Reviewerly/exHarmony huggingface dataset:
A Meta-Analysis of Foreign Direct Investment and Economic Growth: An Empirical evidence from Pakistan during COVID 19 Policymaking

Purpose: Present study is based on the impact of Foreign Direct Investment on the Economic Growth of Pakistan. In this paper, a time-series data has been collected for the variables Foreign Direct Investment (FDI), Gross Capital Formation (GCF), Inflation rate (IR), and Gross Domestic Product (GDP) since the year 1970-2018 from World Bank Development Indicators. Some tests have been performed regarding the significance of data using regression, correlation analysis, and normality test (Kolmogorov-Smirnov) using SPSS software. In this research, an alternative hypothesis has been built to analyze the significance of this data. Discussions and recommendations have been given in this paper to implicate on the economy for growth and development measures. Design/Methodology/Approach: The data from WDI and other worldwide authentic sources were used from 1970-2018 to perform models using regression analysis and advanced machine learning methods. Findings: Regression and correlation analysis have been used on the variables. Its result indicates a positive relationship among the variables FDI and GDP whereas it shows a negative correlation among the variables GCF, IR concerning GDP. Limitations: Data is predictable based on the variables taken. It has been collected for the recent years up till 2018. It can only predict a certain variation caused by the increase of a particular independent variable that is FDI, GCF, and IR. Future variation in variables cannot be predicted. The results are specifically related to Pakistan and cannot be justified for other countries. Practical implications: Strong industrial network clusters must be formed by the policymakers to gain investor loyalty and will also help companies to form capital and also sustain a strong supply chain management. Higher FDI inflows will aid the ongoing project of Pakistan which is the Diamer Bhasha Dam. Due to the pandemic stated as COVID-19, FDI will actively play a role in maintaining ventilators in hospitals that have become a current need. An increase in inflation shows a negative impact on the economy of Pakistan. Considering the prevailing situation where unemployment level, job opportunities not being available and no productivity level is being achieved due to the current Pandemic i.e. COVID-19, policymakers must take initiative in reducing consumer prices so that all the preceding situations could have positive effects on the economic growth of Pakistan.
Electric Circuit Simulator Applying Augmented Reality and Gamification

In Ecuador, the statistics presented by Educational Evaluation National Institution EENI register that 22.8% of the students from the Andean region and 18.3% from the coast, had insufficient grades in areas that include the study of science (Physics, Chemistry, and Biology). This study proposes the development of a physics mobile application that applies the methodology of gamification and increased reality to improve creativity, academic performance, the interaction between students and teachers (collaborative learning) and that is motivating and interesting for reinforce students’ knowledge during use. For the development of the application, the theme will focus on the study of direct current electrical circuits. The proposed application will be made using the development environment called Scratch, which will allow us to create applications in a simpler and faster way with quite efficient results. On the other hand, the gamification methodology is applied which allows the student to use the application, learn, and reinforce their knowledge continuously. The functionalities that will make up the application will consist of the ease and versatility of building various associations of electrical resistances (series, parallel and mixed), circuits (batteries, variable and fixed resistors, motors, etc.) and the use of other components that allow students to set challenges and problems who will be able to solve them in a defined time. This application greatly encourages collaborative work and allows constant information by applying in-creased reality to electronic components. The application creates a safe and dynamic environment for learning since multiple tests can be carried out, fostering creativity, and developing reinforcement learning. The tests carried out have been satisfactory with students, carrying out a control group (traditional teaching) and a test group (with the use of the proposed application). The expected results include an increase in the motivation towards the study of the subject, an increase in teamwork, and an improvement in the academic performance of the students by 20%, through evaluation using questionnaires.
Making Transaction Propagation More Efficient: Deferred Transaction Relay in Bitcoin

Data propagation in the Bitcoin network is inefficient due to its permissionless nature and the lack of multicast/broadcast features. In particular, the number of messages needed to propagate a single transaction is very high which is rather wasteful in terms of bandwidth utilization. In this work we propose a simple modification of Bitcoin software that allows a vast reduction of the number of messages needed for propagating a transaction over the network. The modification consists of deferring the transaction announcements until a certain predefined number of new transactions is collected. We show that the number of messages can be substantially reduced in this manner. The price to pay for this reduction is an increase in transaction propagation delay. However, the tradeoff between the reduction of traffic and transaction delay can be minimized by judicious choice of the threshold number of deferred transactions.
Understanding Traffic Fingerprinting CNNs

HTTPS encrypted traffic can leak information about underlying contents through various statistical properties of traffic flows like packet lengths and timing, opening doors to traffic fingerprinting attacks. Recently proposed traffic fingerprinting attacks leveraged Convolutional Neural Networks (CNNs) and recorded very high accuracies undermining the state-of-the-art mitigation techniques. In this paper, we methodically dissect such CNNs with the objectives of building further accurate and scalable traffic classifiers and understanding the inner workings of such CNNs to develop effective mitigation techniques. By conducting experiments with three datasets, we show that website fingerprinting CNNs focus majorly on the initial parts of traces instead of longer windows of continuous uploads or downloads. Next, we show that traffic fingerprinting CNNs exhibit transfer-learning capabilities allowing identification of new websites with fewer data. Finally, we show that traffic fingerprinting CNNs outperform RNNs because of their resilience to random shifts in data happening due to varying network conditions.
Optimized Software Implementations for the Lightweight Encryption Scheme ForkAE

In this work we develop optimized software implementations for ForkAE, a second round candidate in the ongoing NIST lightweight cryptography standardization process. Moreover, we analyze the performance and efficiency of different ForkAE implementations on two embedded platforms: ARM Cortex-A9 and ARM Cortex-M0. First, we study portable ForkAE implementations. We apply a decryption optimization technique which allows us to accelerate decryption by up to 35%. Second, we go on to explore platform-specific software optimizations. In platforms where cache-timing attacks are not a risk, we present a novel table-based approach to compute the SKINNY round function. Compared to the existing portable implementations, this technique speeds up encryption and decryption by 20% and 25%, respectively. Additionally, we propose a set of platform-specific optimizations for processors with parallel hardware extensions such as ARM NEON. Without relying on parallelism provided by long messages (cf. bit-sliced implementations), we focus on the primitive-level ForkSkinny parallelism provided by ForkAE to reduce encryption and decryption latency by up to 30%. We benchmark the performance of our implementations on the ARM Cortex-M0 and ARM Cortex-A9 processors and give a comparison with the other SKINNY-based schemes in the NIST lightweight competition – SKINNY-AEAD and Romulus.
A logic of knowledge based on abstract arguments

Abstract We introduce a logic of knowledge in a framework in which knowledge is treated as a kind of belief. The framework is based on a standard KD45 characterization of belief, and the characterization of knowledge undergoes the classical tripartite analysis that knowledge is justified true belief, which has a natural link to the studies of logics of evidence and justification. The interpretation of knowledge avoids the unwanted properties of logical omniscience, independent of the choice of the base logic of belief. We axiomatize the logic, prove its soundness and completeness and study the computational complexity results of the model checking and satisfiability problems. We extend the logic to a multi-agent setting and introduce a variant in which belief is characterized in a weaker system to avoid the problem of logical omniscience.
Representing and reasoning about dynamic code

Dynamic code, i.e., code that is created or modified at runtime, is ubiquitous in today's world. The behavior of dynamic code can depend on the logic of the dynamic code generator in subtle and non-obvious ways, e.g., JIT compiler bugs can lead to exploitable vulnerabilities in the resulting JIT-compiled code. Existing approaches to program analysis do not provide adequate support for reasoning about such behavioral relationships. This paper takes a first step in addressing this problem by describing a program representation and a new notion of dependency that allows us to reason about dependency and information flow relationships between the dynamic code generator and the generated dynamic code. Experimental results show that analyses based on these concepts are able to capture properties of dynamic code that cannot be identified using traditional program analyses.
A Hybrid Spherical Search and Whale Optimization Algorithm

In recent years, mixed-element heuristics have received increasing attention in optimization algorithms. The spherical search (SS)-a swarm-based meta-heuristic algorithm is used for solving global optimization problems with nonlinear constraints. The whale optimization algorithm (WOA), inspired by the bubble net hunting strategy, mimics the social behavior of humpback whales, whereas the heuristics method usually fall into a so-called "local optima trap". We proposed a hybrid algorithm based on the whale algorithm and the spherical search optimization hybrid algorithm, so that the two optimization strategies are merged into the presented algorithm which enables both algorithms to work in a co-evolutionary way. Experiment results show that the proposed hybrid algorithm can find the better solutions than some other new algorithms in term of convergence speed and solution accuracy.
The Importance Weighted Autoencoder in End-to-End Speech Synthesis

The modeling of style when synthesizing speech from text aim to enrich the emotional expression of synthesized speech. In this paper, the importance weighted autoencoder(IWAE) is introduced into Tacotron, an end-to-end speech synthesis system, to learn the latent representation of speaking styles independent of text content in an unsupervised way. Compared with the Variational Autoencoder(VAE), a generative model that can be used to model prior data distribution, IWAE has a strictly tighter variational lower bound derive from different weighted importance. In the proposed network, we input the style representation, learned by IWAE, into Tacotron to generate speech with more rhythmic. Finally, the proposed model shows better performance of speech quality in both objective and subjective ways compared to Global Style Token (GST) and VAE-Tacotron.
Characterizing terrain image classification difficulties through reduced-dimension class convex hull analysis

The classification of natural terrain ahead of an autonomous vehicle can help it make decisions regarding the traversability of various paths, but remains an open research problem. Despite the explosion in popularity of deep learning networks,there is still little work available on informed neural network design procedures for specific tasks such as terrain image classification, save through performance measures. A related problem is understanding features of a dataset that lead to difficulties in separating classes of images from one another. This research proposes an algorithm and accompanying analytical procedure to characterize such image classification difficulties; identifying what makes some images easily distinguishable as their class and what makes others readily confused with other classes. This is achieved by learning reduced-dimensionality representations of the input data, constructing a convex hull of class members in the reduced dimensionality representation, then examining between-class overlap within each space, incrementally increasing the dimensionality until overlap is eliminated. Summarizing the between-class overlap statistics reveals trends and anomalies that can be linked back to visual features, characteristic of the original data. Case studies are presented of insights identified through selected example analyses: relative intensities of terrain classes from images taken by Mars rovers, and the impact of color gradients in separating sand from bedrock in color images of terrain. Such insights are discussed as steps toward a more directed approach to designing neural networks for image classification.
Technology and Surveillance in Times of Crisis, and Beyond: Lessons from Mobile Tracking Technology During the Covid-19 Outbreak

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The Ant-Step Algorithms: Reloading the Ant System Heuristic and the Overlooked Basic Variants

Thirty years ago, an heuristic based on the social ants behavior was proposed, to tackle the Traveling Salesman Problem (TSP) called Ant System (AS) or Ant Algorithm (AA). In a pure experimental context, two choices were explored to implement this heuristic. In the first choice, ants, as in nature, autonomously, move and update pheromones. When in the second, ants update pheromones, simultaneously, after fulfilling an entire circuit, in an "abnormal" ants' attitude, defining the Ant-Cycle variant. The first choice offers two options: considering distances between towns, to define the Ant-Quantity variant or not, defining the Ant-Density variant. Exploiting global information in the AS, the Ant-Cycle variant was the most performing, consequently, any improvement of AS was defined upon this best variant.An unexplored "overlooked" third choice was possible and promising; which consist to push more deeper the "abnormal" ants' attitude, forcing them to update pheromones, simultaneously, after every single move, providing the Ant-Step model. This third choice offers, also, the option of considering distances or not.The aim of this paper is to "reload" the Ant Algorithms and define a new AS basic algorithms: the Ant-Step-Quantity and the Ant-Step-Density. These new variants will be validated through an experimental study on various symmetric TSP benchmarks and compared to the previous three basic ones.
Image compression algorithm research based on improved LSTM

With the advent of the era of big data, storing and transferring data is facing tremendous pressure. How to use deep learning to obtain higher compression ratio and higher quality images has become an urgent problem. Recurrent neural network (RNN) can control the bit rate of images with iterative manner to improve compression performance. However, RNN needs to introduce long short term memory (LSTM) to solve the problem of long-term dependence, which leads to the model more complex. In order to speed up training process and reconstruct higher-quality images, firstly, this paper improves the activation function in LSTM to better determine the information to be stored or forgotten, so that the amount of parameters is reduced and the training process is faster. Then, the image recovery block is introduced in the decoder to reconstruct high-resolution images. Finally, instead of L1 loss, we use SmoothL1 loss to accelerate the convergence of loss. Experimental results show that our model can achieve a higher compression ratio, and evaluated by SSIM the value is more nearly to 1.
WITHDRAWN: Implementation patterns of secured internet of things environment using advanced blockchain technologies

Now days, the Blockchain Technology is in focus because of its enormous features associated with privacy, security and integrity of the transactions in the network environment. A blockchain refers to a chain of records which are interconnected with each other and highly secured because of the dynamic hash and cryptography implemented at every phase of the transaction. The blockchain technology avoids the possibilities of hacking or cracking the transactions by intentional or accidental attempts. Blockchain can be used to monitor the measurements of sensor data and avoid replication of any other harmful information. IoT system implementations can be difficult, and a distributed ledger is nice to identify, authenticate and seamlessly migrate IoT devices safely.
AI-Powered Virtual Assistants in the Realms of Banking and Financial Services

This chapter aims at providing a framework for analysis on evolutionary trends in finance that have to do with technological progress and especially with artificial intelligence (AI) applications. The starting point can be identified with a survey on how they have modified the business areas involving banking and financial services and on what can be expected – in terms of future strategic shifts and behavioral changes – on both the supply and the demand sides. The next step revolves around a wider and deeper investigation on the role that virtual assistants have started to – and are likely to further – play in the areas under scrutiny: special attention is requested upon the provision of enhanced customer service support, including conversational AI and sound branding; implications encompass developments that are on the cards, based upon digitalization as a must – not just an option – as shown by the Covid-19 pandemic. Conclusions allow to emphasize the significance, advancing features and value of this conceptual paper, as it leads to sort out best practices and success stories that are worth disseminating and replicating to benefit not only individuals and enterprises having direct interest in them, but society as a whole.
A Compression Resistant Steganography Based on Differential Manchester Code

In the field of image steganography research, more and more attention is paid to the importance of stego image robustness. In order to make steganography accessible from laboratory to practical applications, it becomes critical that the stego images can resist JPEG compression from transmission channel. In this paper, an image steganography algorithm with strong robustness to resist JPEG compression is proposed. First, the robust cover elements are selected using the sign of DCT coefficients which are kept constant before and after JPEG compression. Additionally, a distortion function and a weighted cost adjustment method are designed to assign an appropriate cost to each candidate DCT coefficient. Finally, the message is embedded in the cover image which has minimal embedding distortion by flipping the signs of DCT coefficients, while differential Manchester code is applied to the element positions to obtain the location feature. Compared with the prior art, our algorithm has better undetectability and stronger robustness, and it can resist the attacks from the social network platforms such as Facebook, Twitter, and WeChat.
A dual U-Net algorithm for automating feature extraction from satellite imagery

Quick identification of building and roads is critical for execution of tactical US military operations in an urban environment. To this end, a gridded, referenced, satellite images of an objective, often referred to as a gridded reference graphic or GRG, has become a standard product developed during intelligence preparation of the environment. At present, operational units identify key infrastructure by hand through the work of individual intelligence officers. Recent advances in Convolutional Neural Networks, however, allows for this process to be streamlined through the use of object detection algorithms. In this paper, we describe an object detection algorithm designed to quickly identify and label both buildings and road intersections present in an image. Our work leverages both the U-Net architecture as well the SpaceNet data corpus to produce an algorithm that accurately identifies a large breadth of buildings and different types of roads. In addition to predicting buildings and roads, our model numerically labels each building by means of a contour finding algorithm. Most importantly, the dual U-Net model is capable of predicting buildings and roads on a diverse set of test images and using these predictions to produce clean GRGs.
Optimal wavelet estimators of the heteroscedastic pointspread effects and Gauss white noises model

The problem of estimating a function’s derivative arises in many scientific settings, from medical imaging to astronomy. In this paper, we consider a hard thresholding wavelet approach to certain blind deconvolution problem based on the heteroscedastic data. Motivated by Delaigle & Meister’s work, the adaptive wavelet estimators are proposed and their asymptotic properties are investigated. It is shown that the derivative estimators for the blind deconvolution model are spatially adaptive and attain the optimal rate of convergence up to a logarithmic factor over a range of Besov classes. Our theorems generalize the results of Cai and Navarro et al. in some sense.
An Efficacious Method for Face Recognition Using DCT and Neural Network

Face images have close correlation and redundant information, by making the recognition system inefficient and increasing the complexity of the classifier. High information repetition and relationships to confront pictures result in wasteful aspects when such pictures are explicitly utilized for recognition. To reduce information redundancy we used DCT, which preserves only pertinent information of face, and complexity of classifier reduced as the length of feature vector gets reduced and normalization of a feature vector to increase the robustness of the system. Here in this work, a hybrid method of face recognition is presented in which a feature vector constructed using DCT provided to artificial neural networks. For classification, the multilayer perception (MLP) is used by an artificial neural network with a backpropagation algorithm. The recognition rate increased and the result was tested on a benchmark dataset.
Multi-agent Collaboration in an Adversarial Turret Reconnaissance Task

Although we have previously observed the emergence of collaboration, or more specifically, coordination between cooperative agents in a Predator-Prey Pursuit task, we have yet to demonstrate this interdependence of agents in a different task domain, where the coordination between cooperative agents is expected to depend on specific environmental parameters that may not support collaboration. In this work, we focus on a multi-agent Turret Reconnaissance task (T-RECON) to explore agent interdependence identified through a state-space perturbation technique. This work aims to 1) identify collaboration, and 2) quantify the magnitude of coordination between cooperating agents.