Reviewerly/exHarmony huggingface dataset:
TSInsight: A Local-Global Attribution Framework for Interpretability in Time Series Data

With the rise in the employment of deep learning methods in safety-critical scenarios, interpretability is more essential than ever before. Although many different directions regarding interpretability have been explored for visual modalities, time series data has been neglected, with only a handful of methods tested due to their poor intelligibility. We approach the problem of interpretability in a novel way by proposing TSInsight, where we attach an auto-encoder to the classifier with a sparsity-inducing norm on its output and fine-tune it based on the gradients from the classifier and a reconstruction penalty. TSInsight learns to preserve features that are important for prediction by the classifier and suppresses those that are irrelevant, i.e., serves as a feature attribution method to boost the interpretability. In contrast to most other attribution frameworks, TSInsight is capable of generating both instance-based and model-based explanations. We evaluated TSInsight along with nine other commonly used attribution methods on eight different time series datasets to validate its efficacy. The evaluation results show that TSInsight naturally achieves output space contraction; therefore, it is an effective tool for the interpretability of deep time series models.
Pengaruh Penerapan Model Flipped Learning dan Motivasi Belajar Terhadap Hasil Belajar Siswa

This study aims to reveal the effect of applying the learning model and learning motivation on student learning outcomes. This type of research used a quantitative approach, especially quasi-experiments. Population in this study all students of class XI 2 Painan. The sampling technique is purposive sampling. The sample in this study amounted to 63 students. Data analysis techniques used questionnaires and tests. Data analysis techniques used were descriptive analysis, normality test, homogeneity test, and hypothesis testing. The results of this study indicate that: (1) Differences in learning outcomes obtained between students who have high learning motivation in class who apply the flipped learning model (2) Differences in learning outcomes obtained between students who have low learning motivation in the class applying conventional models (3) Differences learning outcomes obtained between students who have high learning motivation in the class implementing the flipped learning model and students who have high learning motivation in the class applying conventional models (4) Differences learning outcomes obtained between students who have low learning motivation in the class implementing the flipped learning model and students who have low learning motivation in the class applying conventional models (5) There is no interaction between learning models and learning motivation in influencing learning outcomes.Keywords : flipped learning model, learning motivation, learning outcomes
A Study on Usability and Motion Sickness of Locomotion Techniques for Virtual Reality

&#x0D; &#x0D; &#x0D; Virtual Reality (VR) is widely used today in both research and entertainment. The continuous growth of this technology makes VR consumer hardware now available for masses. The new trend in the next generation of VR devices is a VR headset and controllers with inside-out technology. These VR devices will become an important basis for the future of VR applications. Virtual travel or locomotion inside VR experiences is the important part in the VR application development, which affected to the users preference. The goal of this research is to study the difference of locomotion in VR with new trend devices consisting of VR headset and controllers without using other accessories. Three locomotion techniques: controller-based, motion-based and teleportation-based were used to analyze the differences. The VR scene with virtual environments was created to use in the experiment where the users have to move with different locomotion technique. The Usability Questionnaire (UQ) is used to evaluate the usability value of each locomotion technique, while Simulator Sickness Questionnaire (SSQ) is used to assess the motion sickness value. The results showed that the usability (p-value=0.02007) and motion sickness (p-value=0.00014) of all locomotion techniques are different and the usability affected to the user preference. The conclusions of VR locomotion studies were discussed with the limitations of the study and the future work for this research.&#x0D; &#x0D; &#x0D;
Time-Controlled Hierarchical Multikeyword Search Over Encrypted Data in Cloud-Assisted IoT

Internet of Things (IoT) devices and systems are becoming increasingly commonplace, and as such systems scale up, so do the computational and storage requirements. Hence, one recent trend is to outsource data from IoT devices to remote systems. To facilitate both ciphertext retrieval and data confidentiality in the outsourced data, a number of searchable encryption (SE) approaches have been proposed in the literature. However, due to limited keyword space, a number of SE schemes are vulnerable to keyword guessing attacks (KGAs). In addition, existing SE approaches generally do not consider the hierarchical structure in which users at different levels require varying access privileges. Furthermore, existing SE schemes seldom provide time-controlled access control. Therefore, in this article, we propose a time-controlled hierarchical multikeyword search by using a double-server architecture to mitigate KGA. In our approach, we also build a public key tree to support different access permissions for hierarchical users. Formal security analysis shows that our scheme is secure, and extensive experiments demonstrate that our scheme is practical.
A State-of-the-Art Survey on Face Recognition Methods

Face Recognition is an efficient technique and one of the most liked biometric software application for the identification and verification of specific individual in a digital image by analysing and comparing patterns. This paper presents a survey on well-known techniques of face recognition. The primary goal of this review is to observe the performance of different face recognition algorithms such as SVM (Support Vector Machine), CNN (Convolutional Neural Network), Eigenface based algorithm, Gabor Wavelet, PCA (Principle Component Analysis) and HMM (Hidden Markov Model). It presents comparative analysis about the efficiency of each algorithm. This paper also figure out about various face recognition applications used in real world and face recognition challenges like Illumination Variation, Pose Variation, Occlusion, Expressions Variation, Low Resolution and Ageing in brief. Another interesting component covered in this paper is review of datasets available for face recognition. So, must needed survey of many recently introduced face recognition aspects and algorithms are presented.
Designing a Solar Photovoltaic System for Generating Renewable Energy of a Hospital: Performance Analysis and Adjustment Based on RSM and ANFIS Approaches

One of the most favorable renewable energy sources, solar photovoltaic (PV) can meet the electricity demand considerably. Sunlight is converted into electricity by the solar PV systems using cells containing semiconductor materials. A PV system is designed to meet the energy needs of King Abdulaziz University Hospital. A new method has been introduced to find optimal working capacity, and determine the self-consumption and sufficiency rates of the PV system. Response surface methodology (RSM) is used for determining the optimal working conditions of PV panels. Similarly, an adaptive neural network based fuzzy inference system (ANFIS) was employed to analyze the performance of solar PV panels. The outcomes of methods were compared to the actual outcomes available for testing the performance of models. Hence, for a 40 MW target PV system capacity, the RSM determined that approximately 33.96 MW electricity can be produced, when the radiation rate is 896.3 W/m2, the module surface temperature is 41.4 °C, the outdoor temperature is 36.2 °C, the wind direction and speed are 305.6 and 6.7 m/s, respectively. The ANFIS model (with nine rules) gave the highest performance with lowest residual for the same design parameters. Hence, it was determined that the hourly electrical energy requirement of the hospital can be met by the PV system during the year.
Distantly Supervised Relation Extraction using Multi-Layer Revision Network and Confidence-based Multi-Instance Learning

Distantly supervised relation extraction is widely used in the construction of knowledge bases due to its high efficiency.However, the automatically obtained instances are of low quality with numerous irrelevant words.In addition, the strong assumption of distant supervision leads to the existence of noisy sentences in the sentence bags.In this paper, we propose a novel Multi-Layer Revision Network (MLRN) which alleviates the effects of wordlevel noise by emphasizing inner-sentence correlations before extracting relevant information within sentences.Then, we devise a balanced and noise-resistant Confidence-based Multi-Instance Learning (CMIL) method to filter out noisy sentences as well as assign proper weights to relevant ones.Extensive experiments on two New York Times (NYT) datasets demonstrate that our approach achieves significant improvements over the baselines.
Neural Natural Logic Inference for Interpretable Question Answering

Many open-domain question answering problems can be cast as a textual entailment task, where a question and candidate answers are concatenated to form hypotheses.A QA system then determines if the supporting knowledge bases, regarded as potential premises, entail the hypotheses.In this paper, we investigate a neural-symbolic QA approach that integrates natural logic reasoning within deep learning architectures, towards developing effective and yet explainable question answering models.The proposed model gradually bridges a hypothesis and candidate premises following natural logic inference steps to build proof paths.Entailment scores between the acquired intermediate hypotheses and candidate premises are measured to determine if a premise entails the hypothesis.As the natural logic reasoning process forms a tree-like, hierarchical structure, we embed hypotheses and premises in a Hyperbolic space rather than Euclidean space to acquire more precise representations.Empirically, our method outperforms prior work on answering multiple-choice science questions, achieving the best results on two publicly available datasets.The natural logic inference process inherently provides evidence to help explain the prediction process.
Android Based Animation Video Using Millealab Virtual Reality Application for Elementary School

Innovation in education is necessary for teachers and students to follow the learning process because of the limited laboratory facilities in elementary schools; science learning is done. This study aims to analyze student acceptance and understanding concept in using android-based animated videos on Millealab virtual reality in grade V elementary school. The research method process is carried out in several stages, starting from expert testing conducted on three media experts, three subject matter experts, and three teachers from 3 different schools. Test the use of virtual reality video with a sample size of 65 students. Collecting data using a Likert scale questionnaire 1 to 5, and treatment in descriptive analysis. Testing the acceptance of virtual reality videos to students showed that students received virtual reality video media during the learning process. The application of virtual reality (VR) technology in science learning in elementary schools can improve conceptual understanding so that student acceptance of VR is excellent and teachers also feel helped by using VR media to support the learning process. VR technology is better than AR because it is more comprehensive and easier to use by teachers and students.
Maximal Clique Based Non-Autoregressive Open Information Extraction

Open Information Extraction (OpenIE) aims to discover textual facts from a given sentence.In essence, the facts contained in plain text are unordered.However, the popular Ope-nIE systems usually output facts sequentially in the way of predicting the next fact conditioned on the previous decoded ones, which enforce an unnecessary order on the facts and involve the error accumulation between autoregressive steps.To break this bottleneck, we propose MacroIE, a novel non-autoregressive framework for OpenIE.MacroIE firstly constructs a fact graph based on the table filling scheme, in which each node denotes a fact element, and an edge links two nodes that belong to the same fact.Then OpenIE can be reformulated as a non-parametric process of finding maximal cliques from the graph.It directly outputs the final set of facts in one go, thus getting rid of the burden of predicting fact order, as well as the error propagation between facts.Experiments conducted on two benchmark datasets show that our proposed model significantly outperforms current state-of-theart methods, beats the previous systems by as much as 5.7 absolute gain in F1 score.
Frustratingly Simple but Surprisingly Strong: Using Language-Independent Features for Zero-shot Cross-lingual Semantic Parsing

The availability of corpora has led to significant advances in training semantic parsers in English.Unfortunately, for languages other than English, annotated data is limited and so is the performance of the developed parsers.Recently, pretrained multilingual models have been proven useful for zero-shot cross-lingual transfer in many NLP tasks.What else does it require to apply a parser trained in English to other languages for zero-shot cross-lingual semantic parsing?Will simple language-independent features help?To this end, we experiment with six Discourse Representation Structure (DRS) semantic parsers in English, and generalize them to Italian, German and Dutch, where there are only a small number of manually annotated parses available.Extensive experiments show that despite its simplicity, adding Universal Dependency (UD) relations and Universal POS tags (UPOS) as model-agnostic features achieves surprisingly strong improvement on all parsers.We have publicly released our code at https://github.com/GT-SALT/Multilingual-DRS
Improving Unsupervised Commonsense Reasoning Using Knowledge-Enabled Natural Language Inference

Recent methods based on pre-trained language models have shown strong supervised performance on commonsense reasoning.However, they rely on expensive data annotation and time-consuming training.Thus, we focus on unsupervised commonsense reasoning.We show the effectiveness of using a common framework, Natural Language Inference (NLI), to solve diverse commonsense reasoning tasks.By leveraging transfer learning from large NLI datasets, and injecting crucial knowledge from commonsense sources such as ATOMIC 2020 and ConceptNet, our method achieved state-of-the-art unsupervised performance on two commonsense reasoning tasks: WinoWhy and CommonsenseQA.Further analysis demonstrated the benefits of multiple categories of knowledge, but problems about quantities and antonyms are still challenging.
GAML-BERT: Improving BERT Early Exiting by Gradient Aligned Mutual Learning

In this work, we propose a novel framework, Gradient Aligned Mutual Learning BERT (GAML-BERT), for improving the early exiting of BERT.GAML-BERT's contributions are two-fold.We conduct a set of pilot experiments, which shows that mutual knowledge distillation between a shallow exit and a deep exit leads to better performances for both.From this observation, we use mutual learning to improve BERT's early exiting performances, that is, we ask each exit of a multi-exit BERT to distill knowledge from each other.Second, we propose GA, a novel training method that aligns the gradients from knowledge distillation to cross-entropy losses.Extensive experiments are conducted on the GLUE benchmark, which shows that our GAML-BERT can significantly outperform the state-of-the-art (SOTA) BERT early exiting methods.
OpenFraming: Open-sourced Tool for Computational Framing Analysis of Multilingual Data

When journalists cover a news story, they can cover the story from multiple angles or perspectives.These perspectives are called "frames", and usage of one frame or another may influence public perception and opinion of the issue at hand.We develop a web-based system for analyzing frames in multilingual text documents.We propose and guide users through a five-step end-toend computational framing analysis framework grounded in media framing theory in communication research.Users can use the framework to analyze multilingual text data, starting from the exploration of frames in user's corpora and through review of previous framing literature (step 1-3) to frame classification (step 4) and prediction (step 5).The framework combines unsupervised and supervised machine learning and leverages a state-of-the-art (SoTA) multilingual language model, which can significantly enhance frame prediction performance while requiring a considerably small sample of manual annotations.Through the interactive website, anyone can perform the proposed computational framing analysis, making advanced computational analysis available to researchers without a programming background and bridging the digital divide within the communication research discipline in particular and the academic community in general.The system is available online at http://www.openframing.
Sign Language Translator Using YOLO Algorithm

Sign language is a terminology that encloses a motion of hand gestures which is an environment for the auditory impairment, individual (deaf or dumb) to deal with others. Nevertheless, so as to impart with the hearing impaired individual, the communicator obtains to acquire acquaintance in sign language. As follows is frequent to make undoubted that the message provided by the hearing impaired person acknowledged. This implemented system propounds an implementation of real time American Sign Language perception in Convolutional Neural Network (CNN) with the support of You Only Look Once version (YOLO) algorithm. The algorithm initially executes data acquisition, subsequently the pre-processing of gestures and are conducted to trace hand movement utilize a combinational algorithm.
An Efficient RRT Algorithm for Motion Planning of Live-Line Maintenance Robots

The application of robots to replace manual work in live-line working scenes can effectively guarantee the safety of personnel. To improve the operation efficiency and reduce the difficulties in operating a live-line working robot, this paper proposes a multi-DOF robot motion planning method based on RRT and extended algorithms. The planning results of traditional RRT and extended algorithms are random, and obtaining sub-optimal results requires a lot of calculations. In this study, a sparse offline tree filling the planning space are generated offline through the growing–withering method. In the process of expanding the tree, by removing small branches, the tree can fully wiring in the planning space with a small number of nodes. Optimize wiring through a large number of offline calculations, which can improve the progressive optimality of the algorithm. Through dynamic sampling and pruning, the growth of trees in undesired areas is reduced and undesired planning results are avoided. Based on the offline tree, this article introduces the method of online motion planning. Experiments show that this method can quickly complete the robot motion planning and obtain efficient and low-uncertainty paths.
Enhancing the Context Representation in Similarity-based Word Sense Disambiguation

In previous similarity-based WSD systems, studies have allocated much effort on learning comprehensive sense embeddings using contextual representations and knowledge sources.However, the context embedding of an ambiguous word is learned using only the sentence where the word appears, neglecting its global context.In this paper, we investigate the contribution of both word-level and senselevel global context of an ambiguous word for disambiguation.Experiments have shown that the Context-Oriented Embedding (COE) can enhance a similarity-based system's performance on WSD by relatively large margins, achieving state-of-the-art on all-words WSD benchmarks in knowledge-based category.
Adaptive Bridge between Training and Inference for Dialogue Generation

Although exposure bias has been widely studied in some NLP tasks, it faces its unique challenges in dialogue response generation, the representative one-to-various generation scenario.In real human dialogue, there are many appropriate responses for the same context, not only with different expressions, but also with different topics.Therefore, due to the much bigger gap between various ground-truth responses and the generated synthetic response, exposure bias is more challenging in dialogue generation task.What's more, as MLE encourages the model to only learn the common words among different ground-truth responses, but ignores the interesting and specific parts, exposure bias may further lead to the common response generation problem, such as "I don't know" and "HaHa?"In this paper, we propose a novel adaptive switching mechanism, which learns to automatically transit between ground-truth learning and generated learning regarding the word-level matching score, such as the cosine similarity.Experimental results on both Chinese STC dataset and English Reddit dataset, show that our adaptive method achieves a significant improvement in terms of metric-based evaluation and human evaluation, as compared with the state-of-the-art exposure bias approaches.Further analysis on NMT task also shows that our model can achieve a significant improvement.
THE EFFECT OF AUGMENTED REALITY APPLICATIONS ON THE ACADEMIC SUCCESS OF 11TH GRADE STUDENTS IN THE CIRCULATORY SYSTEM

&lt;p&gt;The purpose of this study is to examine the effect of augmented reality applications on 11th grade high school students' success in circulatory system and their attitudes towards augmented reality application. The semi-experimental design was used as a research pattern. In the quantitative part of the study, semi-experimental design with pretest-posttest control group was used. In the qualitative part, a case study was conducted. The sample of the study consists of 11th grade students studying in Siverek district of Şanlıurfa. While the students in the experimental group learned the subject of the circulatory system with “teaching with Augmented Reality applications” the students in the control group learned with “teaching with conventional techniques”. Circulatory system achievement test, attitude scale towards augmented reality practices and semi-structured interviews forms were used as data collection tools in the study. The data were analyzed with SPSS 18 package programme. As a result of the study, it has been observed that augmented reality applications have a positive effect on students’ academic success. When the students’ opinions about teaching with augmented reality applications are examined, it is clearly seen that they have positive thoughts on the themes of using augmented reality applications to be able to see them in an easier, to increase interest and desire, to attract attention and to use technology. As a result, it was determined that augmented reality had an impact on students' achievements and attitudes.&lt;/p&gt;&lt;p&gt; &lt;/p&gt;&lt;p&gt;&lt;strong&gt; Article visualizations:&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;img src="/-counters-/edu_01/0963/a.php" alt="Hit counter" /&gt;&lt;/p&gt;