Reviewerly/exHarmony huggingface dataset:
Progress in Computer Vision: Object Recognition

Computer vision systems aim to analyze data collected from the environment and derive an interpretation from completing a specified task. Vision system tasks are object recognition, low-level processing, representation, model construction, and matching subtasks. One of the classical problems addressed by machine vision is object recognition determining whether or not the image data contains some specific object, feature, or activity. Object recognition or object classification goal is to recognize pre-specified or learning objects or object classes. The application of object recognition provides the ability to recognize objects with their 2D positions in the image or 3D poses in the scene, while the recognition ability of computer vision can also be used for identifying a person’s face or fingerprint, handwritten digits, or a specific vehicle (e.g., Google Goggles).
Partitioning and Gaussian Processes for Accelerating Sampling in Monte Carlo Tree Search for Continuous Decisions

We propose Part-MCTS for sampling continuous decisions at each stage of a Monte Carlo Tree Search algorithm. At each MCTS stage, Part-MCTS sequentially partitions the decision space and keeps a collection of Gaussian processes to describe the landscape of the objective function. A classification criteria based on the estimation of the minimum allows us to focus the attention on regions with better predicted behavior, reducing the evaluation effort elsewhere. Within each subregion, we can use any sampling distribution, and we propose to sample using Bayesian optimization. We compare our approach to KR-UCT (Yee et al. 2016) as state of the art competitor. Part-MCTS achieves better accuracy over a set of nonlinear test functions, and it has the ability to identify multiple promising solutions in a single run. This can be important when multiple solutions from a stage can be preserved and expanded at subsequent stages.
Multi-Motion Segmentation: Combining Geometric Model-Fitting and Optical Flow for RGB Sensors

This paper presents a novel multi-motion segmentation framework by combining the geometric model fitting and optical flow. More precisely, we use the spatial information extracted by the multi-model fitting and the motion information provided by the optical flow to improve the practicality of the motion segmentation algorithm. The real scenes contain challenges such as multiple motions, camera translation, large field of views, and occlusions. To improve the segmentation accuracy of the real scenes, we first propose a cluster selection method to generate several clusters that can cover all motion models in the data, then a cluster merging strategy is proposed to estimate the basic motion models and the number of motions accurately for the scene. Finally, motion assignment of the remaining points is carried out to complete the motion segmentation. Detailed experiments on two challenging real-world benchmark datasets (62-clip and KT3DMoSeg) show that the proposed framework is superior to the state-of-the-art multi-motion segmentation methods.
Amalgamated Evolutionary Approach for Optimized Routing in Time Varying Ultra Dense Heterogeneous Networks

Routing mechanisms in Ultra-Dense Network (UDNs) are expected to be flexible, scalable, and robust in nature and the establishment of the shortest path between the source and destination pairs will always be a critical challenge. Through this projected work, the optimized shortest route of different source-destination pairs is found using a class of evolutionary optimization algorithms namely PSO, GA, and our proposed hybrid PSO–Genetic Mutation (PSO-GM) algorithm which searches for an optimized solution by representing it as a Shortest Path Routing (SPR) problem. The key attribute of the PSO-GM approach is related to the application of an amalgamated strategy with Gaussian, Cauchy, Levy, Single-point, and Chaos mutation operators. Simulation results and application of the above-mentioned algorithms to the SPR problem in UDNs reveal that the hybrid PSO-GM algorithm provides a comparatively enhanced optimized solution. In the case of the rate of convergence to the theoretical limit, the hybrid PSO-GM gives us 20% better results compared to the PSO and GA.
Condensed Representations of Association Rules in n-ary Relations

The problem of association rules mining has given rise to a rich literature, especially in classic binary bidimensional data. In particular, the representation of the set of rules without loss of information is well understood. This is not the case in multidimensional binary data. This paper shows that the knowledge of n-1 components of every closed n-sets of a multidimensional Boolean tensor, as well as the cardinality of the remaining dimension, is enough to allow for the derivation of the confidence of every multidimensional association rule. This generalises well-known results in the bidimensional case. This paper provides experimental comparisons between the numbers of closed n-sets and frequent associations.
Healthcare Security Incident Response Strategy - A Proactive Incident Response (IR) Procedure

The healthcare information system (HIS) has become a victim of cyberattacks. Traditional ways to handle cyber incidents in healthcare organizations follow a predefined incident response (IR) procedure. However, this procedure is usually reactive, missing the opportunities to foresee danger on the horizon. Cyber threat intelligence (CTI) contains information on emerging attacks and should be ideally utilized to inform the IR procedure. However, current research shows that the IR has not been effectively informed by CTI, especially in healthcare organizations. This paper fills in this gap by proposing a proactive IR response procedure based on the National Institute of Standards and Technology (NIST) IR methodology. This paper then presents the NHS WannaCry case study to demonstrate the use of the proposed IR methodology. We collate cyber security advisories from different CTI sources such as US/UK CERT to protect interconnected systems and devices from Ransomware attacks. This research provides novel insights into the IR in healthcare through embedding CTI advisories into IR processes and concludes that our proposed IR procedure can be used to counteract WannaCry Ransomware using CTI advisories. It has the significance of transforming the way of IR from reactive to proactive using the CTI in healthcare.
Identification and Classification of Crowd Activities

The identification and classification of collective people's activities are gaining momentum as significant themes in machine learning, with many potential applications emerging. The need for representation of collective human behavior is especially crucial in applications such as assessing security conditions and preventing crowd congestion. This paper investigates the capability of deep neural network (DNN) algorithms to achieve our carefully engineered pipeline for crowd analysis. It includes three principal stages that cover crowd analysis challenges. First, individual's detection is represented using the You Only Look Once (YOLO) model for human detection and Kalman filter for multiple human tracking; Second, the density map and crowd counting of a certain location are generated using bounding boxes from a human detector; and Finally, in order to classify normal or abnormal crowds, individual activities are identified with pose estimation. The proposed system successfully achieves designing an effective collective representation of the crowd given the individuals in addition to introducing a significant change of crowd in terms of activities change. Experimental results on MOT20 and SDHA datasets demonstrate that the proposed system is robust and efficient. The framework achieves an improved performance of recognition and detection people with a mean average precision of 99.0%, a real-time speed of 0.6 ms non-maximum suppression (NMS) per image for the SDHA dataset, and 95.3% mean average precision for MOT20 with 1.5 ms NMS per image.
Regulation Relatedness Map Creation Method with Latent Semantic Analysis

Regulatory authorities create a lot of legislation that must be followed. These create complex compliance requirements and time-consuming processes to find regulatory non-compliance. While the regulations establish rules in the relevant areas, recommendations and best practices for compliance are not generally mentioned. Best practices are often used to find a solution to this problem. There are numerous governance, management, and security frameworks in Information Technology (IT) area to guide businesses to run their processes at a much more mature level. Best practice maps can used to map another best practice, and users can adapt themselves by the help of this relation maps. These maps are created generally by an expert judgment or top-down relationship analysis. These methods are subjective and easily creates inconsistencies. In order to have an objective and statistical relationships map, we propose a Latent Semantic Analysis (LSA) based modal to generate a specific relatedness correlation map. We created a relatedness map of a banking regulation to a best practice. We analyzed 224 statements of this regulation in relation to Control Objectives for Information Technologies (Cobit) 2019's 1202 activities. Furthermore, we support our LSA results with MCDM analysis methods; Fuzzy Analytics Hierarchy Process (FAHP) to prioritize our criteria and, WASPAS (Weighted Aggregated Sum Product Assessment Method) to compare similarity results of regulation and Cobit activity pairs. Instead of the subjective methods for mapping best practices and regulations, this study suggests creating relatedness maps supported by the objectivity of LSA.
Preserve Privacy on Streaming Data During the Process of Mining Using User Defined Delta Value

A huge volume of data is being regularly generated from different applications, then analyzed by data mining techniques, and summarized in a way so that at the end result would be significant to the users. Published data sets may contain highly sensitive data so; companies are concerned about releasing sensitive information in a public domain. Sharing of such data has been proven to be beneficial for data mining process; however, it could be in violation of privacy laws and eventually be a threat to civil rights. Finding a solution to this problem is the aim of preserving privacy during data mining. Paper recommends preserve privacy on streaming data during the process of mining using user defined Delta value which perturb the sensitive data. Users have the flexibility to change the data attributes that are perturbed based on their security requirements. The outcomes not only protect data privacy but also reliably mine data streams.
Acceleration of Infrared Target Detection via Efficient Channel Pruning

Abstract Deployment of modern detection models is difficult for infrared target detection used in robot vision systems due to their heavy computational burden. To alleviate this situation, a simple but efficient channel pruning method is proposed for model acceleration. Specifically, a soft-gated module combined with batch normalization (SGBN) is designed as a standalone layer to substitute the standard batch normalization (BN) layer during training. The conversion between SGBN and BN is easy, and the training overhead introduced is almost negligible after replacement. By controlling the sparsity of the scaling factor in SGBN, unimportant channels with small output are blocked automatically and globally, which is simultaneous with model training. Removing these redundant channels no longer requires fine-tuning, thus significantly speeding up the pruning process. Experiments of pruning different detection models on the infrared dataset show the effectiveness of our method. For example, the parameters and FLOPs of pruned CetnerNet are reduced by 72.70% and 40.20%, respectively, without accuracy loss. The inference speed on the CPU is 12.01ms faster. Extended studies on the classification task also demonstrate its great potential when transferring to other applications.
Augmented Value Iteration Networks with Artificial Potential Fields for Path Planning

Abstract Value iterative networks (VINs), as a differentiable path planning method, taking environmental images as input, can solve path planning problems in new and unseen environments with powerful generalization capabilities. But the planning success rate decreases rapidly as the planning space scales. We propose the potential field augmented VIN by replacing the environmental map with the potential map as input and implementing this process in the form of dilated convolutions, giving more a priori information to the subsequent planning computation while hardly increasing the computational cost. Experiments on 2D grid-world show that the improved method has higher path planning success rates and smaller difference of predicted paths from shortest paths, as well as smaller performance degradation with increasing map size.
An investigation of personalized entertaining three-tier tests: a prior knowledge perspective

This study proposed a three-tier test to help students learn English grammar. To reduce students' anxiety, game-based learning was incorporated into the three-tier test, where personalization was also implemented to accommodate students' different needs. More specifically, we developed a Personalized Entertaining Three-Tier Test (PET3), which consisted of the three-tier test, game-based learning and personalization. On the other hand, diversities exist among learners, especially prior knowledge. To this end, this study aimed to provide the complete understandings of the effects of prior knowledge in the PET3 context, in terms of test performance, task performance, learning behavior and gaming behavior. Regarding test performance, high prior knowledge learners (HPK) performed better than low prior knowledge learners (LPK). Regarding task performance, HPK was superior to LPK in two aspects, i.e., the reasons of the main questions and advanced questions, while LPK performed better than HPK in the aspect of the review questions. Regarding learning behavior, HPK and LPK favored to use different hints. Regarding gaming behavior, HPK tended to be a winner while LPK had a tendency to be looser. Based on these findings, we developed a framework, which could help designers and instructors to know the preferences of diverse learners.
A Multilevel Input-Oriented English Teaching Model considering Diversified Training Objectives

At present, the existing English teaching model is not constructed according to the LASeR standard, resulting in the low I/O performance of the English teaching model and the decline of students’ learning efficiency. A multilevel input English teaching model considering diversified training objectives is proposed. Split the semantic features of multilevel input English teaching model information, calculate the support value of data items in the English teaching model resource database, and delete the complex and large amount of resource information. And with the probability vector diagram allocation method to match the autocorrelation characteristics of teaching resources, based on the relevant feature extraction results, complete the efficient mining of multilevel input-oriented English teaching model resources and realize the construction of multilevel input-oriented English teaching model considering diversified training objectives. The experimental results show that the designed model has high I/O performance and effectively improves students’ learning efficiency.
Mitigating Poisoning Attacks in Federated Learning

With the world transitioning into the era of artificial intelligence, a prodigious amount of data is being gathered and preserved in different silos. The excessive collection of data has resulted in various threats and privacy issues. This has questioned the use of conventional approaches of Artificial Intelligence and led to the development of a robust approach to processing and using data. Federated Learning, which trains an algorithm across many decentralized servers storing data without exchanging them, has shown promising results under this new reality. But the extant Federated learning mechanism has shown to possess some vulnerabilities that can be exploited to bargain data privacy. Federated Learning schemes have been subject to poisoning attacks. It is therefore of extremely high importance to showcase the prevailing flaws in the current design. This paper discusses the unique characteristics and challenges of federated learning, provides a review of the topic by highlighting two major poisoning attacks in the Federated Learning; and provides a method to mitigate these poisoning attacks.
Embedding Models for Knowledge Graphs Induced by Clusters of Relations and Background Knowledge

Embedding models have been successfully exploited for predictive tasks on Knowledge Graphs (KGs). We propose TransROWL-HRS, which aims at making KG embeddings more semantically aware by exploiting the intended semantics in the KG. The method exploits schema axioms to encode knowledge that is observed as well as derived by reasoning. More knowledge is further exploited by relying on a successive hierarchical clustering process applied to relations, to make use of the several semantic meanings that the very same relation may have. An experimental evaluation on link prediction and triple classification tasks proves the improvement yielded by the proposed approach (coupled with different optimizers) compared to some baseline models.
A Transformation of Oral English Teaching Method in the Network Environment

In order to effectually improve the application effect of the existing oral English teaching methods, this paper studies the conversion of effective oral English teaching methods under the network environment, aiming at the application problems, such as the flexibility of the current education platform. We build an effective oral English teaching platform which is comprised of oral English perception processing module, oral English pronunciation acquisition program module, oral English pronunciation preprocessing module, and oral English pronunciation error detection modeling module. We establish the information technology teaching objectives of the effective oral English teaching model on this basis. This research work is focused on the study of the output driven input-facilitated college oral English teaching model in oral English classroom. It is performed to realize the transformation of effective oral English teaching methods under the network environment. The experimental results show that the transformation effect of the effective teaching method of oral English, under the designed network environment, is better as compared to the existing work. The error detection rate in the proposed methodology reaches up to 98.6%, which is significantly higher than the results obtained by researchers in their existing work. The proposed method has obvious application advantages.
Building a Robust Distributed Voting System Using Consortium Blockchain

The traditional ballot system and the electronic voting system has been used all over the world for a while now but it has numerous flaws. This paper put forth those flaws viz. voter verification, security, transparency, mobility, flexibility and operational cost. Overview of blockchain and unraveling its potential to overcome these flaws is followed. The process of carrying out the entire election process right from voter and contestant registration, till the end result calculation is depicted both technically and functionally based on consortium blockchain model. Potential challenges and their solutions involved in building such a robust and fault-tolerant software system are brainstormed. The motive of this paper is to suggest why a decentralized voting system built using a novel approach by leveraging blockchain is significantly better than current systems and how to build one by elucidating the architecture and pseudocode for the same.
Are You Wearing a Mask? Detecting If a Person Wears a Mask Using a Wristband

Coronavirus 2019 (COVID-19) has posed a serious threat to the lives and health of the majority of people worldwide. Since the early days of the outbreak, South Korea's government and citizens have made persistent efforts to provide effective prevention against further spread of the disease. In particular, the participation of individual citizens in complying with the necessary code of conduct to prevent spread of the infection, through measures such as social distancing and mask wearing, is as instrumental as the geographical tracking of the trajectory of the infected. In this paper, we propose an activity recognition method based on a wristband equipped with an IR array and inertial measurement unit (IMU) to detect individual compliance with codes of personal hygiene management, such as mask wearing, which are recommended to prevent the spread of infectious diseases. The results of activity recognition were comparatively analyzed by applying conventional machine learning algorithms and convolutional neural networks (CNNs) to the IMU time series and IR array thermal images collected from 25 subjects. When CNN and 24 × 32 thermal images were used, 97.8% accuracy was achieved (best performance), and when 6 × 8 low-resolution thermal images were used, similar performance with 97.1% accuracy was obtained. In the case of using IMU, the performance of activity recognition was lower than that obtained with the IR array, but an accuracy of 93% was achieved even in the case of applying machine learning algorithms, indicating that it is more suitable for wearable devices with low computational capability.
Gradual Disintegrations in Etude 16, Pour Irina

Among the etudes of Book 3 it is the second one, Pour Irina (“For Irina”), that is perhaps the most reflective of the techniques Ligeti adapted from chaos theory in mathematics and often deployed in his music—namely, the introduction of a slight disturbances in a system that cause it to eventually spiral out of control. While this chaos principle is a primarily rhythmic one in his earlier work, in Etude 16 Ligeti applies it to scale area as well as tempo in a novel manner that makes the piece perhaps the greatest outlier among all of the 18 etudes. The central claim of this chapter is that Pour Irina is primarily a study in departure from the framework with which it opens; there is never a return home, but rather an arrival at a distant destination. This is effected by a number of processes through the etude, and each section approaches this question of disintegration in unique ways.
Accuracy Gains from Privacy Amplification Through Sampling for Differential Privacy

Abstract Recent research in differential privacy demonstrated that (sub)sampling can amplify the level of protection. For example, for ϵ-differential privacy and simple random sampling with sampling rate r, the actual privacy guarantee is approximately rϵ, if a value of ϵ is used to protect the output from the sample. In this paper, we study whether these amplification effects can be exploited systematically to improve the accuracy of the privatized estimate. Specifically, assuming the agency has information for the full population, we ask under which circumstances accuracy gains could be expected, if the privatized estimate would be computed on a random sample instead of the full population. We find that accuracy gains can be achieved for certain regimes. However, gains can typically only be expected, if the sensitivity of the output with respect to small changes in the database does not depend too strongly on the size of the database. We only focus on algorithms that achieve differential privacy by adding noise to the final output and illustrate the accuracy implications for two commonly used statistics: the mean and the median. We see our research as a first step toward understanding the conditions required for accuracy gains in practice and we hope that these findings will stimulate further research broadening the scope of differential privacy algorithms and outputs considered.