Reviewerly/exHarmony huggingface dataset:
Fabula: Hybridized Weightage Based Book Recommendation System

This paper is about an all in one approach for an industrial level implementation of a recommendation system by applying different recommendation approaches, studying and making a comparison with the state of the art and proper implementation which can be a prototype on an industrial level. In this paper we describe the usage of a hybrid weightage based recommender system focused on books and putting a model into the most used platform application. To make it available for the book readers by making an all in one approach to improvise the state of the art and resolve the cold start problem, making the user experience a major standard for the recommendations. The paper deals with the phases of Software Engineering from the analysis of the requirements, the actual making of the recommender model to deployment and testing of the Application at the user end.
Deep Learning Application for Classification of SEFDM Signals

The paper considers the application of a convolutional neural network for the classification of SEFDM signals for different modulation schemes. A simulation model of the receiver and transmitter has been implemented for the case of a multipath channel and two frequency separations with steps of 0.1 and 0.2. For both cases, the classification accuracy values were obtained, which averaged 99% at signal-to-noise ratio equal to 10 dB.
Mixed Precision Low-Bit Quantization of Neural Network Language Models for Speech Recognition

State-of-the-art language models (LMs) represented by long-short term memory recurrent neural networks (LSTM-RNNs) and Transformers are becoming increasingly complex and expensive for practical applications. Low-bit neural network quantization provides a powerful solution to dramatically reduce their model size. Current quantization methods are based on uniform precision and fail to account for the varying performance sensitivity at different parts of LMs to quantization errors. To this end, novel mixed precision neural network LM quantization methods are proposed in this paper. The optimal local precision choices for LSTM-RNN and Transformer based neural LMs are automatically learned using three techniques. The first two approaches are based on quantization sensitivity metrics in the form of either the KL-divergence measured between full precision and quantized LMs, or Hessian trace weighted quantization perturbation that can be approximated efficiently using matrix free techniques. The third approach is based on mixed precision neural architecture search. In order to overcome the difficulty in using gradient descent methods to directly estimate discrete quantized weights, alternating direction methods of multipliers (ADMM) are used to efficiently train quantized LMs. Experiments were conducted on state-of-the-art LF-MMI CNN-TDNN systems featuring speed perturbation, i-Vector and learning hidden unit contribution (LHUC) based speaker adaptation on two tasks: Switchboard telephone speech and AMI meeting transcription. The proposed mixed precision quantization techniques achieved “lossless” quantization on both tasks, by producing model size compression ratios of up to approximately 16 times over the full precision LSTM and Transformer baseline LMs, while incurring no statistically significant word error rate increase.
Improvement of Performance of K-Nearest Neighbors Used to Classify Sharks into Behaviors

One metric used to measure the classification performance of K-Nearest Neighbors (K-NN) is F1-Score. K-NN is used here to classify data into shark behaviors, namely, Resting, Swimming, Feeding, and Non-Directed Motion (NDM). The objective of this paper is to improve the F1-Score of the K-NN. It is proven that applying Ensemble Averaging (EA) based filters on the data, prior to classification, improves Signal Power to Noise Power Ratio (SNR) and sequentially F1-Score. Data resizing and other Signal Processing (SP) techniques are also used to produce more accurate features.
Multi-View AR Streams for Interactive 3D Remote Teaching

In this work, we present a system that adds augmented reality interaction and 3D-space utilization to educational videoconferencing for a more engaging distance learning experience. We developed infrastructure and user interfaces that enable the use of an instructor’s physical 3D space as a teaching stage, promote student interaction, and take advantage of the flexibility of adding virtual content to the physical world. The system is implemented using hand-held mobile augmented reality to maximize device availability, scalability, and ready deployment, elevating traditional video lectures to immersive mixed reality experiences. We use multiple devices on the teacher’s end to provide different simultaneous views of a teaching space towards a better understanding of the 3D space.
Bio-inspired task-rule retrieval model with auditory sorting test

Cognitive architectures (CAs) are currently used to bring the behavior of computer systems closer to human behavior. One of the main capacities of humans is the ability to plan and make decisions. Thus, part of the human behavior is based on rules associated with the relevant environmental stimuli. Rule management can be divided into six needed processes: rule learning, rule retrieving, rule coding, rule updating, rule reinforcement, and rule changing. A key aspect of rule processing is retrieval, which involves the use of information extracted from memory. This work deals with how rules are stored in coded form in the brain, retrieved and used as the need arises. The proposed model takes inspiration mainly from the processes in the VLPFC and MTL brain areas to extract rules from memory and create a rule-set that is sent to the DLPFC. The DLPFC and VLPFC prefrontal area operations are also described, and a process is proposed to select the appropriate rule and give a response. The experimentation of the implementation of our proposal with different configuration parameters gives rise to different approaches to human behavior in rule retrieval. Our conclusion is that after a virtual entity is endowed with this proposal, it computes in a way similar to human behavior.
A multi-purposed unsupervised framework for comparing embeddings of undirected and directed graphs

Abstract Graph embedding is a transformation of nodes of a network into a set of vectors. A good embedding should capture the underlying graph topology and structure, node-to-node relationship, and other relevant information about the graph, its subgraphs, and nodes themselves. If these objectives are achieved, an embedding is a meaningful, understandable, and often compressed representation of a network. Unfortunately, selecting the best embedding is a challenging task and very often requires domain experts. In this paper, we extend the framework for evaluating graph embeddings that was recently introduced in [15]. Now, the framework assigns two scores, local and global, to each embedding that measure the quality of an evaluated embedding for tasks that require good representation of local and, respectively, global properties of the network. The best embedding, if needed, can be selected in an unsupervised way, or the framework can identify a few embeddings that are worth further investigation. The framework is flexible and scalable and can deal with undirected/directed and weighted/unweighted graphs.
Bitcoin Price Forecasting: Linear Discriminant Analysis with Sentiment Evaluation

Cryptocurrencies such as bitcoin have garnered a lot of attention in recent months due to their meteoric rise. In this paper, we propose a new method for predicting the direction of bitcoin price using linear discriminant analysis (LDA) together with sentiment analysis. Concretely, we train an LDA-based classifier that uses the current bitcoin price information and Twitter headline news in order to forecast the next-day direction of bitcoin price. The proposed model achieves highly accurate results beating several benchmark targets. In particular, the proposed approach produces forecast accuracy of 0.828 and AUC of 0.840 on the test data.
Towards Traffic Scene Description: The Semantic Scene Graph

For the classification of traffic scenes, a description model is necessary that can describe the scene in a uniform way, independent of its domain. A model to describe a traffic scene in a semantic way is described in this paper. The description model allows to describe a traffic scene independently of the road geometry and road topology. Here, the traffic participants are projected onto the road network and represented as nodes in a graph. Depending on the relative location between two traffic participants with respect to the road topology, semantically classified edges are created between the corresponding nodes. For concretization, the edge attributes are extended by relative distances and velocities between both traffic participants with regard to the course of the lane. An important aspect of the description is that it can be converted easily into a machine-readable format. The current description focuses on dynamic objects of a traffic scene and considers traffic participants, such as pedestrians or vehicles.
Supporting Reference Imagery for Digital Drawing

There is little understanding in the challenges artists face when using reference imagery while creating drawings digitally. How can this part of the creative process be better supported during the act of drawing? We conduct formative interviews with artists and reveal many adopt ad hoc strategies when integrating reference into their workflows. Interview results inform the design of a novel sketching interface in form of a technology probe to capture how artists use and access reference imagery, while also addressing opportunities to better support the use of reference, such as just-in-time presentation of imagery, automatic transparency to assist tracing, and features to mitigate design fixation. To capture how reference is used, we tasked artists to complete a series of digital drawings using our probe, with each task having particular reference needs. Artists were quick to adopt and appreciate the novel solutions provided by our probe, and we identified common strategies that can be exploited to support reference imagery in future creative tools.
Implications on the Migration from Ionic to Android

In our past research, we presented an approach to migrate apps implemented by a cross-platform technology (i.e., Ionic-Cordova-Angular) toward a native platform (i.e., Android). We also conducted a study to assess if there was a difference in the user experience and in the affective reactions of end-users when they used the original version of an app and its migrated version. Since we were also interested to study the perspective of developers, we successively conducted a controlled experiment to study possible differences, e.g., in terms of source code comprehension and affective reactions, when developers dealt with the original and migrated versions of a given app. In this paper, we present and discuss implications from both these studies and discuss them from both researchers’ and practitioners’ perspectives. For example, one of the most important takeaway results from the practitioners’ perspective is: it is worthy to develop an app by using a cross-platform technology (e.g., for time-to-market reasons) and then to assess if this app is ready for the market; if this happens, its migration to a native technology is a good option so letting the app penetrate more the market.
Large-scale interactive retrieval in art collections using multi-style feature aggregation

Finding objects and motifs across artworks is of great importance for art history as it helps to understand individual works and analyze relations between them. The advent of digitization has produced extensive digital art collections with many research opportunities. However, manual approaches are inadequate to handle this amount of data, and it requires appropriate computer-based methods to analyze them. This article presents a visual search algorithm and user interface to support art historians to find objects and motifs in extensive datasets. Artistic image collections are subject to significant domain shifts induced by large variations in styles, artistic media, and materials. This poses new challenges to most computer vision models which are trained on photographs. To alleviate this problem, we introduce a multi-style feature aggregation that projects images into the same distribution, leading to more accurate and style-invariant search results. Our retrieval system is based on a voting procedure combined with fast nearest-neighbor search and enables finding and localizing motifs within an extensive image collection in seconds. The presented approach significantly improves the state-of-the-art in terms of accuracy and search time on various datasets and applies to large and inhomogeneous collections. In addition to the search algorithm, we introduce a user interface that allows art historians to apply our algorithm in practice. The interface enables users to search for single regions, multiple regions regarding different connection types and holds an interactive feedback system to improve retrieval results further. With our methodological contribution and easy-to-use user interface, this work manifests further progress towards a computer-based analysis of visual art.
Secure Drone Identification with Hyperledger Iroha

Interet-of-Things (IoT) devices such as drones are becoming more prevalent, but still have blatant security issues. DRIP is a newly proposed protocol for drones that aims to enable users to securely identify drones and trust in messages sent by them. However, some implementation details, such as registries, are yet to be determined. The decentralization, auditability and persistency of blockchain makes it a great candidate to be used as a registry in DRIP. This paper analyzes how Hyperledger Iroha, a permission-based blockchain framework, can be used to fulfill the registry requirements in DRIP, and propose an architecture using it as a registry. Findings show that, thanks to its support for various permissions, multi-signature transactions, and smart contracts, it is able to not only fulfill the requirements, but also enhance other security details of the proposed architecture. Furthermore, this paper also evaluates the performance of Iroha, with results showing that its low consensus delay is adequate to be used for real-time location updates.
An Improved RRT* Algorithm Combining Motion Constraint and Artificial Potential Field for Robot-Assisted Flexible Needle Insertion in 3D Environment

In the field of minimally invasive medical care, compared With rigid needles, the use of flexible needles allows doctors to avoid blood vessels and organs more flexibly during surgery. Due to the non-holonomic constraint of the flexible needle and the tissue deformation caused by the needle tip during the insertion, the accessibility and safety of the path need to be considered when planning. In this paper, we propose an adaptable algorithm based on RRT* algorithm to compute a path for flexible needle to reach the lesion in 3D environment. The improved RRT* (Rapidly-Exploring Random Trees*) algorithm based on the motion constraint of flexible needle makes the path smoother and shorter. The path cost used in the algorithm take the potential field of the obstacle into account to avoid the influence of the local movement of the tissue during the needle puncture process. Finally, the algorithm is conducted by simulations, and the results show that the improved RRT* algorithm can generate a smooth and safe path that conforms to the motion constraint of the flexible needle.
Layering Quantum-Resistance into Classical Digital Signature Algorithms

It is proven that asymmetric key cryptographic systems that rely on Integer Factorization or Discrete Logarithm as the underlying hard problem are vulnerable to quantum computers. Using Shor’s algorithm on a large-enough quantum computer, an attacker can cryptanalyze the public key to obtain the private key in O(logN) time complexity. For systems that use the classical Digital Signature Algorithm (DSA), Rivest-Shamir-Adleman (RSA) algorithm or Elliptic-Curve Digital Signature Algorithm (ECDSA), it means that authentication, data integrity and non-repudiation between the communicating parties cannot be assured in the post-quantum era.In this paper, we present a novel approach using zero-knowledge proofs on the pre-image of the private signing key to layer in quantum-resistance into digital signature deployments that require longer-term post-quantum protection while maintaining backward compatibility with existing implementations. We show that this approach can extend the cryptographic protection of data beyond the post-quantum era and is also easy to migrate to. An implementation of this approach applying a ZKBoo zero-knowledge proof on ECDSA signatures is realized using a RFC3161-compatible time-stamp server with OpenSSL and an Adobe Acrobat Reader DC.
Pricing-Based Deep Reinforcement Learning for Live Video Streaming With Joint User Association and Resource Management in Mobile Edge Computing

Mobile Edge Computing (MEC) is a promising technique in the 5G Era to improve the Quality of Experience (QoE) for online video streaming due to its ability to reduce the backhaul transmission by caching certain content. However, it still takes effort to address the user association and video quality selection problem under the limited resource of MEC to fully support the low-latency demand for live video streaming. We found the optimization problem to be a non-linear integer programming, which is impossible to obtain a globally optimal solution under polynomial time. In this paper, we formulate the problem and derive the closed-form solution in the form of Lagrangian multipliers; the searching of the optimal variables is formulated as a Multi-Arm Bandit (MAB) and we propose a Deep Deterministic Policy Gradient (DDPG) based algorithm exploiting the supply-demand interpretation of the Lagrange dual problem. Simulation results show that our proposed approach achieves significant QoE improvement, especially in the low wireless resource and high user number scenario compared to other baselines.
BreachMob: Detecting Vulnerabilities in Physical Environments Using Virtual Reality

BreachMob is a virtual reality (VR) tool that applies open design principles from information security to physical buildings and structures. BreachMob uses a detailed 3D digital model of a property owner's building. The model is then published as a virtual environment (VE), complete with all applicable security measures and released to the public to test the building's security and find any potential vulnerabilities by completing specified objectives. Our paper contributes a new method of applying VR to crowd source detection of physical environment vulnerabilities. We detail the technical realization of two BreachMob prototypes (a home and an airport) reflecting on static and dynamic vulnerabilities. Our design critique suggests that BreachMob promotes user immersion by allowing participants the freedom to behave in ways that align with the experience of breaching physical security protocols.
A dataset generation for object recognition and a tool for generating ROS2 FPGA node

This paper introduces our autonomous driving system equipped with recognition processing units from a camera image for hazard object / human-doll detection and drive lane detection. In particular, this paper focuses on a dataset generation method for neural networks and a generation tool “FPGA Oriented Easy Synthesizer Tool (FOrEST)” for ROS2-FPGA nodes. The results show that mAP of a neural network trained by the generated dataset is 94%, and a overhead of ROS2-FPGA communication by the FOrEST is 2–3 ms.
Algorithms for Detecting and Preventing Attacks on Machine Learning Models in Cyber-Security Problems

Abstract Machine learning algorithms can be vulnerable to many forms of attacks aimed at leading the machine learning systems to make deliberate errors. The article provides an overview of attack technologies on the models and training datasets for the purpose of destructive (poisoning) effect. Experiments have been carried out to implement the existing attacks on various models. A comparative analysis of cyber-resistance of various models, most frequently used in operating systems, to destructive information actions has been prepared. The stability of various models most often used in applied problems to destructive information influences is investigated. The stability of the models is shown in case of poisoning up to 50% of the training data.
Finitary monads on the category of posets

Abstract Finitary monads on Pos are characterized as precisely the free-algebra monads of varieties of algebras. These are classes of ordered algebras specified by inequations in context. Analogously, finitary enriched monads on Pos are characterized: here we work with varieties of coherent algebras which means that their operations are monotone.
Performance Improvement of Lossy Image Compression Based on Polynomial Curve Fitting and Vector Quantization

Lossy image compression performs a fundamental role in modern communication technology to cope up with the transmission and storage problems. In this paper, we present a new efficient lossy image compression method based on the polynomial curve fitting approximation technique, which represents many pixels of the image by a minimum number of polynomial coefficients. The presented method starts by converting the image into one-dimensional signal and it divides this one-dimensional signal into segments of variable length. Then, the polynomial curve fitting is applied to these segments to construct the coefficients matrix. The number of pixels is selected depending on the Root Mean Squared Error threshold value. Finally, the coefficients matrix is quantized using the vector quantization which composed of three procedures: codebook design procedure, encoding procedure, and decoding procedure. The proposed method is implemented for gray and colored images. Experimentally, the proposed method has improved the reconstruction quality by 2–9 dB with a better compression ratio relative to other techniques. Also, the proposed method obtains a better result than any other compared algorithms.
Role of Restored Underwater Images in Underwater Imaging Applications

Underwater images are extremely sensitive to distortion occurring in an aquatic underwater environment, with absorption, scattering, polarization, diffraction and low natural light penetration representing common problems caused by sea water. Because of these degradation of quality, effectiveness of the acquired images for underwater applications may be limited. An effective method of restoring underwater images has been demonstrated, by considering the wavelengths of red, blue, and green lights, attenuation and backscattering coefficients. The results from the underwater restoration method have been applied to various underwater applications; particularly, edge detection, Speeded Up Robust Feature detection, and image classification that uses machine learning. It has been shown that more edges and more SURF points can be detected as a result of using the method. Applying the method to restore underwater images in image classification tasks on underwater image datasets gives accuracy of up to 89% using a simple machine-learning algorithm. These results are significant as it demonstrates that the restoration method can be implemented on underwater system for various purposes.