Reviewerly/exHarmony huggingface dataset:
Overview of the Impact of Human Error on Cybersecurity based on ISO/IEC 27001 Information Security Management

Information security is the practice of protecting information by mitigating the risk of cyber-attack, and typically includes preventing or reducing the possibility of unauthorized/inappropriate access to data, unlawful use, disclosure, disruption. This concept of information security covers as well various procedures aiming at minimizing the negative effects of such incidents and threats. These threats might be originated from the human behavior which may lead to a wide damage of the organization data assets. Thus, the primary focus of information security is on the balanced protection of confidentiality, integrity and availability of data while maintaining an effective use of the organizations' systems. International standards related to information security such as ISO/IEC 27001 emphasis on effective implementation of the information security policies and applications without hampering the productivity of the organization. This research seeks to draw a set of practical rules to be established within an organization to preserve cybersecurity objectives and protect dada specifically from human errors incidents. The drawn rules are based on ISO/IEC 27001 and its application within organizations will rise the employee’s awareness about their behavior to reduce the impact of such incidents on the organization' systems and data.
Demonstration of Density Matrix Exponentiation Using a Superconducting Quantum Processor

Density matrix exponentiation may offer a natively quantum approach to programming quantum computers. A new experiment presents the first demonstration of the protocol in a superconducting quantum processor.
My Caregiver the Cobot: Comparing Visualization Techniques to Effectively Communicate Cobot Perception to People with Physical Impairments

Nowadays, robots are found in a growing number of areas where they collaborate closely with humans. Enabled by lightweight materials and safety sensors, these cobots are gaining increasing popularity in domestic care, where they support people with physical impairments in their everyday lives. However, when cobots perform actions autonomously, it remains challenging for human collaborators to understand and predict their behavior, which is crucial for achieving trust and user acceptance. One significant aspect of predicting cobot behavior is understanding their perception and comprehending how they "see" the world. To tackle this challenge, we compared three different visualization techniques for Spatial Augmented Reality. All of these communicate cobot perception by visually indicating which objects in the cobot's surrounding have been identified by their sensors. We compared the well-established visualizations Wedge and Halo against our proposed visualization Line in a remote user experiment with participants suffering from physical impairments. In a second remote experiment, we validated these findings with a broader non-specific user base. Our findings show that Line, a lower complexity visualization, results in significantly faster reaction times compared to Halo, and lower task load compared to both Wedge and Halo. Overall, users prefer Line as a more straightforward visualization. In Spatial Augmented Reality, with its known disadvantage of limited projection area size, established off-screen visualizations are not effective in communicating cobot perception and Line presents an easy-to-understand alternative.
The Impact of Serious Game on the Nursing Students' Learning, Behavioral Engagement, and Motivation

Digital game-based learning is a ludic approach that has evolved through the development of information and communication technologies (ICT), making the learner an active participant in the virtual learning environment instead of a mere observer. It is an electronic simulation tool suitable for improving learning efficiency as well as learner engagement and motivation. The integration of this educational technology in the teaching and learning process can contribute to the construction of knowledge, the development of new skills and attitudes among students.&#x0D; The purpose of this study is to investigate the impact of serious game on the learning, behavioral engagement, and motivation of nursing students. To do this, we first conducted a study with a sample of 58 polyvalent nursing students divided into two groups; one experimental and the other as a control. Both groups benefited from the same content taught at a distance, however, the experimental group benefited from an online serious game after the course. By comparing the summative scores of the two groups using the Student's t-test, the results show that the serious game improved the students' clinical knowledge of pediatric nursing (t= -2.706,p=0.009). However, it did not have an effect on clinical skills (t= -0.373, p=0.711). As a second step, we have studied the impact of the game on the learners' behavioral engagement based on the analysis of the digital traces of the students' work. Meanwhile, the motivation is analyzed using a questionnaire developed from the ARCS scale of Killer (2010), which is distributed to the experimental group. By comparing the means and deviation standards, the results show that the students give the satisfaction factor the highest score (4.71±0.34) followed by the relevance, attention, and confidence factors.&#x0D; To obtain reliable results, however, it is necessary to repeat the serious game experience in several nursing modules. In addition, the impact of combining serious game with simulation on the learning of nursing students should be studied.
Performance Evaluation of a Maneuver Classification Algorithm Using Different Motion Models in a Multi-Model Framework

Environment perception is one of the major challenges in the vehicle industry nowadays, as acknowledging the intentions of the surrounding traffic participants can profoundly decrease the occurrence of accidents. Consequently, this paper focuses on comparing different motion models, acknowledging their role in the performance of maneuver classification. In particular, this paper proposes utilizing the Interacting Multiple Model framework complemented with constrained Kalman filtering in this domain that enables the comparisons of the different motions models' accuracy. The performance of the proposed method with different motion models is thoroughly evaluated in a simulation environment, including an observer and observed vehicle.
Local Epochs Inefficiency Caused by Device Heterogeneity in Federated Learning

Federated learning is a new framework of machine learning, it trains models locally on multiple clients and then uploads local models to the server for model aggregation iteratively until the model converges. In most cases, the local epochs of all clients are set to the same value in federated learning. In practice, the clients are usually heterogeneous, which leads to the inconsistent training speed of clients. The faster clients will remain idle for a long time to wait for the slower clients, which prolongs the model training time. As the time cost of clients’ local training can reflect the clients’ training speed, and it can be used to guide the dynamic setting of local epochs, we propose a method based on deep learning to predict the training time of models on heterogeneous clients. First, a neural network is designed to extract the influence of different model features on training time. Second, we propose a dimensionality reduction rule to extract the key features which have a great impact on training time based on the influence of model features. Finally, we use the key features extracted by the dimensionality reduction rule to train the time prediction model. Our experiments show that, compared with the current prediction method, our method reduces 30% of model features and 25% of training data for the convolutional layer, 20% of model features and 20% of training data for the dense layer, while maintaining the same level of prediction error.
Multi-Domain Multilingual Question Answering

Question answering (QA) is one of the most challenging and impactful tasks in natural language processing.Most research in QA, however, has focused on the open-domain or monolingual setting while most real-world applications deal with specific domains or languages.In this tutorial, we attempt to bridge this gap.Firstly, we introduce standard benchmarks in multi-domain and multilingual QA.In both scenarios, we discuss stateof-the-art approaches that achieve impressive performance, ranging from zero-shot transfer learning to out-of-the-box training with open-domain QA systems.Finally, we will present open research problems that this new research agenda poses such as multi-task learning, cross-lingual transfer learning, domain adaptation and training large scale pre-trained multilingual language models.
Organic Food Supply Chain Traceability using Blockchain Technology

Blockchain is an innovation that’s prized for its capacity to record and disperse exchanges in the permanent, scumbled record. In a traditional supply chain, there is no procedure where consumers can be assured about the authenticity of the products as there is no way to probe the hygienist and authenticity. The purpose of this research is to build a decentralized, Blockchain-based system to check the product’s quality and source. To verify the authenticity of the goods, we will create a certified organic food supply chain system. In this paper, we are going to describe how to strengthen the security of data of supply chain members by applying blockchain technology. We will reduce the riskiness of data tampering and fraud data allocation by the ‘Smart contract’ using blockchain technology and by providing a quality assurance certificate customers can be sure of the product quality. This article assists individuals from many industries in understanding the advantages of the blockchain-based system and implementing it to increase the efficiency of the entire system.
A Sentiment Analysis of Gujarati Text using Gujarati Senti word Net

Sentiment Analysis plays vital role in decision making. For English language intensive research work is done in this area. Very less work is reported in this domain for Indian languages compared to English language. Gujarati language is almost unexplored for this task. More data in form of movie reviews, product reviews, social media posts etc are available in regional languages as people like to use their native language on Internet which leads to need of mining these data in order to understand their opinion. Various tools and resources are developed for English language and few for Indian languages. Gujarati is resource poor language for this task. Motive of this paper is to develop sentiment lexical resource for Gujarati language which can be used for sentiment analysis of Gujarati text. Hindi SentiWordNet (H-SWN) [1] and synonym relations of words from IndoWordnet (IWN) [2] [3] are used for developing Gujarati SentiWordNet. Our contribution is twofold. (1) Gujarati SentiWordNet (G-SWN) is developed. (2) Gujarati corpus is prepared in order to evaluate lexical resource created. Evaluation result shows the usefulness of generated resource.
Weakly measured while loops: peeking at quantum states

Abstract A while loop tests a termination condition on every iteration. On a quantum computer, such measurements perturb the evolution of the algorithm. We define a while loop primitive using weak measurements, offering a trade-off between the perturbation caused and the amount of information gained per iteration. This trade-off is adjusted with a parameter set by the programmer. We provide sufficient conditions that let us determine, with arbitrarily high probability, a worst-case estimate of the number of iterations the loop will run for. As an example, we solve Grover’s search problem using a while loop and prove the quadratic quantum speed-up is maintained.
Sports Event Model Evaluation and Prediction Method Using Principal Component Analysis

Aiming at the problems of poor average fitness, low-risk prediction accuracy, high mean square error, low-risk evaluation precision, and long average running time of traditional sports event model evaluation and prediction methods, a sports event model evaluation and prediction method using principal component analysis (PCA) is proposed. Sports event risk monitoring microbase is deployed by ZigBee technology, and sports event risk monitoring data is monitored and packaged at each base station. Optical fiber and Ethernet are used to transmit the data to the monitoring and management center to complete the risk data collection of sports events. After data standardization, the risk evaluation index system of sports events is constructed, and the comprehensive score of each risk index of sports events is obtained by using the PCA method. The BP neural network is improved by genetic algorithm (GA), and the comprehensive score of risk index is input into the network to obtain the evaluation and prediction results of sports event risk. The results show that the proposed method has good average fitness, the predicted value of sports event risk is almost equal to the actual value, the prediction mean square error is less than 0.15, the evaluation precision is high, and the average running time is only 8 s. The cost (time complexity) is low. Overall, the method has a good application prospect in the field of sports event evaluation and prediction.
Quantum conformance test

We introduce a protocol addressing the conformance test problem, which consists in determining whether a process under test conforms to a reference one. We consider a process to be characterized by the set of end products it produces, which is generated according to a given probability distribution. We formulate the problem in the context of hypothesis testing and consider the specific case in which the objects can be modeled as pure loss channels. We demonstrate theoretically that a simple quantum strategy, using readily available resources and measurement schemes in the form of two-mode squeezed vacuum and photon counting, can outperform any classical strategy. We experimentally implement this protocol, exploiting optical twin beams, validating our theoretical results, and demonstrating that, in this task, there is a quantum advantage in a realistic setting.
Gender Classification on Video Using FaceNet Algorithm and Supervised Machine Learning

Gender classification using human face data becomes a trending topic for researchers in the field of image processing and computer vision.The human face is biometric information that can be used to differentiate gender using a computer-aided system.Previous research utilised a local feature algorithm for extracting features on the face.However, the processing speed for one image was more than 2 seconds, making it unsuitable for real-time processing using video data.Processing video data requires a fast feature extraction algorithm because video data collects sequential images (frames).Moreover, the gender classification system's success is also measured by its accuracy, consequently it is necessary to choose the correct classification method to divide the two classes of men and women.In this research, we propose the FaceNet algorithm for feature extraction and explore several supervised machine learning methods (KNN, SVM, and Decision tree) appropriate for gender classification on video data.This study used 23,000 training data on each gender.From the experiment, combination of the FaceNet algorithm and KNN method resulted in the best accuracy of 95.75% with a processing speed of 0.059 seconds on each frame.
Fine-grained Entity Typing without Knowledge Base

Existing work on Fine-grained Entity Typing (FET) typically trains automatic models on the datasets obtained by using Knowledge Bases (KB) as distant supervision.However, the reliance on KB means this training setting can be hampered by the lack of or the incompleteness of the KB.To alleviate this limitation, we propose a novel setting for training FET models: FET without accessing any knowledge base.Under this setting, we propose a two-step framework to train FET models.In the first step, we automatically create pseudo data with fine-grained labels from a large unlabeled dataset.Then a neural network model is trained based on the pseudo data, either in an unsupervised way or using self-training under the weak guidance from a coarse-grained Named Entity Recognition (NER) model.Experimental results show that our method achieves competitive performance with respect to the models trained on the original KB-supervised datasets.
Indicators of the Universities Control Activities

This article presents a detailed analysis and content of the main objects of control of the university’s activities, such as it's potential, educational and scientific activities, as well as such independent objects of control as educational programs. The composition and general characteristics of the analytical indicators of the university’s control activities are disclosed, such as indicators of assessment of its potential, indicators of control activities of the university as a whole, as well as the implementation progress of its educational programs. The results of the study are reflected in the matrix of control objects of university activities, which can be widely applied as methodological support for building a system for controlling universities' activities
Machine learning concepts for correlated Big Data privacy

Abstract With data becoming a salient asset worldwide, dependence amongst data kept on growing. Hence the real-world datasets that one works upon in today’s time are highly correlated. Since the past few years, researchers have given attention to this aspect of data privacy and found a correlation among data. The existing data privacy guarantees cannot assure the expected data privacy algorithms. The privacy guarantees provided by existing algorithms were enough when there existed no relation between data in the datasets. Hence, by keeping the existence of data correlation into account, there is a dire need to reconsider the privacy algorithms. Some of the research has considered utilizing a well-known machine learning concept, i.e., Data Correlation Analysis, to understand the relationship between data in a better way. This concept has given some promising results as well. Though it is still concise, the researchers did a considerable amount of research on correlated data privacy. Researchers have provided solutions using probabilistic models, behavioral analysis, sensitivity analysis, information theory models, statistical correlation analysis, exhaustive combination analysis, temporal privacy leakages, and weighted hierarchical graphs. Nevertheless, researchers are doing work upon the real-world datasets that are often large (technologically termed big data) and house a high amount of data correlation. Firstly, the data correlation in big data must be studied. Researchers are exploring different analysis techniques to find the best suitable. Then, they might suggest a measure to guarantee privacy for correlated big data. This survey paper presents a detailed survey of the methods proposed by different researchers to deal with the problem of correlated data privacy and correlated big data privacy and highlights the future scope in this area. The quantitative analysis of the reviewed articles suggests that data correlation is a significant threat to data privacy. This threat further gets magnified with big data. While considering and analyzing data correlation, then parameters such as Maximum queries executed, Mean average error values show better results when compared with other methods. Hence, there is a grave need to understand and propose solutions for correlated big data privacy.
A SURVEY ON MACHINE LEARNING

Machine learning is a category of an Artificial Intelligence (AI). The goal of machine learning is to understand the structure of data and design a models using that particular data that can be understood and utilized by people. Machine learning forms to be an important application of an Artificial Intelligence (AI). This paper provides the information about the introduction, advantages, disadvantages and applications of Machine Learning. Also, this helps individual to understand why we needs to choose Machine Learning.
Suspicious Object Detection in Environments with Tear Gas

In a digital security surveillance system, detecting suspicious objects is paramount to identifying criminals and thereby reducing the crime rate. At present most of the security surveillance systems are based on automated CCTV cameras. Usually, it is highly challenging to detect objects in environments covered with smoke, water, and dark clouds. One of the main intentions of security surveillance is detecting harmful objects such as guns, knives, axes, and iron rods. Digital surveillance in a situation where the police have used tear gas and water is even more challenging since the background could contain many artefacts. This paper aims to develop a model to detect suspicious objects in footage of protests or riots where the environment encompasses tear gas. The proposed method uses a YOLOv4 object detection framework along with Roboflow. All the fully connected dense layers of the YOLOv4 model were trained with a newly created dataset with different images captured in environments where tear gas is present and containing dangerous objects. Also, the developed dataset uses several Bags of Freebies to improve the detection rate. The model achieved a mean average precision (mAP) of 82% for the test dataset when trained for 105 epochs. Overall, the proposed method could solve problems in suspicious object detection in public security surveillance under challenging environments.
TCL: Tightly Coupled Learning Strategy for Weakly Supervised Hierarchical Place Recognition

Visual place recognition (VPR) is a key issue for robotics and autonomous systems. For the trade-off between time and performance, most of methods use the coarse-to-fine hierarchical architecture, which consists of retrieving top-N candidates using global features, and re-ranking top-N with local features. However, since the two types of features are usually processed independently, re-ranking may harm global retrieval, termed re-ranking confusion. Moreover, re-ranking is limited by global retrieval. In this paper, we propose a tightly coupled learning (TCL) strategy to train triplet models. Different from original triplet learning (OTL) strategy, it combines global and local descriptors for joint optimization. In addition, a bidirectional search dynamic time warping (BS-DTW) algorithm is also proposed to mine locally spatial information tailored to VPR in re-ranking. The experimental results on public benchmarks show that the models using TCL outperform the models using OTL, and TCL can be used as a general strategy to improve performance for weakly supervised ranking tasks. Further, our lightweight unified model is better than several state-of-the-art methods and has over an order of magnitude of computational efficiency to meet the real-time requirements of robots.
Grammatical Profiling for Semantic Change Detection

Semantics, morphology and syntax are strongly interdependent.However, the majority of computational methods for semantic change detection use distributional word representations which encode mostly semantics.We investigate an alternative method, grammatical profiling, based entirely on changes in the morphosyntactic behaviour of words.We demonstrate that it can be used for semantic change detection and even outperforms some distributional semantic methods.We present an in-depth qualitative and quantitative analysis of the predictions made by our grammatical profiling system, showing that they are plausible and interpretable.