Reviewerly/exHarmony huggingface dataset:
Risk Awareness, Self-Efficacy, and Social Support Predict Secure Smartphone Usage

It is widely acknowledged that non-compliance with smartphone security behaviors is widespread and may cause severe harm to people and devices. In addition to device-based security issues, there are psychological factors involved in these behaviors such as self-efficacy, risk awareness, and social support. The present study examines associations of these three factors with smartphone security behaviors and explores possible mechanisms among these variables. In a longitudinal survey with 192 Chinese college students (73.4% women, mean age 24.46 years, SD = 5.15) self-efficacy, risk awareness, and social support were assessed with psychometric scales at two points in time, two weeks apart. Hierarchical regression analyses were performed with follow-up smartphone security behaviors as the dependent variable, controlling for baseline values and demographic and IT-related covariates. Main effects of self-efficacy, risk awareness, and social support on smartphone security behaviors were identified. Moreover, a triple interaction among the three predictors emerged in a synergistic way, indicating that their combination yielded more favorable levels of secure smartphone use. The total model accounted for 50% of the behavioral variance, with all covariates included, and the triple interaction among self-efficacy, risk awareness, and social support accounted for 2.3% of variance. Results document that psychological factors are involved in smartphone security behaviors beyond demographic and IT-related covariates. Interventions could be designed to improve smartphone security behaviors not only by developing privacy enhancing technologies but also by considering psychological factors such as self-efficacy, risk awareness, and social support.
Automatic Emergency Diagnosis with Knowledge-Based Tree Decoding

Automatic diagnosis based on clinical notes is critical especially in the emergency department, where a fast and professional result is vital in assuring proper and timely treatment. Previous works formalize this task as plain text classification and fail to utilize the medically significant tree structure of International Classification of Diseases (ICD) coding system. Besides, external medical knowledge is rarely used before, and we explore it by extracting relevant materials from Wikipedia or Baidupedia. In this paper, we propose a knowledge-based tree decoding model (K-BTD), and the inference procedure is a top-down decoding process from the root node to leaf nodes. The stepwise inference procedure enables the model to give support for decision at each step, which visualizes the diagnosis procedure and adds to the interpretability of final predictions. Experiments on real-world data from the emergency department of a large-scale hospital indicate that the proposed model outperforms all baselines in both micro-F1 and macro-F1, and reduce the semantic distance dramatically.
A Survey on Deep Learning-Based Source Code Defect Analysis

With the rapid development of information technology, various software applications are flooding our daily lives. The development of these application software inevitably generates a lot of source code. How to detect and analyze various defects in the source code, such as API/Function call errors, array misuse, and expression syntax error, etc., which is known as source code defect analysis (SCDA), has attracted the attention of many researchers in the academic field. Since artificial intelligence (AI) technology has achieved excellent results in the field of image processing and natural language processing, researchers have tried to use deep learning algorithms in AI to automatically extract and analyze features of source code. Therefore, we review the recent deep learning-based source code defect analysis methods, including abstract syntax tree-based methods, program dependency graph-based methods, and other deep learning-based methods. Compared to traditional methods, the deep learning-based code defect analysis methods can realize the automatic extraction of source code defect features. This means that there is no longer a need for human experts to pre-define code features, which avoids errors caused by humans to a certain extent. The application research of AI in the source code defect analysis is an interesting and challenging development direction, and we believe it has broad development prospects.
Data-Driven Bayesian Network Learning: A Bi-Objective Approach to Address the Bias-Variance Decomposition

We present a novel bi-objective approach to address the data-driven learning problem of Bayesian networks. Both the log-likelihood and the complexity of each candidate Bayesian network are considered as objectives to be optimized by our proposed algorithm named Nondominated Sorting Genetic Algorithm for learning Bayesian networks (NS2BN) which is based on the well-known NSGA-II algorithm. The core idea is to reduce the implicit selection bias-variance decomposition while identifying a set of competitive models using both objectives. Numerical results suggest that, in stark contrast to the single-objective approach, our bi-objective approach is useful to find competitive Bayesian networks especially in the complexity. Furthermore, our approach presents the end user with a set of solutions by showing different Bayesian network and their respective MDL and classification accuracy results.
A No-Reference Video Streaming QoE Estimator based on Physical Layer 4G Radio Measurements

With the increase in consumption of multimedia content through mobile devices (e.g., smartphones), it is crucial to find new ways of optimizing current and future wireless networks and to continuously give users a better Quality of Experience (QoE) when accessing that content. To achieve this goal, it is necessary to provide Mobile Network Operator (MNO) with real time QoE monitoring for multimedia services (e.g., video streaming, web browsing), enabling a fast network optimization and an effective resource management. This paper proposes a new QoE prediction model for video streaming services over 4G networks, using layer 1 (i.e., Physical Layer) key performance indicators (KPIs). The model estimates the service Mean Opinion Score (MOS) based on a Machine Learning (ML) algorithm, and using real MNO drive test (DT) data, where both application layer and layer 1 metrics are available. From the several considered ML algorithms, the Gradient Tree Boosting (GTB) showed the best performance, achieving a Pearson correlation of 78.9%, a Spearman correlation of 66.8% and a Mean Squared Error (MSE) of 0.114, on a test set with 901 examples. Finally, the proposed model was tested with new DT data together with the network's configuration. With the use case results, QoE predictions were analyzed according to the context in which the session was established, the radio transmission environment and radio channel quality indicators.
Godash 2.0 - The Next Evolution of HAS Evaluation

In this short demo paper, we introduce godash 2.0 godash is a headless HTTP adaptive streaming (HAS) video streaming platform written in the Google programming language GO. godash has been extensively rewritten for this release so as to provide ease of use, and a host of new features. godash includes options for eight different state of the art adaptive algorithms, five HAS profiles, four video codecs, the ability to stream audio and video segments, two transport protocols (TCP and QUIC), realtime output from five Quality of Experience (QoE) models, as well as a collaborative framework for the evaluation of cooperative HAS streaming. In this demo, we will introduce each of the options available in the godash configuration file, and illustrate examples of how to use the collaborative players using both godash on a native machine as well as godash within godashbed. godashbed is an integrated large-scale testbed framework for the evaluation of HAS streaming, which uses a virtual environment to serve video content locally (which allows setting security certificates) through the Mininet virtual emulation tool. In this manner, godash provides a framework for rapid deployment and testing of new HAS algorithms, QoE models and transport protocols.
Robust Vision-Based Control of a Rotorcraft UAV for Uncooperative Target Tracking

This paper investigates the problem of using an unmanned aerial vehicle (UAV) to track and hover above an uncooperative target, such as an unvisited area or an object that is newly discovered. A vision-based strategy integrating the metrology and the control is employed to achieve target tracking and hovering observation. First, by introducing a virtual camera frame, the reprojected image features can change independently of the rotational motion of the vehicle. The image centroid and an optimal observation area on the virtual image plane are exploited to regulate the relative horizontal and vertical distance. Then, the optic flow and gyro measurements are utilized to estimate the relative UAV-to-target velocity. Further, a gain-switching proportional-derivative (PD) control scheme is proposed to compensate for the external interference and model uncertainties. The closed-loop system is proven to be exponentially stable, based on the Lyapunov method. Finally, simulation results are presented to demonstrate the effectiveness of the proposed vision-based strategy in both hovering and tracking scenarios.
E-Mail Spam Detection using Machine Learning and Deep Learning

Here we present an inclusive review of recent and successful content-based e-mail spam filtering techniques. Our focus is primarily on machine learning-based spam filters and variants that are inspired by them. We report on related ideas, techniques, major efforts and cutting-edge art in the field. Preliminary interpretation of prior work shows the basics of e-mail spam filtering and feature engineering. In this we conclude by studying techniques, methods, evaluation criteria, and explore promising apprehensions of the latest developments and suggest future lines of investigation.
Developing a Fine-grained Corpus for a Less-resourced Language: the case of Kurdish

We would like to appreciate the generous assistance of the Ministry of Education of the Kurdistan Regionof Iraq, particularly the General Directorate of Curriculum and Printing, for providing us with the datafor the KTC corpus. Our special gratitude goes to Ms. Namam Jalal Rasheed and Mr. Kawa OmerMuhammad for their assistance in making the required data available and resolving of the copyrightissues.
Augmented Reality and IoT

The technology is taking a brisk pace that the things which were not possible yesterday are possible now. Looking at the recent developments like virtual reality (VR) and augmented reality (AR), it is possible to develop several applications that deal with virtual space and physical space. The physical space around the user is a three dimensional (3D). However, traditional devices can show two-dimensional (2D) images.
Pengaruh Strategi Directed Reading Thinking Activity (DRTA) Berbantuan Media Flip Chart Terhadap Keterampilan Membaca Pemahaman

This study aimed to determine the significant effect of reading comprehension skills between groups of students who are taught by using learning strategies of Directed Reading Thinking Activity (DRTA) with flip chart and group of students who are not learned by using learning strategy of Directed Reading Thinking Activity (DRTA) with flip charts on on fifth graders of Tejakula district primary schools cluster IV academic year 2017/2018. This research is quasi experiment, with non-equivalent research design post-test only control group design. The sample of the study was 41 students, consisting of 21 students of grade V SD N 3 Bondalem as experimental group and 20 students of grade V SD N 4 Bondalem as the control group. Sample selection using random sampling technique. Skill reading comprehension data is collected with multiple choice test instruments. Data analysis used descriptive statistical analysis and inferential statistical analysis (t-test). The results showed that thitung&gt; ttable (tcv = 16,559 &gt; tobs = 2,042). Based on the results of this study, it can be concluded that there is a significant difference of reading comprehension skills of students who are taught by using learning strategy of Directed Reading Thinking Activity (DRTA) with flip chart and students who are not learned by using learning strategy of Directed Reading Thinking Activity (DRTA) chart on on fifth graders of Tejakula district primary schools cluster IV academic year 2017/2018.
Beyond product state approximations for a quantum analogue of Max Cut

We consider a computational problem where the goal is to approximate the maximum eigenvalue of a two-local Hamiltonian that describes Heisenberg interactions between qubits located at the vertices of a graph. Previous work has shed light on this problem’s approximability by product states. For any instance of this problem the maximum energy attained by a product state is lower bounded by the Max Cut of the graph and upper bounded by the standard Goemans-Williamson semidefinite programming relaxation of it. Gharibian and Parekh described an efficient classical approximation algorithm for this problem which outputs a product state with energy at least 0.498 times the maximum eigenvalue in the worst case, and observe that there exist instances where the best product state has energy 1/2 of optimal. We investigate approximation algorithms with performance exceeding this limitation which are based on optimizing over tensor products of few-qubit states and shallow quantum circuits. We provide an efficient classical algorithm which achieves an approximation ratio of at least 0.53 in the worst case. We also show that for any instance defined by a 3 or 4-regular graph, there is an efficiently computable shallow quantum circuit that prepares a state with energy larger than the best product state (larger even than its semidefinite programming relaxation).
Difficulties faced by the undergraduate students in the process writing approach

The purpose of this study is to identify in which stage/stages of the Process Writing Approach students have difficulties most.This posttest-experimental study was applied to the 50 first-year students studying English Translation and Interpreting.The students were expected to produce an essay with the help of the Process Writing Approach at the end of the procedure.Their essays were assessed with a checklist prepared by the researcher according to the steps of the approach.At the end of the analysis, it is found that the students have success in the application of the approach in their writing class.Furthermore, it is seen that the students have difficulties in the application of the second stage, drafting, of the approach.The Process Writing Approach is a difficult and timeconsuming approach in writing.It is found in this study that drafting and revising are problematic stages for undergraduate students.Thus, it is thought that it will be better for teachers to warn students to prepare enough before the production stage.Also, having separate lectures on the organization of paragraphs before the application of the approach will be useful for students.
Character Networks for Narrative Generation

In this position paper, the author proposes the use of social networks of characters as an AI narrative generation mechanism. The first part of the paper offers examples of recent research by literary critics on the relationship between character networks and narrative structure. The second part of the paper offers a simple example of story generation based on a structural balance network model.
Enhancing Transformer with Sememe Knowledge

While large-scale pretraining has achieved great success in many NLP tasks, it has not been fully studied whether external linguistic knowledge can improve data-driven models. In this work, we introduce sememe knowledge into Transformer and propose three sememe-enhanced Transformer models. Sememes, by linguistic definition, are the minimum semantic units of language, which can well represent implicit semantic meanings behind words. Our experiments demonstrate that introducing sememe knowledge into Transformer can consistently improve language modeling and downstream tasks. The adversarial test further demonstrates that sememe knowledge can substantially improve model robustness.
How to Find a Point in the Convex Hull Privately

We study the question of how to compute a point in the convex hull of an input set S of n points in ℝ^d in a differentially private manner. This question, which is trivial without privacy requirements, turns out to be quite deep when imposing differential privacy. In particular, it is known that the input points must reside on a fixed finite subset G ⊆ ℝ^d, and furthermore, the size of S must grow with the size of G. Previous works [Amos Beimel et al., 2010; Amos Beimel et al., 2019; Amos Beimel et al., 2013; Mark Bun et al., 2018; Mark Bun et al., 2015; Haim Kaplan et al., 2019] focused on understanding how n needs to grow with |G|, and showed that n=O(d^2.5 ⋅ 8^(log^*|G|)) suffices (so n does not have to grow significantly with |G|). However, the available constructions exhibit running time at least |G|^d², where typically |G|=X^d for some (large) discretization parameter X, so the running time is in fact Ω(X^d³).In this paper we give a differentially private algorithm that runs in O(n^d) time, assuming that n=Ω(d⁴ log X). To get this result we study and exploit some structural properties of the Tukey levels (the regions D_{≥ k} consisting of points whose Tukey depth is at least k, for k=0,1,…). In particular, we derive lower bounds on their volumes for point sets S in general position, and develop a rather subtle mechanism for handling point sets S in degenerate position (where the deep Tukey regions have zero volume). A naive approach to the construction of the Tukey regions requires n^O(d²) time. To reduce the cost to O(n^d), we use an approximation scheme for estimating the volumes of the Tukey regions (within their affine spans in case of degeneracy), and for sampling a point from such a region, a scheme that is based on the volume estimation framework of Lovasz and Vempala [Laszlo Lovasz and Santosh S. Vempala, 2006] and of Cousins and Vempala [Ben Cousins and Santosh S. Vempala, 2018]. Making this framework differentially private raises a set of technical challenges that we address.
Semi-Axiomatic Sequent Calculus

We present the semi-axiomatic sequent calculus (SAX) that blends features of Gentzen’s sequent calculus with an axiomatic formulation of intuitionistic logic. We develop and prove a suitable analogue to cut elimination and then show that a natural computational interpretation of SAX provides a simple form of shared memory concurrency.
Revisiting Bounded-Suboptimal Safe Interval Path Planning

Safe-interval path planning (SIPP) is a powerful algorithm for finding a path in the presence of dynamic obstacles. SIPP returns provably optimal solutions. However, in many practical applications of SIPP such as path planning for robots, one would like to trade-off optimality for shorter planning time. In this paper we explore different ways to build a bounded-suboptimal SIPP and discuss their pros and cons. We compare the different bounded-suboptimal versions of SIPP experimentally. While there is no universal winner, the results provide insights into when each method should be used.
Bankers' Communication Know-how: An Analysis from Commercial Banks of Kathmandu valley

Background: In the modern era, communication has changed the way of working styles, banking sectors are practicing banking communication in order to make work easier, faster, comfortable and accurate. Banking communication plays the role of mediator in between bank, customer, government, shareholder, suppliers, client, board of directors and employees which strengthen connection between them. Therefore, banker's experience on banking communication enhances banking system, employee's behavior and core banking service facilities management.&#x0D; Objective: This study analyses socio demographic, financial service management, human resource management, corporate influence, organizational regulator, communication culture, responsiveness, organizational culture, e-banking service facilities, organizational functioning and communication encounters.&#x0D; Methodology: This study is based on exploratory research design showing causal relationship between latent and observed variables. The research prepares systematic questionnaire to interview respondents where 355 employees are interviewed by using purposive sampling technique. The result is based on descriptive analysis and Awareness Index.&#x0D; Results: The Employees Awareness Index depicts 12% of employees are inadequately aware about banking communication followed by 46% of employees are moderately aware and 37% of employees are adequately aware about banking communication.&#x0D; Conclusion: This study revealed that, in order to improve banking system, the bank should focus on financial service management and organizational regulator. For smooth control on employee's behavior bank should concentrate on communication culture, responsiveness and organizational culture. Similarly, core banking service facilities management should overview on e-banking service facilities.&#x0D; Originality: This paper is original and not published anywhere else.
A Novel Android Memory Forensics for Discovering Remnant Data

As recently updated on the vulnerability statistics shown in 2019, Android-driven smartphones, tablet PCs, and other Android devices are vulnerable, whether from internal or external threats. Most users store sensitive data like emails, photos, cloud storage access, and contact lists on Android smartphones. This information holds a growing-importance for the digital investigation process of mobile devices, e.g., internal memory or random-access memory (RAM) forensics, or external memory or read-only memory (ROM) forensics on Android smartphones. Internal memory retrieval is considered flawed and difficult by some researchers as it alters the digital evidence in an intrusive way. On the other hand, external memory retrieval also called logical acquisition that implies the image of logical storage items (e.g., files, database, directories, etc.) that locate on logical storage. This research provides a novel methodology that focuses only on internal memory forensic in a forensically sound manner. This research also contributes two algorithms, e.g., collect raw information (CRI) for parsing the raw data, and investigate raw information (IRI) for extracting the digital evidence to be more readable. This research conducted with fourteenth events to be analyzed, and each event was captured by SHA-1 as digital evidence. By using GDrive as the case study, the authors concluded that the proposed methodology could be used as guidance by forensics analyst(s), cyberlaw practitioner(s), and expert witness(es) in the court.