Reviewerly/exHarmony huggingface dataset:
Does financial development promote growth in Kuwait? time- and frequency- domain causality testing

The present study endeavors to explore the dynamic causal relationship between economic growth and financial development in Kuwait, covering the time span between 1991 and 2017. Based on the objective of presenting robust results in relation to the research focus, a combination of time and frequency-domain methodologies has been applied. Using the Toda–Yamamoto and the Fourier Toda–Yamamoto time-domain techniques, no causal direction is found through the former test, while the causality flowing from financial development to economic growth is demonstrated by the latter. Nevertheless, the spectral causality test developed by Breitung and Candelon [(2006). “Testing for short- and long-run causality: A frequency-domain approach.” Journal of Econometrics 132 (2): 363–378. https://doi.org/10.1016/j.jeconom.2005.02.004] gives a more thorough overview of the dynamic causal relationships because it allows the temporary and permanent movements in the linkages between variables to be differentiated. The findings show proof of a bidirectional causality between financial development and economic growth. Our findings highlight the emphasis on covering the frequency causality to provide greater insight into the interrelationship between the variables under consideration.
Country-level cybersecurity posture assessment:Study and analysis of practices

Along with positive impacts, ICT transformation also increases the exposure of critical infrastructure to cyber-attacks. Keeping in view the constantly evolving cyber-threat landscape, it is vital for a country to continuously assure the cybersecurity of its ICT infrastructure. Recently the security of the cyberspace has got a much-needed emphasis from Governments and International Agencies. To generate assurance, actions initiated by a country to counter cyber threats need to be continuously assessed for implementation & effectiveness.This study aims to offer a new perspective on country-wide cybersecurity benchmarking and assurance. The paper presents the study and analysis of the methods and practices adopted by countries for cybersecurity posture assessment and generating assurance on the implemented cybersecurity measures. In this paper, the cybersecurity posture assessment and assurance practices implemented by 37 countries are studied with an objective of understanding the global scenario and identification of different methods adopted for a cybersecurity posture assessment.
Detection and tracking people in real-time with YOLO object detector

In this article, we wrote not a large program to solve tasks for detection and tracking objects in realtime. The program was written in Python programming language. For object detection, a convolutional neural network was used with YOLOV3 architecture. A preliminary analysis was carried out of several variations of YOLO with CNN models. In the article, we justify why we want to use YOLO, and what it is and how to use and process the model output. We will also present the code in the form of a flowchart and as a result of the program's performance, we will show a picture of the program's operation in real-time, which was launched at one of the live lectures at the University.
Dedicated Security Chips in the Age of Secure Enclaves

Secure enclave architectures have become prevalent in modern CPUs. Enclaves provide a flexible way to implement various hardware-assisted security services. But special-purpose security chips can still have advantages. Interestingly, dedicated security chips can also assist enclaves and improve their security.
Circular Arc Based Obstacle Avoiding Blending Trajectory plan

This paper works on the problem of generating a trajectory which is capable to dodge potential obstacles in a blending occasion with a non-holonomic premise. To the problem, this paper offers a solution of a circular blending curve generation with given directions, lanes and obstacle. This technique reforms the two-dimensional information into one-dimensional representation. With this trait, the obstacles could be represented by the constraints generated with the technique. Then, a collision-free corridor could be formed by the constraints. The corridor generating algorithm can provide outcome at a high speed, also cater to one all multiple obstacles situation.
Semi-Supervised Speech Recognition Acoustic Model Training Using Policy Gradient

In this paper, we propose a policy gradient-based semi-supervised speech recognition acoustic model training. In practice, self-training and teacher/student learning are one of the widely used semi-supervised training methods due to their scalability and effectiveness. These methods are based on generating pseudo labels for unlabeled samples using a pre-trained model and selecting reliable samples using confidence measure. However, there are some considerations in this approach. The generated pseudo labels can be biased depending on which pre-trained model is used, and the training process can be complicated because the confidence measure is usually carried out in post-processing using external knowledge. Therefore, to address these issues, we propose a policy gradient method-based approach. Policy gradient is a reinforcement learning algorithm to find an optimal behavior strategy for an agent to obtain optimal rewards. The policy gradient-based approach provides a framework for exploring unlabeled data as well as exploiting labeled data, and it also provides a way to incorporate external knowledge in the same training cycle. The proposed approach was evaluated on an in-house non-native Korean recognition domain. The experimental results show that the method is effective in semi-supervised acoustic model training.
DCEM: A data cell evolution model for service composition based on bigraph theory

Service composition is an important means of integrating individual web services to create new value-added systems that satisfy complex requirements. Effectively analyzing different types of services and identifying the matching similarity between services is a crucial component of service composition, as it allows failed services to be efficiently substituted in a distributed and dynamic environment. In this paper, we propose a data cell evolution model (DCEM) that supports the dynamic adaptation of service compositions. DCEM combines data service information and biological cell behavior analysis to encapsulate data services into data cells. To optimize the adaptations, we analyze the static and dynamic structure of data cells based on bigraph theory to guarantee the consistency of service evolution. To evaluate the proposed approach, a series of simulation experiments and comparisons are conducted to demonstrate the effectiveness of service composition.
BLOCKCHAIN DOUBLE-SPEND ATTACK DURATION

Many cryptocurrencies including Bitcoin are susceptible to a so-called double-spend attack, where someone dishonestly attempts to reverse a recently confirmed transaction. The duration and likelihood of success of such an attack depends on the recency of the transaction and the computational power of the attacker, and these can be related to the distribution of time for counts from one Poisson process to exceed counts from another by some desired amount. We derive an exact expression for this distribution and show how it can be used to obtain efficient simulation estimators. We also give closed-form analytic approximations and illustrate their accuracy.
Static2Dynamic: Video Inference From a Deep Glimpse

In this article, we address a novel and challenging task of video inference, which aims to infer video sequences from given non-consecutive video frames. Taking such frames as the anchor inputs, our focus is to recover possible video sequence outputs based on the observed anchor frames at the associated time. With the proposed Stochastic and Recurrent Conditional GAN (SR-cGAN), we are able to preserve visual content across video frames with additional ability to handle possible temporal ambiguity. In the experiments, we show that our SR-cGAN not only produces preferable video inference results, it can also be applied to relevant tasks of video generation, video interpolation, video inpainting, and video prediction.
Compressed Quadratization of Higher Order Binary Optimization Problems

Recent hardware advances in quantum and quantum-inspired annealers promise substantial speedup for solving NP-hard combinatorial optimization problems compared to general-purpose computers. These special-purpose hardware are built for solving hard instances of Quadratic Unconstrained Binary Optimization (QUBO) problems. In terms of number of variables and precision of these hardware are usually resource-constrained and they work either in Ising space or in Boolean space. Many naturally occurring problem instances are higher-order in nature. The known method to reduce the degree of a higher-order optimization problem uses Rosenberg's polynomial. The method works in Boolean space by reducing the degree of one term by introducing one extra variable. In this work, we prove that in Ising space the degree reduction of one term requires the introduction of two variables. Our proposed method of degree reduction works directly in Ising space, as opposed to converting an Ising polynomial to Boolean space and applying previously known Rosenberg's polynomial. For sparse higher-order Ising problems, this results in a more compact representation of the resultant QUBO problem, which is crucial for utilizing resource-constrained QUBO solvers.
Suffering-Centered Design

Humans are skilled at closing their minds to the suffering of others. This paper argues that, over the past several decades, human computer interaction tools and techniques have both exacerbated and ameliorated this human tendency. It contributes a theoretical framework for thinking about the relationship between HCI and various forms of suffering, and a set of principles to support suffering-centered design — design activities that foreground the suffering of others rather than obscure it. This paper proposes that there is a need for further exploration of ways that HCI can improve the collective human experience by supporting ways to open people's minds to the suffering of others.
Data security improvements on cloud computing using cryptography and steganography

Abstract Cloud computing is a service that eases its users to store data on the internet. However, cloud computing has lack, which is related to stored data security specifically to image file that can be manipulated easily. To improve the data securities of JPG file type on cloud computing, the combination of advanced encryption standard (AES) cryptography and least significant bit (LSB) steganography methods is required. This study implemented AES 128 algorithm cryptography process to scramble the messages that will be inserted and LSB steganography to hide the messages that has been scrambled. The data were assessed using Stegspy. The results of this study indicated that cloud computing service of Platform as a Service (PaaS) has resulted ciphertext that was attached to JPG/JPEG images using Least Significant Bit (LSB). From 5 (five) samples, the encryption result testing on JPG/JPEG files was bigger than the original file. The bigger the number of characters of the inserted text, the bigger the size of JPG/JPEG files will be.
RPRS: a reactive plan repair strategy for rapid response to plan failures of deep space missions

There are always various uncertainties in a deep space exploration mission, which induce a significant impact on plan execution and may cause plan failures. In such case, remote control from the Earth is too late to handle that problem in time, because of the long distance between the probe and the ground station. Therefore, an autonomous reactive plan repair strategy RPRS (Reactive Plan Repair Strategy) for deep space missions is proposed, which enables the probe to recover autonomously and quickly from plan failures with unsatisfactory resources or logical conditions without human intervention. In this strategy, the concept of forwarding dock state is defined to effectively manage the temporal information of planned actions and fortuitous events during execution, to support the repair work of subsequent GRDR (Global Resource Diagnosis & Repair algorithm) and TDPR (Tiny Deviation Plan Repair search algorithm). This strategy iteratively performs a search on the forwarding dock state whenever the original plan fails, i.e., a shortage of resources or invalid logical conditions, and is tested empirically to demonstrate the efficiency and effectiveness with enough domain knowledge.
Time Perceptions as a Material for Designing New Representations of Time

Time is a growing topic of interest in HCI. Technological developments and changes in everyday life routines are creating new perceptions of time that are getting widespread in HCI. While these perceptions expand our current understanding of time, designing for them is an important yet an overlooked challenge. In this work, we investigated five time concepts from HCI literature (right time, clock time, digital time, plastic time, and collective time). We conducted a photography capturing task with four design researchers to understand how they interpret and perceive these time concepts. We then created two design speculations based on their perceptions and interpretations to illustrate how emerging time concepts can inspire new design ideas around representations of time.
Adaptive Relation Discovery from Focusing Seeds on Large Networks

Given a network and a set of seeds related to each other, the problem of relation discovery from focusing seeds aims to discover the relations among all the seeds. Due to its wide applications, the task has been well studied in the literature. However, when facing applications where maybe not all the seeds relate to each other, methods for the task will discover many vertices unrelated to the seeds. To support such applications, a new problem called adaptive relation discovery from focusing seeds (A-RDFS) is proposed and studied in this article. Given a network and a set of seeds that may not be related to each other, discover additional vertices to reveal the relations among the seeds which are related to each other. To solve the A-RDFS problem, a relation sensitive subgraph structure called the α- relation core is proposed to find vertices related to a subset of the seeds. Thereafter, a metric called the relation quality is proposed to measure the quality of discovered relations. The metric is positively correlated with the α value of each discovered α-relation core. Hence, by maximizing the relation quality, a set of α-relation cores with large α values can be discovered, which reveals the relations among the seeds related to each other. Two algorithms are developed to optimize the relation quality. Then, using the algorithms as subroutines, the OPT-A-RDFS method is designed for the A-RDFS problem. Extensive experimental results demonstrate the performance of our methods.
TQVS: Temporal Queries over Video Streams in Action

We present TQVS, a system capable of conducting efficient evaluation of declarative temporal queries over real-time video streams. Users may issue queries to identify video clips in which the same two cars and the same three persons appear jointly in the frames for say 30 seconds. In real-world videos, some of the objects may disappear in frames due to reasons such as occlusion, which introduces challenges to query evaluation. Our system, aiming to address such challenges, consists of two main components: the Object Detection and Tracking (ODT) module and the Query Evaluation module. The ODT module utilizes state-of-art Object Detection and Tracking algorithms to produce a list of identified objects for each frame. Based on these results, we maintain select object combinations through the current window during query evaluation. Those object combinations contain sufficient information to evaluate queries correctly. Since the number of possible combinations could be very large, we introduce a novel technique to structure the possible combinations and facilitate query evaluation. We demonstrate that our approach offers significant performance benefits compared to alternate approaches and constitutes a fundamental building block of the TQVS system.
A Shift-Based Data Augmentation Strategy for Improving Saccade Landing Point Prediction

This paper demonstrates a technique for improving the performance of neural network-based models for saccade landing point estimation. Performance improvement is achieved by augmenting available training data with time-shifted replicates in order to improve prediction robustness to variations in saccade onset timing. The technique is validated for both long short-term memory (LSTM) and feed-forward neural network models for 5,893 saccades extracted from the recordings of 322 individuals during free-viewing of a movie trailer. The proposed augmentation strategy is demonstrated to improve the median accuracy of landing point estimates for LSTM models formulated using both the raw position and relative displacement of the gaze location.
Distributed frequent subgraph mining on evolving graph using SPARK

Within the graph mining context, frequent subgraph identification plays a key role in retrieving required information or patterns from the huge amount of data in a short period. The problem of finding frequent items in traditional mining changed to the innovation of subgraphs that recurrently occur s in graph datasets containing a single huge graph. Majority of the existing methods target static graphs, and the distributed solution for dynamic graphs has not been explored. But, in modern applications like Facebook, robotics utilizes large evolving graphs. The goal is to design a method to find recurrent subgraphs from a single large evolving graph. In this research paper, a novel approach is proposed called DFSME, which uses SPARK to discover frequent subgraphs from an evolving graph in a distributed environment. DFSME maintains a set of subgraphs between frequent and infrequent subgraphs, which is used to decrease the search space. Our experiments with synthetic and real-world datasets authorize the effectiveness of DFSME for mining of recurrent subgraphs from huge evolving graph datasets.
Fast cropping method for proper input size of convolutional neural networks in underwater photography

Abstract The convolutional neural network (CNN) is widely used in object detection and classification and shows promising results. However, CNN has the limitation of fixed input size. If the input image size of the CNN is different from the image size of the system to which the CNN is applied, additional processes, such as cropping, warping, or padding, are necessary. They take additional time to process these processes, and fast cutting methods are required for systems that require real‐time processing. The purpose of our system to which the CNN model will be applied is to classify fish species in real time, using cameras installed in a shallow stream. Therefore, in this paper, we propose a straightforward real‐time image cropping method for fast cutting to the proper input size of CNN. In the experiments, we evaluate the proposed method using CNNs (AlexNet, Vgg 16, Vgg 9, and GoogLeNet).
Patient Representation Transfer Learning from Clinical Notes based on Hierarchical Attention Network.

To explicitly learn patient representations from longitudinal clinical notes, we propose a hierarchical attention-based recurrent neural network (RNN) with greedy segmentation to distinguish between shorter and longer, more meaningful gaps between notes. The proposed model is evaluated for both a direct clinical prediction task (mortality) and as a transfer learning pre-training model to downstream evaluation (phenotype prediction of obesity and its comorbidities). Experimental results first show the proposed model with appropriate segmentation achieved the best performance on mortality prediction, indicating the effectiveness of hierarchical RNNs in dealing with longitudinal clinical text. Attention weights from the models highlight those parts of notes with the largest impact on mortality prediction and hopefully provide a degree of interpretability. Following the transfer learning approach, we also demonstrate the effectiveness and generalizability of pre-trained patient representations on target tasks of phenotyping.
Do You Feel Like Flying? A Study of Flying Perception in Virtual Reality for Future Game Development

Being able to fly is still a dream. Till today, it is only possible with heavy use of technology. Video games incorporated flying as a fancy way of movement inspired by mythical creatures, gods, and fantasies. In virtual reality (VR), with its deep immersion, the avatar control, especially for flying, is still performed by physical controllers that do not feel like a natural extension, thus, hindering the full immersion. To overcome this limitation, this work did an exploratory study on, first, the perception of wings as a natural body extension and, second, the natural movement performed by humans when flying. In a virtual environment, we studied the extent of the virtual presence and body ownership of wings. Results highlight that placing wings on the shoulders in an angelic form and controlling them by arm movements is the preferred way to extend human capabilities toward flying in nontechnologically restricted VR-Games.
Private Empirical Risk Minimization With Analytic Gaussian Mechanism for Healthcare System

With the wide range application of machine learning in healthcare for helping humans drive crucial decisions, data privacy becomes an inevitable concern due to the utilization of sensitive data such as patients records and registers of a company. Thus, constructing a privacy preserving machine learning model while still maintaining high accuracy becomes a challenging problem. In this article, we propose two differentially private algorithms, i.e., Output Perturbation with aGM (OPERA) and Gradient Perturbation with aGM (GRPUA) for empirical risk minimization, a useful method to obtain a globally optimal classifier, by leveraging the analytic Gaussian mechanism (aGM) to achieve privacy preservation of sensitive medical data in a healthcare system. We theoretically analyze and prove utility upper bounds of proposed algorithms and compare them with prior algorithms in the literature. The analyses show that in the high privacy regime, our proposed algorithms can achieve a tighter utility bound for both settings: strongly convex and non-strongly convex loss functions. Besides, we evaluate the proposed private algorithms on five benchmark datasets. The simulation results demonstrate that our approaches can achieve higher accuracy and lower objective values compared with existing ones in all three datasets while providing differential privacy guarantees.