Reviewerly/exHarmony huggingface dataset:
One-Time Key-Encapsulation Mechanisms: Definitions, Constructions and Cybersecurity Applications

Lockable obfuscation, a new primitive that occurred in cryptography, makes possible to execute functions in a supported family and recover a secret under specific conditions. More concretely, if the functions to be executed over the input matches an expected, embedded, value, here denoted by a, the secret s is going to be related. Stated in algebraic terms, if f : X→A has the property that for some x,f(x) = a, s is revealed.One interesting aspect is that lockable obfuscation hides both a,f and s as long as they are chosen independently and meet several conditions related to the conditional min-entropy of sampling the triple (a,s,f). The advantage conferred by such schemes are impressive, especially considering applications which are to be detailed as use cases for our work. However, the drawbacks of such schemes are also considerable, given their inefficiency.Our work explores the possibility to safely decrypt messages, and based on the given message, to reveal the secret. More specifically, we provide a review of existing public key encryption schemes and argue for the efficiency of a novel one.
Reducing Co-Occurrence Bias to Improve Classifier Explainability and Zero-Shot Detection

This paper investigates a new component-class classification (C3) architecture to reduce the impact of co-occurrence bias that can severely degrade multi-label, component-based classification methods. Component-class classification refers to the task of classifying multiple components comprising a single object in a single image. In this paper, the objects are military vehicles with identifiable components such as turret, wheel, and gun. Multi-label architectures are a conventional deep neural network solution used for classifying images containing multiple classes or more commonly multiple class attributes. However, the addition of multiple classes inside single training images introduces the possibility of co-occurrence bias which will be shown to result in unpredictable and poor component-level classification. To mitigate this co-occurrence bias, this paper proposes to replace the multi-label classification architecture that is often used for zero-shot detection, with an architecture that is based on object detection. The result is a dramatic decrease in the effects of co-occurrence bias over that seen with the multi-label solution. Further, this paper shows that this improved component-level classification can translate into a viable approach for classifier interpretability and improve zero-shot detection applications.
A UAV Object Detection Benchmark for Vision-assisted Powerline Element Inspection

Powerline inspection operations involve capturing and inspecting visual footage of powerline elements in electric transmission infrastructures. Current technological advantages in the areas of robotics and machine learning are towards enabling the utilization of completely autonomous Unmanned Aerial Vehicles (UAVs) to carry out such tasks. One of the tasks to be addressed is the robust, precise, and fast powerline object detection problem. To this end, UAVs are required to perform visual object detection autonomously, with high accuracy and fast algorithm execution speed, for providing image regions of interest to be inspected by humans or even be used as input for autonomously controlling the UAV/camera. However, the limited computational resources of the on-board devices of such systems heavily affect the type of neural network architectures that can potentially be deployed. In this work, we study state-of-the-art object detectors in an attempt to find an acceptable trade-off between detection accuracy and inference speed that will allow the exploitation of UAVs for autonomous powerline inspection purposes. To this end, we publicly release a powerline inspection dataset and state a benchmark evaluation with recently proposed object detectors based on deep learning.
Application of the STEAM Approach for the Development of Social Competence in Regular Basic Education Female Teachers of Lima, Peru

This research seeks to determine the contribution of the STEAM (Science, Technology, Engineering, Arts and Mathematics) approach to the development of social competence in Regular Basic Education female teachers of Lima, Peru. The research is carried out using the qualitative approach and a case study. Use an interview guide, which is based on a validated instrument. The course proposed the construction of a video game with Scratch code. Due to this, it represented a proposal for mainstreaming the approach since its goal was to develop social competencies through interaction in a technological project. Likewise, it was sought to replicate the experience of students who enter a new environment and must break down psychological barriers and limiting beliefs. In conclusion, facilitators and participants agreed that the application of the STEAM approach favoured the development of social competence since it provided a safe space for constant interaction. The teachers were able to interact with each other and carry out meticulous teamwork.
Semantic Causal Abstraction for Event Prediction

Causal models promise many benefits if applied correctly to machine learning tasks. However, in order to leverage fine grained causal information, it is often useful to reduce the complexity of the causal connections by producing an abstracted version of the graph. In this paper, we introduce semantic causal abstractions, a scheme for constructing abstracted causal graphs in order to provide a domain-independent approximation to formal causal abstraction for unstructured textual data. We then analyze the effects that this type of abstraction has on the performance of a causal graph-based prediction model under multiple semantic representations. Our experiments on two stock prediction tasks provide evidence for the efficacy of semantic causal abstraction to improve prediction performance and give insight into the consistency of the optimal semantic causal abstraction levels across tasks.
A survey of deep learning based text-to-SQL generation

Text-to-SQL is an important application of automation software engineering. It is also a research hotspot in the field of semantic parsing. The text-to-SQL task aims to automatically generate the SQL statement according to the natural language description. It allows nonprofessionals to access the database without understanding SQL syntax. With the development of large-scale text-to-SQL datasets and artificial intelligence technologies, the text-to-SQL task is also making great progress. Compared with the traditional text-to-SQL generation, the deep learning-based text-to-SQL has the advantages of high accuracy, flexibility, and iterative learning. In recent years, several studies have focused on SQL generation based on deep learning. This research summarizes existing works from the aspects of text-to-SQL scenarios, datasets, model structures, and evaluation methods.
Cool and Useful

Tabletop roleplaying games (TTRPGs) blend aesthetic and mechanical information to tell users not only how to play the game but also what kind of an experience they will have while playing the game. Mörk Borg, a recent TTRPG, has a design that brings such often-ignored technical information to the forefront. This invites us as document designers and those who teach document design to consider the ways that our technical design can be both cool and useful. At the same time, Mörk Borg has some accessibility issues that must be considered when making cool and useful designs.
Designing for Cultural Inclusivity

This paper presents the results of a pilot study on international patrons’ experience interacting with a U.S. university library website. It identifies the unique needs, pain points, and emotional and cognitive state of international students as they use the online library services, and makes recommendations on designing usable and culturally-inclusive experiences for global users.
Variable screening based on Gaussian Centered L-moments

An important challenge in big data is identification of important variables. For this purpose, methods of discovering variables with non-standard univariate marginal distributions are proposed. The conventional moments based summary statistics can be well-adopted, but their sensitivity to outliers can lead to selection based on a few outliers rather than distributional shape such as bimodality. To address this type of non-robustness, the L-moments are considered. Using these in practice, however, has a limitation since they do not take zero values at the Gaussian distributions to which the shape of a marginal distribution is most naturally compared. As a remedy, Gaussian Centered L-moments are proposed, which share advantages of the L-moments, but have zeros at the Gaussian distributions. The strength of Gaussian Centered L-moments over other conventional moments is shown in theoretical and practical aspects such as their performances in screening important genes in cancer genetics data.
Stories from the Circle

This experience report describes our work to design and develop an Extended Reality (XR) application and supporting materials which allow people to experience, in situ, the spoken narratives of a diverse group of individuals affected by the ongoing efforts to remove the large and storied public monument to Christopher Columbus at Heritage Park in Syracuse, New York. Building on recent studies in posthumanist and intercultural rhetorics, this report describes how XR technologies and intercultural research methods can help to bring about the decolonization of the design of communication.
Eye Gaze Estimation Based on Stacked Hourglass Neural Network for Aircraft Helmet Aiming

Helmet-mounted display (HMD) systems allow aircraft pilots to aim at targets by using head postures. However, the direct use of helmet orientation to indicate the aiming direction ignores human eye movements, which are more flexible and efficient for interaction. Since the opaqueness of the helmet goggle blocks the sight of external cameras to capture facial or eye images of the pilots, and traditional eye feature extraction methods may fail when encountering conditions such as poor lighting, occlusion, and shaking, which are common on fighter aircrafts. In this work, an eye gaze based aiming solution that adapts to pilots wearing HMDs is proposed, and a deep learning-based method is proposed to extract eye features robustly. The prototype experiments demonstrate the ability to pick and aim at targets in real-time (60FPS) and are capable to accurately locate the target markers on a screen with an average error of fewer than 2 degrees. Conclusively, the proposed method performs the tasks of eye feature extraction on real-person imagery and the estimation of the 3D aiming direction for users with helmets, displaying competitive results with similar research.
Embrace the Weirdness: Negotiating Values Inscribed into Music Technology

Abstract This article explores the ways specific hardware and software technologies influence the design of musical instruments. We present the outcomes of a compositional game in which music technologists created simple instruments using common sensors and the Pure Data programming language. We identify a clustering of stylistic approaches and design patterns, and we discuss these findings in light of the interactions suggested by the materials provided, as well as makers' technomusical backgrounds. We propose that the design of digital instruments entails a situated negotiation between designer and tools, wherein musicians react to suggestions offered by technology based on their previous experience. Likewise, digital tools themselves may have been designed through a similar situated negotiation, producing a recursive process through which musical values are transferred from the workbench to the instrument. Instead of searching for ostensibly neutral and all-powerful technologies, we might instead embrace and even emphasize the embedded values of our tools, acknowledging their influence on the design of new musical artifacts.
IoT Geography Chain: Blockchain-Based Solution for Logistics Ecosystem

Internet of Things (IoT) is emerging rapidly, thus opening the ear of connectedness and improving business worldwide. IoT devices tend to communicate without human intervention and thus could be more vulnerable to attacks, especially in logistics where it is difficult to reassure the path adopted by the transport services. End-to-end trust is difficult to build, and most of the times IoT requires human intervention to proceed the logistics operations. Our study proposes a solution with blockchain and smart contract to work with GPS data to build trusted paths and premises. Such trusted paths and premises can trigger smart contract automatically to reduce the human intervention in logistics process. For evaluation of our study, we demonstrate the path creation process with graph, smart contract triggering system, and data check-up to authorize IoT devices to proceed with communications. Our proposed method creates secured path and premises for logistic IoT which lead to build end-to-end trust and automated triggering system for smart contract.
A Survey on the Efficiency, Reliability, and Security of Data Query in Blockchain Systems

With the rise of digital currencies, blockchain systems work for storing data efficiently and securely, creating a decentralized and tamper-proof digital platform. However, less works focus on data backtracking and supporting for many complex query functions. Therefore, we explore blockchain queries from both a technical and an application standpoint. Current methods to deliver are based on using distributed databases, data indexing structures, and various cryptographic algorithms to implement rich, verifiable, and secure query functions. As a result, the purpose of this survey is to provide some guidance on blockchain-related query technology. We analyze the issues of query efficiency, query reliability, and privacy protection of queries in blockchain. In addition we summarize some classic scenarios of query schemes in blockchain and discuss future research challenges that still need to be addressed by blockchain query technology.
Nickel alloy coated fuel tank with digital fuel level indicator security system

In current trends we hear a lot about the fuel thefting by drivers and in fuel stations. The most of the fuel stations are cheating the customer by, not filling fuel for the given amount.In India most of the vehicles consist of Analog fuel gauge only, so it is very easy to cheat the customers in additional the commercial vehicle drivers are also cheating their owners by fuel thefting. As we are in the modern world, this project brings an solution for the above problem. The digital fuel indicator in this project uses programmed aurdino UNO with LCD display will show the correct value of the fuel present in storage like numeric values such as 1.5 l, 2.5 l. So that we can check amount of fuel in the tank at any time so that the driver will have knowledge that when will the fuel has to be refilled. Additionally the security system in this project will report the quantity of fuel in tank to owner through SMS. It was achieved by GSM modem, the measured value from aurdino UNO was transferred to GSM. The GSM was programmed with owner's phone number so that it can be done.
The effects of familiarity with mobile-assisted language learning environments on creativity

We explored the effects of familiarity with mobile-assisted language learning environments (MALLE) on creativity. We administered English as a foreign language (EFL) course to university students. They learned EFL in class and then used new knowledge to apply it to the real world using mobile technology after class. All students were assigned to 3 groups: (1) students who learned in unfamiliar MALLE, (2) students who learned in familiar MALLE, and (3) students who learned in unfamiliar MALLE but with familiarization strategies. We evaluated and compared content of essays composed by students from 3 groups to explore the effects of familiarity with MALLE on students’ creativity. Our results evealed that students in the second and third groups (i.e. those who were already familiar with MALLE and those who become familiar with MALLE thanks to familiarization strategies) outperformed students in the first group (i.e. those who were not familiar with MALLE). This result may suggest that creativity of students was affected by their familiarity with MALLE. Based on this finding we suggest that instructors consider familiarization of students with MALLE when their language learning process takes place in the real world.
Response to Open Peer Commentaries on “Privacy and Health Practices in the Digital Age”

This article refers to:Privacy and Health Practices in the Digital AgeHealth Privacy, Racialization, and the Causal Potential of Legal RegulationsPrivacy and the Genetic CommunityProtecting Privacy While Optimizing the Use of (Health)Data: The Importance of Measures and SafeguardsHealthcare Organizations Should Be Accountable Stewards of Patient DataData, Privacy, and Agency: Beyond Transparency to EmpowermentProtecting Health Privacy through Reasonable InferencesRegulatory Angels and Technology Demons? Making Sense of Evolving Realities in Health Data Privacy for the Digital AgePrivacy, Health, and Race Equity in the Digital Age
Career Choice Using Machine Learning Algorithms

Choosing the right career option can be a daunting task in this era due to lack of maturity and knowledge in the field. In addition, students suffer extensively when deciding the beneficial career choices. To avoid future complications, students need to make the right decisions on their career selection in order to maximize their benefits. If people choose an incorrect profession that is not suited to them, they will either find a job that they dislike or will lack knowledge in that subject. To assist the students in making a right career choice, this research study investigates the feasibility of predicting the right career path using survey data in a scientific and methodical manner by considering all the elements. The proposed concept encourages students to choose a career based on their inherent talents and attributes. This is because if students have excellent skills and qualities in a particular subject, they can choose that subject. If not, the existing subject will be removed and different subject will be selected. This research study proposes a machine learning-based career path prediction to assist the students in selecting the suitable vocation for their bright future. The Python programming language is used to create machine learning classifiers.
THE EFFECT OF ROUNDTABLE STRATEGY ON STUDENTS’ ACHIEVEMENT IN WRITING DESCRIPTIVE TEXT

The objective of this study is to find out whether Roundtable Strategy significantly affects students’ achievement in writing descriptive text. It was conducted by using experimental research design. The population of this research was the tenth grade students of SMK Dwi Tunggal Tanjung Morawa which consisted of two parallel classes with the total number of students was 70. By applying cluster random sampling technique, class X TKJ-2 consisting of 35 students was selected as the experimental group and class X TKJ-1 consisting of 35 students was selected as the control group. The instrument for collecting the data was writing test. The data were analyzed by using t-test formula. It was found the value of t-observed was 3.15 with the degree of freedom (df)=58 at the level of significance α=0.05. It meant that t-observed value was higher than t-table value (3.15&gt;2.00). In conclusion, the alternative hypothesis (Ha) was accepted and the null hypothesis (Ho) was succesfully rejected. Thus, Roundtable Strategy significantly affects students’ achievement in writing descriptive text.
Color-Path: Hair Arrangement Reproduction Support System by Displaying Target Motion in AR

Curling irons can create curled hair by heating it. However, because the operation of the curling iron involves six degrees of freedom (6DoF) movements, such as translation and rotation, it is difficult to understand and reproduce by simply watching instructional videos. In this study, we proposed Color-Path, a smart mirror-shaped system that allows users to easily understand how to move a curling iron. The proposed system acquires the moving paths of the curling iron using a camera on a smart mirror and an accelerometer on the curling iron. The system displays the curling iron paths of the target hairstyle on a smart mirror in Augmented Reality (AR). From the evaluation experiments, we confirmed that the system contributed to the reproduction of the moving paths of the curling iron through quantitative evaluation. However, a subjective evaluation showed that our system could not reproduce the appearance of the target hairstyle. The results indicate that the time to heat the hair should be considered.
Multi-Scale and Kernel-Predicting Convolutional Networks for Monte Carlo Denoising

Monte Carlo rendering has been widely used in many fields, such as movies, which pursue the photorealistic rendering effect. Monte Carlo rendering needs high sampling rates to get an accurate rendering effect, but the calculation cost is expensive. To keep costs down, one solution is to reduce the noise of the rendered image at reduced sampling rates. Because the traditional denoising method is based on higher and higher order regression models, it is prone to overfitting noise in the input. The Monte Carlo denoising method based on deep learning shows a certain denoising value. In this paper, we propose a kernel-predicting convolutional network with a multi-scale residual structure. Compared with previous methods, our method can extract features and perform residual learning at different scales, which can further remove low-frequency noise and improve the denoising quality.
Related Questions Retrieval Model in Stack Overflow based on Semantic Matching

As one of the most popular programming forums, Stack Overflow has helped many developers with massive high-quality questions and answers. Particularly, the related questions identified by developers can supply targeted knowledge to solve the programming problems. However, it is difficult to identify all relevant questions by developers from massive questions in Stack Overflow. Although some studies have raised methods for automatically identifying relatedness between questions, only a few of them provided related questions to new query. In addition, the existing methods can not extract the global information between query and candidate questions in a proper way. In this paper, we propose a novel method that recommends the related questions to developers' new queries based on the semantic matching. We introduce a novel integral fusion to improve the global information extraction and use the inter-attention to capture the local interactive information. Besides, we have pre-trained domain-specific word embeddings to enhance the processing of software engineering information. The experiment results show that our model achieves competitive performance in MRR, nDCG@5, and nDCG@10 metrics in the related questions retrieval on Stack Overflow,