Reviewerly/exHarmony huggingface dataset:
Observation Decoding with Sensor Models: Recognition Tasks via Classical Planning

Observation decoding aims at discovering the underlying state trajectory of an acting agent from a sequence of observations. This task is at the core of various recognition activities that exploit planning as resolution method but there is a general lack of formal approaches that reason about the partial information received by the observer or leverage the distribution of the observations emitted by the sensors. In this paper, we formalize the observation decoding task exploiting a probabilistic sensor model to build more accurate hypothesis about the behaviour of the acting agent. Our proposal extends the expressiveness of former recognition approaches by accepting observation sequences where one observation of the sequence can represent the reading of more than one variable, thus enabling observations over actions and partially observable states simultaneously. We formulate the probability distribution of the observations perceived when the agent performs an action or visits a state as a classical cost planning task that is solved with an optimal planner. The experiments will show that exploiting a sensor model increases the accuracy of predicting the agent behaviour in four different contexts.
Instagram: Alternatif Media dalam Pengembangan Maharah Al-Kitabah

This study aims to show that Instagram, which has only been popular for posting photos or videos, is very effective as a medium of al-Kitabah learning and can increase the ability of the students in al-Kitabah Maharah. In addition, it shows positive and negative responses of students to the use of Instagram as al kitabah maharah learning media. The study uses a qualitative descriptive approach and data and information were collected by using observation, interviews and documentation. Data analysis techniques used Miles and Huberman models, namely data reduction, data presentation and data verification. The results of this study indicate that Instagram is one of the social media that strongly supports and motivates students to create and innovate in writing without being limited to place and time. Based on the results of the questionnaire, it was obtained a very positive response from 21 students that all students agreed with the use of Instagram as a medium for learning al-Kitabah maharah. Students give positive responses because they are flexible, not limited in time, learning is more varied and students are more active and creative. As for the negative responses of students needing an internet connection, teachers do not control directly. Researchers hope that with this study, the teachers can use the Instagram application as a medium for learning Arabic, so students can wisely utilize social media.
Preventing Critical Scoring Errors in Short Answer Scoring with Confidence Estimation

Many recent Short Answer Scoring (SAS) systems have employed Quadratic Weighted Kappa (QWK) as the evaluation measure of their systems.However, we hypothesize that QWK is unsatisfactory for the evaluation of the SAS systems when we consider measuring their effectiveness in actual usage.We introduce a new task formulation of SAS that matches the actual usage.In our formulation, the SAS systems should extract as many scoring predictions that are not critical scoring errors (CSEs).We conduct the experiments in our new task formulation and demonstrate that a typical SAS system can predict scores with zero CSE for approximately 50% of test data at maximum by filtering out low-reliablility predictions on the basis of a certain confidence estimation.This result directly indicates the possibility of reducing half the scoring cost of human raters, which is more preferable for the evaluation of SAS systems.
Checkpoint Reranking: An Approach to Select Better Hypothesis for Neural Machine Translation Systems

In this paper, we propose a method of reranking the outputs of Neural Machine Translation (NMT) systems.After the decoding process, we select a few last iteration outputs in the training process as the N -best list.After training a Neural Machine Translation (NMT) baseline system, it has been observed that these iteration outputs have an oracle score higher than baseline up to 1.01 BLEU points compared to the last iteration of the trained system.We come up with a ranking mechanism by solely focusing on the decoder's ability to generate distinct tokens and without the usage of any language model or data.With this method, we achieved a translation improvement up to +0.16 BLEU points over baseline.We also evaluate our approach by applying the coverage penalty to the training process.In cases of moderate coverage penalty, the oracle scores are higher than the final iteration up to +0.99 BLEU points, and our algorithm gives an improvement up to +0.17 BLEU points.With excessive penalty, there is a decrease in translation quality compared to the baseline system.Still, an increase in oracle scores up to +1.30 is observed with the re-ranking algorithm giving an improvement up to +0.15 BLEU points is found in case of excessive penalty.The proposed re-ranking method is a generic one and can be extended to other language pairs as well.
An Advanced Driver Assistance Test Cases Generation Methodology Based on Highway Traffic Situation Description Ontologies

Autonomous cars mainly rely on an intelligent system pilot to achieve the purpose of self-driving. They combine a variety of sensors to perceive their surroundings, such as cameras, radars and lidars. The perception algorithms of the Advanced Driver-Assistance Systems (ADAS) provide observations on the environmental elements based on the data provided by the sensors, while decision algorithms generate the actions to be implemented by these vehicles. To ensure the safety of the autonomous vehicle, it is necessary to specify, validate and secure the dependability of the architecture and the behavioural logic of ADAS running on the vehicle for all the situations that will be met by the vehicle. These situations are described and generated as different test cases. In this work, we propose a methodology to generate automatically test cases of autonomous vehicle for highway. This methodology is based on a three layers hierarchy. The first layer exploits static and mobile concepts we have defined in the context of three ontologies: highway, weather and vehicle. The second layer exploits the relationships between these concepts while the third one exploits the method of test case generation based on the first two layers. Finally, we use the Performance Evaluation Process Algebra (PEPA) for modelling the transitions between the driving scenes. To apply our methodology, we consider a running example about a riding vehicle on the left of the autonomous vehicle to take a right exit lane of a highway.
Cryptanalysis of Forkciphers

The forkcipher framework was designed in 2018 by Andreeva et al. for authenticated encryption of short messages. Two dedicated ciphers were proposed in this framework: ForkAES based on the AES (and its tweakable variant Kiasu-BC), and ForkSkinny based on Skinny. The main motivation is that the forked ciphers should keep the same security as the underlying ciphers, but offer better performances thanks to the larger output. Recent cryptanalysis results at ACNS ’19 have shown that ForkAES actually offers a reduced security margin compared to the AES with an 8-round attack, and this was taken into account in the design of ForkSkinny.In this paper, we present new cryptanalysis results on forkciphers. First we improve the previous attack on ForkAES in order to attack the full 10 rounds. This is the first attack challenging the security of full ForkAES. Then we present the first analysis of ForkSkinny, showing that the best attacks on Skinny can be extended to one round for most ForkSkinny variants, and up to three rounds for ForkSkinny-128-256. This allows to evaluate the security degradation between ForkSkinny and the underlying block cipher.Our analysis shows that all components of a forkcipher must be carefully designed: the attack against ForkAES uses the weak diffusion of the middle rounds in reconstruction queries (going from one ciphertext to the other), but the attack against ForkSkinny uses a weakness of the tweakey schedule in encryption queries (when one branch of the tweakey schedule is skipped).
A Novel Lookahead Strategy for Delete Relaxation Heuristics in Greedy Best-First Search

Best-first width search (BFWS) is a recent approach to satisficing planning that combines traditional heuristics with novelty measures to achieve a balance between exploration and effective search guidance (exploitation). One such novelty measure is based on counting the number of subgoals achieved on the path from a state in which a relaxed plan was computed. We introduce a new lookahead strategy for greedy best-first search based on this idea, where after each expansion, a bounded lookahead search is guided by relaxed subgoal counting. Furthermore, we combine this technique with partial delete relaxation heuristics to improve the subgoals. Using the hCFF heuristic with online-refinement of conjunctions, we obtain a planner that significantly outperforms the state of the art in satisficing planning on the IPC benchmarks.
Unsupervised Fuzzy eIX: Evolving Internal-eXternal Fuzzy Clustering

Time-varying classifiers, namely, evolving classifiers, play an important role in a scenario in which information is available as a never-ending online data stream. We present a new unsupervised learning method for numerical data called evolving Internal-eXternal Fuzzy clustering method (Fuzzy eIX). We develop the notion of double-boundary fuzzy granules and elaborate on its implications. Type 1 and type 2 fuzzy inference systems can be obtained from the projection of Fuzzy eIX granules. We perform the principle of the balanced information granularity within Fuzzy eIX classifiers to achieve a higher level of model understandability. Internal and external granules are updated from a numerical data stream at the same time that the global granular structure of the classifier is autonomously evolved. A synthetic nonstationary problem called Rotation of Twin Gaussians shows the behavior of the classifier. The Fuzzy eIX classifier could keep up with its accuracy in a scenario in which offline-trained classifiers would clearly have their accuracy drastically dropped.
Truncated moment sequences and a solution to the channel separability problem

We consider the problem of separability of quantum channels via the Choi matrix representation given by the Choi-Jamio\l{}kowski isomorphism. We explore three classes of separability across different cuts between systems and ancillae, and we provide a solution based on the mapping of the coordinates of the Choi state (in a fixed basis) to a truncated moment sequence (tms) $y$. This results in an algorithm which gives a separability certificate using semidefinite programming. The computational complexity and the performance of it depend on the number of variables $n$ in the tms and on the size of the moment matrix ${M}_{t}(y)$ of order $t$. We exploit the algorithm to numerically investigate separability of families of two-qubit and single-qutrit channels; in the latter case we can provide an answer for examples explored earlier through the criterion based on the negativity $N$, a criterion which remains inconclusive for Choi matrices with $N=0$.
Virtual Ecological Laboratory to develop a PV module recycling workshop

The article reflects the experience of the development and implementation of a set of laboratory work on the recycling of photovoltaic panels of alternative energy. The main features of the modernization of the educational process for the dismantling of silicon solar panels using virtual reality technologies, as a new educational strategy used in environmental engineering, without risk to the health of students practicing some dangerous activities. This work focuses on the life cycle of sustainable energy systems and on activities to ensure the best educational practices
GRACE: Generating Summary Reports Automatically for Cognitive Assistance in Emergency Response

EMS (emergency medical service) plays an important role in saving lives in emergency and accident situations. When first responders, including EMS providers and firefighters, arrive at an incident, they communicate with the patients (if conscious), family members and other witnesses, other first responders, and the command center. The first responders utilize a microphone and headset to support these communications. After the incident, the first responders are required to document the incident by filling out a form. Today, this is performed manually. Manual documentation of patient summary report is time-consuming, tedious, and error-prone. We have addressed these form filling problems by transcribing the audio from the scene, identifying the relevant information from all the conversations, and automatically filling out the form. Informal survey of first responders indicate that this application would be exceedingly helpful to them. Results show that we can fill out a model summary report form with an F1 score as high as 94%, 78%, 96%, and 83% when the data is noise-free audio, noisy audio, noise-free textual narratives, and noisy textual narratives, respectively.
Dynamic Resource Management for Cloud Spot Markets

Resource management for cloud computing environments that are characterized by many layers emerges as a critical task for cloud computing providers.Such providers are compelled by the demands and strategies of stochastic customers to adopt dynamic resource management for the top-bottom scaling of the cloud resources on the basis of variable needs.Resource management in the infrastructure as a service layer relies on virtual machine (VM) characteristics, such as estimated VM classes.Given that a cloud provider offers a variety of VM classes that differ as regards the size of computing resources (e.g., central processing unit, memory, and input/output devices), optimizing cloud resources to maximize cloud revenue is a challenging dilemma.More specifically, the dynamic management of resources in cloud spot markets is confronted with various severe obstacles.In consideration of these issues, this study investigated a dynamic resource management model for cloud spot markets and put forward an efficient model that manages spare resources for the purpose of expanding cloud revenue.The model estimates the available spare capacity of a spot market, evaluates the maximum expected revenue of stagnant VMs on the basis estimated cumulative capacity, and locates the optimum VM combinations that bear complementary workloads and capacities and can coexist in a certain host.Our model also improves the understanding of cloud resource scaling and generates inferences that can be adopted in managing cloud resources for all layers as well as Reserved and On-Demand markets.
THE INFLUENCE OF READING HABIT ON STUDENTS’ WRITING SKILL

This research aims to investigate the reading habit quality of EFL learners at Widya Kartika University, Surabaya and to find out the influence of reading habit towards students’ writing skill. This is a quantitative ex-post facto study since it examines how the independent variable (reading habit) that has existed affects the dependent variable (writing skill) and the result is in the form of numbers. Sixty two students of Widya Kartika University were selected as the subject of this study. The instruments used in this study were (1) a reading habit questionnaire, to measure the quality of students’ reading habit and (2) a writing test, to examine the students’ writing skill and how it is influenced by reading habit. The results showed that the students’ reading habit quality was fair. Meanwhile, the analysis of Simple Linear Regression concluded that reading habit influences students’ writing skill. Moreover, one of the writing components that was mostly influenced by the reading habit was the content.
Real Time Data Communication for Intelligent Extended Reality Applications

Virtual Reality (VR) is a powerful modern medium. Immersive qualities of VR enables the design of efficient educational, industrial and training solutions. The advent of low-cost head-mounted display (HMD) devices made this technology accessible at large and featured VR with possibilities to monitor interactions and user’s motion. However, due to lack of real–time data communication and collection at present, it is still a challenge to obtain a feedback mechanism related the user’s behavior. This paper addresses an architecture that is designed to implement a feedback mechanism. The architecture consists of a data communication tool, a relevant immersive environment and third party software. The communication tool is used to transmit data with different sample rates in real-time. The feedback mechanism of designed architecture is presented through a number of case studies. A solution for observing the user’s behavior is also presented in this paper with the motivation to complement analysis of performed activities and subjective feedback towards VR based applications.
Development of Pop-Up Book Media based on Balanced Literacy Approach to Improve Skills of Reading Class 1 Students Basic School

The background that drives this research is the low interest in reading and the lack of reading skills in grade 1 students. The purpose of this study is to produce a pop-up book based on a good quality balanced literacy approach. The test subjects in this study were 60 first grade students at SDN Pacarkeling V / 186 Surabaya. The method used in this study is the method of developing Research and Development (R&amp;D). The development model of Borg and Gall in this study goes through 10 stages. The product developed in this research is a valid, practical and effective pop-up book media. The validation results show that the developed media is valid with very good criteria. Practicality in small scale and large scale trials reaches very good category. The effectiveness of the developed media can be seen from the increase in the results of the reading skills test using the t test with the t-test result of -11.6.
Real Elliptically Skewed Distributions and Their Application to Robust Cluster Analysis

This article proposes a new class of Real Elliptically Skewed (RESK) distributions and associated clustering algorithms that allow for integrating robustness and skewness into a single unified cluster analysis framework. Non-symmetrically distributed and heavy-tailed data clusters have been reported in a variety of real-world applications. Robustness is essential because a few outlying observations can severely obscure the cluster structure. The RESK distributions are a generalization of the Real Elliptically Symmetric (RES) distributions. To estimate the cluster parameters and memberships, we derive an expectation maximization (EM) algorithm for arbitrary RESK distributions. Special attention is given to a new robust skew-Huber M-estimator, which is also the maximum likelihood estimator (MLE) for the skew-Huber distribution that belongs to the RESK class. Numerical experiments on simulated and real-world data confirm the usefulness of the proposed methods for skewed and heavy-tailed data sets.
Some combinatorial and geometric constructions of spherical buildings

We survey some known and new combinatorial and geometric constructions of the Lie incidence geometries related to some spherical buildings, in particular of some exceptional types.
Analyzing the Relationship between Student’s Assignment Submission Behaviors and Course Achievement through Process Mining Analysis

In this study, it is aimed to analyze the relationship between student's assignment submission behaviors and course achievement.For this purpose, the behaviors of 75 students' who enrolled in the Operating Systems and Applications course at a public university, submission an assignment through the Moodle learning management system given in the fourth week of the course is analyzed.Students who exhibit different assignment submission behaviors are also analyzed in terms of endof-term grades.During analyzing, the steps followed by the students while submitting their assignments are determined respectively, and students who display a similar pattern are divided into groups by means of cluster analysis.Moreover, using process mining analysis assignment submission processes of students in different groups are analyzed in detail.The analysis shows that students can be divided into three different groups based on their assignment submission behaviors.In terms of course achievement, it is observed that a significant portion of the students who submitted the assignments are successful in the course, while a significant portion of the students who did not submit the assignment failed.The findings will be guideway in determining the students who are likely to fail the course in the early weeks and in designing possible interferences for these students.
Optical topography of rough surfaces using vortex localization of fluorescent markers

Measuring rough surfaces is challenging because the proven topographic methods are impaired by the adverse effects of diffuse light. In our method, the measured surface is marked by fluorescent nanobeads allowing a complete suppression of diffuse light by bandpass filtering. Light emitted by each fluorescent bead is shaped to a double-helix point spread function used for three-dimensional bead localization on the surface. This non-interferometric measurement of rough surface topography is implemented in a vibration resistant setup. The comparison of our method with vertical scanning interferometry shows that a commercial profiler is surpassed when ground glass surfaces with steep slopes are measured.
Behind the Mask: A Computational Study of Anonymous' Presence on Twitter

The hacktivist group Anonymous is unusual in its public-facing nature. Unlike other cybercriminal groups, which rely on secrecy and privacy for protection, Anonymous is prevalent on the social media site, Twitter. In this paper we re-examine some key findings reported in previous small-scale qualitative studies of the group using a large-scale computational analysis of Anonymous' presence on Twitter. We specifically refer to reports which reject the group's claims of leaderlessness, and indicate a fracturing of the group after the arrests of prominent members in 2011-2013. In our research, we present the first attempts to use machine learning to identify and analyse the presence of a network of over 20,000 Anonymous accounts spanning from 2008-2019 on the Twitter platform. In turn, this research utilises social network analysis (SNA) and centrality measures to examine the distribution of influence within this large network, identifying the presence of a small number of highly influential accounts. Moreover, we present the first study of tweets from some of the identified key influencer accounts and, through the use of topic modelling, demonstrate a similarity in overarching subjects of discussion between these prominent accounts. These findings provide robust, quantitative evidence to support the claims of smaller-scale, qualitative studies of the Anonymous collective.
An Efficient Framework for Hand Gesture Recognition based on Histogram of Oriented Gradients and Support Vector Machine

This paper focuses on an empirical hand gesture recognition system in the domain of image processing and machine learning.The hand gesture is probably the most intuitive and frequently used mode of nonverbal communication in human society.The paper analyzes the efficiency of the Histogram of Oriented Gradients (HOG) as the feature descriptor and Support Vector Machine (SVM) as the classification model in case of gesture recognition.There are three stages of the recognition procedure namely image binarization, feature extraction, and classification.The findings of the paper show that the model classifies hand gestures for the given dataset with satisfactory efficiency.The outcome of this work can be further utilized in practical fields of realworld applications dealing with non-verbal communication.