B. On the strength of our experiments, we develop a set of heuristics to aid the theorem-proving environment in finding proofs for set-theoretic problems which could not be obtained using the default settings. A brief exposition of related work in this area is presented towards the end of the paper.
Intelligent Gastric Histopathology Image Classification Using Hierarchical Conditional Random Field based Attention Mechanism

In this paper, an Intelligent Hierarchical Conditional Random Field based Attention Mechanism (HCRF-AM) model is proposed, which can be applied to the Gastric Histopathology Image Classification (GHIC) tasks to assist pathologists in medical diagnosis. However, there exists redundant information in a weakly supervised learning mission. Thus, designing the network that can extract effective distinguishing features has become the focus of research. The HCRF-AM model consists of attention mechanism (AM) module and image classification (IC) module. First, in the AM module, an HCRF model is built to extract attention areas. Then, a convolutional neural network (CNN) model is trained with the attention region selected. Thirdly, an algorithm called classification probability based Ensemble Learning (EL) is used to obtain the image-level result from patch-level output of the CNN. In the experiment, a classification specificity of 96.67% is achieved on a gastric histopathological dataset with 700 images.
Bio-SODA: Enabling Natural Language Question Answering over Knowledge Graphs without Training Data

The problem of natural language processing over structured data has become a growing research field, both within the relational database and the Semantic Web community, with significant efforts involved in question answering over knowledge graphs (KGQA). However, many of these approaches are either specifically targeted at open-domain question answering using DBpedia, or require large training datasets to translate a natural language question to SPARQL in order to query the knowledge graph. Hence, these approaches often cannot be applied directly to complex scientific datasets where no prior training data is available.
A Set of Single YOLO Modalities to Detect Occluded Entities via Viewpoint Conversion

For autonomous vehicles, it is critical to be aware of the driving environment to avoid collisions and drive safely. The recent evolution of convolutional neural networks has contributed significantly to accelerating the development of object detection techniques that enable autonomous vehicles to handle rapid changes in various driving environments. However, collisions in an autonomous driving environment can still occur due to undetected obstacles and various perception problems, particularly occlusion. Thus, we propose a robust object detection algorithm for environments in which objects are truncated or occluded by employing RGB image and light detection and ranging (LiDAR) bird’s eye view (BEV) representations. This structure combines independent detection results obtained in parallel through “you only look once” networks using an RGB image and a height map converted from the BEV representations of LiDAR’s point cloud data (PCD). The region proposal of an object is determined via non-maximum suppression, which suppresses the bounding boxes of adjacent regions. A performance evaluation of the proposed scheme was performed using the KITTI vision benchmark suite dataset. The results demonstrate the detection accuracy in the case of integration of PCD BEV representations is superior to when only an RGB camera is used. In addition, robustness is improved by significantly enhancing detection accuracy even when the target objects are partially occluded when viewed from the front, which demonstrates that the proposed algorithm outperforms the conventional RGB-based model.
PolychoricRM: A Computationally Efficient R Function for Estimating Polychoric Correlations and their Asymptotic Covariance Matrix

Many applications of structural equation modeling involve ordinal (e.g., Likert) variables. A popular way of dealing with ordinal variables is to estimate the model with polychoric correlations rather than Pearson correlations. Such an estimation also requires the asymptotic covariance matrix of polychoric correlations. It is computationally intensive to estimate polychoric correlations and their asymptotic covariance matrices. We describe a computationally efficient R function PolychoricRM to estimate polychoric correlations and their asymptotic covariance matrix. The function invokes the computing power of modern Fortran and exploits multiple-core (multiple-thread) CPUs on nearly all current computers.
A Novel Normalized Variance-Based Differential Power Analysis Against Masking Countermeasures

In this paper, we propose two normalization techniques to reduce the ghost peaks occurring in Differential Power Analysis (DPA). Ghost peaks can be defined as the DPA output generated by the wrong key guesses, having higher amplitudes than the DPA output generated by the correct key guess. We further propose variance-based Differential Power Analysis (vDPA) to attack masked crypto devices. The proposed normalization techniques and vDPA constitute four contributions. First, based on the side-channel signal modeling with the linear coefficient representing the strength of the linear component in a side-channel signal, we formulate the condition function of linear coefficients for the appearance of ghost peaks in DPA. Second, we propose pre-normalization in DPA and mathematically analyze how it can reduce ghost peaks by modulating the strength of the linear components in side-channel signals. Third, we propose post-normalization and mathematically analyze how it can reduce ghost peaks by de-correlating the strength of the linear components in side-channel signals with the condition function for the appearance of ghost peaks. Fourth, we propose vDPA to apply simultaneously with either one of the proposed normalization techniques to effectively attack masked crypto devices. Based on the experiments, we show that the proposed basic vDPA (without normalization), pre-normalized vDPA and post-normalized vDPA are all able to reveal the secret key from ASCAD data set. The pre- and post-normalized vDPAs require up to 18× and 14× fewer traces than the basic vDPA respectively. While attacking ASCAD data set, the proposed pre- and post-normalized vDPAs are both 13, 095× faster than the reported 2nd order CPA, and reveal the key-bytes successfully with only half of side-channel traces required by the reported Zero-offset DPA.
Multi-Modal Context Propagation for Person Re-Identification With Wireless Positioning

Existing person re-identification methods mainly rely on thevisualappearance captured by camerasfor identity matching.However, due to the sensitivity of visual data to occlusion, blur, clothing change, etc.,existing methodsstruggleto distinguish pedestriansin challenging scenarios. Inspired by the fact that most pedestrians carry around smart wireless devices, e.g. mobile phones that can be sensed by WiFi or cellular networks as wireless positioning signals, we propose to exploit the free yet informative wireless signalsto assist person re-identification. It is well recognized thatwireless signals are robust to visual noisesmentioned above, which perform as a good complement to the visual data. To make full use of these multi-modal clues for person re-identification, we propose a multi-modal context propagation framework MCPF that contains a recurrent context propagation module RCPM and an unsupervised multi-modal cross-domain method UMM-ReID. RCPM enables context information to be continuously propagatedand fusedbetween visual data and wireless data. UMM-ReID utilizes wireless signals to constrain the estimation of pseudo labels. To evaluate our approach, we contribute a new wireless positioning person re-identification (WP-ReID) dataset. Extensive experimentsdemonstrate the effectiveness of the proposed method. Benefitingfrom the collaboration of RCPM and UMM-ReID, the proposed framework MCPF achieves a significant performance improvement over existing methods.
How Java Programmers Test Exceptional Behavior

Exceptions often signal faulty or undesired behavior; hence, high-quality test suites should also target exceptional behavior. This paper is a large-scale study of exceptional tests-which exercise exceptional behavior-in 1 157 open-source Java projects hosted on GitHub. We analyzed JUnit exceptional tests to understand what kinds of exceptions are more frequently tested, what coding patterns are used, and how features of a project, such as its size and number of contributors, correlate to the characteristics of its exceptional tests. We found that exceptional tests are only 13% of all tests, but tend to be larger than other tests on average; unchecked exceptions are tested twice as frequently as checked ones; 42% of all exceptional tests use try/catch blocks and usually are larger than those using other idioms; and bigger projects with more contributors tend to have more exceptional tests written using different styles. The paper also zeroes in on several detailed examples involving some of the largest analyzed projects, which refine the empirical results with qualitative evidence. The study's findings, and the capabilities of the tool we developed to analyze exceptional tests, suggest several implications for the practice of software development and for follow-up empirical studies.
Interpretable Anomaly Detection in Event Sequences via Sequence Matching and Visual Comparison

Anomaly detection is a common analytical task that aims to identify rare cases that differ from the typical cases that make up the majority of a dataset. When analyzing event sequence data, the task of anomaly detection can be complex because the sequential and temporal nature of such data results in diverse definitions and flexible forms of anomalies. This, in turn, increases the difficulty in interpreting detected anomalies. In this article, we propose a visual analytic approach for detecting anomalous sequences in an event sequence dataset via an unsupervised anomaly detection algorithm based on Variational AutoEncoders. We further compare the anomalous sequences with their reconstructions and with the normal sequences through a sequence matching algorithm to identify event anomalies. A visual analytics system is developed to support interactive exploration and interpretations of anomalies through novel visualization designs that facilitate the comparison between anomalous sequences and normal sequences. Finally, we quantitatively evaluate the performance of our anomaly detection algorithm, demonstrate the effectiveness of our system through case studies, and report feedback collected from study participants.
Dataset and Benchmarking of Real-Time Embedded Object Detection for RoboCup SSL

When producing a model to object detection in a specific context, the first obstacle is to have a dataset labeling the desired classes. In RoboCup, some leagues already have more than one dataset to train and evaluate a model. However, in the Small Size League (SSL), there is not such dataset available yet. This paper presents an open-source dataset to be used as a benchmark for real-time object detection in SSL. This work also presented a pipeline to train, deploy, and evaluate Convolutional Neural Networks (CNNs) models in a low-power embedded system. This pipeline is used to evaluate the proposed dataset with state-of-art optimized models. In this dataset, the MobileNet SSD v1 achieves $$44.88\%$$ AP ( $$68.81\%$$ AP $$_{50}$$ ) at 94 Frames Per Second (FPS), while running on an SSL robot.
MRPM: Multistep Robust Prediction Machine for Degradation Time Series Projection

Currently, since electric systems are developed with extremely fast speeds and operated under variable environments, existing residual useful life (RUL) estimation methods fail to meet the requirements of both time complexity and case flexibility. Although online RUL estimation has shown great potential for performing accurate predictions, these methods have many limitations regarding probability-based parameter assignment and local similarity measurement with least mean squares (LMS). To overcome these problems, we proposed a novel multistep robust prediction machine (MRPM). The MRPM combines a recurrent neural network (RNN) and an extreme learning machine (ELM) to link historical information and estimated values under the RNN structure with a simple prediction unit used by the ELM. In addition, an error codebook (ECB) is introduced with a correntropy-based similarity measurement to avoid the influence of noise and outlier interruption from measured signals. Finally, a dynamic adjustment process is developed to gain flexibility under variable device operation conditions. It is demonstrated that the MRPM is not influenced by Gaussian noise and other noise with even probability distributions. Even under arbitrary noise, the MRPM can constrain its influence to an acceptable range in most cases. Experiments show that the MRPM has better performance than state-of-art methods and can provide accurate RUL estimates of health conditions for IGBT modules.
FASTEN: Fair and Secure Distributed Voting Using Smart Contracts

Electing democratic representatives via voting has been a common mechanism since the 17 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> century. However, these mechanisms raise concerns about fairness, privacy, vote concealment, fair calculations of tally, and proxies voting on their behalf for the voters. Ballot voting, and in recent times, electronic voting via electronic voting machines (EVMs) improves fairness by relying on centralized trust. Homomorphic encryption-based voting protocols also assure fairness but cannot scale to large scale elections such as presidential elections. In this paper, we leverage the blockchain technology of distributing trust to propose a smart contract-based protocol, namely, FASTEN. There are many existing protocols for voting using smart contracts. We observe that these either are not scalable or leak the vote tally during the voting stage, i.e., do not provide vote concealment. In contrast, we show that FASTEN preserves voter's privacy ensures vote concealment, immutability, and avoids double voting. We prove that the probability of privacy breaches is negligibly small. Further, our cost analysis of executing FASTEN over Ethereum is comparable to most of the existing cost of elections.
Trust Hardware Based Secured Privacy Preserving Computation System for Three-Dimensional Data

Three-dimensional (3D) data are easily collected in an unconscious way and are sensitive to lead biological characteristics exposure. Privacy and ownership have become important disputed issues for the 3D data application field. In this paper, we design a privacy-preserving computation system (SPPCS) for sensitive data protection, based on distributed storage, trusted execution environment (TEE) and blockchain technology. The SPPCS separates a storage and analysis calculation from consensus to build a hierarchical computation architecture. Based on a similarity computation of graph structures, the SPPCS finds data requirement matching lists to avoid invalid transactions. With TEE technology, the SPPCS implements a dual hybrid isolation model to restrict access to raw data and obscure the connections among transaction parties. To validate confidential performance, we implement a prototype of SPPCS with Ethereum and Intel Software Guard Extensions (SGX). The evaluation results derived from test datasets show that (1) the enhanced security and increased time consumption (490 ms in this paper) of multiple SGX nodes need to be balanced; (2) for a single SGX node to enhance data security and preserve privacy, an increased time consumption of about 260 ms is acceptable; (3) the transaction relationship cannot be inferred from records on-chain. The proposed SPPCS implements data privacy and security protection with high performance.
Cooperative Multi-UAV Conflict Avoidance Planning in a Complex Urban Environment

Trajectory planning is of great value and yet challenging for multirotor unmanned aerial vehicle (UAV) applications in a complex urban environment, mainly due to the complexities of handling cluttered obstacles. The problem further complicates itself in the context of autonomous multi-UAV trajectory planning considering conflict avoidance for future city applications. To tackle this problem, this paper introduces the multi-UAV cooperative trajectory planning (MCTP) problem, and proposes a bilevel model for the problem. The upper level is modeled as an extended multiple traveling salesman problem, aiming at generating trajectories based on heuristic framework for multi-UAV task allocation and scheduling and meanwhile considering UAV kinodynamic properties. The lower level is modeled as a holding time assignment problem to avoid possible spatiotemporal trajectory conflicts, where conflict time difference is analyzed based on the proposed state-time graph method. Numerical studies are conducted in both a 1 km2 virtual city and 12 km2 real city with a set of tasks and obstacles settings. The results show that the proposed model is capable of planning trajectories for multi-UAV from the system-level perspective based on the proposed method.
Improved Short-video User Impact Assessment Method Based on PageRank Algorithm

The short-video platform is a social network where users’ content accelerates the speed of information dissemination. Hence, it is necessary to identify important users to effectively obtain information. Four algorithms (Followers Rank, Average Forwarding, K Coverage, and Expert Survey and Evaluation) have been proposed to calculate users’ influence and determine their importance. These methods simply take the number of a user’s fans or posts as the standard of influence evaluation, ignoring factors such as the paid posters, which makes such evaluations inaccurate. To solve these problems, we propose the short-video user influence rank (SVUIR) algorithm, which combines direct and indirect influence to comprehensively measure the influence of short-video users, using reference factors such as the number of fans, likes, number of users’ works, users’ work quality, focus on behavior, comments, and forwarding behavior. An experiment verifies the algorithm on Douyin (i.e., TikTok), which is a typical short-video platform, and confirms that SVUIR is more comprehensive and objective than the above four algorithms.
On the Benefits and Security Risks of a User-Centric Data Sharing Platform for Healthcare Provision

With data breaches on the rise especially after a Covid pandemic, a huge challenge is to design secure platforms for sensitive data sharing and to support vital decisions for both healthcare provision and enhanced personalised patient care. Recently proposed is the design of a patient-centric tool chain to integrate cross-border medical records. The aim is to demonstrate how emerging technologies for authentication, authorisation, and big data storage can converge in a healthcare platform to enable citizens (and researchers) to securely retrieve vital patient health information whilst aligned with data protection regulations and standards. We develop an initial risk model with four common threat scenarios, discussing risk factors such as threat, vulnerability, impact, and likelihood. We detail how the healthcare platform design can mitigate the underlying vulnerabilities with countermeasures that do not compromise the data sharing process transparency and trust for users.
Comparing of Multi-class Text Classification Methods for Automatic Ratings of Consumer Reviews

Consumer reviews show inconsistent ratings when compared to their contents as a result of sarcastic feedback. Consequently, they cannot provide valuable feedback to improve products and services of the firms. One possible solution is to utilize consumer review contents to identify the true ratings. In this work, different multi-class classification methods were applied to assign automatic ratings for consumer reviews based on a 5-star rating scale, where the original review ratings were inconsistent with the content. Two term weighting schemes (i.e. tf-idf and tf-igm) and five supervised machine learning algorithms (i.e. k-NN, MNB, RF, XGBoost and SVM) were compared. The dataset was downloaded from the Amazon website, and language experts helped to correct the real rating for each consumer review. After verifying the effectiveness of the proposed methods, the multi-class classifier model developed by SVM along with tf-igm returned the best results for automatic ratings of consumer reviews, with average improved scores of accuracies and F1 over the other methods at 11.7% and 10.5%, respectively.
Rigid Body Movement Prediction Using Dual Quaternion Recurrent Neural Networks

This paper presents a novel approach for the data based prediction of rigid body movements. To this end, we combine data based learning with a physically motivated neural network architecture using the theory of dual quaternions. Particularly, we develop a novel neural network architecture based on dual quaternion algebra which is particular suitable for representing rigid body movements. To account for multi-step predictions and the inherent dynamics of rigid bodies, we particularly focus on recurrent neural networks. As such we propose both dual quaternion recurrent neural networks as well as dual quaternion long short term memories. We apply the approach to a simplified simulation environment developed using the discrete element method which allows for a very detailed simulation of the movements. The obtained results underline the applicability and potential of the approach in terms of improved prediction performance.