Reviewerly/exHarmony huggingface dataset:
Authentic Materials For Teaching Writing: A Critical Look

Teaching writing is not easy. Writing teachers often face problems with what kind of materials suitable for the students. This paper is intended to describe 1) the theoretical relationship between Content-Based Instruction and authentic materials that can be used by language teachers to teach writing in the classroom, 2) the previous studies of authentic materials used for teaching writing, 3) the type of authentic materials which can be used for teaching writing, and 4) the advantages and disadvantages of using authentic materials for teaching writing. The method used was qualitative research focused on content analysis. The instrument used was mainly human instrument and documentation. The result of the extensive review of literature shows that 1) one of the main feature of content-based instruction is the use of authentic materials for teaching; 2) most studies in the area of authentic materials for teaching writing shows the effectiveness of authentic materials for teaching procedure texts, descriptive texts and essay writing; 3) the type of authentic materials used for teaching writing is categorized based on how it is transmitted, namely audio, visual, and printed material materials; 4) aside from having its advantages, authentic materials also give challenges for the teacher to use it for classroom teaching, among others are practicality, comprehensibility and appropriateness.
A Study on Factors Influencing the Introduction of Smart Factory : Focusing on Small and Medium-sized Enterprises in Korea

In this study, we analyzed the factors affecting the introduction of Smart Factory by domestic SMEs through AHP analysis and tried to provide...
ModelLens: An Interactive System to Support the Model Improvement Practices of Data Science Teams

This demo presents ModelLens, an interactive system designed to support data science teams in their model improvement practices. A central component of improving models is analyzing model errors, often incorporating feedback on the model's precision and recall performance (e.g., differences between the model's predicted label and its actual label). Today, error analysis is typically an ad hoc, team-specific process, largely accomplished through spreadsheets. ModelLens offers a unified view of feedback from multiple sources, the ability for data scientists to explore the context of an individual feedback instance, as well as a customizable ontology to enable collaborative and systematic annotations of model errors.
Sense disambiguation for Punjabi language using supervised machine learning techniques

Automatic identification of a meaning of a word in a context is termed as Word Sense Disambiguation (WSD). It is a vital and hard artificial intelligence problem used in several natural language processing applications like machine translation, question answering, information retrieval, etc. In this paper, an explicit WSD system for Punjabi language using supervised techniques has been analysed. The sense tagged corpus of 150 ambiguous Punjabi noun words has been manually prepared. The six supervised machine learning techniques Decision List, Decision Tree, Naive Bayes, K-Nearest Neighbour (K-NN), Random Forest and Support Vector Machines (SVM) have been investigated in this proposed work. Every classifier has used same feature space encompassing lexical (unigram, bigram, collocations, and co-occurrence) and syntactic (part of speech) count based features. The semantic features of Punjabi language have been devised from the unlabelled Punjabi Wikipedia text using word2vec continuous bag of word and skip gram shallow neural network models. Two deep learning neural network classifiers multilayer perceptron and long short term memory have also been applied for WSD of Punjabi words. The word embedding features have experimented on six classifiers for the Punjabi WSD task. It has been observed that the performance of the supervised classifiers applied for the WSD task of Punjabi language has been enhanced with the application of word embedding features. In this work, an accuracy of 84% has been achieved by LSTM classifier using word embedding feature.
Towards an Online approach for Knowledge Communication Planning: Extended Abstract

Explicit coordination among robot usually requires a method to exchange information regarding their intentions and their knowledge of the world. Most of the research found in the multi-robot coordination literature uses ad-hoc approaches for communication. This work is an initial attempt to tackle the communication problem in a generic way. It tries to answer the question of when to communicate, what to communicate and with whom to communicate. It proposes the use of dynamic action sets combined with a Determinized Sparse Partially Observable Tree structure that can address the communication decision problem in an online fashion. Compared with a state-of-the-art approach used for sharing knowledge among robotic teams it shows a constantly better performance.
Mining Emerging High Utility Itemsets over Streaming Database

HUIM (High Utility Itemset Mining) is a classical data mining problem that has gained much attention in the research community with a wide range of applications. The goal of HUIM is to identify all itemsets whose utility satisfies a user-defined threshold. In this paper, we address a new and interesting direction of high utility itemsets mining, which is mining temporal emerging high utility itemsets from data streams. The temporal emerging high utility itemsets are those that are not high utility in the current time window of the data stream but have high potential to become a high utility in the subsequent time windows. Discovery of temporal emerging high utility itemsets is an important process for mining interesting itemsets that yield high profits from streaming databases, which has many applications such as proactive decision making by domain experts, building powerful classifiers, market basket analysis, catalogue design, among others. We propose a novel method, named EFTemHUI (Efficient Framework for Temporal Emerging HUI mining), to identify Emerging High Utility Itemsets better. To improve the efficiency of the mining process, we devise a new mechanism to evaluate the high utility itemsets that will emerge, which has the ability to capture and store the information about potential high utility itemsets. Through extensive experimentation using three datasets, we proved that the proposed method yields excellent accuracy and low errors in the prediction of emerging patterns for the next window.
TwinCam Go: Proposal of Vehicle-Ride Sensation Sharing with Stereoscopic 3D Visual Perception and Vibro-Vestibular Feedback for Immersive Remote Collaboration

Personal vehicles such as the Segway have been actively used for security patrols or supervision of construction sites, because of their mobility capabilities. In the current study, we proposed a vehicle-ride sensation sharing system enabling a rider to remotely collaborate with a driver, and to receive both 3D visual perception and vibro-vestibular sensation. We developed a prototype personal vehicle system with two 360° cameras attached to the Segway with a stabilizer to capture stereoscopic 3D images and send them to each eye of a head-mounted display worn by a remotely collaborating rider. We also developed a prototype of vibro-vestibular display by modifying a conventional wheelchair with a simple lightweight mechanism for actuation and vibration by two DC motors. In our presentation algorithm, each wheel of the wheelchair is accelerated or decelerated proportionally to the acceleration of each wheel of the Segway. When the velocity of each wheel was almost constant and the acceleration was nearly zero, the wheelchair slowly moved to the initial position, with movement that the rider could not perceive, to keep the wheelchair accelerating or decelerating in a limited space.
HMLFC: Hierarchical Motion‐Compensated Light Field Compression for Interactive Rendering

Abstract We present a new motion‐compensated hierarchical compression scheme (HMLFC) for encoding light field images (LFI) that is suitable for interactive rendering. Our method combines two different approaches, motion compensation schemes and hierarchical compression methods, to exploit redundancies in LFI. The motion compensation schemes capture the redundancies in local regions of the LFI efficiently (local coherence) and the hierarchical schemes capture the redundancies present across the entire LFI (global coherence). Our hybrid approach combines the two schemes effectively capturing both local as well as global coherence to improve the overall compression rate. We compute a tree from LFI using a hierarchical scheme and use phase shifted motion compensation techniques at each level of the hierarchy. Our representation provides random access to the pixel values of the light field, which makes it suitable for interactive rendering applications using a small run‐time memory footprint. Our approach is GPU friendly and allows parallel decoding of LF pixel values. We highlight the performance on the two‐plane parameterized light fields and obtain a compression ratio of 30–800× with a PSNR of 40–45 dB. Overall, we observe a ∼2–5× improvement in compression rates using HMLFC over prior light field compression schemes that provide random access capability. In practice, our algorithm can render new views of resolution 512 × 512 on an NVIDIA GTX‐980 at ∼200 fps.
Information-Project Technology for the Formation of General Competencies of Students by Means of Electronic Information and Educational Environment

The relevance of presented research is determined by the need for development and implementation of training technologies that provide high-quality professional training of students in the context of increasing the amount of time devoted to independent work. The goal of this work is to develop and test pedagogical technology aimed at the formation of general competences of students with the effective use of electronic information – environment (EIE). Methods of research used by authors: analysis of scientific literature, modeling, pedagogical experiment, methods of mathematical statistics, etc. The article considers experience and new possibilities of organization of independent work of students in conditions of informatization. Also, analysis of scientific and pedagogical researches devoted to the study of methods of organization of independent work is carried out. Theoretical and methodological substantiation of new author’s information and project technology of independent work of students with the use of electronic educational-methodical complexes implemented, including methodological approaches and principles. Authors proposed the original system, which includes principles: cumulative, modular and integrative. All stages of interaction between teachers and students in the implementation of technology are described, namely: motivational, cognitive, integrative activity, evaluative and reflexive. Estimates of efficiency of implementation of the author’s teaching technology are offered. The results of approbation and project technology are presented. The efficiency of the developed technology was assessed, and possible directions for the further development of EIE due to the expansion of methodological opportunities for the systemic use of interdisciplinary methodical complexes are presented. The result of the pedagogical experiment was a statistically significant increase in the components of general competences of students: motivational, cognitive, activity, evaluative and reflexive. Thus, the information and project technology proposed by authors of formation of general competences of students can be proposed for use in educational organizations.
Streamline Distribution Method based on Vector-magnitude-aware Entropy

Streamline is widely used in flow field visualization. Information-theoretic streamline distribution method performs well on demonstrating feature regions but only considers the direction component of vector. Our algorithm places streamlines based on information entropy calculated by both vector direction and vector magnitude, to cover more information of the flow field. By considering vector magnitude, the initial streamlines derived from entropy and supplementary streamlines derived from conditional entropy can convey the steepness of speed variation. An advanced streamline pruning method is also applied to improve the efficiency of our algorithm. Results prove that the streamlines produced by our algorithm can reflect the vector magnitude variation of a flow field without losing sight of the salient features.
A Failure Diagnosis Method of Oil-Filled Transformer Using Expert System Based on Duval Triangle Method and IEC 60599

DGA(Dissolved Gas Analysis) is a reliable method for detection of fault in oil-filled transformer. Among all the available techniques, IEC 60599 and DTM(Duval Triangle Method) is widely used. Most DGAs, including IEC 60599 and DTM, diagnose failure in the transformer through gas composition ratio. If the gas composition ratio is not calculated properly, the failure in the transformer can not be accurately diagnosed. Also, conventional techniques are difficult to consider the operating characteristics of the transformer. This paper proposes failure diagnosis technique of a transformer using expert system. The expert system was created from fuzzy logic based on IEC 60599 and DTM. The effectiveness of the expert system was verified by using MATLAB.
Data-Driven Syllabification for Middle Dutch

The task of automatically separating Middle Dutch words into syllables is a challenging one. A first method was presented by Bouma and Hermans (2012), who combined a rule-based finite-state component with data-driven error correction. Achieving an average word accuracy of 96.5%, their system surely is a satisfactory one, although it leaves room for improvement. Generally speaking, rule-based methods are less attractive for dealing with a medieval language like Middle Dutch, where not only each dialect has its own spelling preferences, but where there is also much idiosyncratic variation among scribes. This paper presents a different method for the task of automatically syllabifying Middle Dutch words, which does not rely on a set of pre-defined linguistic information. Using a Recurrent Neural Network (RNN) with Long-Short-Term Memory cells (LSTM), we obtain a system which outperforms the rule-based method both in robustness and in effort.
On Non-Completeness in Threshold Implementations

In this note we study non-completeness, the key property of Threshold Implementations (TIs). TIs have proved to be a popular method for mitigating side-channel leakage of sensitive information in hardware implementations of cryptographic algorithms.
Hey Siri. Ok Google. Alexa: A topic modeling of user reviews for smart speakers

User reviews provide a significant source of information for companies to understand their market and audience.In order to discover broad trends in this source, researchers have typically used topic models such as Latent Dirichlet Allocation (LDA).However, while there are metrics to choose the "best" number of topics, it is not clear whether the resulting topics can also provide in-depth, actionable product analysis.Our paper examines this issue by analyzing user reviews from the Best Buy US website for smart speakers.Using coherence scores to choose topics, we test whether the results help us to understand user interests and concerns.We find that while coherence scores are a good starting point to identify a number of topics, it still requires manual adaptation based on domain knowledge to provide market insights.We show that the resulting dimensions capture brand performance and differences, and differentiate the market into two distinct groups with different properties.
Chaos and IWT Blended Image Encryption for Grey Scale Image Security

Cybercrimes have increased manifold with the development in recent technologies. Thus security has become a huge concern in this digitalized world. Hence, there is an increasing need for efficient techniques to achieve secure transmission of images over the internet. Among the three methods of image security, encryption is chosen to ensure the confidentiality of secrets. In this paper, the secrecy of a grey-scale image is ensured through a new encryption scheme. The proposed encryption method comprises two phases, namely confusion and diffusion. Prior to encryption, the image pixels are transformed to transform domain through Integer Wavelet Transform (IWT) technique. The transformed pixels are then shuffled between rows and columns based on the keys generated from chaotic maps. The shuffled image is then diffused with the pseudo-random matrices from three chaotic maps. The strength of the developed algorithm is tested with various security analyses. The experimental results demonstrates that the entropy is 7.9974 (≈8) and correlation coefficient is nearer to zero with key space 10140which proves the randomness of cipher image. Further, other obtained results substantiate that the developed chaos-based encryption has realized desired security for grey scale images.
Developing the Old Tibetan Treebank

This paper presents a full procedure for the development of a segmented, POS-tagged and chunk-parsed corpus of Old Tibetan.As an extremely lowresource language, Old Tibetan poses non-trivial problems in every step towards the development of a searchable treebank.We demonstrate, however, that a carefully developed, semisupervised method of optimising and extending existing tools for Classical Tibetan, as well as creating specific ones for Old Tibetan, can address these issues.We thus also present the very first Tibetan Treebank in a variety of formats to facilitate research in the fields of NLP, historical linguistics and Tibetan Studies.
Knowledge Management in Development of a Project-Oriented Education Institute

The paper is about urgency and necessity of using the technology of knowledge management in the development of a project-oriented educational institution. The essence of managing the development of a project-oriented educational institution is revealed. Author proposes characteristic of technology of knowledge management that provides sustainable development of educational establishment.
Theoretical Domain Framework to Identify Cybersecurity Behaviour Constructs

Humans are still the weakest link in the cyber security system. In order to correct cybersecurity behaviour, it is important to understand both the behaviour as well as the cause of the behaviour. In an effort towards the latter, researchers have conducted empirical studies that investigate the constructs of cybersecurity behaviour. This approach has led to a plethora of constructs being proposed as the determinates of cybersecurity behavior. The large number of constructs make it difficult to decide which constructs to focus on when designing cybersecurity behavior interventions. This problem is not unique to cybersecurity behaviour. A similar problem exists in the medical domain. One proposed solution, that achieved good results in the medical domain, is the use of the Theoretical Domain Framework. The contribution of the current paper is a mapping of the constructs found in cybersecurity behaviour, to the Theoretical Domain Framework. This has been achieved by a systematic literature survey. The significance of the study is the identification and of the main behavioural constructs used in the cybersecurity domain. The findings of this research are aimed at being used as a basis when planning theory-based interventions for cybersecurity behaviour change.
A Wizard-of-Oz Experiment to Demonstrate Water Reduction and User Training With an “Autonomous” Faucet

Abstract Resource-conserving products and commercial smart products abound in the market, but the intersection of the two is largely unexplored from the human-centered-design community. Research has shown that people (users) have different cognitive styles that influence their methods of approaching challenges and how they interpret the world. Utilizing this knowledge of cognitive styles, energy conserving products could (1) reduce resource consumption of its users and (2) increase user satisfaction with interacting with those products. Passive products — such as a flow-limiting showerhead — do not seek to change the user behavior and solely change the behavior of the product to conserve water. In this work, we design and test an “active smart” product to see if it can change users through product interaction. A custom faucet was designed and built to conduct an experiment with the Wizard of Oz (WoZ) technique of remotely operating a device to create the impression of autonomy/smartness. Participants were asked to wash multiple sets of dishes to test if: (1) participants use less water when washing dishes with a smart faucet and (2) participants remember this behavior change and use less water in a alter interaction with a normal faucet. Results confirmed the hypotheses and showed that those interacting with the faucet reduced their consumption by 26.5% during WoZ treatment and, importantly, 10.9% while washing after interacting with the WoZ treatment. Limitations include the implementation of the smart algorithm and the willingness-to-pay for a smart faucet in the home. This study demonstrates that smart products can conserve resources and train for further conservation even when the user is not using the smart product.
Survey of Uralic Universal Dependencies development

This paper attempts to evaluate some of the systematic differences in Uralic Universal Dependencies treebanks from a perspective that would help to introduce reasonable improvements in treebank annotation consistency within this language family.The study finds that the coverage of Uralic languages in the project is already relatively high, and the majority of typically Uralic features are already present and can be discussed on the basis of existing treebanks.Some of the idiosyncrasies found in individual treebanks stem from language-internal grammar traditions, and could be a target for harmonization in later phases.
An Augmented Reality Game for Energy Awareness

Energy efficiency requires a behavioral shift towards sustainable consumption. Such a change can be supported by persuasive IT applications, which employ a variety of stimuli to increase the energy literacy and awareness of consumers. We describe FunergyAR, an Augmented Reality digital game targeting children and their families. FunergyAR incorporates Computer Vision and Augmented Reality components within traditional game mechanics and can be used either in a standalone manner or together with Funergy, a card game designed for improving energy savvy behaviors in children.
Online Expert System for Diagnosis Psychological Disorders Using Case-Based Reasoning Method

Abstract Expert system of diagnosis of psychological disorders is a system designed to diagnose early symptoms of psychological disorders that may be suffered by individuals, online-based expert systems by applying the method of Case-Based Reasoning allows users to check the psychological condition directly with the results that can be used as a basis for more examination continue. The use of Case-Based Reasoning method facilitates the examination process for each user consultation because the result of past examination data can be used to compare with consultation data, psychological disorder examination with Case-Based Reasoning method can be done well.