Reviewerly/exHarmony huggingface dataset:
Improved GCN Framework for Human Motion Recognition

Human recognition models based on spatial-temporal graph convolutional neural networks have been gradually developed, and we present an improved spatial-temporal graph convolutional neural network to solve the problems of the high number of parameters and low accuracy of this type of model. The method mainly draws on the inception structure. First, the tensor rotation is added to the graph convolution layer to realize the conversion between graph node dimension and channel dimension and enhance the model’s ability to capture global information for small-scale tasks. Then the inception temporal convolution layer is added to build a multiscale temporal convolution filter to perceive temporal information under different time domains hierarchically from 4-time dimensions. It overcomes the shortcomings of temporal graph convolutional networks in the field of joint relevance of hidden layers and compensates for the information omission of small-scale graph tasks. It also limits the volume of parameters, decreases the arithmetic power, and speeds up the computation. In our experiments, we verify our model on the public dataset NTU RGB + D. Our method reduces the number of the model parameters by 50% and achieves an accuracy of 90% in the CS evaluation system and 94% in the CV evaluation system. The results show that our method not only has high recognition accuracy and good robustness in human behavior recognition applications but also has a small number of model parameters, which can effectively reduce the computational cost.
Configurable Deep Learning Accelerator with Bitwise-accurate Training and Verification

This paper introduces an end-to-end solution to a deep neural network (DNN) inference system. We customize and enrich a family of deep-learning accelerators (DLA) based on the NVIDIA open-source deep-learning accelerator (NVDLA). Our exclusive enhancement includes hardware and software parts. The hardware part is the shared multiplier array of both high-efficient regular and depth-wise convolutions. The software part includes a new DLA toolchain that generates various specifications of DLA and provides corresponding tests. Users can either verify trained or untrained DNN graphs to simulate the accuracy and performance results. Considering the hard-to-debug accuracy loss in the integer inference, a hardware-aware and bitwise-accurate flow are proposed. The DLAs were verified using FPGA prototypes and silicon chips. The 65nm test chip has 256 convolutional multipliers, and the peak computing power is 200 GOPs, achieving 2.5TOPs/W peak energy efficiency. The average power consumption is 65mW when running an object detection DNN model.
English Translation of Chinese Topic Sentences with Gap Subject Based on Internet Environment

Machine translation is showing an increasingly broad application prospect as the Internet becomes more widely used. The method based on the principle of maximum entropy is primarily used in this paper to determine the subject clause for translation. Its feature set does not necessitate extensive linguistic knowledge, and it is less reliant on it than other methods. It can also add arbitrary features and flexibly combine a large amount of scattered and fragmented knowledge. As a result, the maximum entropy model is used as the classifier in this paper, and lexical and sentence features are used to effectively combine the rules and statistical knowledge. The subsentence recognition process is broken down into three stages, with different features extracted at different stages and the maximum entropy model applied multiple times. At each stage, the classifiers are trained. The translation accuracy and recall rate of Chinese gapped subject topic sentences are improved by more than 5% in a maximum entropy model, according to the results. This method of feature description is particularly useful for identifying sentence endings in Chinese subject topic sentences with gaps. Its translation is closer to the correct translation, indicating that the proposed method’s basic concept—the rationality of segmentation—is correct. The algorithm based on the maximum entropy model performs better in practice.
Resource Profiling and Performance Modeling for Distributed Scientific Computing Environments

Scientific applications often require substantial amount of computing resources for running challenging jobs potentially consisting of many tasks from hundreds of thousands to even millions. As a result, many institutions collaborate to solve large-scale problems by creating virtual organizations (VOs), and integrate hundreds of thousands of geographically distributed heterogeneous computing resources. Over the past decade, VOs have been proven to be a powerful research testbed for accessing massive amount of computing resources shared by several organizations at almost no cost. However, VOs often suffer from providing exact dynamic resource information due to their scale and autonomous resource management policies. Furthermore, shared resources are inconsistent, making it difficult to accurately forecast resource capacity. An effective VO’s resource profiling and modeling system can address these problems by forecasting resource characteristics and availability. This paper presents effective resource profiling and performance prediction models including Adaptive Filter-based Online Linear Regression (AFOLR) and Adaptive Filter-based Moving Average (AFMV) based on the linear difference equation combining past predicted values and recent profiled information, which aim to support large-scale applications in distributed scientific computing environments. We performed quantitative analysis and conducted microbenchmark experiments on a real multinational shared computing platform. Our evaluation results demonstrate that the proposed prediction schemes outperform well-known common approaches in terms of accuracy, and actually can help users in a shared resource environment to run their large-scale applications by effectively forecasting various computing resource capacity and performance.
An Effective Payload Management (2,3) Scheme for Visual Secret Sharing Over Remote Sensing Images

Usually, visual cryptographic techniques are widely used when the secret content or images are shared in a network transmission or cloud storages. Data confidentiality and integrity should be taken much more care when it deals with these applications like defense, military, remote sensing and space researches even more in medical data. Many existing techniques have been used for visual secret sharing in cloud network but it having less concentration in transmission payloads. In existing such (2, 2) techniques and (2, 3) techniques are concentrated only in integrity and resolution of both cover and secret images. Here an novel and effective payload management (2, 3) scheme has proposed and devised for visual secret sharing over remote sensing images in large cloud networks to manage the image payloads based on image blending scheme. It devised the framework to manage the transmission payloads and implement the blending scheme to avoid image pixel tampering. The implementation results show that this effective scheme have more potential than the existing secret sharing schemes.
Finding Optimal Moving Target Defense Strategies: A Resilience Booster for Connected Cars

During their life-cycle, modern connected cars will have to face various and changing security threats. As for any critical embedded system, security fixes in the form of software updates need to be thoroughly verified and cannot be deployed on a daily basis. The system needs to commit to a defense strategy, while attackers can examine vulnerabilities and prepare possible exploits before attacking. In order to break this asymmetry, it can be advantageous to use proactive defenses, such as reconfiguring parts of the system configuration. However, resource constraints and losses in quality of service need to be taken into account for such Moving Target Defenses (MTDs). In this article, we present a game-theoretic model that can be used to compute an optimal MTD defense for a critical embedded system that is facing several attackers with different objectives. The game is resolved using off-the-shelf MILP solvers. We validated the method with an automotive use case and conducted extensive experiments to evaluate its scalability and stability.
Envision of Solar System using Augmented Reality

The potential for using augmented reality (AR) technology is investigated in this article, and the solar system is constructed as a software model. The software tools for modelling the solar system that are currently available are explored. The software programme created using augmented reality technology depicts the behavior of solar system objects in great detail. By reading the object's description, primary characteristics, and intriguing information, as well as daily updates of NASA's tweets on the dashboard, users may visually and informatively explore each planet. The AR models are built utilizing Newtonian physics, as well as the proper proportions in object size, velocity, form, and distance between orbits of celestial bodies.
Scale-equivariant Steerable Networks for Crowd Counting

In the crowd counting based on density map estimation, the scale variance is the main factor causing the difficulty of crowd density map estimation. To handle this issue, we utilize the scale-equivariant steerable convolution and attention mechanism to build the network for the crowd counting. Meanwhile, we propose a method of generating high-precision pseudo bounding boxes about heads of crowd to generate the density maps based on the length and width of the pseudo bounding boxes which further obtain the scale information of the crowd. The density map improves the accuracy of density map estimation without changing our network configuration. In order to verify the effectiveness of scale-equivariant steerable convolution in crowd density map estimation, experiments are conducted on ShanghaiTech dataset and compared with other crowd counting networks. Experiments show that the proposed network structure and the generated density map are effective for the task of crowd counting based on density map estimation.
A Low Power Clock Gated Median Filter for Gray Level Images

Based on a sorting network, the proposed approximate median filters (APMF) produce acceptable image quality on low-cost hardware. A specialized comparator is being developed to increase the noise-elimination capabilities of such filters. The inexact median filters (IMF) have a regular and modular architecture. Further, to reduce the power consumed by digital systems clock gating is employed. Clock with a View Into the Future Gating is a unique way of computing each FF&#x0027;s clock enabling signals one cycle ahead of time, based on the current cycle data of the FFs it depends on. By allocating a whole clock cycle for the computation of the enabling signals and their propagation, it avoids the strict timing constraints of adaptive geometric features-based filtering (AGFF) and data-driven. In addition, to give a better assessment of intrinsic mode filter (IMF) performance, a novel error evaluation approach based on a histogram-based error dispersion plot was presented. The proposed filter is effective in terms of power, area, and speed, according to simulation findings. The filter&#x0027;s output quality is comparable to that of a precise filter, despite the trade- off between filtering precision and circuit features. In addition, the deterioration is almost unnoticeable to the naked eye. When the clock power is reduced, the entire system&#x0027;s power is reduced as well.
Federated Learning Techniques for 5G Mobile Networks

The growing adoption of AI/ML in 5G mobile networks results in the increased amount of the sensitive data sharing by the end users, which may cause certain privacy and security concerns. Therefore, the general efforts in the direction of privacy protection by AI/ML community so far are focused on the federated learning techniques. In this paper, we consider the main advantages of the decentralized federated learning approach in the case of 5G mobile networks, in terms of the data privacy for subscribers. We implement the federated learning model to the task of network load prediction. Simulation results show that proposed federated learning model provided the same accuracy as conventional learning model, while reducing the redundancy and ensuring the privacy of user's data.
The Original Method of Controlling a Computer Using Distance Sensors

The article provides a brief overview of human-computer interaction methods and systems, and the research made in this area. We have designed the architecture of the system and tested a method of controlling a computer using ultrasonic distance sensors. In the course of the experiment, the following problems have been solved: obtaining and processing data from several distance sensors, data transfer to gesture recognition algorithm, uncontrolled oscillations of sensor indicators. The suggested method of controlling a computer can be used in ubiquitous computing.
Machine Learning-Based Automatic Utterance Collection Model for Language Development Screening of Children

To assess a child’s language development, utterance data are required. The approach of recording and transcribing the conversation between the expert and the child is mostly utilized to obtain utterance data. Because data are obtained through one-on-one interactions, this approach is costly. In addition, depending on the expert, subjective dialogue situations may be incorporated. To acquire speech data, we present a machine learning-based phrase generating model. It has the benefit of being able to cope with several children, which reduces costs and allows for the collection of objectified utterance data through consistent conversation settings. Children’s utterances are initially categorized as topic maintenance or topic change, with rule-based replies based on scenarios being formed in the instance of a topic change. When it comes to topic maintenance, it encourages the child to say more by answering with imitative phrases. The strategy we suggest has the potential to reduce the cost of collecting data for evaluating children’s language development while maintaining data collection impartiality.
Comment on “Quantum coherence measures based on Fisher information with applications”

In their recent paper [Li et al., Phys. Rev. A 103, 012401 (2021)], the authors proposed two quantities ${C}_{\mathrm{F}}^{D}(\ensuremath{\rho}|\mathcal{K})$ in Eq. (11) and ${C}_{\mathrm{F}}^{C}(\ensuremath{\rho}|\mathcal{K})$ in Eq. (12). The authors proved that ${C}_{\mathrm{F}}^{C}(\ensuremath{\rho}|\mathcal{K})$ is a valid coherence measure in Theorem 5 and proved that ${C}_{\mathrm{F}}^{D}(\ensuremath{\rho}|\mathcal{K})={C}_{\mathrm{F}}^{C}(\ensuremath{\rho}|\mathcal{K})$ in Proposition 4. In this Comment, we show that the proof of Proposition 4 is not justified, that is, ${C}_{\mathrm{F}}^{D}(\ensuremath{\rho}|\mathcal{K})={C}_{\mathrm{F}}^{C}(\ensuremath{\rho}|\mathcal{K})$ is not true, in general. Consequently, whether ${C}_{\mathrm{F}}^{D}(\ensuremath{\rho}|\mathcal{K})$ is a valid coherence measure is still an open problem.
A Study on Visual Based Optical Sensor for Depth Sense Estimation

In this study we analyzed the working of Intel RealSense depth camera D455 which is latest depth sense camera in Intel Family. This visual based optical sensor will be useful for mapping the unknown environment in indoor and outdoor applications and it is also useful for longer range fast moving autonomous robots for inspection and more accurate for collision avoidance capabilities. By understanding the usefulness of visual sensor here we performed experiment on camera position at different places and observed the streaming of depth sense images at different positions, and x, y, z co-ordinates of accelerometer and gyroscope measurements are visualized.
Compression and Decompression of Biomedical Signals Using Chinese Remainder Theorem

The biomedical data signals captured from the patient is sent for compression where the size of the digital data is 24 bits. Compression is executed for two consecutive data elements. One is current data where data is shared for compression and previous data stores old values. In the starting, previous data storage contains no data. Compression is accomplished using log2 sub-band methodology that involves the identification of changes by making use of XOR logic. The XOR-generated data are further calculated using OR gates that are required to generate flags. Using the Chinese remainder theorem that involves the division method the 24-bit data is compressed into 12 bits. According to the theorem, the dividend should be divided with divisor where the degree of divisor must be less than that of dividend. In our work, the compressed data generated before the division process can be up to 26 bits which is divided only with a prime number as per the theorem rule. The division process produces 12-bit data which is given to the decoder along with the quotient and remainder obtained after division for decompression. It should satisfy the division rule where the decompressed result should be the same as the data to be compressed then the overall compression & decompression is correct and is satisfied in our project.
Symposium on Cognitive Architecture

Between 1965 and 1982, classically symbolic AI was often touted as the most promising basis for a general theory of Cognitive Architecture. By 1986, however, prominent researchers began to herald the emergence of a new paradigm, Connectionism, which would (they argued) eventually displace the classically symbolic methods dominant in AI and Cognitive Science. Since then, a variety of competing positions have emerged, some purely connectionist, some hybrid, and some purely classical. This symposium examines the present and future prospects for contending paradigms in this realm. A variety of arguments, arising from computational, psychological, biological and philosophical bases, are presented.
Extrapolation of Human Estimates of the Concreteness/ Abstractness of Words by Neural Networks of Various Architectures

In a great deal of theoretical and applied cognitive and neurophysiological research, it is essential to have more vocabularies with concreteness/abstractness ratings. Since creating such dictionaries by interviewing informants is labor-intensive, considerable effort has been made to machine-extrapolate human rankings. The purpose of the article is to study the possibility of the fast construction of high-quality machine dictionaries. In this paper, state-of-the-art deep learning neural networks are involved for the first time to solve this problem. For the English language, the BERT model has achieved a record result for the quality of a machine-generated dictionary. It is known that the use of multilingual models makes it possible to transfer ratings from one language to another. However, this approach is understudied so far and the results achieved so far are rather weak. Microsoft’s Multilingual-MiniLM-L12-H384 model also obtained the best result to date in transferring ratings from one language to another. Thus, the article demonstrates the advantages of transformer-type neural networks in this task. Their use will allow the generation of good-quality dictionaries in low-resource languages. Additionally, we study the dependence of the result on the amount of initial data and the number of languages in the multilingual case. The possibilities of transferring into a certain language from one language and from several languages together are compared. The influence of the volume of training and test data has been studied. It has been found that an increase in the amount of training data in a multilingual case does not improve the result.
SKILL-IL: Disentangling Skill and Knowledge in Multitask Imitation Learning

In this work, we introduce a new perspective for learning transferable content in multi-task imitation learning. Humans are capable of transferring skills and knowledge. If we can cycle to work and drive to the store, we can also cycle to the store and drive to work. We take inspiration from this and hypothesize the latent memory of a policy network can be disentangled into two partitions. These contain either the knowledge of the environmental context for the task or the generalisable skill needed to solve the task. This allows an improved training efficiency and better generalization over previously unseen combinations of skills in the same environment, and the same task in unseen environments. We used the proposed approach to train a disentangled agent for two different multi-task IL environments. In both cases, we out-performed the SOTA by 30% in task success rate. We also demonstrated this for navigation on a real robot.
The Value of Deeds in a Digital Society

The act as a way of mastering the reality given to a person goes beyond stereotypical actions and is not recognized by modern AI as a necessary condition for human development. The act is connected by semantic fields with the world where it takes place and the dynamics of the multi-level components of natural language do not always correspond to the dynamics of the digital situation. Thus, they stimulate a person to commit acts to determine the boundaries of acceptability, ethics, and the norms of the digital space. AI acts as a representative of that other world, which is also not mirrored but is a reflection of a person in the digital space.
A Novel Methodology to Ensure Data Integrity in Enterprise Information Systems Using Blockchain Technology

Data integrity is one of the most important issues in information management systems. Periodic database auditing is the only way to ensure data integrity. Blockchain is a technology that supports and employs a variety of cryptographic models. Initially, the Blockchain technology was developed as a methodology to record cryptocurrency transactions (bitcoin technology). Additionally, blockchain has the potential to transform the current data auditing practices in a decentralized manner. In this paper, we make an attempt to review the application of blockchain in an enterprise information management system and explore closely on how to incorporate blockchain technology in CampusStack, an integrated information management system, to audit the database for ensuring data integrity.