Reviewerly/exHarmony huggingface dataset:
Constraints Penalized Q-learning for Safe Offline Reinforcement Learning

We study the problem of safe offline reinforcement learning (RL), the goal is to learn a policy that maximizes long-term reward while satisfying safety constraints given only offline data, without further interaction with the environment. This problem is more appealing for real world RL applications, in which data collection is costly or dangerous. Enforcing constraint satisfaction is non-trivial, especially in offline settings, as there is a potential large discrepancy between the policy distribution and the data distribution, causing errors in estimating the value of safety constraints. We show that naïve approaches that combine techniques from safe RL and offline RL can only learn sub-optimal solutions. We thus develop a simple yet effective algorithm, Constraints Penalized Q-Learning (CPQ), to solve the problem. Our method admits the use of data generated by mixed behavior policies. We present a theoretical analysis and demonstrate empirically that our approach can learn robustly across a variety of benchmark control tasks, outperforming several baselines.
YNU-HPCC at SemEval-2021 Task 11: Using a BERT Model to Extract Contributions from NLP Scholarly Articles

This paper describes the system we built as the YNU-HPCC team in the SemEval-2021 Task 11: NLPContributionGraph.This task involves first identifying sentences in the given natural language processing (NLP) scholarly articles that reflect research contributions through binary classification; then identifying the core scientific terms and their relation phrases from these contribution sentences by sequence labeling; and finally, these scientific terms and relation phrases are categorized, identified, and organized into subject-predicateobject triples to form a knowledge graph with the help of multiclass classification and multilabel classification.We developed a system for this task using a pre-trained language representation model called BERT that stands for Bidirectional Encoder Representations from Transformers, and achieved good results.The average F 1 -score for Evaluation Phase 2, Part 1 was 0.4562 and ranked 7th, and the average F 1 -score for Evaluation Phase 2, Part 2 was 0.6541, and also ranked 7th.
Factors influencing information security compliance: an institutional perspective

Information is the critical resource of modern organization that needs to be protected from both internal and external threats so as to sustain in this competitive business environment. In order to do so, comprehensive security policy must be formulated and implemented. Every employee of the organization must comply with the organization’s security policy. Although organizations implement information security policy, it is commonly observed that employees do not comply with the organization information security policy. The purpose of this research was to identify organizational factors that shape employees behavior to comply with information system security policy in Ethio-telecom. Data were collected via using survey method. Multiple linear regression was used as data analysis method. The study result showed that management support, awareness and training, and accountability are leading organizational factors that shape employees behavior to comply with the existing information system security policy. This is a single case study; it cannot be generalized for other organizations. Other researchers can replicate this research for generalizability of the research findings across different contexts.
Arduino Based Solar Tracking System for Energy Improvement of PV Solar Panel

Solar energy is a clean, easily accessible and abundantly available alternative energy source in nature. Getting solar energy from nature is very beneficial for power generation. Using a fixed Photovoltaic panels extract maximum energy only during 12 noon to 2 PM in Nigeria which results in less energy efficiency. Therefore, the need to improve the energy efficiency of PV solar panel through building a solar tracking system cannot be over-emphasized. Photovoltaic panels must be perpendicular with the sun in order to get maximum energy. The methodology employed in this work includes the implementation of an Arduino based solar tracking system. Light Dependent Resistors (LDRs) are used to sense the intensity of sunlight and hence the PV solar panel is adjusted accordingly to track maximum energy. The mechanism uses servo motor to control the movement of the solar panel. The microcontroller is used to control the servo motor based on signals received from the LDRs. The result of this work has clearly shown that the tracking solar panel produces more energy compared to a fixed panel.
A Comparative Study of Transfer Learning Models for Offline Signature Verification and Forgery Detection

Recognising one’s identity to enter a system is called authentication. This process can take various forms where users input the system with a set of identifying credentials to access the system. Signatures belong to behavioural biometric, where the distinct features of every individual are considered in order to corroborate the person’s identity. The act of falsely imitating one’s signature biometric to impersonate and leverage access to their asset is called signature forgery. Our paper presents a comparative study of various deep learning models using Siamese architecture, over a wide catalogue of signature images. Openly available datasets like CEDAR, Handwritten Signatures dataset from Kaggle, ICDAR 2011 SigComp, and BH-Sig260 signature corpus are used to train the models. A set of classifiers – Support Vector Classifiers (SVC), Gaussian Naïve Bayes (GNB), Logistic Regression (LR) and K-Nearest Neighbours (KNN) are applied sequentially to classify the signature as genuine or forged.
Securing Text Messages Using Graph Theory and Steganography

Data security is an important component of data communication and transmission systems. Its main role is to keep sensitive information safe and integrated from the sender to the receiver. The proposed system aims to secure text messages through two security principles encryption and steganography. The system produced a novel method for encryption using graph theory properties; it formed a graph from a password to generate an encryption key as a weight matrix of that graph and invested the Least Significant Bit (LSB) method for hiding the encrypted message in a colored image within a green component. Practical experiments of (perceptibility, capacity, and robustness) were calculated using similarity measures like PSNR, MSE, and SSIM. These measures had proved the efficiency of the system for image quality and hiding messages with PSNR ratio more than 85 dB, MSE ranged (4.537e-05 to 5.27546e-04) and SSIM=1.0 for using a cover file with size ranged from 256×300 to 1200×760 pixels and message ranged from 16 to 300 characters.
Rydberg entangling gates in silicon

In this paper we propose a Rydberg entangling gate scheme which we demonstrate theoretically to have an order-of-magnitude improvement in fidelities and speed over existing cold atom protocols. It requires a large Rabi frequency compared to the interaction strength, which is difficult in cold atoms, but natural in donors in silicon, where it could help overcome the strenuous requirements on atomic precision donor placement and substantial gate tuning, which so far has hampered scaling. Furthermore, the gate operation would be ultrafast, on the order of picoseconds. We calculate multivalley van der Waals, induced electric dipole and total Rydberg interactions for several donor species using the finite-element method and show that they are important even for low-lying excited states. We show that Rydberg gate operation is possible within the lifetime of donor excited states with 99.9% fidelity for the creation of a Bell state in the presence of decoherence.
Credit scoring by one-class classification driven dynamical ensemble learning

It is very useful to endow machines with the ability to measure credit scores of loan applicants. Conventional methodologies always train Credit Scoring (CS) models by using data from clients who passed previous credit examination (i.e. who were considered adequately creditworthy and took out a loan). However, the CS models trained on data from the applicants who with good credit background may not work well for new applicants with plain or ambiguous credit backgrounds. Previous work always alleviates this by techniques of rejected inference and semisupervised learning. In this article, we propose a novel approach called as “One-class Classification Driven Dynamical Ensemble Learning” (abbreviated as OCDDEL). Different from rejected inference or semisupervised learning, OCDDEL does not use inferred labels of past rejected applications. Instead, OCDDEL only relies on past accepted applications and their true labels. It builds a dynamical ensemble model which deal with different test applications in different ways. To determine the ensemble weights for a specific test case, OCDDEL will learn a one-class classifier to separate test applications into groups, according to their similarities with training applicants. An experimental evaluation with 2 real-world datasets demonstrates the effectiveness of our approach.
MVP-BERT: Multi-Vocab Pre-training for Chinese BERT

Despite the development of pre-trained language models (PLMs) significantly raise the performances of various Chinese natural language processing (NLP) tasks, the vocabulary (vocab) for these Chinese PLMs remains to be the one provided by Google Chinese BERT (Devlin et al., 2019), which is based on Chinese characters (chars).Second, the masked language model pre-training is based on a single vocab, limiting its downstream task performances.In this work, we first experimentally demonstrate that building a vocab via Chinese word segmentation (CWS) guided sub-word tokenization (SGT) can improve the performances of Chinese PLMs.Then we propose two versions of multi-vocab pretraining (MVP), Hi-MVP and AL-MVP, to improve the models' expressiveness.Experiments show that: (a) MVP training strategies improve PLMs' downstream performances, especially it can improve the PLM's performances on span-level tasks; (b) our AL-MVP outperforms the recent AMBERT (Zhang & Li, 2020) after large-scale pre-training, and it is more robust against adversarial attacks.
An Improved Algorithm Based on Fast Search and Find of Density Peak Clustering for High-Dimensional Data

The find of density peak clustering algorithm (FDP) has poor performance on high-dimensional data. This problem occurs because the clustering algorithm ignores the feature selection. All features are evaluated and calculated under the same weight, without distinguishing. This will lead to the final clustering effect which cannot achieve the expected. Aiming at this problem, we propose a new method to solve it. We calculate the importance value of all features of high-dimensional data and calculate the mean value by constructing random forest. The features whose importance value is less than 10% of the mean value are removed. At this time, we extract the important features to form a new dataset. At this time, improved t-SNE is used for dimension reduction, and better performance will be obtained. This method uses t-SNE that is improved by the idea of random forest to reduce the dimension of the original data and combines with improved FDP to compose the new clustering method. Through experiments, we find that the evaluation index NMI of the improved algorithm proposed in this paper is 23% higher than that of the original FDP algorithm, and 9.1% higher than that of other clustering algorithms ( <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>K</mi> </math> -means, DBSCAN, and spectral clustering). It has good performance in high-dimensional datasets that are verified by experiments on UCI datasets and wireless sensor networks.
A Survey of Citation Recommendation Tasks and Methods

Scientific articles store vast amounts of knowledge amassed through many decades of research. They serve to communicate research results among scientists but also for learning and tracking progress in the field. However, scientific production has risen to levels that make it difficult even for experts to keep up with work in their field. As a remedy, specialized search engines are being deployed, incorporating novel natural language processing and machine learning methods. The task of citation recommendation, in particular, has attracted much interest as it holds promise for improving the quality of scientific production. In this paper, we present the state-of-the-art in citation recommendation: we survey the methods for global and local approaches to the task, the evaluation setups and datasets, and the most successful machine learning models. In addition, we overview two tasks complementary to citation recommendation: extraction of key aspects and entities from articles and citation function classification. With this survey, we hope to provide the ground for understanding current efforts and stimulate further research in this exciting and promising field.
A SIFT-Like Feature Detector and Descriptor for Multibeam Sonar Imaging

Multibeam imaging sonar has become an increasingly important tool in the field of underwater object detection and description. In recent years, the scale-invariant feature transform (SIFT) algorithm has been widely adopted to obtain stable features of objects in sonar images but does not perform well on multibeam sonar images due to its sensitivity to speckle noise. In this paper, we introduce MBS-SIFT, a SIFT-like feature detector and descriptor for multibeam sonar images. This algorithm contains a feature detector followed by a local feature descriptor. A new gradient definition robust to speckle noise is presented to detect extrema in scale space, and then, interest points are filtered and located. It is also used to assign orientation and generate descriptors of interest points. Simulations and experiments demonstrate that the proposed method can capture features of underwater objects more accurately than existing approaches.
NLP_UIOWA at Semeval-2021 Task 5: Transferring Toxic Sets to Tag Toxic Spans

We leverage a BLSTM with attention to identify toxic spans in texts.We explore different dimensions which affect the model's performance.The first dimension explored is the dataset the model is trained on.Besides the provided dataset, we explore the transferability of 5 different toxic related sets, including offensive, toxic, abusive, and hate sets.We find that the solely offensive set shows the highest promise of transferability.The second dimension we explore is methodology, including leveraging attention, employing a greedy remove method, using a frequency ratio, and examining hybrid combinations of multiple methods.We conduct an error analysis to examine which types of toxic spans were missed and which were wrongly inferred as toxic along with the main reasons why they occurred.Finally, we extend our method via ensembles, which achieves our highest F1 score of 55.1.
Color Image Retrieval Method Fusing Principal Curvature and Color Information

Aiming at the problems of high feature dimensionality, long retrieval time, and low precision of existing algorithms in image retrieval system, an image retrieval algorithm based on the fusion of color information and texture information is proposed.Firstly, the color image is quantized and counted to obtain color histogram.Secondly, the Hessian matrix is used to extract curvature information of the color image, and the weighted joint histogram of oriented gradient based on curvature information is constructed.Then, the obtained color histogram and histogram of oriented gradient are fused to obtain the final joint histogram.And the experimental results on the Corel-1k, FTVL, and COIL-100 datasets show that the proposed algorithm has the advantages of high precision and low dimensionality.
HuBeRo - a Framework to Simulate Human Behaviour in Robot Research

Social robots' software is commonly tested in a simulation due to the safety and convenience reasons as well as an environment configuration repeatability assurance. An interaction between a robot and a human requires taking a person presence and his movement abilities into consideration. The purpose of the article is to present the HuBeRo framework, which can be used to simulate human motion behaviour. The framework allows independent control of each individual's activity, which distinguishes the presented approach from state-of-the-art, open-source solutions from the robotics domain. The article presents the framework assumptions, architecture and an example application with respect to presented scenarios.
Quantum non-Gaussianity criteria based on vacuum probabilities of original and attenuated state

Abstract Quantum non-Gaussian states represent an important class of highly non-classical states whose preparation requires quantum operations or measurements beyond the class of Gaussian operations and statistical mixing. Here we derive criteria for certification of quantum non-Gaussianity based on probability of vacuum in the original quantum state and a state transmitted through a lossy channel with transmittance T . We prove that the criteria hold for arbitrary multimode states, which is important for their applicability in experiments with broadband sources and single-photon detectors. Interestingly, our approach allows to detect quantum non-Gaussianity using only one photodetector instead of complex multiplexed photon detection schemes, at the cost of increased experimental time. We also formulate a quantum non-Gaussianity criterion based on the vacuum probability and mean photon number of the state and we show that this criterion is closely related to the criteria based on pair of vacuum probabilities. We illustrate the performance of the obtained criteria on the example of realistic imperfect single-photon states modeled as a mixture of vacuum and single-photon states with background Poissonian noise.
HamiltonDinggg at SemEval-2021 Task 5: Investigating Toxic Span Detection using RoBERTa Pre-training

This paper presents our system submission to task 5: Toxic Spans Detection of the SemEval-2021 competition.The competition aims at detecting the spans that make a toxic span toxic.In this paper, we demonstrate our system for detecting toxic spans, which includes expanding the toxic training set with Local Interpretable Model-Agnostic Explanations (LIME), fine-tuning RoBERTa model for detection, and error analysis.We found that feeding the model with an expanded training set using Reddit comments of polarizedtoxicity and labeling with LIME on top of logistic regression classification could help RoBERTa more accurately learn to recognize toxic spans.We achieved a span-level F1 score of 0.6715 on the testing phase.Our quantitative and qualitative results show that the predictions from our system could be a good supplement to the gold training set's annotations.
UAntwerp at SemEval-2021 Task 5: Spans are Spans, stacking a binary word level approach to toxic span detection

This paper describes the system developed by the Antwerp Centre for Digital humanities and literary Criticism [UAntwerp] for toxic span detection.We used a stacked generalisation ensemble of five component models, with two distinct interpretations of the task.Two models attempted to predict binary word toxicity based on ngram sequences, whilst 3 categorical span based models were trained to predict toxic token labels based on complete sequence tokens.The five models' predictions were ensembled within an LSTM model.As well as describing the system, we perform error analysis to explore model performance in relation to textual features.The system described in this paper scored 0.6755 and ranked 26 th .
Blockchain Framework for Communication between Vehicle through IoT Devices and Sensors

The advent of autonomous vehicles is indeed a potential field of research in today's situation. Connected Vehicles (CV) have received a lot of attention in the last decade, which has resulted in CV as a Service (CVaaS). With the advent of taxi services, there is a need for or demand for robust, seamless, and secure information transmission between the vehicles connected to a vehicular network. Thus, the concept of vehicular networking is transformed into novel concept of autonomous and connected vehicles. These autonomous vehicles will serve as a better experience by providing instant information from the vehicles via congestion reduction. The significant drawback faced by the invention of autonomous vehicles is the malicious floor of intruders, who tend to mislead the communication between the vehicles resulting in the compromised smart devices. To address these concerns, the best methodology that will protect and secure the control system of the autonomous vehicle in real time is blockchain. This research work proposes a blockchain framework in order to address the security challenges in autonomous vehicles. This research work enhances the security of smart vehicles thereby preventing intruders from accessing the vehicular network. To validate the suggested technique, money security criteria such as changing stored user ratings, probabilistic authentication scenarios, smart device compromise, and bogus user requests were employed. The observed findings have been documented and analysed, revealing an 82% success rate.
DIRECT : A Transformer-based Model for Decompiled Identifier Renaming

Decompiling binary executables to high-level code is an important step in reverse engineering scenarios, such as malware analysis and legacy code maintenance.However, the generated high-level code is difficult to understand since the original variable names are lost.In this paper, we leverage transformer models to reconstruct the original variable names from decompiled code.Inherent differences between code and natural language present certain challenges in applying conventional transformer-based architectures to variable name recovery.We propose DIRECT, a novel transformer-based architecture customized specifically for the task at hand.We evaluate our model on a dataset of decompiled functions and find that DIRECT outperforms the previous state-of-the-art model by up to 20%.We also present ablation studies evaluating the impact of each of our modifications.We make the source code of DIRECT available to encourage reproducible research.
A Video Prediction Method Based on Optical Flow Estimation and Pixel Generation

Video prediction has developed rapidly after the booming of deep learning.As an important part of unsupervised representation learning, it plays an important role in anomalous behavior detection, autonomous driving, video games, and other fields.However, the prediction method based on optical flow estimation is susceptible to brightness change and camera shake, and it is difficult to predict occluded objects.While the prediction method based on pixel generation is difficult to fit ambiguous and complex scenes, which leads to a blurry prediction.In this work, we proposed an end-to-end video prediction framework that combines the optical flow estimation module with the pixel generation module by a learnable mask weight to predict high-fidelity videos.To further improve the prediction effect, we introduce adversarial training to the framework.We introduced a frame discriminator and a sequence discriminator to ensure the consistency of the spatio-temporal distribution of predicted video frames and real video frames.The results of experiments on challenging datasets demonstrate the practicability and effectiveness of our proposed video prediction framework.On the one hand, our proposed framework has achieved an equal quality compared with the current latest model, which requires fewer parameters and has a faster prediction speed.On the other hand, the results of ablation experiments demonstrate the effect of fusing different modules and the effectiveness of adversarial training.