Reviewerly/exHarmony huggingface dataset:
Desktop based Smart Voice Assistant using Python Language Integrated with Arduino

A voice assistant is also known as voice-based Artificial Intelligence is one of the hot topics in the current world, that are programs that listens to human’s verbal command and responds to them which makes it a human-computer device interaction. In the current days, a voice assistant is everywhere which is a lot useful in these busy days. Nowadays, almost everyone in the current world is using voice assistant because it’s everywhere starting from Google Assistant which even 5 years old kids can use, because of the current world pandemic which increased the utilization of smartphones. Amazon's Alexa equally competes with the Google Assistant and is very useful to do variety of works starting from entertaining the users till turning on and off the household products using Internet of Things (IoT). One of the greatest features is that it is helpful to physically challenged people, for example people who aren't able to walk use the Internet of Things feature to operate the household products and maintain them. So, a voice assistant has been developed which will be convenient for the users, and similar as the other voice assistants which are currently in trend.
Data Encryption Basing on the Existence of Eulerian Circuits in a Group of Random Graphs

In cyber security, data encryption is conversion of data from readable form to coded form so that an intended and authorized person can read the original data. Information security has become an essential tool in the present digital world. Applied graph theory plays a crucial role in encryption technique as it has widespread special features equipped with easy and effective representation. The present paper explores a symmetric encryption technique using Hamiltonian circuits of weighted Eulerian graphs, circular bit shift operation, and simple logical XOR operations.
Test-retest Reliability on Movement Times and Error Rates in Target Pointing

Target-selection tasks have been frequently conducted to evaluate novel input devices and pointing-facilitation techniques. Recently, Sharif et al. showed that a unified metric to evaluate the pointing performance, called throughput TP, was not stable across two sessions performed by the same participant group, which indicates poor test-retest reliability. Because there are cases in which using TP is inappropriate depending on the research topic, we extend their finding to two other metrics: movement time MT and error rate ER. We demonstrated that, even for the participants who kept their TPs across two sessions stable, they would exhibit unstable MTs and ERs. Thus, if time allows, researchers should design their experiments to run multiple sessions for obtaining the central tendency of user performance, which increases the validity of their user studies.
Cascaded Q-tables with Two States Corrected the Synchronicity of Power Supplies in Synchrotrons

It is related to the synchronicity of power supplies that ions accelerated successfully in a synchrotron. The weakened synchronicity will lead to a failure of the acceleration. By improving the Q-learning algorithm, we proposed an online method, which can correct the synchronicity of power supplies automatically and quickly. Inspired by the operating of a microscope, the single q-table with multiple states replace by many cascaded q-tables which contain only two states. When selecting actions, the randomness mechanism replaces by the circular one. Due to the changes, the algorithm achieves faster learning of delay time. Experimental results show that the method obviously saves learning time. At the same time, it is superior to the accuracy of standard Q learning.
The hurdles of current data citation practices and the adding-value of providing PIDs below study level

This paper discusses the data reuse principle from the end-user standpoint, addressing the data citation hurdles, particularly in the Social Sciences. Referencing research data and their inherited detailed entities by Persistent Identifiers (PIDs) supports FAIR data usage. However, in the Social Sciences, PIDs are only available on the study level, but not on the level of the inline data objects, such as survey variables. Since citing research data is the backbone of proper data reuse, this paper proposes an infrastructure to reference specific attributes within data sets, assigning PIDs to the finegrained level of attributes. By assigning PIDs to these attributes, individual elements of the data files can be referenced and retrieved with the required metadata for both machine-actionable as well as human access.
Classification of Fault Prediction: A Mapping Study

Software fault prediction is an important activity in the testing phase of the software development life cycle and involves various statistical and machine learning techniques. These techniques are useful for making accurate predictions to improve software quality. Researchers have used different techniques on different datasets to build fault prediction in software projects, but these techniques vary and are not generalised. As a result, it creates challenges that make it difficult to choose a suitable technique for software fault prediction in a particular context or project. This mapping study focuses on research published from 1997 to 2020 involving fault prediction techniques, intending to determine a classification of fault prediction techniques based on problem types that researchers need to solve. This study conducted a systematic mapping study to structure and categorise the research evidence that has been published in fault prediction. A total of 82 papers are mapped to a classification scheme. This study identified research gaps and specific issues for practitioners, including the need to classify fault prediction techniques according to problem types and to provide a systematic way to identify suitable techniques for fault prediction models.
Axiological Reformation of Higher Education in the Global World

The chapter deals with problems of the transformation of value-based landmarks of university education. The author demonstrated that changes in global interactions in political, economical and cultural spheres lead to transformations of the axiological dimension of the modern university. The key tendencies of this kind are historical and axiological de-ideologization, as well as maintenance of ecological mindset, as a sort of special format of ethical and epistemological approach to the world.
Performance Benchmarking of Local Differential Privacy for Mobile Devices

Collecting large amounts of data from mobile devices has become pervasive as a basis for data-driven decision making, but this collection dramatically increases the surface area for undesirable actions against user’s privacy, e.g., leakage of user information. Local Differential Privacy (LDP) is a high-level architecture for privacy preservation based on probability and statistical inference theories that ensures reasonable doubt about any information provided by devices in exchange for time spent on information encoding-decoding and some level of noise in the resulting data analysis. This paper presents some benchmarks on the processing time and accuracy of two competing LDP mechanisms, i.e., Hadamard encoding and RAPPOR, on the task of counting how many clients reported a given string, i.e., the heavy hitter (most frequent item) identification task. The experimental results show that Hadamard encoding employing unknown dictionary setup takes three times longer to encode strings than using a known dictionary setup, while the RAPPOR encoding with unknown dictionary takes 387 times longer to encode strings than the RAPPOR with known dictionary. The two other parts of the experiments, i.e., decoding data and exporting statistics, also show significant differences in terms of processing time when comparing the two types of data dictionary setups.
Correcting Perspective Distortion for Circle Centerpoint Coordinates in Camera Based Visible Light Positioning

This paper proposes an algorithm for circle center-point deviation correction in camera based Visible Light Positioning (VLP) systems using circular light fixtures as references for the position estimation, without the use of additional sensors. We present an analysis of the centerpoint coordinates deviation when tilting is introduced on the image plane and derive an expression to correct this deviation. Using the solvePnP algorithm of the OpenCV computer vision library, we propose an iterative algorithm, using the latter, to reduce the position estimation error, which does not require the internal modification of solvePnP. The proposed algorithm is validated using a simulation setup comprised of a camera and four circular light fixtures, with a camera tilt introduced around a single axis. Our results show that in just two iterations, our algorithm reduces the position estimation error to below 0.1mm, compared to a previous maximum error of around 1 cm.
Gaze Estimation with Imperceptible Marker Displayed Dynamically using Polarization

Conventional eye-tracking methods require NIR-LEDs at the corners and edges of displays as references. However, extensive eyeball rotation results in the loss of reflections. Therefore, we propose imperceptible markers that can be dynamically displayed using liquid crystals. Using the characteristics of polarized light, the imperceptible markers are shown on a screen as references for eye-tracking. Additionally, the marker positions can be changed using the eyeball pose in the previous frame. The point-of-gaze was determined using the imperceptible markers based on model-based eye gaze estimation. The accuracy of the estimated PoG obtained using the imperceptible marker was approximately 1.69°, higher than that obtained using NIR-LEDs. Through experiments, we confirmed the feasibility and effectiveness of relocating imperceptible markers on the screen.
Private Messaging Service using AES Encryption and Toxicity Detection

With the advancement of digital technology in recent decades, the style of communication and use of digital accessories in our daily lives has changed dramatically. It is undeniable that the introduction of the mobile phone/smart phone has improved our standard of living and made life easier. A private messenger is an application that enables for encrypted communication. To provide safe data transmission between the sender and recipient, a protected private instant messenger is required. The messenger gives main emphasis on textual value by checking the text toxicity using a ML model in a novel way by not storing meta data of user atany juncture without compromising the quality of messaging service which is not present in main stream messaging application. The architecture of private messengers is presented in this work. The proposed architecture was created and tested on a private messaging application, and the findings show that it can increase data security for messenger applications.
Research on Path Planning of Mobile Robot in Static Environment Based on Improved Ant Colony Algorithm

Abstract In order to solve the problems of ant colony algorithm in the path planning of mobile robots, such as slow convergence speed and easy to fall into local optimum, an improved algorithm of adaptive ant colony algorithm is proposed in this paper. The algorithm improves the ant’s transition probability function by adding the number of revolutions and the angle of rotation to the heuristic function, and adding the target guide element, which can make the algorithm converge faster. The pheromone update formula is based on the wolfs food competition and distribution principle, which adaptively updates the pheromone volatility that satisfies the Cauchy distribution to avoid the local optimal solution found by the algorithm. Finally, by comparing and analyzing the statistical data results obtained by simulation, it can be concluded that the improved ant colony algorithm in this paper is better.
TJOSConf: Automatic and Safe System Environment Operations Platform

With exploding number of servers in large IT corporations, system environment management of servers at scale is a big challenge. System environment needs to be regularly updated to meet the system demand of services or fix emerging bugs. It is of vital importance to ensure stability of services during these system updates. However, the heterogeneity of workload and diversity of update scripts make it difficult to estimate how a system update affects the servers and services running on them. Unexpected failures caused by various reasons can also arise during or after update execution, making the situation even worse.
Near-Lossless Coding of Plenoptic Camera Sensor Images for Archiving Light Field Array of Views

In this paper we propose a near-lossless encoder for sensor images acquired by plenoptic cameras, and we investigate its usage for encoding in an archive all information needed for reconstructing high quality versions of the light field (LF) array of views(AoV). The near-lossless encoding of the plenoptic camera sensor image is realized by a modified version of the recently published sparse relevant regressors and contexts (SRRC) encoder. The lossy reconstruction is obtained in two nested loops: the outer one operates over the sensor image patches (each patch corresponding to a microlens image), and the inner loop operates over the pixels in the patch. In the latter, we enforce the SRRC predictors to use the already reconstructed lossy version of the sensor image. Then, we examine the usage of the near-lossless SRRC (NL-SRRC) codec as a building block for an archiving scheme including all information needed for running the plenoptic processing pipeline and obtaining the LF-AoV. Finally, we replace in the archiving scheme the NL-SRRC codec with other state of the art lossy codecs and compare the results, which show that NL-SRRC based archiving scheme achieves better performance for the range of high bitrates.
Personal Protective Equipment use inspection, real time surveillance with YOLO

Two situations are necessary to improve the safety and health conditions of people in industries: A proper use of Personal Protective Equipment by workers who develop their jobs into industrial areas and a continuous inspection promoted by employers. In this paper, we review the best known object detectors for prediction tasks, evaluating the features that are needed for a real-time surveillance system that can provide reliable predictions in a safety and health context. We present a tool based on Computer Vision which make predictions about if people are using their respective High Visibility Vest and Hard Hat. Additionally, the same model predicts if a person is wearing a face cover or there is an absence of this protector on the person's face. Our dataset was built by collecting images from available online datasets and we added own images. It has six classes which were defined by the combination of four instances. For detection task, we explored and evaluated different versions of You-only-look-once (YOLO). The best performance for a real time surveillance was reached by YOLO v5-nano, 125.3 FPS. On the other hand, the most important outcomes in terms of average precision were achieved by YOLO v4 and YOLO v5-small with 0.892 and 0.916 respectively.
Two-stage reward allocation with decay for multi-agent coordinated behavior for sequential cooperative task by using deep reinforcement learning

Abstract We propose a two-stage reward allocation method with decay using an extension of replay memory to adapt this rewarding method for deep reinforcement learning (DRL), to generate coordinated behaviors for tasks that can be completed by executing a few subtasks sequentially by heterogeneous agents. An independent learner in cooperative multi-agent systems needs to learn its policies for effective execution of its own responsible subtask, as well as for coordinated behaviors under a certain coordination structure. Although the reward scheme is an issue for DRL, it is difficult to design it to learn both policies. Our proposed method attempts to generate these different behaviors in multi-agent DRL by dividing the timing of rewards into two stages and varying the ratio between them over time. By introducing the coordinated delivery and execution problem with an expiration time, where a task can be executed sequentially by two heterogeneous agents, we experimentally analyze the effect of using various ratios of the reward division in the two-stage allocations on the generated behaviors. The results demonstrate that the proposed method could improve the overall performance relative to those with the conventional one-time or fixed reward and can establish robust coordinated behavior.
Reconfigurable Architecture for Real-time Decoding of Canonical Huffman Codes

Data compression is an important algorithm which has found its use in modern day algorithms such as Convolutional Neural Networks (CNNs). Reconfigurable platforms (like FPGAs) have strong capabilities to implement time complex tasks like CNNs, however, these algorithms present a big challenge due to high resource demand. Data compression is one of the most utilized techniques to reduce memory utilization in FPGAs. The weights of CNN architecture are usually encoded to store in FPGA. In this paper, we propose design of an efficient decoder based on Canonical Huffman that can be utilized for the efficient decompression of weights in CNN. The proposed design makes use of Hash functions to effectively decode the weights eliminating the need for searching dictionary. The proposed design decodes a single weight in a single clock cycle. Our proposed design has a maximum frequency of 408.97MHz utilizing 1% of system LUTs when tested for Aritix 7 platform.
Towards efficient and robust intelligent mobile vision system via small object aware parallel offloading

As mobile devices continuously generate streams of images and videos, intelligent mobile vision applications are rapidly emerging. An ideal object detection system for mobile vision applications should be accurate and real-time. Nevertheless, it is non-trivial to achieve these goals utilizing resource-constrained mobile devices. In this work, we propose an efficient and robust intelligent mobile vision system AREdge via small object aware parallel offloading. We find that the detection performance of small objects is a core factor that affects detection accuracy. To tackle this, we design a local lightweight DNN model that runs on mobile devices to detect big objects fast and identify the regions of interest (RoIs) that may have small objects. These areas are then cropped and offloaded to multiple edge servers for more accurate detection based on complex and large-scale DNN models. To further improve the performance, we propose a dynamic area-aware parallel offloading scheme for fine-grained parallel execution on multiple edge servers. Experimental results show that the accuracy of AREdge is 214 . 27 % higher than that of the local detection in small objects. It also reduces the detection latency by 20 . 68 % on average over the offloading method based on full images and well-used object detection models.
IoT Group Membership Management Using Decentralized Identifiers and Verifiable Credentials

Many IoT use cases can benefit from group communication, where a user requests an IoT resource and this request can be handled by multiple IoT devices, each of which may respond back to the user. IoT group communication involves one-to-many requests and many-to-one responses, and this creates security challenges. In this paper, we focus on the provenance that has been received by an authorized device. We provide an effective and flexible solution for securing IoT group communication using CoAP, where a CoAP client sends a request to a CoAP group and receives multiple responses by many IoT devices, acting as CoAP servers. We design a solution that allows CoAP servers to digitally sign their responses in a way that clients can verify that a response has been generated by an authorized member of the CoAP group. In order to achieve our goal, we leverage Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs). In particular, we consider that each group is identified by a DID, and each group member has received a VC that allows it to participate in that group. The only information a client needs to know is the DID of the group, which is learned using DNSSEC. Our solution allows group members to rotate their signing keys, it achieves group member revocation, and it has minimal communication and computational overhead.
Survey Paper on Communication System Using Blockchain and Cryptography

Abstract: Decentralized application make use of peer-to-peer networks, this ensures that no network failure can occur due to central node failure. Blockchain serves as a ledger which allows messaging to take place in a decentralized manner. Decentralized application for the communication and resource sharing is need in today’s world, where keeping data on a centralized server can be risky and a very expensive experience. With the help of some consensus, we can implement different ways to share resources and communicate. Together with Blockchain and Decentralized Applications, we can create a secure and very reliable messaging application that overcomes the drawbacks of traditional messaging applications. Keywords: Blockchain, Cryptography, Communication System, Secure Hash Algorithm, Encryption and Decryption
A relative approach for uncertainty quantification of inversely identified material behavior using a heterogeneous test for sheet metal

Abstract The process of calibrating the material parameters of a constitutive model has inherent uncertainty sources. These can lead to inadequate material behavior characterization and, consequently, errors in the numerical simulations. A robust mechanical test is necessary for accurate material mechanical characterization. In this work, it is relatively quantified the uncertainties of the parameter identification of the Swift hardening law and the Yld2000-2d yield function using Digital Image Correlation (DIC) and a DIC-levelled approach. The levels of uncertainty are calculated with a derivative-based local method, having into consideration the Karush-Kuhn-Tucker (KKT) conditions for optimization problems. The mechanical test used for the parameter calibration was an optimized interior notched specimen that presents several strain and stress states simultaneously. The synthetic images were generated based on the numerical data, and the strain fields were accessed using DIC techniques. It was concluded that the step size is the DIC parameter that most influences the material parameter identification.
Detection of serious falls by camera for the safety of old person in intelligent building

An elderly person who lives alone is exposed to dangerous situations, for example, a serious fall, a stroke, a heart attack, a serious illness. In these dangerous situations this person risks dying or having serious complications. Among these dangerous situations, we have chosen to study the serious falls of a person, which prevents him from asking for help. In this article we propose a system for detecting serious falls of a person living alone in, intelligent building in an efficient and reliable way, with a minimum rate of false alarms, to ensure instant intervention and reduce risks of injury. This detection is done in video scenes. The serious fall information as well as the images of the event will be sent to another system which informs an assistance center to call for help for this person. This detection is carried out by the classification of the images obtained by the video scenes, which shows the state of this person in each place of the intelligent building. We used the SIFT method and for the parametrization of the images, and in order to obtain a robust system, we opted for a hybrid Neuro-Markovian system, neural networks for the learning phase, and hidden Markov model for the classification phase. In order to validate our obtained results.