Reviewerly/exHarmony huggingface dataset:
A Novel Privacy Paradigm for Improving Serial Data Privacy

Protecting the privacy of individuals is of utmost concern in today's society, as inscribed and governed by the prevailing privacy laws, such as GDPR. In serial data, bits of data are continuously released, but their combined effect may result in a privacy breach in the whole serial publication. Protecting serial data is crucial for preserving them from adversaries. Previous approaches provide privacy for relational data and serial data, but many loopholes exist when dealing with multiple sensitive values. We address these problems by introducing a novel privacy approach that limits the risk of privacy disclosure in republication and gives better privacy with much lower perturbation rates. Existing techniques provide a strong privacy guarantee against attacks on data privacy; however, in serial publication, the chances of attack still exist due to the continuous addition and deletion of data. In serial data, proper countermeasures for tackling attacks such as correlation attacks have not been taken, due to which serial publication is still at risk. Moreover, protecting privacy is a significant task due to the critical absence of sensitive values while dealing with multiple sensitive values. Due to this critical absence, signatures change in every release, which is a reason for attacks. In this paper, we introduce a novel approach in order to counter the composition attack and the transitive composition attack and we prove that the proposed approach is better than the existing state-of-the-art techniques. Our paper establishes the result with a systematic examination of the republication dilemma. Finally, we evaluate our work using benchmark datasets, and the results show the efficacy of the proposed technique.
Factors affecting inter-rater agreement in human classification of eye movements: a comparison of three datasets

Manual classification of eye-movements is used in research and as a basis for comparison with automatic algorithms in the development phase. However, human classification will not be useful if it is unreliable and unrepeatable. Therefore, it is important to know what factors might influence and enhance the accuracy and reliability of human classification of eye-movements. In this report we compare three datasets of human manual classification, two from earlier datasets and one, our own dataset, which we present here for the first time. For inter-rater reliability, we assess both the event-level F1-score and sample-level Cohen's κ, across groups of raters. The report points to several possible influences on human classification reliability: eye-tracker quality, use of head restraint, characteristics of the recorded subjects, the availability of detailed scoring rules, and the characteristics and training of the raters.
Combination of Resnet and Spatial Pyramid Pooling for Musical Instrument Identification

Abstract Identifying similar objects is one of the most challenging tasks in computer vision image recognition. The following musical instruments will be recognized in this study: French horn, harp, recorder, bassoon, cello, clarinet, erhu, guitar saxophone, trumpet, and violin. Numerous musical instruments are identical in size, form, and sound. Further, our works combine Resnet 50 with Spatial Pyramid Pooling (SPP) to identify musical instruments that are similar to one another. Next, the Resnet 50 and Resnet 50 SPP model evaluation performance includes the Floating-Point Operations (FLOPS), detection time, mAP, and IoU. Our work can increase the detection performance of musical instruments similar to one another. The method we propose, Resnet 50 SPP, shows the highest average accuracy of 84.64% compared to the results of previous studies.
A Multilingual Perspective Towards the Evaluation of Attribution Methods in Natural Language Inference

Most evaluations of attribution methods focus on the English language. In this work, we present a multilingual approach for evaluating attribution methods for the Natural Language Inference (NLI) task in terms of faithfulness and plausibility. First, we introduce a novel cross-lingual strategy to measure faithfulness based on word alignments, which eliminates the drawbacks of erasure-based evaluations.We then perform a comprehensive evaluation of attribution methods, considering different output mechanisms and aggregation methods. Finally, we augment the XNLI dataset with highlight-based explanations, providing a multilingual NLI dataset with highlights, to support future exNLP studies. Our results show that attribution methods performing best for plausibility and faithfulness are different.
Towards Improvement of LSTM and SVM Approach for Multiclass Fall Detection System

Telemonitoring of human physiological data helps detect emergency occurrences for subsequent medical diagnosis in daily living environments. One of the fatal emergencies in falling incidents. The goal of this paper is to detect significant incidents such as falls. The fall detection system is essential for human body movement investigation for medical practitioners, researchers, and healthcare businesses. Accelerometers have been presented as a practical, low-cost, and dependable approach for detecting and predicting outpatient movements in the user. The accurate detection of body movements based on accelerometer data enables the creation of more dependable systems for incorporating long-term development in physiological remarks. This research describes an accelerometer-based platform for detecting users' body movement when they fall. The ADXL345, MMA8451q, and ITG3200 body sensors capture activity data, subsequently classified into 15 fall incident classes based on SisFall dataset. Falling incidents classification is performed using Long Short-Term Memory results in best AUC-ROC value of 97.7% and best calculation time of 6.16 seconds. Meanwhile, Support Vector Machines results in the best AUC-ROC value of 98.5% and best calculation times of 17.05 seconds.
Generalizing Adversarial Explanations with Grad-CAM

Gradient-weighted Class Activation Mapping (Grad- CAM), is an example-based explanation method that provides a gradient activation heat map as an explanation for Convolution Neural Network (CNN) models. The drawback of this method is that it cannot be used to generalize CNN behaviour. In this paper, we present a novel method that extends Grad-CAM from example-based explanations to a method for explaining global model behaviour. This is achieved by introducing two new metrics, (i) Mean Observed Dissimilarity (MOD) and (ii) Variation in Dissimilarity (VID), for model generalization. These metrics are computed by comparing a Normalized Inverted Structural Similarity Index (NISSIM) metric of the Grad-CAM generated heatmap for samples from the original test set and samples from the adversarial test set. For our experiment, we study adversarial attacks on deep models such as VGG16, ResNet50, and ResNet101, and wide models such as InceptionNetv3 and XceptionNet using Fast Gradient Sign Method (FGSM). We then compute the metrics MOD and VID for the automatic face recognition (AFR) use case with the VGGFace2 dataset. We observe a consistent shift in the region highlighted in the Grad-CAM heatmap, reflecting its participation to the decision making, across all models under adversarial attacks. The proposed method can be used to understand adversarial attacks and explain the behaviour of black box CNN models for image analysis.
Structured access: an emerging paradigm for safe AI deployment

Structured access is an emerging paradigm for the safe deployment of artificial intelligence (AI). Instead of openly disseminating AI systems, developers facilitate controlled, arm's length interactions with their AI systems. The aim is to prevent dangerous AI capabilities from being widely accessible, whilst preserving access to AI capabilities that can be used safely. The developer must both restrict how the AI system can be used, and prevent the user from circumventing these restrictions through modification or reverse engineering of the AI system. Structured access is most effective when implemented through cloud-based AI services, rather than disseminating AI software that runs locally on users' hardware. Cloud-based interfaces provide the AI developer greater scope for controlling how the AI system is used, and for protecting against unauthorized modifications to the system's design. This chapter expands the discussion of "publication norms" in the AI community, which to date has focused on the question of how the informational content of AI research projects should be disseminated (e.g., code and models). Although this is an important question, there are limits to what can be achieved through the control of information flows. Structured access views AI software not only as information that can be shared but also as a tool with which users can have arm's length interactions. There are early examples of structured access being practiced by AI developers, but there is much room for further development, both in the functionality of cloud-based interfaces and in the wider institutional framework.
Dynamic Posture Prediction Model in Informationized Combat Environment

The several local wars that broke out at the turn of the century have shown to the world that the rapid development of high and new technology with information technology as the core is effectively promoting the pace of world military reform, and its most essential feature is information dominance. In local wars under modern high-tech conditions, rapid response and multiarms joint and coordinated operations have become the key factors to win, which requires multiarms, multiweapon platforms, and each combat unit software to work autonomously and dynamically. This research mainly discusses the dynamic posture prediction model in the informationized combat environment. It designs the organization of command elements, related data (products and tools), competency, personnel matching checklists, and activities in joint operational planning and relies on activities to model the binding of groups, capabilities, and related data. Finally, it forms process segments through the combined design of activities and then sets the final state and background knowledge constraints on the process segments to construct the process knowledge, turning each combat unit into an agent unit, so that the combat software unit has the performance characteristics of the agent’s autonomy, reactivity, and sociality. At the same time, it has good communication performance and autonomous negotiation ability, so it can realize the autonomous connection, communication, and cooperative work among multiagent systems. During the process execution or business processing, the seat staff can input the goal and background of the activity according to the current operating environment. It relies on the background constraints of the process knowledge to quickly match the corresponding process knowledge, insert the process fragments in the process knowledge into the original process, realize the rapid and efficient decomposition of the process redefinition, and effectively ensure the orderly development of the combat command. Techniques and methods for automating integration testing of combat systems are provided. These technologies are further applied to the process of combat system integration tests to improve the efficiency of combat system integration. In addition, through the research on the integration testing technology of combat systems, the problems of low generality of integration testing software and many structural repetitive codes can also be solved and the existing integration testing technology can be further improved. In the research, the time-consuming of the script interpretation module accounts for a relatively low proportion of about 5% of the simulation. The article embodies the platform to explore the realization of multiagent-based dynamic cooperative operations, to achieve the purpose of simulating the battlefield situation, rehearsing the combat plan, and evaluating the strike effect. This study will improve the efficiency of overall combat system integration.
INFLUENCE OF QUANTUM TEACHING-LEARNING MODEL ON SOCIAL STUDIES LEARNING OUTCOMES

The purpose of this study was to determine the effect of the application of the quantum teaching-learning model on the learning outcomes of Theme 5 on the social studies content of grade V elementary school students in Demak Sub District. The research method used a quasi-experimental design. The research sample was class V Elementary School Bintoro Number 5 Elementary School (35 students). The control class was class V Elementary School Bintoro Number 2 with a total of 41 students. The sampling technique used purposive random sampling. Data analysis used t-test, by first doing data description, average similarity test, and analysis requirements test (normality test and homogeneity test). The results showed that the use of the quantum teaching-learning model had an effect on the learning outcomes of Theme 5 on social studies subject matter, as indicated by the results of the t-test of 3.446&gt; 2,000 and the percent N-gain test of 59.50 (quite effective); Based on the research, it was concluded that there was an influence on the use of the quantum teaching-learning model on the learning outcomes of Theme 5 on the IPS subject content of elementary school students in Demak Sub District.
Transformaton of urban spaces into information age

The article describes the prerequisites that are global in the perception of the context of digital culture which implies a deep reorganization of social relations in time and space. This article analyzes phased development of means of artistic expressiveness in digital format. Several examples of transformation of urban space due to the emergence of media units of urban structure (MEGS) are considered. The importance of the appearance of media units in the structure of the city was noted. The presence of media units allows to change and adapt the urban environment in a variety of ways as the macro level in a smart city and simple local interventions related to specific sections of the city.
Precision Medicine in Emergency Medicine

Personalized medicine was always a part of medicine. However, with technological advances in data mining, machine learning, artificial intelligence and computing, the term “personalized” has been surpassed by precision medicine, a multidisciplinary bridge that aims to provide unique approaches for each patient. Acute care is an area where current precision medicine methods is starting to transform. In this mini review, we describe in brief some of the applications used in emergencies that promote precision medicine.
Citation and Similarity in Academic Texts: Colombian Engineering Case

Abstract This article provides the results of a citation determinants model for a set of academic engineering texts from Colombia. The model establishes the determinants of the probability that a text receives at least one citation through the relationship among previous citations, journal characteristics, the author and the text. Through a similarity matrix constructed by Latent Semantic Analysis (LSA), a similarity variable has been constructed to capture the fact that the texts have similar titles, abstracts and keywords to the most cited texts. The results show: i) joint significance of the variables selected to characterize the text; ii) direct relationship of the citation with similarity of keywords, published in an IEEE journal, research article, more than one author; and authored by at least one foreign author; and iii) inverse relationship between the probability of citation with the similarity of abstracts, published in 2016 or 2017, and published in a Colombian journal.
Identification of the cause of fall during the pre-impact fall period

[Purpose] This study aimed to develop and validate a method for identifying factors that may cause a fall during the pre-impact fall period using wearable sensors. [Participants and Methods] The participants were 23 young people from the public data set (mean age, 23.4 years). Acceleration and angular velocity information obtained from sensors attached to the participant's waist was used to generate the pre-impact fall. The cause of the fall (slip, trip, fainting, get up, sit down) was then classified with and without the addition of activity of daily living data using three different support vector machine. In addition, we investigated the influence of lead time (0-2.0s) on accuracy. [Results] The quadratic and cubic support vector machine identified the activity of daily living and fall patterns more accurately than the linear support vector machine, and the cubic support vector machine was better for classification, although the difference was slight. The greatest accuracy for predicting the cause of the fall (87.9%) was obtained when the cubic support vector machine was used, activity of daily living was factored into the analysis, and the lead time was 0.25 sec. [Conclusion] Support vector machine can identify the cause of the fall during the pre-impact fall period. Appropriate individualized interventions may be designed based on the most likely cause of fall as identified by this analysis method.
A tighter generalization bound for reservoir computing

While reservoir computing (RC) has demonstrated astonishing performance in many practical scenarios, the understanding of its capability for generalization on previously unseen data is limited. To address this issue, we propose a novel generalization bound for RC based on the empirical Rademacher complexity under the probably approximately correct learning framework. Note that the generalization bound for the RC is derived in terms of the model hyperparameters. For this reason, it can explore the dependencies of the generalization bound for RC on its hyperparameters. Compared with the existing generalization bound, our generalization bound for RC is tighter, which is verified by numerical experiments. Furthermore, we study the generalization bound for the RC corresponding to different reservoir graphs, including directed acyclic graph (DAG) and Erdős-R e´nyi undirected random graph (ER graph). Specifically, the generalization bound for the RC whose reservoir graph is designated as a DAG can be refined by leveraging the structural property (i.e., the longest path length) of the DAG. Finally, both theoretical and experimental findings confirm that the generalization bound for the RC of a DAG is lower and less sensitive to the model hyperparameters than that for the RC of an ER graph.
A Ternary Neural Network with Compressed Quantized Weight Matrix for Low Power Embedded Systems

In this paper, we propose a method of transforming a real-valued matrix to a ternary matrix with controllable sparsity. The sparsity of quantized weight matrices can be controlled by adjusting the threshold during the training and quantizing process. A 3-layer ternary neural network was trained with the MNIST dataset using the proposed adjustable dynamic threshold. The sparsity of the quantized weight matrices varied from 0.1 to 0.6 and the obtained recognition rate reduced from 91% to 88%. The sparse weight matrices were compressed by the compressed sparse row format to speed up the ternary neural network, which can be deployed on low-power embedded systems, such as the Raspberry Pi 3 board. The ternary neural network with the sparsity of quantized weight matrices of 0.1 is 4.24 times faster than the ternary neural network without compressing weight matrices. The ternary neural network is faster as the sparsity of quantized weight matrices increases. When the sparsity of the quantized weight matrices is as high as 0.6, the recognition rate degrades by 3%, however, the speed is 9.35 times the ternary neural network's without compressing quantized weight matrices. Ternary neural network work with compressed sparse matrices is feasible for low-cost, low-power embedded systems.
Primary total knee arthroplasties under the Brazilian Public Health Unic System (SUS) - Number of procedures, regional distribution, hospitalization costs, average length of hospital stay and mortality (2012-2021)

The aim of this study was to describe the number and regional distribution of hospital admission authorization (AIH), hospitalization costs, average length of stay (LOS), and mortality rates related to primary total knee arthroplasty (TKA) procedures funded by the Brazilian Public Health Unic System (SUS) from 2012 to 2021. A cross-sectional descriptive study on the number and regional distribution of AIH, hospitalization costs, average LOS, and mortality rates related to primary TKA procedures funded by the SUS from 2012 to 2021 was done. As results, 65,602 AIH were released in the evaluated period; in 2020 and 2021 there was a sharp decrease of procedures, probably due to the COVID-19 pandemic. The total value spent in the evaluated period was of 271,297,010.60 BRL. The average LOS was of 4.15 days. The absolute number of deaths was of 103, with 0.16% of mortality rate. It was concluded that the number and regional distribution of AIH was of 37,370 (57%) to the Southeast; 18,907 (29%) to the South, 5,250 (8%) to the Northeast; 2,758 (4%) to the Midwest and 1,317 (2%) to the North, totaling 65,602 admissions, with a total expenditure of 271,297,010.60 BRL (156,252,541.60 BRL in Southeast, 77,150,946.31 BRL in South, 21,194,955.07 BRL in Northeast, 12,077,393.69 BRL in Midwest and 4,621,173.96 BRL in North). The average LOS (in days) was of 4.15 (4.15 in Southeast; 3.83 in South; 4.6 in Northeast; 4.81 in Midwest; and, 5.71 in North). The mortality rate was of 0.16% (Southeast=0,14%; South=0,1%; Northeast=0,16%; Midwest=0,17%; and North=0,14%).
A protocol for conferencing through shared multipartite entanglement

In this paper, we introduce a protocol for mutual sharing of known quantum information between different parties. We show that it is possible to exchange known non-maximally entangled Bell states between three parties through a protocol by use of a multipartite entangled state. The scheme is overseen by a controller.
Integrating Quality Function Deployment with Fuzzy Cognitive Maps for Resolving Correlation Issues in the Roof Matrix

The roof matrix represents correlations among engineering characteristics (EC) in the house of quality (HoQ) in Quality Functions Deployment (QFD).Correlations are usually measured qualitatively and omitted in the analysis.However, ignoring them may cause duplication of effort, decreased product performance, and unsatisfied customer requirements (CR).Hence, this paper intends to propose an approach to considering the correlations quantitatively.Fuzzy Cognitive Maps (FCM) were used for this purpose.Additionally, Axiomatic Design (AD), for examining relationships between CRs and ECs, and Fuzzy Analytic Hierarchy Process (FAHP) with the Extent Analysis (EA) were used for checking the consistency of the evaluations.The proposed approach was applied in a sheet metal die-making company for ranking CRs and ECs.Results show that FCM enables analysing the quantitative roof matrix practically.The square roof matrix that supports FCM's adjacency matrix structure successfully represents asymmetric relationships among ECs.Integrating the correlations into the analysis resulted in a change in the final ranking.It also helped determine the most manageable ECs, better satisfiable CRs, and most critical/least manageable ECs.