Reviewerly/exHarmony huggingface dataset:
MTL-SLT: Multi-Task Learning for Spoken Language Tasks

Language understanding in speech-based systems has attracted extensive interest from both academic and industrial communities in recent years with the growing demand for voice-based applications.Prior works focus on independent research by the automatic speech recognition (ASR) and natural language processing (NLP) communities, or on jointly modeling the speech and NLP problems focusing on a single dataset or single NLP task.To facilitate the development of spoken language research, we introduce MTL-SLT, a multi-task learning framework for spoken language tasks.MTL-SLT takes speech as input, and outputs transcription, intent, named entities, summaries, and answers to text queries, supporting the tasks of spoken language understanding, spoken summarization and spoken question answering respectively.The proposed framework benefits from three key aspects: 1) pre-trained sub-networks of ASR model and language model; 2) multitask learning objective to exploit shared knowledge from different tasks; 3) end-to-end training of ASR and downstream NLP task based on sequence loss.We obtain state-of-the-art results on spoken language understanding tasks such as SLURP and ATIS.Spoken summarization results are reported on a new dataset: Spoken-Gigaword.
Real time photovoltaic power forecasting and modelling using machine learning techniques

Photovoltaic (PV) system installations have increased in recent years partly due to growing energy needs from a rising population. Such PV systems producing electricity contribute in promoting green energy. However, solar energy is highly intermittent and uncontrollable due to its high spatial and temporal variations of atmospheric conditions. With such variability, PV power forecasting is therefore crucial for full integration of solar energy into the grid. In this study, Support Vector Regression (SVR) and Random Forest Regression (RFR) models were built and used to forecast real-time PV power output of a 1.5kW solar PV system installed at the Department of Physics, University of Nairobi in Kenya. SVR model outperforms RFR model with root mean square (RMSE) of 43.16 adjusted R 2 of 0.97 and mean absolute error (MAE) of 32.57 on the validation. Dataset compared to RMSE of 86, adjusted R 2 of 0.90, MAE of 69 were obtained for RFR model. A real time power forecast application based on the SVR model was successfully built using the Shiny application in R software. This shows that SVR model is more robust than RFR and has capabilities of reducing errors during computations.
Integrative KnowGen: Integrative Knowledge Base Generation for Criminology as a Domain of Choice

Knowledge bases are essential in developing expert intelligent systems to solve domain specific problems. The low availability of detailed knowledge bases is a byproduct of the fact that addition and reasoning of information entities is largely a manual process. This paper proposes an architecture for automatic generation of knowledge bases from various heterogenous sources. Criminology has been chosen as a domain of choice because extensive knowledge in this area can help in curbing crimes in our society by creating preventive and predictive analysis systems. The proposed architecture utilizes seed criminology ontology as a base and adds information via multiple inputs i.e., Google’s news API, criminology journals, e-books, web portals and available datasets. The approach incorporates classification using LSTM and alignment of the instances using transformers. Furthermore, reasoning has been done using description logics and a large extensive knowledge base has been obtained. The model yields an average accuracy of 95.35% which surpasses the previous approaches.
An Improvement Method of Kd-Tree Using k-Means and k-NN for Semantic-Based Image Retrieval System

In this paper, an improved method of KD-Tree using k-Means and k-NN is proposed and applied to a semantic-based image retrieval system. Firstly, the image data is clustered by the k-Means algorithm at each layer on KD-Tree. Secondly, a process of image classification according to the k-NN algorithm is conducted at leaf nodes. So, an algorithm of KD-Tree construction based on the k-Means and k-NN is implemented. Each query image is extracted to a feature vector and performed image classification on KD-Tree. Then, a SPARQL query is created to retrieve a set of similar images by semantics on ontology. Based on this theory, a model of semantic-based image retrieval is proposed. The experiment is performed on Wang, Caltech101, and Caltech256 data sets. The experiment results are compared with the published works on the same data set to demonstrate the efficiency of our proposed method.
Multi-view Monocular Depth and Uncertainty Prediction with Deep SfM in Dynamic Environments

3D reconstruction of depth and motion from monocular video in dynamic environments is a highly ill-posed problem due to scale ambiguities when projecting to the 2D image domain. In this work, we investigate the performance of the current State-of-the-Art (SotA) deep multi-view systems in such environments. We find that current supervised methods work surprisingly well despite not modelling individual object motions, but make systematic errors due to a lack of dense ground truth data. To detect such errors during usage, we extend the cost volume based Deep Video to Depth (DeepV2D) framework [26] with a learned uncertainty. Our resulting Deep Video to certain Depth (DeepV2cD) model allows (i) to perform en par or better with current SotA and (ii) achieve a better uncertainty measure than the naive Shannon entropy. Our experiments show that we can significantly reduce systematic errors by taking the uncertainty into account. This results in more accurate reconstructions both on static and dynamic parts of the scene.
Approximate Divisor Multiples – Factoring with Only a Third of the Secret CRT-Exponents

We address Partial Key Exposure attacks on CRT-RSA on secret exponents $$d_p, d_q$$ with small public exponent e. For constant e it is known that the knowledge of half of the bits of one of $$d_p, d_q$$ suffices to factor the RSA modulus N by Coppersmith’s famous factoring with a hint result. We extend this setting to non-constant e. Somewhat surprisingly, our attack shows that RSA with e of size $$N^{\frac{1}{12}}$$ is most vulnerable to Partial Key Exposure, since in this case only a third of the bits of both $$d_p, d_q$$ suffices to factor N in polynomial time, knowing either most significant bits (MSB) or least significant bits (LSB). Let $$ed_p = 1 + k(p-1)$$ and $$ed_q = 1 + \ell (q-1)$$ . On the technical side, we find the factorization of N in a novel two-step approach. In a first step we recover k and $$\ell $$ in polynomial time, in the MSB case completely elementary and in the LSB case using Coppersmith’s lattice-based method. We then obtain the prime factorization of N by computing the root of a univariate polynomial modulo kp for our known k. This can be seen as an extension of Howgrave-Graham’s approximate divisor algorithm to the case of approximate divisor multiples for some known multiple k of an unknown divisor p of N. The point of approximate divisor multiples is that the unknown that is recoverable in polynomial time grows linearly with the size of the multiple k. Our resulting Partial Key Exposure attack with known MSBs is completely rigorous, whereas in the LSB case we rely on a standard Coppersmith-type heuristic. We experimentally verify our heuristic, thereby showing that in practice we reach our asymptotic bounds already using small lattice dimensions. Thus, our attack is highly efficient.
Fiber-optic microphone based on bionic silicon micro-electro-mechanical system diaphragm

The bionic sound detector based on the principle of the Ormia ochracea fly’s coupled ears is a miniature directional microphone that is sensitive to the sound pressure gradient. In this work, a silicon micro-electro-mechanical system bionic diaphragm consisting of two interconnected wings is designed and prepared, and a fiber-optic Fabry-Pérot interferometric microphone is constructed using the bionic diaphragm, and the characteristics of this microphone are studied theoretically and experimentally. According to the simulation results, the bionic diaphragm has two vibration modes of rocking and bending, and the rocking-mode displacement amplitude at a given sound pressure depends on the frequency and the propagation direction of the incident sound wave, and the closer to the eigenfrequency of the rocking mode the sound frequency, the greater the amplitude is; the rocking-mode displacement amplitude changes with the propagation direction in the three-dimensional space, resulting in a spindle-shaped distribution, and the long axis of the spindle is parallel to the long axis of the diaphragm, implying that the microphone is most sensitive to the sound wave propagating along the long axis of the diaphragm. The rocking-mode resonance frequency of the fiber-optic bionic microphone is measured to be slightly smaller than the simulated value. The output signal amplitude of the microphone changes with the horizontal azimuth angle of the sound source, producing a figure-8 polar pattern. A linear relationship between the microphone response and the azimuth angle is obtained in a range of 0° – ±60°, and in this angle range the directional sensitivity of the microphone is 39.98 mV/(°).
A new Secure Speech Communication Scheme Based on Hyperchaotic Masking and Modulation.

This paper presents a new, effective, simple, and more secure speech communication technique based on the combination of hyperchaotic masking and modulation. One hyperchaotic system is used for both masking and modulation operations. Speech communication techniques based on only chaotic masking have weak resistance to several attacks. So, further encryption is applied using chaotic modulation. Second-level encryption has enhanced the security of speech communication, as evidenced by the performance metrics. The encryption correlation coefficient between the original signal and the encrypted signal after chaotic masking is 0.0101. However, the correlation coefficient has become 0.0007 after the second level of encryption. Again, the speech signal segmental spectral signal to noise ratio (SSSNR) is -32.5160 dB after the first level of encryption, but the SSSNR becomes - 47.2612 dB after performing chaotic modulation in the second level of encryption. The decryption process is achieved successfully and the original speech signal is retrieved. The required synchronization is performed using an active controller.
EduTalk: An IoT Environment for Learning Computer Programming and Physics

This article proposes EduTalk, an out-of-the-box Internet of Things (IoT)-based smart learning environment for programming education. In particular, EduTalk enables the students to write VPython programs that render 3-D animations in the browser without installing extra software or using any specific (and typically expensive) hardware. EduTalk takes the user’s smartphone as a controller for cyber–physical interaction, which nicely integrates with learning of other core courses such as physics and mathematics. EduTalk allows building science exhibition projects by writing VPython programs to show 3-D animation, where the cost for EduTalk’s cyber–physical interaction is low and is almost maintenance free. The major contribution of this article is the IoT-based EduTalk proposal that subtly utilizes an IoT platform IoTtalk to conveniently generate cyber–physical interaction for learning how to program as well as learning core courses such as physics. The programming exercises can be easily extended to science exhibition projects and then the development of digital twin applications. A mechanism is provided to easily integrate GlowScript animation demos with EduTalk, which significantly simplifies the effort for teachers to prepare the lecturers. Finally, we show how data collected from EduTalk can be analyzed to improve learning design for cyber–physical interactive animation.
Asymptotically Quasi-Optimal Cryptography

The question of minimizing the computational overhead of cryptography was put forward by the work of Ishai, Kushilevitz, Ostrovsky and Sahai (STOC 2008). The main conclusion was that, under plausible assumptions, most cryptographic primitives can be realized with constant computational overhead. However, this ignores an additive term that may depend polynomially on the (concrete) computational security parameter $$\lambda $$ . In this work, we study the question of obtaining optimal efficiency, up to polylogarithmic factors, for all choices of n and $$\lambda $$ , where n is the size of the given task. In particular, when $$n=\lambda $$ , we would like the computational cost to be only $$\tilde{O}(\lambda )$$ . We refer to this goal as asymptotically quasi-optimal (AQO) cryptography. We start by realizing the first AQO semi-honest batch oblivious linear evaluation (BOLE) protocol. Our protocol applies to OLE over small fields and relies on the near-exponential security of the ring learning with errors (RLWE) assumption. Building on the above and on known constructions of AQO PCPs, we design the first AQO zero-knowledge (ZK) argument system for Boolean circuit satisfiability. Our construction combines a new AQO ZK-PCP construction that respects the AQO property of the underlying PCP along with a technique for converting statistical secrecy into soundness via OLE reversal. Finally, combining the above results, we get AQO secure computation protocols for Boolean circuits with security against malicious parties under RLWE.
A Progressive Web App for Virtual Campus Tour

Virtual Tour can be created stored in some MB&#x0027;s or GB&#x0027;s and can be accessed by a user from any corner of the world having a strong internet connection. Many colleges have represented their campus in digital format so many student&#x0027;s can have an idea how college campus looks. The way of representing virtual tour of most of the colleges are the same using a 360-degree virtual tour which are the 2D images stitched together to form a long continuous image. But our virtual has real objects that are represented in a 3D Gaming environment. We have combined the idea of 3D Gaming and 360-degree images to create an actual campus environment where user can move around. We have used First Person Perspective Approach which results in when the user controls it he feels that he is walking on a real college campus. For this, we have developed our 3D Model using as popular open-source modelling tool Blender2.8. And for giving a taste of gaming we are exporting our model into the web using the Babylon.js library which is new in the market but provides all assets to develop a 3D game. So are represent our virtual in a unique way where a user has all control and can roam inside the college campus smoothly.
Proposal of a Real-time American Sign Language Detector using MediaPipe and Recurrent Neural Network

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Data Placement for Multi-Tenant Data Federation on the Cloud

Due to privacy concerns of users and law enforcement in data security and privacy, it becomes more and more difficult to share data among organizations. Data federation brings new opportunities to the data-related cooperation among organizations by providing abstract data interfaces. With the development of cloud computing, organizations store data on the cloud to achieve elasticity and scalability for data processing. The existing data placement approaches generally only consider one aspect, which is either execution time or monetary cost, and do not consider data partitioning for hard constraints. In this paper, we propose an approach to enable data processing on the cloud with the data from different organizations. The approach consists of a data federation platform named <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FedCube</b> <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> and a Lyapunov-based data placement algorithm. <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FedCube</b> <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> enables data processing on the cloud. We use the data placement algorithm to create a plan in order to partition and store data on the cloud so as to achieve multiple objectives while satisfying the constraints based on a multi-objective cost model. The cost model is composed of two objectives, i.e., reducing monetary cost and execution time. We present an experimental evaluation to show our proposed algorithm significantly reduces the total cost (up to 69.8%) compared with existing approaches.
Estimation and Comparison of Linear Regions for ReLU Networks

We study the relationship between the arrangement of neurons and the complexity of the ReLU-activated neural networks measured by the number of linear regions. More specifically, we provide both theoretical and empirical evidence for the point of view that shallow networks tend to have higher complexity than deep ones when the total number of neurons is fixed. In the theoretical part, we prove that this is the case for networks whose neurons in the hidden layers are arranged in the forms of 1x2n, 2xn and nx2; in the empirical part, we implement an algorithm that precisely tracks (hence counts) all the linear regions, and run it on networks with various structures. Although the time complexity of the algorithm is quite high, we verify that the problem of calculating the number of linear regions of a ReLU network is itself NP-hard. So currently there is no surprisingly efficient way to solve it. Roughly speaking, in the algorithm we divide the linear regions into subregions called the "activation regions", which are convex and easy to propagate through the network. The relationship between the number of the linear regions and that of the activation regions is also discussed.
Modeling and verification of authentication threats mitigation in aspect-oriented mal sequence woven model

The modeling of security threats is equally important as the modeling of functional requirements at the design stage of software engineering. However, unlike functional requirements modeling, the modeling of security threats is neglected, which consequently introduces software defects during the early stages of software engineering. Hence, there is a need to mitigate these threats at the design stage. Security threats, specifically authentication threats, crosscut other functional and non-functional requirements when modeled using the object-oriented paradigm. This not only makes the design complex but also results in tangling and scattering problems. We therefore model authentication threats using the aspect-oriented modeling (AOM) technique since it separates crosscutting concerns and localizes them as separate units called aspects. Our main research aim is to remove scattering and tangling in security threats modeling using all the core features of the aspect-oriented technique. In this paper, we propose a research approach to model security threats and their mitigation in mal sequence diagram. Using this approach, our contribution makes a clear difference from previous work. Our first contribution is the modeling of authentication threats in the mal sequence diagram using the security profile and AOM profile. Our second contribution is the mathematical verification of the aspect-oriented mal sequence woven model in terms of correctness and completeness. Using the proposed approach, the scattering and tangling from the resultant woven model are successfully removed at the design stage. Thus, the complexity of models and the time and effort required for future modifications of design models are reduced.
Random forest and recurrent neural network for sentiment analysis on texts in Bulgarian language

The purpose of this paper is to further explore and search for the most accurate methods and algorithms to use in sentiment analysis on comments in Bulgarian language. In the previous works of the authors were applied standard algorithms like Naïve Bayes, Support Vector Machines and Logistic Regression on two self-developed databases with customers’ reviews in Bulgarian. The objective of the current project is to find weather Random Forest and Recurrent Neural Network could give better accuracy in sentiment prediction than the other algorithms or reduce the difference in the results of the two databases. The aim of the authors is to find the most accurate model for sentiment analysis in Bulgarian language, given the specifics of the language.
Synergy of Deep Learning and Artificial Potential Field Methods for Robot Path Planning in the Presence of Static and Dynamic Obstacles

In a fast-changing world we are in today, unmanned vehicles are displacing old, obsolete and frustrating tasks. Since unmanned vehicles are intended to work in an environment without any conduction, finding a collision-free path of movement is of paramount importance and a definite asset in practice. The objective of a global path planner consists in generating an optimized path without considering unknown obstacles. In contrast, a local path planner properly avoids the unknown obstacles while getting close to the goal. This results in an inefficient movement, particularly in complex circumstances. In this paper, a complete global and local path planning method is proposed for avoiding both static and dynamic obstacles. The proposed method benefits the advantages of both approaches while covering each other’s weaknesses. First, a method for generating numerous obstacle-free paths from random pairs of start and goal points is introduced. Next, a novel deep-learning approach is proposed in order to train the robot in an environment free of moving obstacles. After extracting the desired via points, an efficient Artificial Potential Field (APF) approach for attaining the local path planning is introduced with the aim of avoiding dynamic obstacles while the robot travels through the aforementioned via points. The proposed method can be well extended to different platforms such as mobile robots, arm robots, quadrotors, etc; in this paper, both local and global path planning methods are implemented on a simulated quadrotor to examine the robot’s performance for both approaches. Furthermore, it has been revealed that implementing both approaches should be implemented seamlessly in order to attain a complete efficient path planning with the presence of both static and dynamic obstacles. Finally, both local and global path planning methods are implemented on a detailed simulated quadrotor to evaluate the robot’s performance for the aforementioned approaches.