Reviewerly/exHarmony huggingface dataset:
PROJECT-BASED LEARNING AS A LIFE SKILL

Abstract. The article considers the features of the project-based learning as a mean of forming students’ skills of independent work, critical thinking, problem solving, teamwork, etc. Project-based learning is a special method that helps students to acquire knowledge and skills through continuing work on a problem or issue that can be interesting and exciting, but at the same time challenging. The main feature of the project-based learning is the presence of a specific educational goal, the focus on research, the authenticity of the problems studied in the project, and the emphasis on the importance of the work.&#x0D; Planning of the project-based learning is a complex and time-consuming task as it includes both theoretical information and development of various handouts and additional materials of practical nature to be used in the process of work. For sure, they need clear instructions to be fully understandable for students. The teacher should think about making the students ready for each stage of the project, developing the key ideas of the project, clearing the standards of the project, developing connections with other subject areas, selecting the examples and tools for a feedback.&#x0D; Engaging students in project-based learning effectively helps them to process the types of complex problems, to develop their ability to analyze, synthesize, evaluate, think critically and ask questions about the ideas to focus on.&#x0D;
Learning the Depths of Moving People by Watching Frozen People

We present a method for predicting dense depth in scenarios where both a monocular camera and people in the scene are freely moving. Existing methods for recovering depth for dynamic, non-rigid objects from monocular video impose strong assumptions on the objects' motion and may only recover sparse depth. In this paper, we take a data-driven approach and learn human depth priors from a new source of data: thousands of Internet videos of people imitating mannequins, i.e., freezing in diverse, natural poses, while a hand-held camera tours the scene. Because people are stationary, training data can be generated using multi-view stereo reconstruction. At inference time, our method uses motion parallax cues from the static areas of the scenes to guide the depth prediction. We demonstrate our method on real-world sequences of complex human actions captured by a moving hand-held camera, show improvement over state-of-the-art monocular depth prediction methods, and show various 3D effects produced using our predicted depth.
What HR Managers Must Know About Employee Sabotage

A suspended computer operator who worked for a communications company entered a number of unauthorized commands, taking 32 tape drives off-line. The following morning, the company found that it was unable to serve its customers. Hostility between two employee factions in a laboratory led to incidents in which individuals poured a strong adhesive on partially assembled components of sensitive electronic machines. Each time this occurred, it took the laboratory a day and a half to repair the damage.
Enhancing Semantic Word Representations by Embedding Deeper Word Relationships

Word representations are created using analogy context-based statistics and lexical relations on words. Word representations are inputs for the learning models in Natural Language Understanding (NLU) tasks. However, to understand language, knowing only the context is not sufficient. Reading between the lines is a key component of NLU. Embedding deeper word relationships which are not represented in the context enhances the word representation. This paper presents a word embedding which combines an analogy, context-based statistics using Word2Vec, and deeper word relationships using Conceptnet, to create an expanded word representation. In order to fine-tune the word representation, Self-Organizing Map is used to optimize it. The proposed word representation is compared with semantic word representations using Simlex 999. Furthermore, the use of 3D visual representations has shown to be capable of representing the similarity and association between words. The proposed word representation shows a Spearman correlation score of 0.886 and provided the best results when compared to the current state-of-the-art methods, and exceed the human performance of 0.78.
Neural Decipherment via Minimum-Cost Flow: from Ugaritic to Linear B

In this paper we propose a novel neural approach for automatic decipherment of lost languages. To compensate for the lack of strong supervision signal, our model design is informed by patterns in language change documented in historical linguistics. The model utilizes an expressive sequence-to-sequence model to capture character-level correspondences between cognates. To effectively train the model in an unsupervised manner, we innovate the training procedure by formalizing it as a minimum-cost flow problem. When applied to the decipherment of Ugaritic, we achieve a 5.5% absolute improvement over state-of-the-art results. We also report the first automatic results in deciphering Linear B, a syllabic language related to ancient Greek, where our model correctly translates 67.3% of cognates.
Heterogeneous Data Analysis in Intelligent Fraud Detection Systems

Fraud is being considered as an anomaly in the data in the work. The work is devoted to the development of a method of heterogeneous data analysis in intelligent fraud detection systems. The detection of fraud as an anomaly in heterogeneous data during mobile applications installation using set theory, which allowed further data analysis in such systems, is formalized. The mathematical model of the process of heterogeneous data analysis, the algorithm of heterogeneous data analysis, the method of heterogeneous data analysis on the basis of the proposed scales and coefficients that allowed processing of various input data — data of various metrics, templates, dimensions, which in the analysis process makes it possible to form a generalized fingerprint of fraudster, is proposed. The developed method uses the databases and knowledge bases, through which a generalized fingerprint of the fraudster is formed, the presence of which allows accelerating the detection of fraudsters in new data sets and detecting even implicit fraudsters. The proposed method is designed to use it in intelligent systems for fraud detection based on anomalies in data that, unlike existing ones, will allow analyzing heterogeneous data on the basis of which fraudulent decisions are made, to reduce the dimensionality of data and to classify users. Experimental researches of the proposed method of heterogeneous data analysis as a part of detection of fraud as anomalies in heterogeneous data and a classification model developed using fully connected deep neural networks with three hidden layers using the developed software and using a representative sample have been carried out. The scheme of experimental research of detection of anomalies in heterogeneous data during the mobile applications installation, based on which method of heterogeneous data analysis was presented has been proposed. The efficiency of using the proposed method in the fraud detection system is shown, the classification accuracy of which was 99,14&nbsp;%, the accuracy of the fraud detection is 82,76 %. However, with the increase of rules in the developed knowledge base, which will grow with each launch on the new data, the accuracy of the system will increase.
Locality Preserving Joint Transfer for Domain Adaptation

Domain adaptation aims to leverage knowledge from a well-labeled source domain to a poorly-labeled target domain. A majority of existing works transfer the knowledge at either feature level or sample level. Recent researches reveal that both of the paradigms are essentially important, and optimizing one of them can reinforce the other. Inspired by this, we propose a novel approach to jointly exploit feature adaptation with distribution matching and sample adaptation with landmark selection. During the knowledge transfer, we also take the local consistency between samples into consideration, so that the manifold structures of samples can be preserved. At last, we deploy label propagation to predict the categories of new instances. Notably, our approach is suitable for both homogeneous and heterogeneous domain adaptation by learning domain-specific projections. Extensive experiments on five open benchmarks, which consist of both standard and large-scale datasets, verify that our approach can significantly outperform not only conventional approaches but also end-to-end deep models. The experiments also demonstrate that we can leverage handcrafted features to promote the accuracy on deep features by heterogeneous adaptation.
Differentiable Sampling with Flexible Reference Word Order for Neural Machine Translation

Despite some empirical success at correcting exposure bias in machine translation, scheduled sampling algorithms suffer from a major drawback: they incorrectly assume that words in the reference translations and in sampled sequences are aligned at each time step. Our new differentiable sampling algorithm addresses this issue by optimizing the probability that the reference can be aligned with the sampled output, based on a soft alignment predicted by the model itself. As a result, the output distribution at each time step is evaluated with respect to the whole predicted sequence. Experiments on IWSLT translation tasks show that our approach improves BLEU compared to maximum likelihood and scheduled sampling baselines. In addition, our approach is simpler to train with no need for sampling schedule and yields models that achieve larger improvements with smaller beam sizes.
Investigating Robustness and Interpretability of Link Prediction via Adversarial Modifications

Representing entities and relations in an embedding space is a well-studied approach for machine learning on relational data. Existing approaches, however, primarily focus on improving accuracy and overlook other aspects such as robustness and interpretability. In this paper, we propose adversarial modifications for link prediction models: identifying the fact to add into or remove from the knowledge graph that changes the prediction for a target fact after the model is retrained. Using these single modifications of the graph, we identify the most influential fact for a predicted link and evaluate the sensitivity of the model to the addition of fake facts. We introduce an efficient approach to estimate the effect of such modifications by approximating the change in the embeddings when the knowledge graph changes. To avoid the combinatorial search over all possible facts, we train a network to decode embeddings to their corresponding graph components, allowing the use of gradient-based optimization to identify the adversarial modification. We use these techniques to evaluate the robustness of link prediction models (by measuring sensitivity to additional facts), study interpretability through the facts most responsible for predictions (by identifying the most influential neighbors), and detect incorrect facts in the knowledge base.
Strong chaos of fast scrambling yields order: Emergence of decoupled quantum information capsules

The information loss problem in black hole evaporation is one of fundamental issues. Its resolution requires more profound understanding of information storage mechanism in quantum systems. In this Letter, we argue that when multiple unknown parameters are stored in large entangled qudits, strong chaos generated by fast scrambling in high temperature limit yields an ordered information storage structure with decoupled quantum information capsules (QICs). A rotational isometry emerges in the quantum Fisher information metric. The isometry is expected to be observed in future experiments on cold atoms in a pure entangled state. We provide a QIC speculation of black hole evaporation.
Deep Learning for X-ray Image to Text Generation

Motivated by the recent success of supervised and weakly supervised common object discovery, in this work we move forward one step further to tackle common object discovery in a fully unsupervised way.Mainly, object colocalization aims at simultaneously localizing the objects of the same class across a group of images.Traditional object localization/ detection usually trains the specific object detectors which require bounding box annotations of object instances, or at least image-level labels to indicate the presence/absence of objects in an image.Given a collection of images without any annotations, our proposed fully unsupervised method is to simultaneously discover images that contain common objects and also localize common objects in corresponding images.
Towards Real-Time Action Recognition on Mobile Devices Using Deep Models

Action recognition is a vital task in computer vision, and many methods are developed to push it to the limit. However, current action recognition models have huge computational costs, which cannot be deployed to real-world tasks on mobile devices. In this paper, we first illustrate the setting of real-time action recognition, which is different from current action recognition inference settings. Under the new inference setting, we investigate state-of-the-art action recognition models on the Kinetics dataset empirically. Our results show that designing efficient real-time action recognition models is different from designing efficient ImageNet models, especially in weight initialization. We show that pre-trained weights on ImageNet improve the accuracy under the real-time action recognition setting. Finally, we use the hand gesture recognition task as a case study to evaluate our compact real-time action recognition models in real-world applications on mobile phones. Results show that our action recognition models, being 6x faster and with similar accuracy as state-of-the-art, can roughly meet the real-time requirements on mobile devices. To our best knowledge, this is the first paper that deploys current deep learning action recognition models on mobile devices.
ENHANCING ENGLISH VOCABULARY THROUGH THE USE OF SNAKE AND LADDER MEDIA FOR 4 TO 6-YEAR-OLD CHILDREN

This study aims to improve the English vocabulary of 4 to 6-year-old children at TK Pertiwi 29 Semarang. This research includes classroom action research using snake and ladder media. The research subjects in this study were 20 students from kindergarten group B. Data collection techniques were observation, interview and discussion to classroom teachers and principals, as well as documentation. The research instrument used in this research was observation sheet in the form of checklist. The analysis is using observation data done gradually, then processed into tabulation of observation score and calculated its percentage according to children’s name. The result of this study showed the improvement of percentage from pre-cycle that is 52%, cycle I, which is 59% and 77% in cycle II. Snake and ladder media used in English language learning gradually increase children’s vocabulary.
Enhanced Manhattan-based Clustering using Fuzzy C-Means Algorithm for High Dimensional Datasets

The problem of mining a high dimensional data includes a high computational cost, a high dimensional dataset composed of thousands of attribute and or instances. The efficiency of an algorithm, specifically, its speed is oftentimes sacrificed when this kind of dataset is supplied to the algorithm. Fuzzy C-Means algorithm is one which suffers from this problem. This clustering algorithm requires high computational resources as it processes whether low or high dimensional data. Netflix data rating, small round blue cell tumors (SRBCTs) and Colon Cancer (52,308, and 2,000 of attributes and 1500, 83 and 62 of instances respectively) dataset were identified as a high dimensional dataset. As such, the Manhattan distance measure employing the trigonometric function was used to enhance the fuzzy c-means algorithm. Results show an increase on the efficiency of processing large amount of data using the Netflix ,Colon cancer and SRCBT an (39,296, 38,952 and 85,774 milliseconds to complete the different clusters, respectively) average of 54,674 milliseconds while Manhattan distance measure took an average of (36,858, 36,501 and 82,86 milliseconds, respectively) 52,703 milliseconds for the entire dataset to cluster. On the other hand, the enhanced Manhattan distance measure took (33,216, 32,368 and 81,125 milliseconds, respectively) 48,903 seconds on clustering the datasets. Given the said result, the enhanced Manhattan distance measure is 11% more efficient compared to Euclidean distance measure and 7% more efficient than the Manhattan distance measure respectively.
Measuring User Assessments and Expectations: The Use of WebQual 4.0 Method and Importance-Performance Analysis (IPA) to Evaluate the Quality of School Websites

As time goes by and technology advances, any information can be easily obtained from many media, both from various print media or digital electronic media. One of the fastest growing digital media and the main choice for individuals or agencies to get fast and accurate information is the website. One of the benefits of the website is that it can be used as an effective promotional media because it can be accessed by anyone and at any time. This study discusses the evaluation of school websites with a case study at the SMK Muhammadiyah 1 Banjarmasin using the WebQual 4.0 and Importance-Performance Analysis (IPA) methods. This study aims to measure user ratings of performance and expectations of users/visitors to the website of the SMK Muhammadiyah 1 Banjarmasin. The data in this study were obtained from the questionnaire using a sample of web visitors themselves, both through online based questionnaires and offline questionnaires with paper. This study uses data processing software, namely Structural Equation Modeling (SEM) 2.0. It can be concluded that in this study the results are measured by the level of performance (actual) and the level of importance (expectations) which shows that there is an overall gap for all dimensions -0.38 ie the website needs improvement and tends to still not be as expected. The biggest gap is in the Usability dimension with the largest value being -1.07 on USE5 variables, which means that the appearance of this website tends to be unattractive, not in line with expectations, and needs improvement. Then the acquisition of the value of R Square for the variable user satisfaction is 0.61, which means that the value indicates that the variable user satisfaction can be explained by usability, information quality, and service interaction with a value of 67.7%, while the remaining 32.3% is influenced by variables others not found in the research model.
USING THE VIRTUAL CHEMICAL LABORATORY VLAB IN TEACHING CHEMISTRY OF 9th GRADE STUDENTS

The article discusses the importance of the skills of school students to solve experimental problems in chemistry and the conditions for the use of virtual chemical laboratories in the process of the formation of these skills. The essence of the concept of “virtual chemical laboratories” is considered and their main types, advantages and disadvantages that define the methodically reasonable limits of the use of these software products in the process of teaching chemistry, in particular, to support the educational chemical experiment are described. The capabilities of the virtual chemical laboratory VLab to support the process of solving experimental problems in chemistry in grade 9 have been determined. The main advantages and disadvantages of the virtual chemical laboratory VLab on the modeling of chemical processes necessary for the creation of virtual experimental problems in chemistry are analyzed. The features of the virtual chemical laboratory VLab, the essence of its work and the creation of virtual laboratory work in it are described. The results of the study are the development of a set of experimental tasks in chemistry for students in grade 9 on the topic “Solutions” in the cloud-oriented virtual chemical laboratory VLab.
Entanglement detection with scrambled data

In the usual entanglement detection scenario the possible measurements and the corresponding data are assumed to be fully characterized. We consider the situation where the measurements are known, but the data is scrambled, meaning the assignment of the probabilities to the measurement outcomes is unknown. We investigate in detail the two-qubit scenario with local measurements in two mutually unbiased bases. First, we discuss the use of entropies to detect entanglement from scrambled data, showing that Tsallis and R\'enyi entropies can detect entanglement in our scenario, while the Shannon entropy cannot. Then, we introduce and discuss scrambling-invariant families of entanglement witnesses. Finally, we show that the set of nondetectable states in our scenario is nonconvex and therefore in general hard to characterize.
Penciptaan Film Animasi “Chase!” Dengan Teknik “Digital Drawing”

A short animation that combines several personal experiences and surroundings into a simple yet entertaining story. Although the story is presented simply, visualization is made unique and flexible with digital two-dimensional animation techniques. This animated film is titled “CHASE!” Which means chasing, taken from the core of the film which tells the story of a young man who chases a mysterious woman who turns out to be a transvestite.The animated film “CHASE!” Lenght 3 minutes 45 seconds, make the story made shorter and directly to the point at the core of what will be delivered. The number of shots reached 45 shots with a total of 5400 frames of HDTV 1920 x 1080 px 24 fps (frames per second). In the process of making this film also applied 12 principles of animation. Making an animated film “CHASE!” uses a digital process in it. For animating, 2D drawing frame by frame technique are used digitally by using computers, background images are also digitally created without leaving the artistic value of traditional drawing techniques using hands.Keywords: Film, 2D Animation, Personal Experience, Digital