Reviewerly/exHarmony huggingface dataset:
An evaluation of a 3D multimodal marker-less motion analysis system

Motion analysis is a technique used by clinicians (among many others) that quantifies human movement by using camera-based systems. Marker-based motion analysis systems have been used across a variety of application domains, from Interactive 3D Tele-Immersion (i3DTI) environments to the diagnosis of neuromuscular and musculoskeletal diseases. Although such analysis is performed in several laboratories in many countries, numerous issues exist: (1) the high cost of precise motion capture systems; (2) scarcity of qualified personnel to operate them; (3) expertise required to interpret their results; (4) space requirements to install and store these systems; (5) complexity in terms of measurement protocol required for such systems; (6) limited availability; (7) and in some situations the use of markers means they are unsuitability for certain clinical use cases (e.g. for patients recovering from orthopaedic surgery). In this paper, we present, from a system perspective, an alternative, cheaper, and more accessible system for motion analysis. The ultimate aim is to use the output of this multimodal marker-less system as part of an immersive multimedia gait re-education tool. In real-time, it will advise the user on their gait performance (as well as potentially providing accurate clinical data to clinicians). With the initial focus on the capture system, we have developed and evaluated a novel multimodal system which integrates Multiple Microsoft Kinects (which employ RGB-D cameras) with multiple Shimmer Inertial Measurement Unit (IMU) sensors. We have compared this system with the VICON system (the gold standard in motion capture). Our marker-less motion capture system combines data from 4 skeletons generating 3D and complete 360 degrees in view skeleton. The system combines unit quaternions from each Kinect joint with quaternions from 4 inertial measurement units to promote integration. We used our system to measure 3D points of 12 joints from the Kinect fused skeleton and flexion-extension angles of the knee and hip in a walking trial in 8 participants with 8--10 trials per participant. The analysis found component similarity of 0.97 for knee angles and 0.98 for hip angles. These results show that our system, through combination of Multi Kinect system and Shimmer IMUs, offers a cheaper, sufficiently accurate and more accessible human motion analysis system.
Cybercrime and Digital Forensics: Bridging the gap in Legislation, Investigation and Prosecution of Cybercrime in Nigeria

The advancement of Information and Communication Technologies (ICT) opens new avenues and ways for cybercriminals to commit crime. The primary goal of this paper is to raise awareness regarding gaps that exist with regards to Nigeria’s capabilities to adequately legislate, investigate and prosecute cases of cybercrimes. The major source of cybercrime legislation in Nigeria is an act of the National Assembly which is majorly a symbolic legislation rather than a full and active legislation. In perusing these avenues of inquiry, the authors seek to identify systemic impediments which hinder law enforcement agencies, prosecutors, and investigators from properly carrying out their duties as expected.
Steganalysis of Content-Adaptive Steganography Based on Massive Datasets Pre-Classification and Feature Selection

For current steganalysis of the image content-adaptive steganography, there are multiple problems to be improved, such as high difficulty and low accuracy, when detecting images with various contents and textures. For this problem, an improved steganalytic method is proposed in this paper based on the pre-classification and feature selection. First, using the features extracted based on the dependency analysis of image adjacent data, the images with various content and texture complexities are pre-classified as multiple clusters by the K-means algorithms. Then, the performance of existing various steganalytic features are analyzed for different clusters of images, and the optimal features for each cluster are selected for final classification. The experimental results show that the detection accuracy could be improved by the proposed method, and the rationality and availability are also verified. At the same time, the analysis and experimental results in this paper also show that the images with rich content and complex texture should be paid more attention both in steganography and in steganalysis.
Preserved multisensory body representations in advanced age

The internal representation of the body emerges via the integration of multisensory body cues. Sensory signal transfer and the ability to integrate multisensory information deteriorate significantly with increasing age. However, there is little empirical evidence on age-related changes in body representations based on multisensory integration. Here, we used a standard paradigm for evaluating body representations based on multisensory integration, the rubber hand illusion, and compared the amount of proprioceptive drift and changes in perceived body ownership triggered by the integration of visual, tactile, and proprioceptive cues between younger and older adults. To account for potential age-related differences in the temporal stability of the illusion, proprioceptive drift was measured at five different time points. Our results show that older adults used synchronous visuo-tactile cues similarly to younger adults to update both the position of their own hand, and their feeling of ownership over the artificial hand. Independent of visuo-tactile synchrony, older adults perceived their hand as closer to their body than younger adults did, and showed a less stable representation of this in-depth hand position. This proprioceptive bias towards the body did not correlate with the strength of the illusion. Our results indicate that the integration of visual and tactile cues is largely preserved in advanced age when used to update limb position, whereas proprioception worsens with age. This may be linked to two different pathways that underlie changes in body representations over the life span.
Fast and Effective Copy-Move Detection of Digital Audio Based on Auto Segment

Detecting digital audio forgeries is a significant research focus in the field of audio forensics. In this article, the authors focus on a special form of digital audio forgery—copy-move—and propose a fast and effective method to detect doctored audios. First, the article segments the input audio data into syllables by voice activity detection and syllable detection. Second, the authors select the points in the frequency domain as feature by applying discrete Fourier transform (DFT) to each audio segment. Furthermore, this article sorts every segment according to the features and gets a sorted list of audio segments. In the end, the article merely compares one segment with some adjacent segments in the sorted list so that the time complexity is decreased. After comparisons with other state of the art methods, the results show that the proposed method can identify the authentication of the input audio and locate the forged position fast and effectively.
WristSpy: Snooping Passcodes in Mobile Payment Using Wrist-worn Wearables

Mobile payment has drawn considerable attention due to its convenience of paying via personal mobile devices at anytime and anywhere, and passcodes (i.e., PINs or patterns) are the first choice of most consumers to authorize the payment. This paper demonstrates a serious security breach and aims to raise the awareness of the public that the passcodes for authorizing transactions in mobile payments can be leaked by exploiting the embedded sensors in wearable devices (e.g., smartwatches). We present a passcode inference system, WristSpy, which examines to what extent the user's PIN/pattern during the mobile payment could be revealed from a single wrist-worn wearable device under different passcode input scenarios involving either two hands or a single hand. In particular, WristSpy has the capability to accurately reconstruct fine-grained hand movement trajectories and infer PINs/patterns when mobile and wearable devices are on two hands through building a Euclidean distance-based model and developing a training-free parallel PIN/pattern inference algorithm. When both devices are on the same single hand, a highly challenging case, WristSpy extracts multi-dimensional features by capturing the dynamics of minute hand vibrations and performs machine-learning based classification to identify PIN entries. Extensive experiments with 15 volunteers and 1600 passcode inputs demonstrate that an adversary is able to recover a user's PIN/pattern with up to 92% success rate within 5 tries under various input scenarios.
From Mechanics to Meaning

While generative approaches to game design offer great promise, systems can only reliably generate what they can “understand,” which is often represented in a limited, implicit form in hand-crafted evaluation functions or constructive rules. Proceduralist readings, a semiformal approach for interpreting the meaning of a game based on its underlying processes and interactions in conjunction with aesthetic and cultural cues, offer a novel, systematic approach to game understanding. We formalize proceduralist argumentation as a logic program that performs static reasoning over game specifications to derive higher level meanings, as part of Gemini, a bidirectional game analysis and generation system.
Perancangan Aplikasi Komik Hadist Berbasis Multimedia

Abstract - Comics are visual communication media that contain information, ideas, messages that are poured into images as attractive as possible. The combination of comics and hadiths is suitable to be used as an alternative to da'wah media because there are moral and moral messages contained in hadiths. The hadith comic application is made to make it easier for readers to understand the contents of comic stories and minimize the common mistakes that often occur. The method used in making the application is to use PHP. The hadith comic application has the features of presenting comics collaborated with animation, this is so that users listen to the comic storyline correctly and sequentially so as to minimize failure in understanding the core story. Hadist comic applications accompanied by attractive images and color variations make it an interesting application to use especially for teenagers and children. Keywords - Applications, Comics Hadits, Information, Multimedia
Aerial Infrared Target Tracking in Complex Background Based on Combined Tracking and Detecting

Aerial infrared target tracking is the basis of many weapon systems, especially the air-to-air missile. Till now, it is still challenging research to track the aircraft in the event of complex background. In this paper, we focus on developing an algorithm that could track the aircraft fast and accurately based on infrared image sequence. We proposed a framework composed of a tracker <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:mi>T</mml:mi></mml:mrow></mml:math> based on correlation filter and a detector <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mrow><mml:mi>D</mml:mi></mml:mrow></mml:math> based on deep learning, which we call combined tracking and detecting (CTAD). With such collaboration, the algorithm enjoys both the high efficiency provided by correlation filter and the strong discriminative power provided by deep learning. Finally, we performed experiments on three representative infrared image sequences and two sequences from VOT-TIR2016 dataset to quantitatively evaluate the performance of our algorithm. To evaluate our algorithm scientifically, we present the experiments performed on two sequences from AMCOM FLIR dataset of the proposed algorithm. The experimental results demonstrate that our algorithm could track the infrared target reliably, which shows comparable performance with the deep tracker, while running at a fast speed of about 18.1 fps.
A Systematic Approach Towards Web Preservation

The main purpose of the article is to divide the web preservation process into small explicable stages and design a step-by-step web preservation process that leads to creating a well-organized web archive. A number of research articles are studied about web preservation projects and web archives, and designed a step-by-step systematic approach for web preservation. The proposed comprehensive web preservation process describes and combines strengths of different techniques observed during the study for preserving digital web contents into a digital web archive. For each web preservation step, different approaches and possible implementation techniques have been identified that can be adopted in digital archiving. The potential value of the proposed model is to guide the archivist, related personnel, and organizations to effectively preserved their intellectual digital contents for future use. Moreover, the model can help to initiate a web preservation process and create a well-organized web archive to efficiently manage the archived web contents. A section briefly describes the implementation of the proposed approach in a digital news stories preservation framework for archiving news published online from different sources.
The mediating effect of knowledge sharing among intrinsic motivation, high-performance work system and authentic leadership on university faculty members’ creativity

The aim of this research was to investigate the relationship among intrinsic motivation, authentic leadership and high-performance work system (HPWS) on university faculty members with the mediating role of knowledge sharing.A total of 286 full-time faculty members of public universities from 30 universities of Punjab, Pakistan were interviewed using a five-point Likert-scale questionnaire, adapted from the literature.The results of PLS-SEM indicate that authentic leadership and HPWS had a significant effect on faculty member's creativity whereas intrinsic motivation showed an insignificant relationship with creativity.Results further highlight that knowledge sharing mediated the relationship between HPWS and employee's creativity, however, no mediation effect was found for intrinsic motivation and authentic leadership with employee creativity.
On the Dynamics of Gradient Descent for Autoencoders

We provide a series of results for unsupervised learning with autoencoders. Specifically, we study shallow two-layer autoencoder architectures with shared weights. We focus on three generative models for data that are common in statistical machine learning: (i) the mixture-of-gaussians model, (ii) the sparse coding model, and (iii) the sparsity model with non-negative coefficients. For each of these models, we prove that under suitable choices of hyperparameters, architectures, and initialization, autoencoders learned by gradient descent can successfully recover the parameters of the corresponding model. To our knowledge, this is the first result that rigorously studies the dynamics of gradient descent for weight-sharing autoencoders.Our analysis can be viewed as theoretical evidence that shallow autoencoder modules indeed can be used as feature learning mechanisms for a variety of data models, and may shed insight on how to train larger stacked architectures with autoencoders as basic building blocks.
A variational gamma correction model for image contrast enhancement

Image contrast enhancement plays an important role in computer vision and pattern recognition by improving image quality. The main aim of this paper is to propose and develop a variational model for contrast enhancement of color images based on local gamma correction. The proposed variational model contains an energy functional to determine a local gamma function such that the gamma values can be set according to the local information of the input image. A spatial regularization of the gamma function is incorporated into the functional so that the contrast in an image can be modified by using the information of each pixel and its neighboring pixels. Another regularization term is also employed to preserve the ordering of pixel values. Theoretically, the existence and uniqueness of the minimizer of the proposed model are established. A fast algorithm can be developed to solve the resulting minimization model. Experimental results on benchmark images are presented to show that the performance of the proposed model are better than that of the other testing methods.
Development of Titanium-Based Transition-Edge Single-Photon Detector

Superconducting transition-edge sensors (TESs), working as single-photon detectors with high quantum efficiency, low dark count rate, and photon number resolving capability, have been successfully used in quantum information experiments as well as astronomical transient object observations. Aiming to achieve high count rate, we are developing titanium-based superconducting TES single-photon detectors with a critical temperature of ~300 mK. Ti-based TESs with active area from 10 × 10 μm to 40 × 40 μm are first fabricated on silicon substrate coated with SiO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</sub> . The fabricated TES devices show a thermal conductance scaling with the active area and an effective time constant of ~4 μs from pulsed laser diode illumination. In order to get high quantum efficiency, we further design and fabricate a 1550-nm optical cavity based on dielectric mirror and antireflection layer, and study its absorption with an IR spectrometer. Ti TES devices on the dielectric mirror are then fabricated and the superconducting transition is measured.
A TRANSFORMATION METHOD FOR ASPECT-BASED SENTIMENT ANALYSIS

Along with the explosion of user reviews on the Internet, sentiment analysis has becomeone of the trending research topics in the field of natural language processing. In the last five years,many shared tasks were organized to keep track of the progress of sentiment analysis for various lan-guages. In the Fifth International Workshop on Vietnamese Language and Speech Processing (VLSP2018), the Sentiment Analysis shared task was the first evaluation campaign for the Vietnamese lan-guage. In this paper, we describe our system for this shared task. We employ a supervised learningmethod based on the Support Vector Machine classifiers combined with a variety of features. Weobtained the F1-score of 61% for both domains, which was ranked highest in the shared task. For theaspect detection subtask, our method achieved 77% and 69% in F1-score for the restaurant domainand the hotel domain respectively.
Circuit complexity and 2D bosonisation

A bstract We consider the circuit complexity of free bosons and free fermions in 1+1 dimensions. Motivated by the results of [1, 2, 3] who found different behavior in the complexity of free bosons and fermions, in any dimension, we consider the 1+1 dimensional case where, thanks to the bosonisation equivalence of the Hilbert spaces, we can consider the same state from both the bosonic and the fermionic perspectives. This allows us to study the dependence of the complexity on the choice of the set of gates, which explains the discrepancy. We study the effect in two classes of states: i) bosonic-coherent / fermionic- gaussian states; ii) states that are both bosonic- and fermionic-gaussian. We consider the complexity relative to the ground state. In the first class, the different complexities can be related to each other by introducing a mode-dependent cost function in one of the descriptions. The differences in the second class are more important, in terms of the structure of UV divergencies and the overall behavior of the complexity.
Crisis Warning Apps: Investigating the Factors Influencing Usage and Compliance with Recommendations for Action

Effectively disseminating warnings of threats such as floods, thunderstorms, or terrorist attacks is essential for saving lives in affected areas.With the widespread use of mobile devices such as smartphones, mobile warning applications (warning apps) enable the efficient transmission of warnings via pushnotifications.Use of warning apps in crisis or threat situations, however, has received little attention by researchers.Therefore, we investigate in this study the factors that affect the use of warning apps and the intention to comply with recommendations for action transmitted via such apps.We rely on prior research that studied compliance intention during campus emergencies, research on warning and risk communication, and research on technology usage.We find that risk perception, trust, and subjective norm positively influence both use of a warning app and compliance intention, whereas concerns about data security have negative effects.Our findings inform research in the context of risk communication and technology usage as well as providers of warning apps seeking to promote their apps effectively.
Hourglass

This paper addresses the key problems that emerge when one attempts to use transient resources to reduce the cost of running time-constrained jobs in the cloud. Previous works fail to address these problems and are either not able to offer significant savings or miss termination deadlines. First, the fact that transient resources can be evicted, requiring the job to be re-started (even if not from scratch) may lead provisioning policies to fall-back to expensive on-demand configurations more often than desirable, or even to miss deadlines. Second, when a job is restarted, the new configuration can be different from the previous, which might make eviction recovery costly, e.g., transferring the state of graph data between the old and new configurations. We present HOURGLASS, a system that addresses these issues by combining two novel techniques: a slack-aware provisioning strategy that selects configurations considering the remaining time before the job's termination deadline, and a fast reload mechanism to quickly recover from evictions. By switching to an on-demand configuration when (but only if) the target deadline is at risk of not being met, we are able to obtain significant cost savings while always meeting the deadlines. Our results show that, unlike previous work, HOURGLASS is able to significantly reduce the operating costs in the order of 60-70% while guaranteeing that deadlines are met.
Blockchain and Cryptocurrencies – Considerations for Treatment and Reporting for Financial Services Professionals

The rise and continued implementation of cryptocurrencies and other cryptoassets is having, and will continue to have, a disruptive impact on the accounting, federal income taxation, and broader financial services industries.Much has been written, researched, and discussed about both cryptocurrencies and the underlying blockchain technology, but uncertainty remains as it pertains to how these items should be reported for accounting and tax purposes.What this research attempts to accomplish, through both a review of the literature and publicly available guidance issued by tax and accounting authorities, is to document and analyze what current guidance represents for financial reporting.Additionally, this research proposes how future iterations of cryptoassets and cryptocurrencies might be reported, with recommendations applicable for both practitioners and academics seeking to expand on this work.