Reviewerly/exHarmony huggingface dataset:
Beyond Horror and Fear

Digital gameplay experience depends not only on the type of challenge that the game provides, but also on how the challenge be presented. With the introduction of a novel type of emotional challenge and the increasing popularity of virtual reality (VR), there is a need to explore player experience invoked by emotional challenge in VR games. We selected two games that provides emotional challenge and conducted a 24-subject experiment to compare the impact of a VR and monitor-display version of each game on multiple player experiences. Preliminary results show that many positive emotional experiences have been enhanced significantly with VR while negative emotional experiences such as horror and fear have less been influenced; participants' perceived immersion and presence were higher when using VR than using monitor-display. Our finding of VR's expressive capability in emotional experiences may encourage more design and research with regard to emotional challenge in VR games.
Formally verified software countermeasures for control-flow integrity of smart card C code

Fault attacks can target smart card programs to disrupt an execution and take control of the data or the embedded functionalities. Among all possible attacks, control-flow attacks aim at disrupting the normal execution flow. Identifying harmful control-flow attacks and designing countermeasures at the software level are tedious and tricky for developers. In this paper, we propose a methodology to detect harmful inter- and intra-procedural jump attacks at the source code level and automatically inject formally proven countermeasures into a C code. The proposed software countermeasures protect the integrity of individual statements at the granularity of single C statements. They support many control-flow constructs of the C language. The countermeasure scheme can detect an attack early either inside a control-flow construct or only at its exit. The secured source code defeats 100% of attacks that jump over at least two C source code statements. Experiments showed that the resulting code is also hardened against unexpected function calls and jump attacks at the assembly code level. Securing a source code automatically and extensively with our scheme degrades the performance. The performance overhead of our countermeasures on three well-known encryption algorithms available in C ranged from +41% to +138% on an x86 platform and from +45% to +217% on an ARM-v7 platform. However, combining code rewriting with hardening of sensitive code regions identified by the weakness detection step enables an application to be fully hardened while limiting the overhead.
PEMANFAATAN TEKNOLOGI DIGITAL DALAM KEGIATAN PEMBELAJARAN

This research study aims to systematically review a variety of digital technologies that are currently developing so rapidly to be optimally utilized according to the ability of teachers and technological devices owned by schools, teachers, and students. This study uses the method of review of integrative literature. This method combines various empirical and research-based articles, books, and other published literature on digital competencies and their use in 21st century education. The literature research concludes: 1) Digital literacy is the awareness, attitude, and ability of individuals to properly use tools and digital facilities to identify, access, manage, integrate, evaluate, analyze, and synthesize digital resources, build new knowledge, make media expressions, and communicate with others, in the context of certain life situations, to enable constructive social action; 2) Digital competency is a set of knowledge, skills, attitudes needed when using ICT and digital media for various needs; 3) Digital technology that can be used by schools / teachers in the learning process including interactive whiteboards (IWB), software applications, Web 2.0, and social media; and 4) Utilization of digital technology in learning can increase students' interest and motivation in learning, the quality of learning processes, and learning outcomes, but need to pay attention to various aspects of the ability of human resources, security, and health of teachers and students.
Multilingual Entity, Relation, Event and Human Value Extraction

Manling Li, Ying Lin, Joseph Hoover, Spencer Whitehead, Clare Voss, Morteza Dehghani, Heng Ji. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations). 2019.
High precision computer-generated moiré profilometry

Abstract Recently, a computer-generated moiré profilometry was proposed by our research group. It can effectively avoid the influence of the transient caused by moiré fringes’ direct acquisition and generally owns a higher accuracy. But when the spatial spectrum of the captured deformed pattern is severely aliased caused by the measured object, the accuracy of this method may be affected to some extent due to the impure background light component extraction. So, a high precision computer-generated moiré profilometry based on background light component’s accurate elimination is proposed. By adding an additional special phase-shifting sinusoidal grating to accurately extract valid information in the spatial domain and improve the sinusoidal feature of the pattern, the measurement precision can be improved effectively. Though the single-shot feature is broken, the real-time measuring feature is still maintained successfully. Experimental results show the feasibility and validity of the proposed method.
Designing Safety Critical Software Systems to Manage Inherent Uncertainty

Deploying machine learning algorithms in safety critical systems raises new challenges for system designers. The opaque nature of some algorithms together with the potentially large input space makes reasoning or formally proving safety difficult. In this paper, we argue that the inherent uncertainty that comes from using certain classes of machine learning algorithms can be mitigated through the development of software architecture design patterns. New or adapted patterns will allow faster roll out time for new technologies and decrease the negative impact machine learning components can have on safety critical systems. We outline the important safety challenges that machine learning algorithms raise and define three important directions for the development of new architectural patterns.
Responding to victimisation in a digital world: a case study of fraud and computer misuse reported in Wales

This paper presents the early results of a study exploring computer misuse and fraud victimisation in Wales, United Kingdom (UK). The results presented here describe the quality of the data available to local forces, the characteristics and heterogeneity of the victims who report incidents and the nature of the police response at a local level. The significance of these results is considered within the enforcement and victimisation policy context that surrounds computer misuse and fraud. It is argued that while Action Fraud data provides a rich source of data with respect to victims’ needs, specific improvements in data collection and processing could aid local forces in the delivery (or facilitation) of a more victim-focused response. Alongside this, the results highlight how an adequate police response must take victim heterogeneity into account, both at national and local levels. Finally, better understandings of vulnerability (both theoretically informed and empirically tested) are necessary, on which to build an adequate victim-response to these crime types.
Explicable Planning as Minimizing Distance from Expected Behavior

In order to achieve effective human-AI collaboration, it is necessary for an AI agent to align its behavior with the human's expectations. When the agent generates a task plan without such considerations, it may often result in inexplicable behavior from the human's point of view. This may have serious implications for the human, from increased cognitive load to more serious concerns of safety around the physical agent. In this work, we present an approach to generate explicable behavior by minimizing the distance between the agent's plan and the plan expected by the human. To this end, we learn a mapping between plan distances (distances between expected and agent plans) and human's plan scoring scheme. The plan generation process uses this learned model as a heuristic. We demonstrate the effectiveness of our approach in a delivery robot domain.
Gradient algorithm for Hamiltonian identification of open quantum systems

In this paper, we present a gradient algorithm for identifying unknown parameters in an open quantum system from the measurements of time traces of local observables. The open system dynamics is described by a general Markovian master equation based on which the Hamiltonian identification problem can be formulated as minimizing the distance between the real time traces of the observables and those predicted by the master equation. The unknown parameters can then be learned with a gradient descent algorithm from the measurement data. We verify the effectiveness of our algorithm in a circuit QED system described by a Jaynes-Cummings model whose Hamiltonian identification has been rarely considered. We also show that our gradient algorithm can learn the spectrum of a non-Markovian environment based on an augmented system model.
Visualizing Knowledge Evolution and Hotspots of Rural Environment and Health: A Systematic Review and Research Direction

With global warming, energy scarcity, water shortages, and air, soil, and water pollution, the situation of environments in countries around the world is getting more and more serious and in some countries, rural environmental issues are more prominent. Health problems in rural areas also cannot be ignored, chronic diseases and infectious diseases have become the greatest threat to human life, while good environment and human health are the foundation of social and economic sustainable development. This paper adopts the bibliometrics method to conduct a visual analysis of 6,971 studies in the field of the rural environment and health published on the Web of Science between 2000 and 2017, including time knowledge map analysis, space knowledge map analysis, knowledge base analysis, and research focus analysis. This paper reveals the development status of research in the field of rural environment and health, analyzes, and discusses the research hotspots and future development trends in this field, and provides important knowledge support for researchers to carry out follow-up research.
Human Pose Estimation With Deeply Learned Multi-Scale Compositional Models

Compositional models are meant for human pose estimation (HPE) due to their abilities to capture relationships among human body parts.Deeply learned compositional model (DLCM) utilizes deep neural networks to learn compositionality of human body parts and has achieved great improvements in human pose estimation.The DLCM has a hierarchical compositional architecture and bottom-up/top-down inference stages.The previous works have proven that multi-scale deep features are beneficial for computer vision tasks, such as classification and human body keypoints detection.However, learning multi-scale feature pyramids in DLCM has not been well explored.In this paper, we propose a new method to apply the multi-scale feature pyramid module to further improve the performance of the DLCM, which is named as deeply learned multi-scale compositional model (DLMSCM).We design multi-scale residual modules as the basic blocks to learn multi-scale deep features which can capture the scale variations of different body parts.With the multi-scale mechanism in the framework of the DLCM, the model can not only deal with scale variations of body parts but also find joints dependencies, therefore enforce the entire body joints structural constrains.As a result, more precise body keypoints detection can be acquired.Our approach outperforms the other state-of-the-art methods on two standard benchmarks datasets MPII and LSP for human pose estimation.
Virtually There: The Potential, Process and Problems of Using 360° Video in the Classroom

Aim/Purpose: This paper presents an exploratory case study into using 360° videos to present small segments of lecture content for IT students in an Australian University. The aim of this study was to understand; what is the impact of incorporating 360° videos into class content for students and teaching staff? In this study the 360° videos are described as “learning atoms”. Learning atoms are short duration videos (1 to 5 minutes) captured in 360°. Background: Within this paper we conducted experiments in the classroom using 360° videos to determine if they have an impact on student's feeling of presence with class content. Additionally, to follow up, how does the inclusion of 360° impact on the teaching experience. Methodology: The methodology used in this study focused on both quantitative and qualita-tive aspects. Data was captured at the same time during the teaching period to address the research questions. In order to gauge the feeling of presence within the classroom a short survey was administered to students in the undergraduate IT class at the start (pre) and end (post) of the semester using the same questions to measure any change. Contribution: The main contributions from this study were that we demonstrated there is a potential for providing an alternative ‘immersive’ content presentation for students. This alternative content took the form of 360° learning atoms, whereas further showed our nuance process for creating and publishing of these atoms. Findings: The results show that for students, learning atoms can help improve the sense of presence, particularly for remote students, however the interactive experience can take student’s attention away from the lecturer. The results present potential for providing an alternative ‘immersive’ content presentation for students, however problems for uptake are present for both students and teachers, such as image capture quality and file size Impact on Society: We foresee this approach as being a new approach to teaching students in higher education within online spaces to increase engagement and move towards having a richer virtual experience no matter the location. Future Research: Future research will be conducted to resolve whether presence and engagement is supported by the inclusion of 360° videos in the classroom.
Transaction Fees, Block Size Limit, and Auctions in Bitcoin

Confirmation of Bitcoin transactions is executed in blocks, which are then stored in the Blockchain. As compared to the number of transactions in the mempool, the set of transactions which are verified but not yet confirmed, available space for inclusion in a block is typically limited. For this reason, successful miners can only process a subset of such transactions, and users compete with each other to enter the next block by offering confirmation fees. Assuming that successful miners pursue revenue maximization, they will include in the block those mempool transactions that maximize earnings from related fees. In the paper we model transaction fees as a Nash Equilibrium outcome of an auction game with complete information. In the game the successful miner acts as an auctioneer selling block space, and users bid for shares of such space to confirm their transactions. Moreover, based on expected fees we also discuss what the optimal, revenue maximizing, block size limit should be for the successful miner. Consistently with the intuition, the optimal block size limit resolves the trade-off between including additional transactions (which possibly lower the unit fees collected) and keeping the block capacity limited (with, however, higher unit fees).
Hybrid Framework for Speaker-Independent Emotion Conversion Using i-Vector PLDA and Neural Network

Expressive speech can be synthesized using acoustic feature modeling by mapping the spectral and fundamental frequency parameters between neutral speech and target emotions based on context.Speaker and text-independent emotion conversion are challenging modeling problems in this paradigm.In this paper, spectral mapping using an i-vector-based framework of fixed dimensions is proposed for the speaker-independent emotion conversion, considering the entire problem in the utterance domain, rather than the existing approaches using frame-level processing.The high dimensionality of i-vectors and reduced utterances for i-vector training necessitate the use of Probabilistic Linear Discriminant Analysis (PLDA) to derive the emotion dependent latent vector.The i-vector setup does not require parallel data or alignment procedures at any stage of training.F 0 training is conducted on a multilayer feed-forward neural network using limited aligned seed parallel data.The framework is tested on three different languages (datasets) viz.German (EmoDB), Telugu (IITKGP), and English (SAVEE).The proposed approach delivered superior performance compared to the baseline under both clean and noisy data conditions considered for analysis.Under clean data conditions, the proposed model was found to perform better than the baseline with a Mel Cepstral Distortion as low as 3.8 (fear), an F 0 -RMSE of 26.31 (happiness), and a Perceptual Evaluation of Speech Quality (PESQ) of 3.64 (anger) across datasets.Subjective testing provided a maximum CMOS of 4.10 (anger), 4.44 (fear), and 3.43 (happiness).
Evaluating Computational Language Models with Scaling Properties of Natural Language

In this article, we evaluate computational models of natural language with respect to the universal statistical behaviors of natural language. Statistical mechanical analyses have revealed that natural language text is characterized by scaling properties, which quantify the global structure in the vocabulary population and the long memory of a text. We study whether five scaling properties (given by Zipf’s law, Heaps’ law, Ebeling’s method, Taylor’s law, and long-range correlation analysis) can serve for evaluation of computational models. Specifically, we test n-gram language models, a probabilistic context-free grammar, language models based on Simon/Pitman-Yor processes, neural language models, and generative adversarial networks for text generation. Our analysis reveals that language models based on recurrent neural networks with a gating mechanism (i.e., long short-term memory; a gated recurrent unit; and quasi-recurrent neural networks) are the only computational models that can reproduce the long memory behavior of natural language. Furthermore, through comparison with recently proposed model-based evaluation methods, we find that the exponent of Taylor’s law is a good indicator of model quality.
Learning How to Active Learn by Dreaming

Heuristic-based active learning (AL) methods are limited when the data distribution of the underlying learning problems vary.Recent data-driven AL policy learning methods are also restricted to learn from closely related domains.We introduce a new sample-efficient method that learns the AL policy directly on the target domain of interest by using wake and dream cycles.Our approach interleaves between querying the annotation of the selected datapoints to update the underlying student learner and improving AL policy using simulation where the current student learner acts as an imperfect annotator.We evaluate our method on cross-domain and cross-lingual text classification and named entity recognition tasks.Experimental results show that our dream-based AL policy training strategy is more effective than applying the pretrained policy without further fine-tuning, and better than the existing strong baseline methods that use heuristics or reinforcement learning.
Inferential Machine Comprehension: Answering Questions by Recursively Deducing the Evidence Chain from Text

This paper focuses on the topic of inferential machine comprehension, which aims to fully understand the meanings of given text to answer generic questions, especially the ones needed reasoning skills.In particular, we first encode the given document, question and options in a context aware way.We then propose a new network to solve the inference problem by decomposing it into a series of attentionbased reasoning steps.The result of the previous step acts as the context of next step.To make each step can be directly inferred from the text, we design an operational cell with prior structure.By recursively linking the cells, the inferred results are synthesized together to form the evidence chain for reasoning, where the reasoning direction can be guided by imposing structural constraints to regulate interactions on the cells.Moreover, a termination mechanism is introduced to dynamically determine the uncertain reasoning depth, and the network is trained by reinforcement learning.Experimental results on 3 popular data sets, including MCTest, RACE and MultiRC, demonstrate the effectiveness of our approach.
Intuitionistic fuzzy ridge regression functions

Developing technology shows how useful fuzzy inference systems in lots of applications. Fuzzy functions approach which is one of the important fuzzy inference system for time series forecasting. In fuzzy functions approach, the membership values and their non-linear transformations are used together with original input variables to increase the prediction power. However, multicollinearity problem can be occured because of using these correlated variables. Purpose of the paper is to propose a new fuzzy forecasting method with intuitionistic fuzzy sets which has addition information known as hesitation degree. In this case, both intuitionistic fuzzy sets and their non-linear transformations is used to increase the prediction power. Ridge regression method is preferred to obtain intuitionistic fuzzy functions without exposed to multicolinearity problem. To demonstrate the performances of proposed method, some real world time series data are used and the results have shown that the effectiveness of the proposed method in conrast to other methods.
Pauli semigroups and unistochastic quantum channels

We adopt the perspective of similarity equivalence, in gate set tomography called the gauge, to analyze various properties of quantum operations belonging to a semigroup, Φ=eLt, and therefore given through the Lindblad operator. We first observe that the non unital part of the channel decouples from the time evolution. Focusing on unital operations we restrict our attention to the single-qubit case, showing that the semigroup embedded inside the tetrahedron of Pauli channels is bounded by the surface composed of product probability vectors and includes the identity map together with the maximally depolarizing channel. Consequently, every member of the Pauli semigroup is unitarily equivalent to a unistochastic map, describing a coupling with one-qubit environment initially in the maximally mixed state, determined by a unitary matrix of order four.
CogNet: A Large-Scale Cognate Database

This paper introduces CogNet, a new, large-scale lexical database that provides cognates-words of common origin and meaning-across languages.The database currently contains 3.1 million cognate pairs across 338 languages using 35 writing systems.The paper also describes the automated method by which cognates were computed from publicly available wordnets, with an accuracy evaluated to 94%.Finally, statistics and early insights about the cognate data are presented, hinting at a possible future exploitation of the resource 1 by various fields of lingustics.