Reviewerly/exHarmony huggingface dataset:
QUEST: Quantum Computing-Based Reversible Hybrid Encoder/Decoder for Error Resilient Transmission

Quantum technology is an emerging area of engineering and used in bio-informatics, Cryptography, etc. Reversible logic helps Quantum gates in the computation process. Reversible computation is different from conventional computation as it safeguards data while processing it. Reversible logic is used in contemporary research works on combinational circuits. Encoder and Decoder are critical parts of advanced digital electronic devices, which are used in combinational logic, telecommunication and clock generators which are multi-phased. So, this research work focuses on reversible 3:8 decoder and 8:3 encoder and these designs are further extended to linear error correcting codes mainly hamming codes. Further, this work also proves that proposed encoder/decoder can also be used for Error resilient transmission. The proposed circuits consist of rudimentary reversible gates like CCNOT and CNOT so that analysis can be made easy. The methodology of QUEST is implemented in IBM-Q processor where Garbage outputs and constant inputs are equal to zero.Vangala, Swathi Rishi kiran, E. Ravindra, J. V. R.
A Design of a Developable Automatic Avoidance System of UAV Based on ADS-B

There are two primary defects in the existing UAV avoidance systems: the system is memoryless; airborne radars are used to detect long-distance barriers, which are unreliable and expensive. The paper adopts the deep learning algorithm and ADS-B communication system based on a satellite base station to solve the above problems. It divides the avoidance problem into two parts: short-distance obstacle avoidance and long-distance route planning. On the one hand, the system establishes the knowledge base storing the previous avoidance experience and the matching mechanism, realizing the correspondence between input and experience through a deep learning algorithm. They can dramatically improve the reaction speed and safety of UAVs. On the other hand, the system realizes the interconnection between UAV and the satellite base station through the ADS-B communication system to replace the radars, putting the task of route planning on the satellite platform. Therefore, the satellite can achieve large-scale and all-weather detection to improve the overall safety of UAVs depending on its high and long-range characteristics. The paper also illustrates the design elements of the RF baseband integrated ADS-B transceiver and the simulation performance of the short-distance avoidance system in the end, whose results show that the system can be applied to dense obstacle environments and significantly improve the security of UAVs in a complex domain.
MOVI-Codec: Deep Video Compression without Motion

Traditional video codecs follow the predictive coding architecture of motion-compensated prediction and residual transform coding. Inspired by recent advances in deep learning, we propose a new deep learning video compression architecture that does not require motion estimation, which is the most expensive component in traditional video codecs. Our network consists of three components: a Displacement Calculation Unit (DCU), a Displacement Compression Network (DCN), and a Frame Reconstruction Network (FRN). The DCU exploits displaced frame differences as motion information, thus removing the need for motion estimation found in hybrid codecs. DCN utilizes an RNN-based network to learn temporal dependencies between frames. In the FRN, a new version of the UNet model, called LSTM-UNet is proposed and utilized to learn space-time differential representations of the videos. Our experimental results show that our compression model, MOtionless VIdeo Codec (MOVI-Codec), learns how to efficiently compress videos without computing motion and outperforms the video coding standard H.264 and exceeds the performance of the modern global standard HEVC codec as measured by MS-SSIM, especially on higher resolution videos.
Optimized Convolution Neural Network (OCNN) for Voice-Based Sign Language Recognition: Optimization and Regularization

Human–computer interactions (HCIs) are very helpful for current technology world. Later, the trendy data policies restrict the instinctive nature and velocity of HCI use, and the sign language acknowledgment framework has multiplied many significances. Completely one of a kind sign language is frequently accustomed to specific intentions and intonations or for dominant units like domestic robots. In previous work, ICNN was once in contrast to a baseline CNN and in preceding work OCNN gain 99.96% recognition rate. The foremost center of attention of this evaluation is to enhance the loss and execution time of ICNN model by using and optimizing the preference for alternate OCNN. New model is improving configuration by applying optimizing function in bottom layer. Due to the fact it is previously acknowledged, one optimizer will never give higher accuracy for all situation. The desire for the optimizer is to be created through thinking about the variability of facts and, consequently, the nonlinearity degree of the connection designs that happen inside the facts. As a result of the theoretical calculation, it is not always sufficient to work out the easiest optimization function. The study is required to visualize the complete optimization function of model that definitely suits the behaviors of the facts being examined. Gradient descent method is well known for its convenient computing and step of coming together on real-time data. This research paper compared three optimization types of gradient descent method, i.e., stochastic gradient descent algorithm, adaptive gradient algorithm, root mean square propagation algorithm. From the test result, it is recognized that RMSProp is that the first-class optimizer to preserve the loss of model performs of OCNN capacity in managing sign language recognition.
Research and Design of Intelligent Detection System in Teaching Manner Training of Normal School Students

With the progress of society and the increase of computer, network and digital media technology, computer technology has become an indispensable part of modern service industry. The application of the intelligent detection system in the teaching attitude training of normal students has developed in all aspects. As an intelligent detection system, it not only serves teaching, but also serves students. How to use more methods to train students and teaching attitudes is a future job Innovation. This article mainly introduces the research and design of the intelligent detection system in the teaching training of normal students, and uses the intelligent detection system to make a reasonable analysis of the teaching situation in the classroom. This paper uses the research and design of the intelligent detection system in the teaching training of teachers’ students, adopts microteaching for classroom analysis, and intelligently and systematically constructs the teaching training of teachers’ students to improve the standardization of teaching training in the classroom. The experimental results of this paper show that the application of the intelligent detection system in the teaching training of normal students increases the efficiency of teaching training for normal students by 15% and improves the teaching quality of teaching training for normal students.
User Preference and Suitability-Aware Eyeglasses Recommender System

This paper proposes a system that uses Interactive Evolutionary Computation (IEC) to recommend eyeglasses based on user aesthetic preference and suitability. The system presents the user with various eyeglasses, and the user evaluates the design based on aesthetic preference. Then, the system uses an Interactive Genetic Algorithm (IGA) to optimize the design based on the user and suitability evaluation. An experiment was conducted to determine the effectiveness of the proposed system toward optimizing eyeglass design. In the experiment, subjects were presented with two versions of the proposed system, which differed in the selection process adopted for the IGA. After system interaction, the subjects answered a questionnaire to determine any differences between the two versions. As new designs were generated, the system was could optimize the eyeglass design, to improve preference and suitability evaluations. We also found a difference in the optimization performance and user satisfaction of the two proposed methods.
Effectiveness of Manga Technique in Expressing Facial Expressions of Welfare Robot

We have been developing a communication robot “advice robot” that encourages and suggests actions to an elderly person, such as having a meal, taking a bath, taking medicine, etc. If the robot’s affective expressions are weak, an elderly user may not be able to understand or follow the robot’s suggestions. We focused on the vertical line and shadow techniques used in manga, or Japanese comics, to strengthen the affective expressions of disgust and fear, which can be difficult for the robot to express. The results of our experiment showed that the impression of disgust was strengthened by adding vertical lines. On the other hand, the impression of fear could not be strengthened by using the manga techniques. However, when the vertical lines were added to a happy face, the impression of fear was greater than when the lines were added to a fearful face.
Assessing Effectiveness and Interpretability of Light Behaviors in Smart Speakers

Light is an important communication modality. Smart speakers leverage light behaviors with different colors and shapes to deliver a wide range of information. However, there has not been sufficient work to assess the effectiveness of these light behaviors. That is, can users correctly interpret light behaviors from smart speakers? How effective are these light behaviors across different cultures? To answer these questions, we conducted a survey with 1,006 smart speaker users from seven countries. On average, they were able to correctly identify only 37% of light behaviors used in smart speakers. Overall, Amazon Echo users accurately identified a higher proportion of light behaviors than Google Home users. Furthermore, the perceived usability varied considerably across different countries. We also interviewed six experts to design more effective light behaviors for smart speakers. Our findings have important implications for future smart speaker design and the use of light as a communication modality in HCI.
AFF‐UNIT: Adaptive feature fusion for unsupervised image‐to‐image translation

The task of image-to-image translation is to generate images closer to the target domain style while preserving the significant features of the original image. This paper contends an adaptive feature fusion method for unsupervised image translation. The proposed architecture, termed as AFF-UNIT, is based on a compact network structure to further improve the quality of generated images. First of all, a feature extraction module based on an adaptive feature fusion method is proposed, which combines low-level fine-grained information and high-level semantic information to obtain feature maps with richer information. At the same time, a feature-similarity loss is proposed to guide the feature extraction module to extract features that are more conducive to improving the translation result. In addition, AFF-UNIT reuses the feature extraction module in the generator and discriminator to simplify the framework. Extensive experiments on five popular benchmarks demonstrate the superior performance of AFF-UNIT over state-of-the-art methods in terms of FID, KID, IS, and also human preference. Comprehensive ablation studies are also carried out to isolate the validity of each proposed component.
Performance Enhancing Secure Erasure Code (PESEC) for Cloud Storage using Random N Blocks Encryption

Security in Cloud Storage is of utmost importance given that data is not physically protected and breaches have been consistently notified since the creation of the Cloud. With the introduction of Erasure Code to provide a higher usage of storage capacity with a higher failure tolerance rate, the need for security is still prevalent. On top of encoding, blocks of data need to be encrypted as they are stored on arbitrary servers within or across data centers. Till date, all security solutions for Erasure Coded Distributed Storage System, have been to encrypt all blocks for any given file. In this paper, a random N blocks encryption scheme is proposed to enhance the performance of such secured systems. With smaller RS (Reed-Solomon) policies, N will be small, and with larger RS policies, N will be large. Given that all blocks are required to reconstruct the original file, whether one block or several blocks are encrypted, it will result in the same complexity. However with random N Block, complexity is doubled, and even tripled, if Random N Blocks are encrypted with different Keys and Algorithms. The results show enhanced performance of Random N Blocks compared with existing systems with higher complexities when using random keys and algorithms.
Building an Integrated Development Environment (IDE) on top of a Build System

When developing a Haskell IDE we hit upon an idea – why not base an IDE on an build system? In this paper we’ll explain how to go from that idea to a usable IDE, including the difficulties imposed by reusing a build system, and those imposed by technical details specific to Haskell. Our design has been successful, and hopefully provides a blue-print for others writing IDEs.
Rule-Based Recognition of Associated Entities in Hindi Text: A Domain Centric Approach

There are several computing applications that process unstructured texts available in various natural languages on the Web. These applications provide worthwhile outcomes that are of interest to the end users, other application developers and researchers. Various approaches of Natural Language Processing (NLP) and related fields are used to develop these applications. Application’s domain related lexical resources, in same language as the language of the text being processed, are considered useful and effective in improving performance of the related application. Several such resources are available in English, but there are certain languages that have scope for development and research. There is a need to develop the resources in those languages and bring them at par with their English counterparts. One such identified language is Hindi. Lexical resources in Hindi are developed in an ongoing research in the field of Opinion Mining. These are used in a rule based named entity recognition system developed as part of the research. The unstructured texts from Hindi Weblogs in the domain of home remedies are used in this research. The methodology used to develop these resources, outcomes achieved, experimental setup used for evaluation of the developed resources and the results obtained thereof, are discussed in this paper.
Coded Distributed Path Planning for Unmanned Aerial Vehicles

To enable urban air mobility (UAM), an efficient shortest path planning algorithm is required to ensure safe UAV navigation in large-scale urban environments. Existing optimal shortest path planning algorithms, e.g., the Dijkstra's algorithm, are computationally infeasible for complicated scenarios and large-scale problems. This paper aims to conquer this challenge by exploring a novel distributed implementation of the classical centralized Dijkstra's algorithm. The proposed algorithm explores the coding theory and the idea of load balancing to address the practical issues prominent in UAV-based distributed computing systems, including uncertain disturbances and node heterogeneity. Comprehensive experimental studies on Amazon EC2 demonstrate the high resiliency of the proposed algorithm to uncertain disturbances and its high efficiency compared with existing solutions.
Software Agent-Based Simulation for Pan-European Transport Corridor Management in Supply Chain

Software agent-based computing is emerging as an essential technology for developing commercial distributed systems to deal with the uncertainty in a dynamic business environment. Supply chain management (SCM) systems help to manage industry-specific business processes, services, and information flow among the stakeholders. Transportation network design and development is an essential part of effective supply chain management. The transport networks use different travel corridors for regular operations. The global supply chain transport corridors include various infrastructural components (e.g., border clearance authority, package handling machinery, weighbridge). The traffic and transportation systems domain are well suited for an agent-based approach because transportation systems are usually geographically distributed in a dynamic changing environment. This chapter describes a multi-agent software system to model and simulate transportation corridor use cases. The experimental simulation results provide potential guidance.
An Approach to Identifying the Most Predictive and Discriminant Features in Supervised Classification Problems

In this paper, we are interested in explaining datasets used in supervised classification problems by identifying the features that are most predictive and discriminant of the class. We first propose a definition of predictive and discriminant features based on the impact of the features on the performance of classification models. We then propose an approach to identifying the most predictive and discriminant features using multicriteria decision making. Finally, we present and discuss an experiment on a public dataset illustrating the potential of the approach.
PoolParty: Efficient Blockchain-Agnostic Decentralized Mining Pool

Blockchain mining is increasingly dominated by a few centralized mining pools [1]. Large pools can censor transactions on the blockchain and can coordinate their efforts to potentially carry out a 51% attack [2]. There have been 2 previous decentralized mining pools P2Pool and SmartPool, but both had unique limitations and are no longer actively mining. PoolParty is a novel decentralized blockchain-agnostic mining pool. By using smart-contracts to validate shares and punish miner misbehavior, as well as employing fines to disincentivize cheating, PoolParty is both secure and has low overhead operating costs. PoolParty can provide 0.05% overhead costs on Ethereum, and 0.003% overhead costs on Ethereum Classic for users with 20% pool hash-power. Finally, PoolParty can be implemented on less expensive smart contract hostable blockchains to improve scalability and overhead.
Depth Completion and Super-Resolution with Arbitrary Scale Factors for Indoor Scenes

Depth sensing has improved rapidly in recent years, which allows for structural information to be utilized in various applications, such as virtual reality, scene and object recognition, view synthesis, and 3D reconstruction. Due to the limitations of the current generation of depth sensors, the resolution of depth maps is often still much lower than the resolution of color images. This hinders applications, such as view synthesis or 3D reconstruction, from providing high-quality results. Therefore, super-resolution, which allows for the upscaling of depth maps while still retaining sharpness, has recently drawn much attention in the deep learning community. However, state-of-the-art deep learning methods are typically designed and trained to handle a fixed set of integer-scale factors. Moreover, the raw depth map collected by the depth sensor usually has many depth data missing or misestimated values along the edges and corners of observed objects. In this work, we propose a novel deep learning network for both depth completion and depth super-resolution with arbitrary scale factors. The experimental results on the Middlebury stereo, NYUv2, and Matterport3D datasets demonstrate that the proposed method can outperform state-of-the-art methods.
A Homotopy Approach to Coherent Quantum LQG Control Synthesis Using Discounted Performance Criteria

This paper is concerned with linear-quadratic-Gaussian (LQG) control for a field-mediated feedback connection of a plant and a coherent (measurement-free) controller. The plant and controller are multimode open quantum harmonic oscillators governed by linear quantum stochastic differential equations. The control objective is to make the closed-loop system internally stable and to minimize the infinite-horizon quadratic cost for the plant variables and the controller output subject to quantum physical realizability (PR) constraints. This coherent quantum LQG (CQLQG) control problem, which has been of active research interest for over ten years, does not admit a solution in the form of separation principle and independent Riccati equations known for its classical counterpart. We apply variational techniques to a family of discounted CQLQG control problems parameterized by an effective time horizon. This yields a homotopy algorithm, which is initialized with a PR (but not necessarily stabilizing) controller and aims at a locally optimal stabilizing controller for the original problem in the limit.
Averages don’t characterise the heavy tails of ransoms

The Bitcoin block-chain is the scoreboard of Ransomware. By mining the data in it and within the malware itself, we can understand the distribution of ransoms and characterise ransomware risk. Ransoms follow the power-law distribution in their amounts. The alpha parameter (α) of those power laws suggest they do not have a well defined average for most years in our study. Indeed, there has not been an α above 2 since 2015 and so there has not been a stable ransomware average since that time. The changing α has strong implications for cyber risk management and policy responses to ransomware attacks.
Organizational Challenges of Blockchain Adoption: An Exploratory Literature Review

This paper analyzes the challenges for blockchain technology adoption in organizations. Exploratory literature review included 16 relevant papers that aren't related to blockchain and cryptocurrencies like Bitcoin, but discussing primarily non-technical aspects of blockchain adoption in organizations. Since blockchain is a hype technology and its application outside the financial sector is not yet mature, challenges and theoretical models used in current research for examining technology adoption are explored. Guidelines for future research and application in practice are also disclosed so blockchain could reach its full potential in organizations.
Vehicle Behavior Recognition using Multi-Stream 3D Convolutional Neural Network

Vehicle behavior recognition take an essential part in the community of intelligent driving assistance systems. Recent approaches with 3D convolutional neural network (3DCNN) achieve reasonable recognition performance in laboratory settings. However, due to the complexity of network, the model is large and the reasoning is slow, so it is difficult to be applied in practice. In order to better handle the trade-off between size, precision and reasoning speed,a lightweight multi-stream 3DCNN model is proposed in this paper, which achieves fast reasoning speed and small model size while maintaining high precision. The 3DCNN model consists of three parts. Firstly, the module of SELayer-3DCNN is developed to extract appearance information from the RGB image sequence. The motion and edge information are also extracted from the optical flow sequence and edge image sequence, respectively. The edge information is applied to enhance the optical flow features. Secondly, a novel channel attention fusion strategy is proposed to improve the feature fusion and network ability. Finally, a 3D-RFB module is proposed to enhance the receptive field of the convolutional kernel. Furthermore, this paper presents a dataset of vehicle behavior. The advantages of the proposed method are verified by ablation experiments and comparative experiments while the real-time characteristics are maintained.