Reviewerly/exHarmony huggingface dataset:
Depth And Skeleton Based View-invariant Human Action Recognition

Recognition of human activity plays an important role in computer-human interaction, surveillance, reconnaissance, robotics for humans, and understanding interpersonal behaviour relationships. These activities can be recorded as a sequence of still images but only using vision to solve the HAR poses a major task due to problems like scale variation, wide change, in contrast, lighting, viewpoint and occlusions. Thus to address this our work is concentrated on developing and training two deep learning pipelines one Spatiotemporal based and the other being skeletal based on publicly available human activity classification datasets. Moreover, we merge the two pipelines using late fusion and provide a comparison between the three with the existing state of the art algorithms for various activities in the dataset. Finally, we present the future work for the same problem.
High-Efficiency Multi-Sensor System for Chair Usage Detection

Recognizing Activities of Daily Living (ADL) or detecting falls in domestic environments require monitoring the movements and positions of a person. Several approaches use wearable devices or cameras, especially for fall detection, but they are considered intrusive by many users. To support such activities in an unobtrusive way, ambient-based solutions are available (e.g., based on PIRs, contact sensors, etc.). In this paper, we focus on the problem of sitting detection exploiting only unobtrusive sensors. In fact, sitting detection can be useful to understand the position of the user in many activities of the daily routines. While identifying sitting/lying on a sofa or bed is reasonably simple with pressure sensors, detecting whether a person is sitting on a chair is an open problem due to the natural chair position volatility. This paper proposes a reliable, not invasive and energetically sustainable system that can be used on chairs already present in the home. In particular, the proposed solution fuses the data of an accelerometer and a capacitive coupling sensor to understand if a person is sitting or not, discriminating the case of objects left on the chair. The results obtained in a real environment setting show an accuracy of 98.6% and a precision of 95%.
Cryptocurrencies

Following Satoshi Nakamoto's famous Bitcoin Whitepaper and the subsequent success of the Bitcoin network, cryptocurrencies have emerged as a new asset class. As an area of constant innovation, the cryptocurrency space is expanding very quickly. The surrounding policy debates keep shifting and legal development is trying to keep pace. This chapter charts the emergence of cryptocurrencies with a focus on their underlying technical properties and provides an overview of pertinent current legal issues. It discusses the most recent iterations and tries to anticipate the legal challenges that are likely to arise in the future.
NAEM: Noisy Attention Exploration Module for Deep Reinforcement Learning

Recently, deep reinforcement learning (RL) has been a hot topic due to its high capability in solving complex decision-making tasks.Although deep RL has achieved remarkable results in many fields, efficient exploration remains one of its most challenging issues.Conventional exploration heuristic methods, such as random exploration, have proven to be not suitable for complex RL tasks along with high-dimensional states and large action spaces.In this paper, we propose a novel lightweight and general neural network module for effective global exploration, called the Noisy Attention Exploration Module (NAEM), of which the key insight is to introduce parametric and learnable Gaussian noise into the attention mechanism for global exploration.NAEM is a general structure based on the Convolutional Block Attention Module (CBAM), which retains the ability of attention to enhance feature representation for any CNNs.In order to evaluate our module, we embed it into both value-based and actor-critic RL algorithms to test their performance improvement over related agents.The experimental results show that both of the modified agents achieve a performance improvement of more than 130% on most Atari games when compared with their original versions.In addition, for the NoisyNet agents, the training time can be reduced by about 30% through using NAEM as an alternative exploration.
Trajectory Privacy Protection Scheme for Different Travel Modes

The problem of trajectory privacy is getting more attention in recent years. It is unreasonable to adopt the same privacy protection scheme in diversified travel modes, since the trajectories have different constraints under different travel modes. Aiming at this problem, a mobile trajectory privacy protection scheme for different travel modes is proposed in this paper. At first, the overall privacy protection scheme is designed according to the classification of privacy level of road sections and different travel modes of users. And then, by considering both service and privacy, two false trajectory generation algorithms are designed, which can protect the real trajectory. The simulation experiments show that, the proposed scheme can process trajectory anonymity effectively. Compared with the algorithm in non-road network environment, the road network based algorithm can improve the average service accuracy by 36%, and decrease the privacy leak by 78%, which can provide users with more efficient and secure location services.
Research on 3D multi-target tracking algorithm

Aiming at the problem of data association errors caused by frequent occlusion of vehicles in complex scenes and poor tracking results, a multi-target tracking algorithm based on Frustum PointNets is proposed. Firstly, the algorithm uses RGBD information and Frustum PointNets as the detector to detect the target's 3D bounding box. Secondly, the 3D Kalman state estimation is performed. Finally, the weighted Hungarian matching algorithm is used for data association to achieve accurate tracking in complex situations. Qualitative experiments and quantitative analysis on KITTI dataset show that this algorithm can effectively deal with occlusion, improve the accuracy of data association, and obtain 83.48% tracking precision and 80.71% tracking accuracy, which greatly improves the accuracy of target tracking algorithm.
Tanh discrete estimate for all-opical neural network based on MZI

Optical neural networks (ONNs) can process information in parallel and have low energy advantages which researched more and more recently aims to replace the electrical Artificial neural networks (ANN s) solutions. The MZI with Gridnet or FFTnet can realize the convolution calculation is already proved by lots of researches. But the activation functions still have to use the DAC/ ADC to do the photoelectric conversion and then calculated in electronic-based hardware systems. We proposed a discrete estimate scheme for all-optical activation function in this paper. The scheme can give different accurate results with different implementation cost.
The books and booklets on librarianship published in post-Soviet Russia (1992–2020 bibliological review)

The publishing business in post-Soviet Russia differs a lot from that of the Soviet period. The number of books and brochures has significantly increased, though the circulations have drastically decreased, which is evidenced by the national press statistics without interpreting indicators related to the subject. After USSR’s collapsed, many complete and reliable resources have appeared to study librarianship publishing repertoire in various aspects, e. g.: target readership of publications, their target audience, publishing chronology, main publishers; circulations. With these criteria, expert assessment of key publishing repertoire components is made. The Russian Book Chamber databases based on acquired mandatory copies of publications, as well as Rosinforkultura, Institute of scientific Information for Social Sciences of the Russian Academy of Sciences, largest library catalogs and several bibliographic catalogs and indexes on the subject under examination, make the main source of statistical and bibliographic information for the authors. The authors identify the main problems and trends in librarianship publishing so to change situation to the better. The authors also focus on publication identification elements, i. e. their imprint.
Design of Ball Game Data Acquisition and Visualization Platform Supporting Mobile Terminal

In recent years, the popularity of tennis in China continues to improve, tennis has become one of the most concerned and favorite sports of our people. In the process of tennis teaching or training, coaches (teachers) spend a lot of time and energy to help students acquire the correct tennis skills. On the basis of previous research results, this paper proposes a motion state recognition method based on deep learning. This method first uses mobile phones to collect data, then uses different types of networks to build neural network models, and finally makes a visual analysis of the data.
An External Calibration Method for the Circular Scanning Device Used for Bend Tube Intelligent Inspection

In order to realize the intelligent inspection of bend tube processing defects, such as the wrinkles and the ovality, a 3D scanning device composed of a linear translation stage and four uniformly distributed laser profilers (LPs) is built in this paper. For the external calibration of this type of device, the major contribution of this paper are: 1) A 3D calibration object composed of three non-collinear target balls (TBs) is designed. Even if the scanning angle of each LP is limited, there are still common geometric features. 2) A frame-by-frame fusion method for scan data from multiple LPs that only requires one set of rail parameters is proposed to avoid the introduction of multiple sets of rail errors that affect equipment calibration and data fusion. When calculating the parameters, the shape error of the TBs after fusing the scan data of each LP is taken as the objective function, and the nonlinear least square method is used to solve the problem. Finally, the scan data fusion can maintain the accuracy and stability of the shape. The fitting error of the TB with a nominal radius of 19.05mm does not increase with the increase of the guideway stroke, the average fitting error of the radius is stable at about 0.0014mm, and the average RMSE of the surface point is stable at about 0.0426mm.
Public Auditing for Secure Cloud Storage based on DHT

Today cloud computing has been the most popular service enjoyed by people due to the easy maintenance provided by it. Cloud computing is cost-efficient and people pay according to the services they use. Many organizations are using cloud storage and the reason behind it is that the outsourcing services are provided by the cloud computing. Most of people do not trust the legality of the services provided by cloud (CSPs i.e. cloud service providers) because they are afraid of the security breach of their data. The public auditing of the data by their owners is a technique that can maintain the trust of people on cloud services. This research paper is about cloud storage services based on the distributed hash table (DHT).This is required for dynamic auditing of information as this is new two-dimensional data and Third-party Auditor (TPA) is responsible for recording the information to do dynamic auditing and the dimensional data is located at TPA. The computational costs gets reduced when the authorized information is migrated to the two dimensional data and the Cloud service provider shifts it to the TPA DHT has many structural advantages and the services can be updated efficiently. The comparison with the present system is also made and is assured that it is the security system for the cloud storage. To secure the data information by blinding it, random masking is provided as a proof for securing process. The authentication is done via hashing technique and integrity and performance checks are made with this authentication process.
Artistic reality of the TV series “The Marvelous Mrs. Maisel”

This article analyzes the artistic reality of the popular TV series &amp;ldquo;The Marvelous Mrs. Maisel&amp;rdquo;. There author discusses the feedback of the viewers, including negative. The question is raised on the reasons of its series, as in terms of rigorous analysis it seems mosaic and contradictory. The article explores the artistic techniques (split mind of the hero, bringing modern problems to the past, which create the artistic reality). It is demonstrated that the TV series enjoys popularity namely for representation of the modern problems and situations, despite the use of historical records. Following the logic of comedy-drama streaming TV series, its creators my Sherman-Palladino and Rachel Brosnahan readjusted all historical and non-historical themes and plotline so it would be entertaining for the audience, rather than give knowledge of history. The reality of the series is not a stand-up story based on the example of Mrs. Maisel, but the artistic reality in the comedy-drama genre. Detailed analysis is conducted on the techniques of depicting split personality of the hero, as well as presentation of the actor&amp;rsquo;s personal life and problems as a skillful acting. The author wonders whether the TV series &amp;ldquo;The Marvelous Mrs. Maisel&amp;rdquo; reflects rather the structure of the mosaic mentality of the modern viewer, oriented towards entertainment and aesthetic experiences than the knowledge of life. At the end of the research, the author discusses how the producers managed to create a holistic and organic artistic reality of the TV series.
Visualizing Ruwah Related Data By Interactive Graph

With the continuous achievements in Information Technology and its applications in different life fields, huge amounts of data are generated daily that makes searching for specific data items is a time/effort consuming process. However, several techniques are implemented and used to seek information such as search engines and information generation centers. Requesting data from historical warehouses is a famous routine as well, since extracting knowledge from historical repositories is needed in several daily life applications. The Arabic language has a lot of historical repositories represented in literary periodicals and books. Prophet Mohammad's (PBUH) talks are one of these important historical sources that can be used for knowledge extraction. These talks are collected and verified by a set of Muslim scholars in which Al-Bukhari was a famous one of them. This work is related to visualize the narrators of prophet Mohammad's (PBUH) talks as an interactive graph for both the narrator's related information and the talks themselves. Moreover, a set of graph centrality measures have been executed in order to quantify the importance of each narrator in the process of talks narration. The conducted experimental test emerges the importance of using the Interactive Graph versus the manual searching of Ahadith.
Co-design as a strategy to improve ecological awareness of communities

Abstract One strategy of communities to easily embrace ecological awareness is by involving directly in the improvement of their environmental quality. This paper discusses the case study research of the co-design process of the neighborhood regeneration project in Kampung Tongkol at the Ciliwung riverbank, Jakarta. This project is carried out collaboratively involving residents of the area and facilitated by ASF (Architecture Sans Frontières) Indonesia. The main important object of this regeneration project is the construction of a self-supporting sample house. This house aims as an ideal model which another neighborhood can replicate. Residents are directly involved in the organizing, the design process, to the construction stage. The primary purpose of regeneration is to improve the quality of the residential environment. The principles of sustainable design are employed as the main guidance from the beginning of the process. This study concludes that the final achievement of this project not only results in a higher quality environment but also raises the ecological awareness of the residents of the neighborhood.
Embedded Vision-Based Intelligent Device for the Visually Impaired

Computer Vision, a branch of Artificial Intelligence has been showing tremendous potential in solving day to day problems. The world has been tuning itself to the language of embedded systems. Dedicated, distributed and intelligent systems are making lives easier. The important things about intelligent embedded systems have started becoming more important. Computer Vision is achieved using deep learning, one of the most computationally expensive domains in computing. Deploying a computer vision solution in the form of deep learning models on single board computers that work with constrained resource allocation is indeed a challenge. The problem that this work takes aims to help visually impaired people by augmenting the way they interact with their surroundings. The proposed systems aim to make use of Embedded Vision-Computer Vision deployed on a single board computer like the Raspberry Pi, to recognize and convey emotions, age and gender of a person in front of a visually impaired person to him as audio output. Wide ResNet is used to implement age and gender classifier, while emotion classification uses a mini Xception Net with ImageNet weights, fine-tuned over the FER-2013 dataset. The reasons for selecting the respective algorithms and their deployment on the hardware shall be discussed. The system helps in adding more behaviour to the way a visually impaired person interacts with his or her peers. A few critical hardware deployment strategies that the work entails shall also be discussed.
Speech recognition with Transformers - creating and testing datasets for Bulgarian language

Speech is a natural way of communicating between human beings and as such, it triggers an interest of transforming it to a way of interaction with a computer as well. Once it is converted into a sequence of words, it can easily be applied for executing commands, thus enabling people to perform tasks easily or in some cases - tasks that are even impossible for them to be performed. The automatic speech recognition becomes a task solved with various success based on the accuracy, online/offline availability, speed. However, with the constant improvement of the neural networks, it is getting easier to solve and the most prominent solution seems to be the Transformer architecture of neural networks. In this paper, we describe the design of an encoder-decoder TensorFlow architecture and the process of training the model with a specially created Bulgarian dataset.
Quantum Sensing Enhanced Fiber Optic Sensors

Quantum sensing techniques can greatly enhance imaging but most of these require nonclassical light sources which are inappropriate for use in an optical fiber. One exception which uses low intensity laser light is explored herein.
Messages, Money, and Messengers: Do Migrant Remittances Strengthen Corporate Governance in Developing Countries?

Research has long acknowledged the salutary impact of foreign capital on business practices, including corporate governance practices, in developing countries. But past research has tended to assume that the relevant change agents were foreign organizations, such as multinational corporations. In the 2000s, remittances to developing countries from migrants living abroad rival or surpass the dollar value of other foreign capital inflows, yet there is neither theory nor broad-sample evidence to guide understanding about whether and when these transnational individuals might affect business practices back home. In response, we develop and test hypotheses from a “naturalistic agency” theory of transnational institutional change, highlighting both the base influential weight of many small remittance transactions and an enhancement when those remittances come from better-informed and better-positioned migrant agents. We develop and test hypotheses concerning the effects of migrant remittances on corporate governance practices in 46 developing countries from 2006 to 2013. We find that remittances positively affect the strength of home-country corporate governance when they come from better-educated migrant elites living in host countries where there exist stronger corporate governance practices. We conclude that under certain conditions, remittances strengthen underlying corporate governance practices vital to longer-term, business-led economic growth and poverty alleviation.
Fall Detection and Risk Assessment with New Technologies

Recent advances in technology allow for remote fall detection and risk assessment in the home environment. Technology has the potential to contribute to the prevention of falls, and associated physical and psychological trauma, and thereby improve the lives of older people. The aim of this chapter is to provide a comprehensive overview of the fields of remote fall detection and risk assessment with a focus on wearable technology and its clinical utility.
Image Data Hiding Based on Logical Operation of LSB

Digital communication has become the main way of disseminating information in the world. The image data occupies a very important part. In some specific applications, people hope to hide the secret information to be transmitted in the image and spread it along the network, so that only specific receivers can get the secret information. As a result, many image data hiding technologies have been produced, including LSB and its various improved versions. In this paper, based on the simple LSB method, a new LSB method based on logical operations will be proposed. Through logical calculation encryption, sequence decryption, and asymmetric encryption three times encryption, on the basis of sacrificing a certain amount of information hiding, the confidentiality of information hiding technology is greatly improved. And conduct a series of experiments to verify the performance of this new method.
Bayesian Non-parametric Priors Based on Random Sets

We study the construction of random discrete distributions, taking values in the infinite dimensional simplex, by means of a latent random subset of the natural numbers. The derived sequences of random weights are then used to establish a Bayesian non-parametric prior. A sufficient condition on the distribution of the random set is given, that assures the corresponding prior has full support, and taking advantage of the construction, we propose a general MCMC algorithm for density estimation purposes. This method is illustrated by building a new distribution over the space of all finite and non-empty subsets of \(\mathbb {N}\), that subsequently leads to a general class of random probability measures termed Geometric product stick-breaking process. It is shown that Geometric product stick-breaking process approximate, in distribution, Dirichlet and Geometric processes, and that the respective weights sequences have heavy tails, thus leading to very flexible mixture models.
Enabling civil-military information sharing in federated smart environments

Future emergency response and disaster recovery operations will increasingly rely on an up-to-date operational picture obtained from IoT devices deployed in the operational theatre. Some of these IoT devices will be present in the environment as a part of the civilian smart infrastructure, while others will be a part of the deployable military infrastructure. We present a high-level design for a federated smart environment enabling secure civil-military information sharing in support of emergency response and disaster recovery operations. Our security architecture, relying on standardized protocols, data formats and interfaces, spans from physical sensors through network and processing layers to applications. The proposed novel security mechanisms cover confidentiality, integrity and availability of information at rest, in transfer and in use.