Reviewerly/exHarmony huggingface dataset:
Error Analysis of English Sentence Structure in Students’ Written Paragraphs

This research aimed to find out the types of sentence structure errors in English paragraphs written by tertiary English students and the factors causing the errors. This research employed an explanatory mixed-method design. Fourth-semester students from the English department of IAIN Curup were engaged as the subjects of this research. Positivism-governed document analyses and constructivism-based interviews were conducted to solicit the data as desired. The quantitative findings garnered from document analyses endorsing a ready-to-use construct proposed by Dulay, Burt, and Krashen (1982), revealed that there were four types of sentence structure errors students made, namely omission, addition, misformation, and misordering. Those types of errors were exhibited in a proximate composition which meant that the students had compatible difficulties in terms of the four types of errors. As uncovered from students' writing works, the four types of errors were found in the domains of words, phrases, and clauses. Subsequently, the qualitative findings, elicited from interviews, demonstrated that the factors of sentence structure errors extended to students' mother tongue interference, overgeneralization in the use of English rules and norms, and the lecturer's teaching material delivery and method. Anchored in the data gained, this research discussed the data from the perspective of interlanguage theory, wherein some reviews of SLA and EFL pedagogy-related theories were offered to help lower the factors causing English sentence structure errors in writing skills. Keywords: Errors, Sentence Structure, English writing skill
Enhancing Situational Awareness for Tutors of Cybersecurity Capture the Flag Games

Supervised Capture the Flag games represent a popular method of practical hands-on training in cybersecurity education. However, as cybersecurity training sessions are process-oriented, tutors have only a limited insight into what trainees are doing and how they deal with the tasks. From their perspective, it is necessary to have situational awareness, enabling them to identify and react to any issues during a training session as soon as they emerge. We propose a tool designed in collaboration with cybersecurity educators. Based on user requirements, we developed the Progress Visualization Tool, which provides educators with timely feedback through the session. More specifically, the tool informs educators of the training progression, helps identify the students who might struggle with their tasks, and reveals overall deviation from the schedule. We validated the tool through formative and summative qualitative in-lab evaluations. The participants appraised the impact on the training workflow and gave further insights regarding the tool. We discuss the insights and recommendations that arose from the evaluations as they could aid the design of future tools for supporting educators, not only of CTFs but also in other domains.
Evaluating Automatic Speech Recognition Quality and Its Impact on Counselor Utterance Coding

Automatic speech recognition (ASR) is a crucial step in many natural language processing (NLP) applications, as often available data consists mainly of raw speech.Since the result of the ASR step is considered as a meaningful, informative input to later steps in the NLP pipeline, it is important to understand the behavior and failure mode of this step.In this work, we analyze the quality of ASR in the psychotherapy domain, using motivational interviewing conversations between therapists and clients.We conduct domain agnostic and domain-relevant evaluations using evaluation metrics and also identify domain-relevant keywords in the ASR output.Moreover, we empirically study the effect of mixing ASR and manual data during the training of a downstream NLP model, and also demonstrate how additional local context can help alleviate the error introduced by noisy ASR transcripts.
Strategies for Safety Goal Decomposition for Neural Networks

Neural networks (NNs) have become a key technology for solving highly complex tasks, and require integration into future safety argumentations. New safety relevant aspects introduced by NN based algorithms are: representativity of test cases, robustness, inner representation and logic, and failure detection for NNs. In this paper, a general argumentation structure for safety cases respecting these four aspects is proposed together with possible sources of evidence.
QUALITY OF EXPERIENCES (QOE) WITH E-LEARNING AMONG NURSING STUDENTS IN A PRIVATE HEALTHCARE UNIVERSITY SETTING IN MALAYSIA.

Background: During the COVID-19 pandemic, students in higher education institutions are not allowed to be on campuses due to safety reasons. However, educational activities need to continue, and students need to graduate on time. To facilitate teaching, learning, and assessment, E-learning had been adopted globally by all educational institutions from schools to Higher learning institutes. Therefore, this research aims to assess nursing students’ satisfaction and behavioural intentions towards E-learning, mainly on the Moodle-based Learning Management System (LMS). Methods: This cross-sectional survey study includes 160 nursing students from one of the private nursing colleges in Malaysia. A validated questionnaire SERVQUAL was used for data collection. The questionnaire has five (5) quality dimension constructs to measure E-learning quality. The questionnaire also included questions related to satisfaction with the E-learning and behavioural intentions of learners regarding the use of E-learning in the future. The data was collected using Google forms. Results: The study findings show that respondents positively rated E-learning quality on a Likert scale of 1-4, ranging from strongly disagree to strongly agree (M=2.5; SD=0.36). Pearson Correlation test showed that all five quality dimensions, namely “Empathy,” “Assurance,” “Reliability,” “Responsiveness,” and web content quality, had positive Correlation with E-learning quality. However, “Reliability” and website content had the most positive influence on E-learning quality (r=0.733; p=0.000). The findings also suggest that E-learning quality was not a big driver for the behavioural intentions in promoting online E-learning (r =0.185; p =0.019). However, grade expectations were found to have a strong positive relationship with the satisfaction of learners with E-learning. Conclusion: Providing quality E-learning platforms and experiences can help improve the E-learning quality experience of the learners, promote satisfaction and grade expectations of the learners of higher education institutions. Given the characteristics of nursing students, it is necessary to develop and apply an effective and viable E-learning educational program to sustain motivation and produced the desired learning outcome among nursing students.
Retrieval of behavior trees using map-and-reduce technique

There has been an increased interest in the creation of AI social agents who possess complex behaviors that allow them to perform social interactions. Behavior trees provide a plan model execution that has been widely used to build complex behaviors for AI social agents. Behavior trees can be represented in the form of a memory structure known as cognitive scripts, which would allow them to evolve through further development over multiple exposure to repeated enactment of a particular behavior or similar ones. Behavior trees that share the same context will then be able to learn from each other resulting in new behavior trees with richer experience. The main challenge appears in the expensive cost of retrieving contextually similar behavior trees (scripts) from a repertoire of scripts to allow for that learning process to occur. This paper introduces a novel application of map-and-reduce technique to retrieve cognitive with low computational time and memory allocation. The paper focuses on the design of a corpus of cognitive scripts, as a knowledge engineering key challenge, and the application of map-and-reduce with semantic information to retrieve contextually similar cognitive scripts. The results are compared to other techniques used to retrieve cognitive scripts in the literature, such as Pharaoh which uses the least common parent (LCP) technique in its core. The results show that the map-and-reduce technique can be successfully used to retrieve cognitive scripts with high retrieval accuracy of 92.6%, in addition to being cost effective.
Low-Cost Side-Channel Secure Standard 6T-SRAM-Based Memory With a 1% Area and Less Than 5% Latency and Power Overheads

Side-channel attacks constitute a concrete threat to IoT systems-on-a-chip (SoCs).Embedded memories implemented with 6T SRAM macrocells often dominate the area and power consumption of these SoCs.Regardless of the computational platform, the side-channel sensitivity of low-hierarchy cache memories can incur significant overhead to protect the memory content (i.e., data encryption, data masking, etc.).In this manuscript, we provide a silicon proof of the effectiveness of a low cost side-channel attack protection that is embedded within the memory macro to achieve a significant reduction in information leakage.The proposed solution incorporates low-cost impedance randomization units, which are integrated into the periphery of a conventional 6T SRAM macro in fine-grain memory partitions, providing possible protection against electromagnetic adversaries.Various blocks of unprotected and protected SRAM macros were designed and fabricated in a 55 nm test-chip.The protected ones had little as 1% area overhead and less than 5% performance and power penalties compared to a conventional SRAM design.To evaluate the security of the proposed solution, we applied a robust mutual information metric and an adaptation to the memory context to enhance this evaluation framework.Assessment of the protected memory demonstrated a significant information leakage reduction from 8 bits of information exposed after only 100 cycles of attack to less than ∼1.5 bits of mutual information after 160K traces.The parametric nature of the protection mechanisms are discussed while specifying the proposed design parameters.Overall, the proposed methodology enables designs with higher security-level at a minimal cost.
Seed Word Selection for Weakly-Supervised Text Classification with Unsupervised Error Estimation

Weakly-supervised text classification aims to induce text classifiers from only a few userprovided seed words.The vast majority of previous work assumes high-quality seed words are given.However, the expert-annotated seed words are sometimes non-trivial to come up with.Furthermore, in the weakly-supervised learning setting, we do not have any labeled document to measure the seed words' efficacy, making the seed word selection process "a walk in the dark".In this work, we remove the need for expert-curated seed words by first mining (noisy) candidate seed words associated with the category names.We then train interim models with individual candidate seed words.Lastly, we estimate the interim models' error rate in an unsupervised manner.The seed words that yield the lowest estimated error rates are added to the final seed word set.A comprehensive evaluation of six binary classification tasks on four popular datasets demonstrates that the proposed method outperforms a baseline using only category name seed words and obtained comparable performance as a counterpart using expert-annotated seed words 1 .
Translating the Discrete Logarithm Problem on Jacobians of Genus 3 Hyperelliptic Curves with $$(\ell ,\ell ,\ell )$$-Isogenies

We give an algorithm to compute $(\ell,\ell,\ell)$-isogenies from the Jacobians of genus three hyperelliptic curves to the Jacobians of non-hyperelliptic curves. An important application is to reduce the discrete logarithm problem in the Jacobian of a hyperelliptic curve to the corresponding problem in the Jacobian of a non-hyperelliptic curve.
Exploration in Approximate Hyper-State Space for Meta Reinforcement Learning

To rapidly learn a new task, it is often essential for agents to explore efficiently - especially when performance matters from the first timestep. One way to learn such behaviour is via meta-learning. Many existing methods however rely on dense rewards for meta-training, and can fail catastrophically if the rewards are sparse. Without a suitable reward signal, the need for exploration during meta-training is exacerbated. To address this, we propose HyperX, which uses novel reward bonuses for meta-training to explore in approximate hyper-state space (where hyper-states represent the environment state and the agent’s task belief). We show empirically that HyperX meta-learns better task-exploration and adapts more successfully to new tasks than existing methods.
Generalization in Instruction Following Systems

Understanding and executing natural language instructions in a grounded domain is one of the hallmarks of artificial intelligence.In this paper, we focus on instruction understanding in the blocks world domain and investigate the language understanding abilities of two topperforming systems for the task.We aim to understand if the test performance of these models indicates an understanding of the spatial domain and of the natural language instructions relative to it, or whether they merely over-fit spurious signals in the dataset.We formulate a set of expectations one might have from an instruction following model and concretely characterize the different dimensions of robustness such a model should possess.Despite decent test performance, we find that state-of-the-art models fall short of these expectations and are extremely brittle.We then propose a learning strategy that involves data augmentation and show through extensive experiments that the proposed learning strategy yields models that are competitive on the original test set while satisfying our expectations much better. 1.
Blockchain Based Formal Modelling of Patient Management in Hospital Emergency System

An emergency department is a fundamental part of hospital systems providing medical treatment services and critical care that specializes in emergency care of patients. In the emergency department, patients are treated without an appointment. Most of the traditional hospital emergency management systems utilize inefficient manual record management procedures that may lead to serious problems. Specifically, a failed emergency services system may result in loss of lives which need modern technologies for developing smart health emergency systems. There exists a lot of work in this area but still, it needs many improvements making use of the state-of-the-art technologies. Therefore, in the present paper blockchain-based formal model of patient management in a hospital emergency system is proposed by integrating the Internet of Things (IoT), Unified Modelling Language (UML), and formal methods. Emergency patient management system functionality includes patient registration, payments, and patient discharge modules. In software development, UML is a standardized modelling language that is used to portray the model of a software system requirement comprehensively. IoT healthcare devices are utilized to track a patient's health condition and actors are employed to take decisions as specified. The blockchain-based model offered system security and privacy mechanism additionally preventing alteration of the record. Formal methods using mathematical models will be applied for validation and model analysis.
Postprocessing for Skin Detection

Skin detectors play a crucial role in many applications: face localization, person tracking, objectionable content screening, etc. Skin detection is a complicated process that involves not only the development of apposite classifiers but also many ancillary methods, including techniques for data preprocessing and postprocessing. In this paper, a new postprocessing method is described that learns to select whether an image needs the application of various morphological sequences or a homogeneity function. The type of postprocessing method selected is learned based on categorizing the image into one of eleven predetermined classes. The novel postprocessing method presented here is evaluated on ten datasets recommended for fair comparisons that represent many skin detection applications. The results show that the new approach enhances the performance of the base classifiers and previous works based only on learning the most appropriate morphological sequences.
The Magnitude of the Frequency Jitter of Acoustic Waves Generated by Wind Instruments Is of Relevance for the Live Performance of Music

It is shown that a gold-plated device mounted on a tenor saxophone, forming a small bridge between the mouthpiece and the S-bow, can change two characteristics of the radiated sound: (1) the radiated acoustic energy of the harmonics with emission maxima around 1500–3000 Hz, which is slightly reduced for tones played in the lower register of the saxophone; (2) the frequency jitter of all tones in the regular and upper register of the saxophone show a two-fold increase. Through simulated phase-shifted superimpositions of the recorded waves, it is shown that the cancellation of acoustic energy due to antiphase superimposition is significantly reduced in recordings with the bridge. Simulations with artificially generated acoustic waves confirm that acoustic waves with a certain systematic jitter show less cancelling of the acoustic energy under a phase-shifted superimposition, compared to acoustic waves with no frequency jitter; thus, being beneficial for live performances in small halls with minimal acoustic optimization. The data further indicate that the occasionally hearable “rumble” of a wind instrument orchestra with instruments showing slight differences in the frequency of the harmonics might be reduced (or avoided), if the radiated acoustic waves have a systematic jitter of a certain magnitude.
Technologies used to support the nursing process: scoping review

Abstract Objective: To identify and map the technologies used to support the nursing process. Methods: This is a scoping review carried out in November and December 2019, in 15 national and international databases. Data for analysis were extracted from indicators for a spreadsheet of Microsoft Excell 2010®. Results: The study sample consisted of 14 studies, published mainly in 2017 and from Brazil. The technologies developed are mainly software, aimed at teaching and as target audience professionals who already work in [...]
SiDD: The Situation-Aware Distributed Deployment System

Most of today’s deployment automation technologies enable the deployment of distributed applications in distributed environments, whereby the deployment execution is centrally coordinated either by a central orchestrator or a master in a distributed master-workers architectures. However, it is becoming increasingly important to support use cases where several independent partners are involved. As a result, decentralized distributed deployment automation approaches are required, since organizations typically do not provide access to their internal infrastructure to the outside or leave control over application deployments to others. Moreover, the choice of partners can depend heavily on the current situation at deployment time, e.g. the costs or availability of resources. Thus, at deployment time it is decided which partner will provide a certain part of the application depending on the situation. To tackle these challenges, we demonstrate the situation-aware distributed deployment (SiDD) system as an extension of the OpenTOSCA ecosystem.
Designing a Smart Gateway for Data Fusion Implementation in a Distributed Electronic System Used in Automotive Industry

This paper focuses on the interdisciplinary research on the design of a smart gateway for managing the dynamic error code testing collected and generated by the Electronic Control Unit (ECU) from the automotive industry. The techniques used to exchange information between the ECU code errors and knowledge bases, based on data fusion methods, allowed us to consolidate and ensure data reliability, and then to optimize processed data in our distributed electronic systems, as the basic state for Industry 4.0 standards. At the same time, they offered optimized data packets when the gateway was tested as a service integrator for ECU maintenance. The embedded programming solutions offered us safe, reliable, and flexible data packet management results on both communication systems (Transmission Control Protocol/Internet Provider (TCP/IP) and Controller Area Network (CAN) Bus) on the Electronic Control Unit (ECU) tested for diesel, high-pressure common rail engines. The main goal of this paper is to provide a solution for a smart, hardware–software, Industry-4.0-ready gateway applicable in the automotive industry.
A Comparative Study of User Experience According to One-person Media Virtual YouTuber (VTuber) and General YouTuber

With the spread of advanced computer graphics (CG) technology and online video platforms' popularization, 3D content is emerging in the one-person broadcast field.A new type of content area called Virtual YouTuber (VTuber) using VR equipment has emerged.Therefore, this study is aimed to analyze the differences in content user experience quantitatively and qualitatively by comparing existing general and virtual YouTubers.A survey method was employed to collect data using a questionnaire developed by the AttrakDiff evaluation method.The questionnaire was designed to measure four dimensions of the usability (pragmatic quality, hedonic quality-identity, hedonic quality-simulation, and attractiveness) with 28 items (pairs of opposite adjectives positioned at both ends) rated on a 7-point scale (-3: negative adjective ~ 3: positive adjective).A total of 50 people (25 men and 25 women) in their 20s to 30s participated in the survey.The results of the study revealed the characteristics of the user experience of ordinary and virtual YouTubers.This work is significant as an attempt to further identify the preferences and features of the user experience using AttrakDiff evaluation techniques from the simple rating comparison for existing virtual YouTuber (Vtuber).Starting with this research, we hope that more related research will be conducted on virtual YouTuber (VTuber).
Adaptive Model for Biofeedback Data Flows Management in the Design of Interactive Immersive Environments

The interactivity of an immersive environment comes up from the relationship that is established between the user and the system. This relationship results in a set of data exchanges between human and technological actors. The real-time biofeedback devices allow to collect in real time the biodata generated by the user during the exhibition. The analysis, processing and conversion of these biodata into multimodal data allows to relate the stimuli with the emotions they trigger. This work describes an adaptive model for biofeedback data flows management used in the design of interactive immersive systems. The use of an affective algorithm allows to identify the types of emotions felt by the user and the respective intensities. The mapping between stimuli and emotions creates a set of biodata that can be used as elements of interaction that will readjust the stimuli generated by the system. The real-time interaction generated by the evolution of the user’s emotional state and the stimuli generated by the system allows him to adapt attitudes and behaviors to the situations he faces.