Reviewerly/exHarmony huggingface dataset:
Analysis of particle swarm optimization by dynamical systems theory

Particle Swarm Optimization (PSO) is one of the most effective optimization methods for the black-box optimization problem. PSO involves a large number of particles sharing information with each other to search for the optimal solution. The method in which a large number of search individuals cooperate to search for the optimal solution is called swarm intelligence optimization. In the group intelligence optimization, the balance between exploration and exploitation is important. However, in PSO, it is unclear to what extent each parameter affects exploration and exploitation. Therefore, we proposed a deterministic PSO without probabilistic elements and analyzed the dynamics of PSO using dynamical systems theory. Each particle in deterministic PSO has its motion determined by its eigenvalue. In order to make this motion clearer, a canonical deterministic PSO on a regularized phase space was proposed. The results of these analyses clarified what is attributed to the parameters for exploration and exploitation, i.e., global and local search capabilities. Based on this fact, we proposed a nonlinear map optimization (NMO) with improved local search capability. In this paper, we present the background of our proposal and consider the solution-search capability of nonlinear map optimization.
Considering Interaction Sequence of Historical Items for Conversational Recommender System

Different from the traditional recommender systems with content-based and collaborative filtering, conversational recommender systems (CRS) can dynamically dialogue with users to capture fine-grained preferences. Although several efforts have been made for CRS, they neglect the importance of interaction sequences, which seek to capture the ‘context’ of users’ activities based on actions they have performed recently. Therefore, we propose a framework that considers interaction Sequence of historical items for Conversational Recommendation (SeqCR). Specifically, SeqCR first scores candidate items through the sequence which users interact with. Then it can generate the recommendation list and attributes to be asked based on the scores. We restrict candidate attributes to the ones with high-scoring (high-relevance) items, which effectively reduces the search space of attributes and leads to user preferences that can be hit more quickly and accurately. Finally, SeqCR utilizes the policy network to decide whether to recommend or ask. We conduct extensive experiments on two datasets from MovieLens 10M and Yelp in multi-round conversational recommendation scenarios. Empirical results demonstrate our SeqCR significantly outperforms the state-of-the-art methods.
The AI Liability Puzzle and A Fund-Based Work-Around

Confidence in the regulatory environment is crucial to enable responsible AI innovation and foster the social acceptance of these powerful new technologies. One notable source of uncertainty is, however, that the existing legal liability system is unable to assign responsibility where a potentially harmful conduct and/or the harm itself are unforeseeable, yet some instantiations of AI and/or the harms they may trigger are not foreseeable in the legal sense. The unpredictability of how courts would handle such cases makes the risks involved in the investment and use of AI difficult to calculate with confidence, creating an environment that is not conducive to innovation and may deprive society of some benefits AI could provide. To tackle this problem, we propose to draw insights from financial regulatory best practices and establish a system of AI guarantee schemes. We envisage the system to form part of the broader market-structuring regulatory frameworks, with the primary function to provide a readily available, clear, and transparent funding mechanism to compensate claims that are either extremely hard or impossible to realize via conventional litigation. We propose it to be at least partially industry-funded. Funding arrangements should depend on whether it would pursue other potential policy goals aimed more broadly at controlling the trajectory of AI innovation to increase economic and social welfare worldwide. Because of the global relevance of the issue, rather than focusing on any particular legal system, we trace relevant developments across multiple jurisdictions and engage in a high-level, comparative conceptual debate around the suitability of the foreseeability concept to limit legal liability. The paper also refrains from confronting the intricacies of the case law of specific jurisdictions for now and—recognizing the importance of this task—leaves this to further research in support of the legal system’s incremental adaptation to the novel challenges of present and future AI technologies.&#x0D; This article appears in the special track on AI and Society.
Reversible difference expansion multi-layer data hiding technique for medical images

Maintaining the privacy and security of confidential information in data communication has always been a major concern. It is because the advancement of information technology is likely to be followed by an increase in cybercrime, such as illegal access to sensitive data. Several techniques were proposed to overcome that issue, for example, by hiding data in digital images. Reversible data hiding is an excellent approach for concealing private data due to its ability to be applied in various fields. However, it yields a limited payload and the quality of the image holding data (Stego image), and consequently, these two factors may not be addressed simultaneously. This paper addresses this problem by introducing a new non-complexity difference expansion (DE) and block-based reversible multi-layer data hiding technique constructed by exploring DE. Sensitive data are embedded into the difference values calculated between the original pixels in each block with relatively low complexity. To improve the payload capacity, confidential data are embedded in multiple layers of grayscale medical images while preserving their quality. The experiment results prove that the proposed technique has increased the payload with an average of 369999 bits and kept the peak signal to noise ratio (PSNR) to the average of 36.506 dB using medical images' adequate security the embedded private data. This proposed method has improved the performance, especially the secret size, without reducing much the quality. Therefore, it is suitable to use for relatively big payloads.
Hardware Design of a Turbo Product Code Decoder

Nair, Gana C. Yamuna, B. Balasubramanian, Karthi Mishra, DeepakIn a communication channel, message transfer happens through a noisy medium which can introduce errors in the transmitted message. It is required to maintain an acceptable bit error rate for a reliable transmission. This can be handled by an efficient error control coding scheme. This paper discusses the hardware design of an error control decoder, namely turbo product code decoder. The iterative Chase-Pyndiah decoding algorithm has been used in the turbo decoder design. The decoder has been designed using both MATLAB and Verilog, and its performance has been analysed. To study the robustness of the decoder in dealing with different data types, performance analysis is done with both the received data and the encrypted version of the same. Advanced Encryption Standard (AES) has been used for the encryption process, and it is shown that there is only a negligible difference in the performance for both the data sets.
Implementation of Feature Selection using Information Gain Algorithm and Discretization with NSL-KDD Intrusion Detection System

Feature selection is one of the research on data mining for datasets that have relatively many attributes. Eliminating some attributes that are irrelevant to the label class will be able to improve the performance of the classification algorithm. The Information Gain algorithm is one of the algorithms for searching for features that are irrelevant to the label class. This algorithm uses wrapper techniques to eliminate irrelevant attributes. This research aims to implement feature selection using the Information Gain algorithm against the NSL KDD intrusion detection dataset which has a large number of relative attributes. The dataset of the selected attribute will be performed by a classification algorithm so that an attribute reduction can improve the compute process and improve the accuracy of the algorithm model used.
Opportunities, Challenges and Promotion Countermeasures of Central Bank Digital Currency

Blockchain is a shared ledger and database with distributed data storage, asymmetric encryption, consensus mechanism and intelligent contract as the core technologies. It has the characteristics of decentralized, tamper-proof, traceable and retrospective, collectively-maintained, open and transparent. Therefore, Blockchain becomes the bottom but also the most important technical means of Central Bank Digital Currency (CBDC), which are organically combined and closely linked. This paper studies the opportunities and challenges of Central Bank Digital Currency (CBDC) issuance in the context of the rapid development of Blockchain technology, and proposes the main technical routes and countermeasures for CDBC: improving the management mechanisms, formulating CBDC standards and regulations; deepening international cooperation and actively innovating regulatory approaches to meet future challenges; focusing on technology empowerment, planning for protecting the rights and interests of financial consumers under new business models; strengthening data governance, building data transaction and privacy protection mechanisms. This study has reference value for the risk control, technology adoption and countermeasures in the process of issuing digital currency by central banks of all countries.
PEEP: A Parallel Execution Engine for Permissioned Blockchain Systems

Unlike blockchain systems in public settings, the stricter trust model in permissioned blockchain opens an opportunity for pursuing higher throughput. Recently, as the consensus protocols are developed significantly, the existing serial execution manner of transactions becomes a key factor in limiting overall performance. However, it is not easy to extend the concurrency control protocols, widely used in database systems, to blockchain systems. In particular, there are two challenges to achieve parallel execution of transactions in blockchain as follows: (i) the final results of different replicas may diverge since most protocols just promise the effect of transactions equivalent to some serial order but this order may vary for every concurrent execution; and (ii) almost all state trees that are used to manage states of blockchain do not support fast concurrent updates. In the view of above challenges, we propose a parallel execution engine called PEEP, towards permissioned blockchain systems. Specifically, PEEP employs a deterministic concurrency mechanism to obtain a predetermined serial order for parallel execution, and offers parallel update operations on state tree, which can be implemented on any radix tree with Merkle property. Finally, the extensive experiments show that PEEP outperforms existing serial execution greatly.
Local data and upstream reporting as sources of error in the administrative data undercount of Covid 19

The Covid 19 pandemic illuminates the role data has in public policy-making, i.e. datafication of society, and the importance of exploring the local sources of data to reveal errors in what has assuredly been from the beginning an undercount of cases and deaths. I note four interrelated error sources. The first two are common to any quantitative data collection project: (1) representation, measurement, and data processing; and (2) problems of data standardization from unequally resourced local and national data providers. Covid 19 casts a special light on (3) the possibility of government intervention in at least the public presentation of these data; and (4) human errors in the data chain caused by a stressful data collection environment. To identify errors, we should look to national pressures and the local contexts from which these data are collected and the upstream reporting process.
An Approach to Sentimental Analysis of Drug Reviews using RNN-BiLSTM Model

In past years, large amount of data in various forms is available for use in medical domain to analyse various health conditions. The reviews in the medical industry are based on price, side- effects, user experience and proper dosage. The drug reviews are much more complicated as they contain medical terminologies like chemical used in drugs, medical health conditions etc. To overcome these issues this paper proposes a recommendation system based on a hybrid RNN stacked with bi-directional LSTM model and a gradient boosting framework, LightGBM classifier, which takes health condition as the input and recommends the best drugs based on the analysis of the reviews with 83% accuracy.
TRAINING SIMULATORS IN THE PROFESSIONAL EDUCATION SYSTEM: PEDAGOGICAL ASPECT

Professional dynamism, as a characteristic feature of modern society, requires updating the forms and methods of training specialists. In the current system of training is increasingly in demand training procedure, adaptive to different conditions of the educational process, including using distance learning technologies and e-learning. In the system of professional training of specialists, virtual simulators and simulators are actively used, focused on the formation of stable behavior models in the professional environment. The question of pedagogical aspects of the use of these teaching tools in the system of professional training becomes relevant. The paper defines the prerequisites for the use of these tools in training, sets goals and objectives, describes interaction models, and defines the classification of educational tasks. The author highlights the levels of simulation based on the degree of immersiveness and interactivity of the environment, methodological approaches and methods of organizing educational activities of students. The paper summarizes the experience of using simulators and virtual simulators in the system of professional training of specialists, lays the conceptual foundations for building a simulation model of training.
Spatiotemporal Transformer for Video-based Person Re-identification

Recently, the Transformer module has been transplanted from natural language processing to computer vision. This paper applies the Transformer to video-based person re-identification, where the key issue is to extract the discriminative information from a tracklet. We show that, despite the strong learning ability, the vanilla Transformer suffers from an increased risk of over-fitting, arguably due to a large number of attention parameters and insufficient training data. To solve this problem, we propose a novel pipeline where the model is pre-trained on a set of synthesized video data and then transferred to the downstream domains with the perception-constrained Spatiotemporal Transformer (STT) module and Global Transformer (GT) module. The derived algorithm achieves significant accuracy gain on three popular video-based person re-identification benchmarks, MARS, DukeMTMC-VideoReID, and LS-VID, especially when the training and testing data are from different domains. More importantly, our research sheds light on the application of the Transformer on highly-structured visual data.
Virtual reality as a didactic tool for the development of professional competencies

Questions of development of secondary vocational education are raised. Digital technologies of the future require training for new generation workers. Digital competencies are required to work on the digital railway. Examples of the use of virtual reality in the system of technical training at the enterprises of railway transport are given. The formation of professional competencies among students is effective in a virtual educational environment. Describes the experience of the virtual laboratory of traffic control in a railway college. Examples and options for using high-tech equipment for training mid-level specialists are given. The features of the educational process during distance learning in the information environment are revealed. The perspective of education in the development of mentality and the preservation of the culture of learning generations is explained. An example of the variability of meta-subject assignment techniques is shown. The methods of constructing learning situations for a design solution in virtual reality are listed. Virtual reality in vocational education is considered from the point of view of enhancing the cognitive interest of students, conducting a demonstration exam on principles Worldskills. Indicator indicators of the development of professional competencies in the system of continuing professional education are proposed. In a first approximation, the problematic issues of organizing distance learning during self-isolation during the coronavirus pandemic are listed. The function of education is substantiated to ensure a prosperous future for humanity.
Advanced Multiple Linear Regression Based Dark Channel Prior Applied on Dehazing Image and Generating Synthetic Haze

Haze removal is an extremely challenging task, and object detection in the hazy environment has recently gained much attention due to the popularity of autonomous driving and traffic surveillance.In this work, the authors propose a multiple linear regression haze removal model based on a widely adopted dehazing algorithm named Dark Channel Prior.Training this model with a synthetic hazy dataset, the proposed model can reduce the unanticipated deviations generated from the rough estimations of transmission map and atmospheric light in Dark Channel Prior.To increase object detection accuracy in the hazy environment, the authors further present an algorithm to build a synthetic hazy COCO training dataset by generating the artificial haze to the MS COCO training dataset.The experimental results demonstrate that the proposed model obtains higher image quality and shares more similarity with ground truth images than most conventional pixel-based dehazing algorithms and neural network based haze-removal models.The authors also evaluate the mean average precision of Mask R-CNN when training the network with synthetic hazy COCO training dataset and preprocessing test hazy dataset by removing the haze with the proposed dehazing model.It turns out that both approaches can increase the object detection accuracy significantly and outperform most existing object detection models over hazy images.
A simple method for rejection sampling efficiency improvement on SIMT architectures

We derive a probability distribution for the possible number of iterations required for a SIMT (single instruction multiple thread) program using rejection sampling to finish creating a sample across all threads. This distribution is found to match a recently proposed distribution from Chakraborty and Gupta (in: Communications in statistics: theory and methods, 2015) that was shown as a good approximation of certain datasets. This work demonstrates an exact application of this distribution. The distribution can be used to evaluate the relative merit of some sampling methods on the GPU without resort to numerical tests. The distribution reduces to the expected geometric distribution in the single thread per warp limit. A simplified formula to approximate the expected number of iterations required to obtain rejection iteration samples is provided. With this new result, a simple, efficient layout for assigning sampling tasks to threads on a GPU is found as a function of the rejection probability without recourse to more complicated rejection sampling methods.
Study of Holoportation: Using Network Errors for Improving Accuracy and Efficiency

Holoportation is a technique in which two persons communicate with each with anyone’s virtual presence in front of the other. There have been efforts to make it as much smoother and real as possible by the researchers in the recent past. But the challenges are many in this field not only due to unavailability of software resources but due to hardware constraints as well. Major hardware constraints are based on the transmission of a lot of data being collected by the camera and audio devices which require good data transfer rates between the communicating devices. Reason of challenge is viewed in two faces, i.e., one is slow data transfer speed and the second is huge amount of data transfer. Slow data transfer speed of resources is being tackled, and a good data transfer rate has been reached to but still not suffice and unavailable in all areas around the world. A huge amount of data transfer may also suffer from network lag spikes and dropouts of the signals which will lead to disruption in reproduced Holoportation on the receiver’s end. In this paper, the focus is on proposing a buffering and correction mechanism which will require to be applied on both sender and receiver ends. The system will produce high accuracy and will not increase network latency and hence the smoothness of service. The system will leverage normal human behaviour and persistence of vision delays to provide better accuracies.
A Blockchain-Based Loyalty Program for a Smart City

The subject of this paper is the development of a loyalty program based on blockchain, tailored for the application in smart cities. The goal is to propose a collaborative loyalty scheme that would enable consumers to collect loyalty points across a smart city, through traditional buying of products and services from partners, but also through participating in different collaborative activities, such as crowdsourcing, crowdsensing or crowdvoting. Additionally, the points can be spent across the smart city with any of the partners. In the paper, we give details on the business model for the proposed loyalty scheme and present a prototype of the implementation on the Hyperledger Fabric platform. We discuss the advantages of the proposed loyalty program and give implications for different stakeholders.
Big Data Process Engineering under Manifold Coordinate Systems

This paper involves techniques for improving the quality indices of big data process engineering with respect to high-performance coded design, transmission speed, and reliability under manifold coordinate systems. The system formed with limited number of basis vectors. The set of modular sums of the vectors including themselves form t-dimensional toroidal coordinate grid over the toroid, and the basis is sub-set of general number of grid coordinate set. These design techniques make it possible to configure high performance information technology for big data coding design and vector signal processing. The underlying mathematical principles relate to the optimal placement of structural elements in spatially or temporally distributed systems by the appropriate algebraic constructions based on cyclic groups in extensions of Galois fields, and development of the scientific basis for optimal solutions for wide classes of technological problems in big data process engineering and computer science.
Three-Dimensional Gaze Projection Heat-Mapping of Outdoor Mobile Eye-Tracking Data

The mobilization of eye-tracking for use outside of the laboratory provides new opportunities for the assessment of pedestrian visual engagement with their surroundings. However, the development of data representation techniques that visualize the dynamics of pedestrian gaze distribution upon the environment they are situated within remains limited. The current study addresses this through highlighting how mobile eye-tracking data, which captures where pedestrian gaze is focused upon buildings along urban street edges, can be mapped as three-dimensional gaze projection heat-maps. This data processing and visualization technique is assessed during the current study along with future opportunities and associated challenges discussed.