Reviewerly/exHarmony huggingface dataset:
A Detailed and Direct Proof of Skorohod-Wichura's Theorem

The representation Skorohod theorem of weak convergence of random variables on a metric space goes back to Skorohod (1956) in the case where the metric space is the class of real-valued functions defined on [0,1] which are right-continuous and have left-hand limits when endowed with the Skorohod metric. Among the extensions of that to metric spaces, the version by Wichura (1970) seems to be the most fundamental. But the proof of Wichura seems to be destined to a very restricted public. We propose a more detailed proof to make it more accessible at the graduate level. However we do far more by simplifying it since important steps in the original proof are dropped, which leads to a direct proof that we hope to be more understandable to a larger spectrum of readers. The current version is more appropriate for different kinds of generalizations.
Detecting semantic difference

Semantic discrimination among concepts is a daily exercise for humans when using natural languages.For example, given the words, airplane and car, the word flying can easily be thought and used as an attribute to differentiate them.In this study, we propose a novel automatic approach to detect whether an attribute word represents the difference between two given words.We exploit a combination of knowledge-based and cooccurrence features (collocations) to capture the semantic difference between two words in relation to an attribute.The features are scores that are defined for each pair of words and an attribute, based on association measures, n-gram counts, word similarity, and Concept-Net relations.Based on these features we designed a system that run several experiments on a SemEval-2018 dataset.The experimental results indicate that the proposed model performs better, or at least comparable with, other systems evaluated on the same data for this task.
A Novel Phase-Domain Speech Watermarking Method using Perceptual Orthogonal Matching Pursuit

This paper proposes a new blind method for speech watermarking utilizing the perceptual orthogonal matching pursuit (POMP). The employed POMP algorithm, which has been recently introduced to achieve a high-quality sparse representation of speech signals, is combined into a watermarking method in this work. The proposed watermarking approach is carried out as the following. First, an orthogonal dictionary is constituted of gammatone basis functions to be used in the POMP method. Thereafter, the POMP algorithm is executed on short-time speech frames to find the most relevant gammatone kernels and the corresponding coefficients. Finally, the watermark bitstream is embedded into the phase of extracted coefficients and the reconstruction step is performed. By utilizing POMP representation with an orthogonal gammatone dictionary, a high secure speech watermarking scheme has been attained with a high capacity and an adequate robustness against various types of attacks.
Speaker Diarization in Broadcast News Using Sub Glottal Resonances

Recently by the increasing use of digital media especially multimedia archives, researchers have been looking for safe reliable and easy to use methods by means of these Medias. There have been different methods developed in which among them speaker diarization can be pinpointed. In the latter method, vocal tract is one of the features which has been taken into account. Since the structure of larynx changes rapidly during a talk or conversation, more stable features can be used for speaker identification purposes. In this paper we tried to use lower parts of larynx known as SGR (Sub Glottal Resonance), as features in the segmentation stage of speaker diarization. After simulations and analysing the results segmentation and diarization phases, the proposed method has given 63 percent of accuracy in the first phase and 91.67 percent in the second phase.
RTS Smoother for GLMB filter

In this paper, we implement a low-cost but effective smoothing strategy to smooth estimated tracks returned by the GLMB filter. While the forward filtering step is carried out via the GLMB filtering procedure, the backward smoothing step is recursively implemented from the final time step to the first time step via a smoothing algorithm. In particular, the smoothing algorithm is based on the Rauch-Tung-Striebel (RTS) of fixed-interval smoother. We demonstrate our smoothing strategy on a linear Gaussian model and the experimental results show consistent improved tracking performance over 100 Monte Carlo runs.
Inverse Lighting from Cast Shadows Under Unknown Radiometric Response Function

Inverse lighting is a technique for estimating the illumination distribution of a scene from a single image. Conventionally, inverse lighting assumes either a linear radiometric response function or a convex scene without cast shadows. Unfortunately, however, consumer cameras usually have unknown and nonlinear radiometric response functions, and then the existing methods do not work well for images such as Internet photos taken by using those cameras. Moreover, it is known that the high-frequency components of an illumination distribution cannot be recovered from diffuse reflection components without cast shadows. In this paper, we propose a method for jointly estimating both an illumination distribution and a radiometric response function from cast shadows. Specifically, our proposed method represents the illumination distribution and the response function by using Haar wavelets with the sparseness constraint and polynomials respectively, and then estimates their coefficients. We conducted a number of experiments by using both synthetic and real images, and confirmed that our method works better than the existing methods. In addition, we showed that masking pixels near edges makes the joint estimation robust for real images with approximate geometry.
Online Portal Inquiry for DOST-8(Department of Science and Technology-Region 8) Programs and Services with SMS Features

The dissemination of information is a critical thing in the everyday routine of an organization to share it with the community or within the premises of their locality. DOST-8 Regional Office is located in Palo, Leyte, and its primary concern is to strengthen information about its programs and services not only within the reachable areas but to remote areas who can barely find it hard to receive recent updates about different details of the agency.The primary purpose of why the researchers were bringing this study is it aims to develop an Online Portal Inquiry System that can be used to access information of programs and services of the DOST VIII specifically by its scholars and stakeholders, wherein the respondents can inquire either online or through SMS messaging. Thus, making it easier for the community to reach out information on what the agency is extending to them.Gathering of data was considered to get ideas on the concept of making this study, and to understand better the flow and purpose, particularly in the dissemination of information of its programs and services to the community, was done to its respondent. Researchers used an iterative model as a guide to complete the task in developing the project since it is a useful guide to create the purpose of the study and that whenever there are iterations as suggested by the respondent, it can be quickly taken into account.Online Portal Inquiry for DOST 8 Programs and Services with SMS Features is a great help to the rural/urban areas for easy dissemination of information regarding programs and services of the said agency and give updates of any news also to DOST scholars. It also provides an easy and effective storage information related to beneficiaries and the community (e.g., business owners of SMEs, LGU, BLGU, and small organization).
Quantum Fisher information matrix of a single qutrit in Xi or cascade configuration

Quantum sensing is an important application of Quantum Information techniques. In this work, a mathematical model for a single Qutrit in three different representations is presented and their Shannon Mutual Information is compared.
Activity Recognition Using Temporal Features and Deep Bottleneck 3D-ResNeXt

Due to the advancements in the field of artificial intelligence, object detection and classification are becoming easier. New deep learning and machine learning-based approaches are being used for video analysis, activity recognition and activity detection, too. But, training of deep learning-based neural networks for large video datasets is computationally expensive. Also, it lacks in terms of either speed or correctness and has drastic variations due to the background, scale, etc. Thus, while deep learning has given outstanding results for image classification and detection, activity detection and recognition can still use better approaches for faster and more accurate results. In this paper, we built a model based on ResNet classifier and ResNeXt classifier, which gives improved performance in action recognition. We start with introductory topics and network architecture. Then, we discuss the experiments that we have tried, and also, we show the process of how we get to deploy this method in edge devices like Nvidia Jetson Nano and Intel UP Squared board.
Evaluation of Attacker Skill Level for Multi-stage Attacks

Information security risks caused by difficult to exploit vulnerabilities are often treated with countermeasures as last due to their low likelihood of occurrence and should be given a high priority in security monitoring. In this paper, we propose an evaluation of detected attacks in terms of their difficulty - by assigning them an attacker's skill level. We draw similarities between vulnerability's exploitability score and aim to evaluate intrusion detection system alerts within the same framework. We also present the methodology on attacks from a dataset intended for evaluation of intrusion detection systems.
Data Science Applications

In this chapter, we shall study a few applications of linear programming to an area of statistics called regression and to an area of machine learning called support vector machines. As a specific example for our study of regression, we shall use size and iteration-count data collected from a standard suite of linear programming problems to derive a regression estimate of the number of iterations needed to solve problems of a given size.
Big Data Recommender System for Encouraging Purchases in New Places Taking into Account Demographics

Recommendation systems have gained popularity in recent years. Among them, the best known are those that select products in stores, movies, videos, music, books, among others. The companies, and in particular, the banking entities are the most interested in implementing these types of techniques to maximize the purchases of potential clients. For this, it is necessary to process a large amount of historical data of the users and convert them into useful information that allows predicting the products of interest for the user and the company. In this article, we analyze two essential problems when using systems, one of which is to suggest products of one commerce to those who have never visited that place, and the second is how to prioritize the order in which users buy certain products or services. To confront these drawbacks, we propose a process that combines two models: latent factor and demographic similarity. To test our proposal, we have used a database with approximately 65 million banking transactions. We validate our methodology, achieving an increase in the average consumption in the selected sample.
Handwritten Digital Detection Based on Tensorflow Building SSD Model

A computer vision recognition model based on real-time detection is built, and relevant tests are made by using the theory of deep learning. The SSD convolution neural network model is built on the Tensorflow platform, and it is used for the recognition and detection of handwritten numerals. The method of making the data set which can be used to convert MNIST into SSD is given, and the training flow is given, and the experimental results are analyzed. After 50000 training, the recognition accuracy reaches 99.19%, and the location accuracy reaches 99.99%, and the recognition effect is good.
ROLE OF CYBER SECURITY IN INDUSTRY 4.0

Nation Crime Record Bureau (NCRB) report indicates that speedy increase in Cyber- attacks in India by 50% from 2018 to 2020. In future the cyber-attacks will continue to become more innovative and sophisticated. There have been several incidences of cyber- attacks on business and individual level of digital transactions in the past few years. Putting the data of billions of people on the cloud could be risky and could threaten the security. Hence the Digital India project demands very strong network security at all levels of operation. Industry 4.0 (I.4.0) can be the substance of changes in different fields like governance , management and administration of smart cities and other applications which are driving the vision of Digital India. Theme of the paper presentation is to make awareness on the cyber attacks and levels of security measures in the field of Digital transactions, footprints of data and E-commerce websites. The essential steps that to be implemented in the process of Industry 4.0 .
Multi-scale Correlation Filter Tracking with Adaptive Re-detection Mechanism

Tracking is a popular research topic in artificial intelligence, but how to handle the severe occlusion and deformation remains a challenging problem. Focusing on this issue, we propose a multi-scale correlation filter tracker using a re-detection module. Specifically, we utilize a reliable confidence strategy to estimate the reliability of initial results, and introduce a novel template matching technique to solve the target relocation problem. Experiment results demonstrate that our method can outperform several classic trackers.
Multi-group target GM-PHD filter combined with rapid detection mechanism

A multi-group target Gaussian mixture probability hypothesis density (GM-PHD) filter algorithm combined with rapid detection mechanism is proposed for multi-group target tracking problem. The algorithm is based on the GM-PHD filter algorithm, using the characteristics of the group target, combined with the rapid detection mechanism to detect and track the new group. In order to effectively remove the clutter, after the prediction phase is completed, the correlation gates of all predicted Gaussian components are used to screen the measurements. In order to avoid missing new groups, a cyclic threshold aggregation method is used to detect whether a new group target appears in the measurement set at each moment. Simulation experiments show that the proposed method can track multi-group targets quickly and effectively, and has better ability to cope with sudden changes.
Data Augmentation Under Scarce Condition for Neural Machine Translation

Neural Machine Translation (NMT) has achieved state-of-the-art performance depending on the availability of copious parallel corpora. However, for low-resource NMT task, the scarcity of training data will inevitably lead to poor translation performance. In order to relieve the dependence on scale of bilingual corpus and to cut down training time, we propose a novel data augmentation method named SMC under scarce condition that can Sample Monolingual Corpus containing difficult words only in back-translation process for Mongolian-Chinese (Mn-Ch) and English-Chinese (En-Ch) NMT. Inspired by work in curriculum learning, our approach takes into account the various difficulty-degree of the sample and the corresponding model capabilities. Experimental results show that our method improves translation quality respectively by up to 2.4 and 1.72 BLEU points over the baselines on En-Ch and Mn-Ch datasets while greatly reducing training time.
Policy Space Exploration for Linear Quadratic Regulator (LQR) Using Augmented Random Search (ARS) Algorithm

Considering the recent developments in embedded systems and automotive industry, it is quite evident that in very near future many application-based electronic devices will adapt the automation in its daily based activities. This automation will make the devices more powerful and will enhance its services. Currently, automation is the result of the algorithms which are pre-coded into the devices, but its future is the result of algorithms which enable devices to learn from environment in which it needs to work. It can be achieved utilizing the resources developed for a particular domain popularly known as reinforcement learning (RL). Main objective of this paper is to enable an agent to explore a policy for achieving a control of dynamic system such that it will be capable to find an optimal solution to solve the environment. It can be achieved using an algorithm known as augmented random search algorithm. To improve the training speed, we will use concept of multiprocessing and environment-specific customizations along with ARS algorithm.
Image-Text Cross-Media Feature Correlation based on Adversarial Network

With the emergence of mass multimedia data on Internet, there is an increasing demand for cross-media retrieval applications. The modal feature representation capability is crucial for cross-media retrieval. This paper proposes an improved feature correlation model of image and text modalities based on adversarial network, called FCMAN, which uses the adversarial network to establish feature correlation between image and text modalities. Firstly, different features of the image modality are fused to enhance the feature representation capability. Secondly, based on the feature correlation modeling of a single adversarial network, two new adversarial networks are introduced to model the real labels and predictive labels of image and text modalities respectively. Experimental results show that the proposed approach can establish feature correlation between image and text modalities effectively.
Research and Design of Key Management System in Power Distribution Network Based on Quantum Private Communication

Power distribution operation involves the supervision and control of the operating states of a large distribution power grid. This technology includes several features, e.g. an abundant amount of application nodes, diversity of communication channels, etc. To accelerate the construction of State Grid-electric Internet of things and to guarantee the security of power grids data network and to consider the feature of power grids, a synthetic key management system is proposed. In the first section of this paper, from the perspective of maturity and practicality of related technology, the feasibility of combining quantum private communication and automatic power grid key management system is analyzed and demonstrated. In the next section, a key-management method, which is designed to combine quantum private communication technology and interaction with encryption devices, is designed and implemented. In the concluding section, to guarantee the integrity and atomicity in key storage processes and to achieve the quantum key storage, rollback mechanism of large-capacity quantum key storage is studied and discussed.
Design and Implementation Voice Scrambling Model Based on Hybrid Chaotic Signals

One of the most common theories used in cryptography is chaos theory, as it has sufficient encryption properties. Such properties can be used to analyse diffusion and confusion processes, which is one of cryptography's key features; a speech scrambling is an important tool in wireless communication systems for enhancing data security. The main target is achieving security to resist malicious eavesdroppers and attacks. This paper presents a speech scrambling model using three various chaotic maps/flows (standard map SM, Unified system US and Chen system CS). The use of the chaotic keys in scrambling techniques are to enhance the transmitted data security, which is strong over the effects of AWGN. Five different tests were included in the evaluation measures: cepstral distance d <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CD</sub> , Linear predict coding distance d <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LPC</sub> , Mean Square Error MSE, Entropy H, and Correlation Factor R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</sub> . The important criteria are residual intelligibility of speech signal between the (original and scrambling/descrambling signal) for the proposed models. The results are good enough to protect the encryption signal of the brute force attack, and when applying a model, the results are being evaluated. The offered model was carried out using SIMULINK (R2018b) by using MATLAB.
Prediction of Juvenile Delinquencies in Correlation with Education

Juvenile Crime is not a healthy trait for any developing country and certainly not for a widely cultured and populous country like India. India has been suffering from overpopulation and low literacy for many years now and it affects the growth of the nation. Education is the key to improve the state of the country and more juvenile crime, children receive lesser quality education and it keeps pulling down the nation. Not only that, it also poisons the next generation and is not healthy for the future of the nation. Even though education is provided for the convicted children, not everyone who is released continues to study which leads to nothing but more uneducated adults who are less productive in this competitive world. This project analyses the juvenile delinquency rate over the course of 30 Years and predicts the possible rate of juvenile crime in India in the forthcoming years, taking into account the various factors that would affect said juvenile crime rate, which include but not limited to: overpopulation, education especially between the ages 16 to 18, financial state of family, cultural differences, regional differences and past criminal history within the convicted juvenile's family. Since factoring in all the aforementioned features into account is near impossible, various machine learning (Linear Regression, Polynomial Regression regression, Back Propagation Network and Adam Optimization Algoritms) and deep learning models are created to primarily focus on the admission rates at different education levels in India. The results of said models provide us with insight on how important education is and how would the future generations shape up when the current trend continues, and also which of the selected learning models is the most accurate.
A Novel Two-Level One-vs-Rest Classifier

Classification plays a significant role in pharmacokinetics, pharmacology dynamics and etc. This paper proposes a novel Two-Level assembling Classifier (TLC) that fulfills label detection with two steps, with aim to achieve higher accuracy and feasibility. TLC constructs the micro classifier based on local boundary information, combines them in an informed way to form basic classifiers and integrates the local decisions of basic classifiers to give the global decision. TLC is equipped with parameterization heuristics to foster computation. Empirical evidence on real datasets demonstrates the performance of TLC compared with the peers.
Development of a Weather Application in Android and iOS Operating Systems

This paper is meant for a better understanding of the main mobile operating systems, Android and iOS, and the overall development of a weather application, determining the strengths and flaws of each platform. The goal is to provide a detailed study of the two mobile platforms, how they work, their market share and the time and costs needed for development.