Reviewerly/exHarmony huggingface dataset:
Kemampuan Mahasiswa PGSD dalam Merancang dan Melaksanakan Pembelajaran Berbasis High Order Of Thinking Skills

The results of a survey of the world body in the fields of writing, mathematics, and science as a measure of literacy progress, the results show the ability of high-level thinking or High Order of Thinking Skills (HOTS) students in Indonesia is low and is ranked 69 out of 76 countries. The results are a reflection of the renewal of the learning system in LPTK, including students in the Primary School Teacher Education (PGSD) study program. This research is a descriptive study because it describes the ability of PGSD students in planning and implementing HOTS-based learning in elementary schools. Respondents 154 people. The results showed the ability in basic knowledge of HOTS learning was still low, only 52.24% had passed. The remaining 47.76% did not meet the Minimum Mastery Criteria (KKM). The aspects of planning HOTS learning are still low, only 59.58% of students meet the KKM. Aspects of the ability to carry out HOTS-based learning, 49.03% of students do not meet the KKM. In the aspect of designing and implementing HOTS-based assessment, 51.30% did not reach the KKM. Conclusions of the study, the majority of students have not demonstrated good skills in planning and implementing HOTS learning in elementary schools.
Surface Codes Based Quantum Networking

We propose a multipartite quantum communication network (QCN) based on surface codes (SCs). We describe how simultaneously to entangle multiple nodes in an arbitrary network topology by employing the SCs. We further describe how to extend the transmission distance between arbitrary two nodes by using the SCs as well. Finally, we describe how to operate the proposed QCN by employing the SDN concept.
Mining the Software Engineering Forums: What’s New and What’s Left

Software maintenance is an important part of the software life cycle. People use bug tracking systems to collect bugs in the system. There are a lot of bug reports in open source software. Researchers conducted a series of studies on these reports, such as automatically determining whether two bug reports were duplicates. This article provides a detailed survey of the researchers’ analysis of bug reports. In this paper, we conduct a comprehensive survey of the works concerning the mining of the software engineering forums. Specifically, we formulate these works in a three-dimensional style, i.e., we classify these studies according to the data formats they used, the methodology they are adopting, and most importantly, the questions they are dealing with. With this three dimensional partition, it can be clearly known what has been done, and what is left, along with the question, say, why left? To go further, beyond this three-dimensional partition, we are seeking to add research space through new data, novel techniques, and upcoming research questions.
Penerapan Data Mining Dalam Clustering Produksi Daging Sapi Di Indonesia Menggunakan Algoritma K-Means

Cows are animals that are found in Indonesia, cattle provide many benefits for humans ranging from milk that is rich in nutrients to meat as a source of high animal protein for humans. Beef production in Indonesia is not sufficient to meet domestic needs, so the government needs to ask for other meat and most still need beef to meet the daily protein needs of the community. The discussion of this study about the Application of Data Mining in the Cluster of Beef Production in Indonesia Using the K-Means Algorithm. The data source of this study was collected based on documents about beef production produced by the National Statistics Agency. The data used in this study are data from 2009-2016 consisting of 34 provinces. This study clustered in 3 groups, namely medium and low. The results of this study were 9 provinces included in the high group, 3 provinces included in the middle group and 22 provinces entered the low group.
71‐2: Subpixel Non‐Uniformity Correction for Displays

For emissive display such as OLED or MicroLED displays, the non‐uniformity or Mura is caused by subpixel (r, g, b subpixels) level non‐uniformity from TFT driving or material uniformity. This non‐uniformity affects each subpixel, thus requires sub‐pixel non‐uniformity correction. The measurement accuracy of the brightness of red, green, and blue subpixels is a key factor that affects the performance of the demura system. In this paper we modeled the imaging chain of measuring the display subpixel brightness and described an image processing algorithm to derive the subpixel brightness of each subpixels. The accuracy of subpixel brightness is evaluated with the contrast transfer function of grating pattern of different frequencies. The contrast of the grating from the measured single subpixel grating is about 82% of the original contrast and two‐pixel and above lines almost 100% of the original.
Answer students’ questions anytime, using technology they embrace

Providing good information that's easy to access is key to the success of any organization, said Lenord McGownd, Assistant Registrar for Communications at Ozarks Technical Community College. OTCC's chancellor wanted a way to answer students’ questions in the format they wanted in the time frame they expected. That's why the institution implemented a chatbot.
A Lexical Simplification Tool for Promoting Health Literacy

This paper presents MedSimples, an authoring tool that combines Natural Language Processing, Corpus Linguistics and Terminologyto help writers to convert health-related information into a more accessible version for people with low literacy skills. MedSimplesapplies parsing methods associated with lexical resources to automatically evaluate a text and present simplification suggestions thatare more suitable for the target audience. Using the suggestions provided by the tool, the author can adapt the original text and makeit more accessible. The focus of MedSimples lies on texts for special purposes, so that it not only deals with general vocabulary, butalso with specialized terms. The tool is currently under development, but an online working prototype exists and can be tested freely.An assessment of MedSimples was carried out aiming at evaluating its current performance with some promising results, especially forinforming the future developments that are planned for the tool.
Multi-Sensor Fusion in Dynamic Environment using Evidential Grid Mapping

Grid mapping is widely used to represent the environment surrounding a car or a robot for autonomous navigation. This paper describes an algorithm for evidential occupancy grid (OG) mapping that fuses measurements from different sensors, based on the Dempster-Shafer theory, and is intended for scenes with stationary and moving (dynamic) objects. Conventional OGmapping algorithms tend to struggle in the presence of moving objects because they do not explicitly distinguish between moving and stationary objects. In contrast, evidential OG mapping allows for dynamic and ambiguous states (e.g. a LIDAR measurement: cannot differentiate between moving and stationary objects) that are more aligned with measurements made by sensors. In this paper, we present a framework for fusing measurements as they are received from disparate sensors (e.g. radar, camera and LIDAR) using evidential grid mapping. With this approach, we can form a live map of the environment, and also alleviate the problem of having to synchronize sensors in time. We also designed a new inverse sensor model for radar that allows us to extract more information from object level measurements, by incorporating knowledge of the sensor&#x2019;s characteristics. We have implemented our algorithm in the OpenVX framework to enable seamless integration into embedded platforms. Test results show compelling performance especially in the presence of moving objects.
Parsing Icelandic Alþingi Transcripts: Parliamentary Speeches as a Genre

We introduce a corpus of transcripts from Alþingi, the Icelandic parliament. The corpus is syntactically parsed for phrase structure according to the annotation scheme of the Icelandic Parsed Historical Corpus (IcePaHC). This addition to IcePaHC makes it more diverse with respect to text types and we argue that having a syntactically parsed corpus facilitates research on differt types of texts. We furthermore argue that the speech corpus can be treated somewhat like spoken language even though the transcripts differ in various ways from daily spoken language. We also compare this text type to other types and argue that this genre can shed light on their properties. Finally, we exhibit how this addition to IcePaHC has helped us in identifying and solving issues with our parsing scheme.
Business Taxonomy Construction Using Concept-Level Hierarchical Clustering

Business taxonomies are indispensable tools for investors to do equity research and make professional decisions. However, to identify the structure of industry sectors in an emerging market is challenging for two reasons. First, existing taxonomies are designed for mature markets, which may not be the appropriate classification for small companies with innovative business models. Second, emerging markets are fast-developing, thus the static business taxonomies cannot promptly reflect the new features. In this article, we propose a new method to construct business taxonomies automatically from the content of corporate annual reports. Extracted concepts are hierarchically clustered using greedy affinity propagation. Our method requires less supervision and is able to discover new terms. Experiments and evaluation on the Chinese National Equities Exchange and Quotations (NEEQ) market show several advantages of the business taxonomy we build. Our results provide an effective tool for understanding and investing in the new growth companies.
A STUDY ON THE STUDENTS’ FACTOR DIFFICULTY IN WRITING NARRATIVE TEXT AT VIII GRADE OF MTs SABILAL MUHTADIN TEMBILAHAN

Writing is one of the language skills, which is taught to secondary school students. In reality, it is still found that students have difficulties in writing. It is needed to investigate why it happened. Thus, the purpose of this research is to determine the students' factor difficulties in writing a narrative text. Moreover, it was designed as descriptive research which took sixty students of the eighth grade of MTs Sabilal Muhtadin Tembilahan as the samples. In collecting the data, the researchers used questionnaires and interviews. Then the data were analyzed by using descriptive statistical analysis and qualitative analysis. The research findings show that there are two main factors; students' factors and teachers' factors. After analyzing the data, the results revealed that the students' factor gained a percentage of 62.43% which was placed at a strong level. Also, the teachers' factor obtained a percentage of 72.95% which was classified as a strong level. The highest students’ factor indicator was the statement “English is very different from Bahasa Indonesia." It was proven by the score 75.3% which was categorized as a strong level. Then the teachers’ factor found that the highest indicator was the statement “Using up to date method and technique." It was classified as a strong level. In conclusion, the factors of students' difficulties in writing narrative text cause both students' and teachers' factors.
URIGHT Publishers’ Electronic Library System: Representing publications in natural sciences

The structure of electronic library system of URIGHT Publishers (Moscow) specializing in academic book publishing for universities and colleges is discussed. The system comprises publications in natural sciences. Hypertextuality, interactivity and multimedia are the key features used efficiently and completely to design digital derivative learning materials in natural disciplines and corresponding parts of the electronic library system specifically for gaining knowledge and learning in natural sciences. This specific character is defined by objective and universal character of this knowledge, its accuracy, uniqueness, invariance, as well as by the structuredness of scholarly knowledge. Many objects and processes of the nature call for static and dynamic visualization. Links to the core structural elements, full-text search, bookmarkers, video, animation, and tests are available. URIGHT Electronic Library System also comprises the works by prominent natural scientists of the past and classics of popularization. With few exceptions, the classical scholarly works are published as texts with hypertext links which is a sufficient fitting. The classic popular publications are additionally supplied with videos which is seen as a rational solution. The article will be of interest to the librarians in universities and colleges teaching natural sciences and disciplines as well as to academic publishers specializing in the area.
Planning High-Level Paths in Hostile, Dynamic, and Uncertain Environments

&#x0D; &#x0D; &#x0D; This paper introduces and studies a graph-based variant of the path planning problem arising in hostile environments. We consider a setting where an agent (e.g. a robot) must reach a given destination while avoiding being intercepted by probabilistic entities which exist in the graph with a given probability and move according to a probabilistic motion pattern known a priori. Given a goal vertex and a deadline to reach it, the agent must compute the path to the goal that maximizes its chances of survival. We study the computational complexity of the problem, and present two algorithms for computing high quality solutions in the general case: an exact algorithm based on Mixed-Integer Nonlinear Programming, working well in instances of moderate size, and a pseudo-polynomial time heuristic algorithm allowing to solve large scale problems in reasonable time. We also consider the two limit cases where the agent can survive with probability 0 or 1, and provide specialized algorithms to detect these kinds of situations more efficiently.&#x0D; &#x0D; &#x0D;
A Framework for Executing Complex Querying for Relational and NoSQL Databases (CQNS)

The increasing of data on the web poses major confrontations. The amount of stored data and query data sources have become needful features for huge data systems. There are a large number of platforms used to handle the NoSQL database model such as: Spark, H2O and Hadoop HDFS / MapReduce, which are suitable for controlling and managing the amount of big data. Developers of different applications impose data stores on difficult tasks by interacting with mixed data models through different APIs and queries. In this paper, a complex SQL Query and NoSQL (CQNS) framework that acts as an interpreter sends complex queries received from any data store to its corresponding executable engine called CQNS. The proposed framework supports application queries and database transformation at the same time, which in turn speeds up the process. Moreover, CQNS handles many NoSQL databases like MongoDB and Cassandra. This paper provides a spark framework that can handle SQL and NoSQL databases. This work also examines the importance of MongoDB block sharding and composition. Cassandra database deals with two types of sections vertex and edge Portioning. The four scenarios criteria datasets are used to evaluate the proposed CQNS to query the various NOSQL databases in terms of optimization performance and timing of query execution. The results show that among the comparative system, CQNS achieves optimum latency and productivity in less time.
Adaptive CP-Based Lagrangian Relaxation for TSP Solving

M. Sellmann showed that CP-based Lagrangian relaxation gave good results but the interactions between the filtering algorithms and the Lagrangian multipliers were quite difficult to understand. In other words, it is difficult to determine when filtering algorithms should be triggered. There are two main reasons for this: the best multipliers do not lead to the best filtering and each filtering disrupts the solving of the Lagrangian multiplier problem. In this paper, we study these interactions for the Traveling Salesman Problem (TSP) because the resolution of the TSP in CP is mainly based on a Lagrangian relaxation. We propose to match the calls to the filtering algorithms with the strong variations of the objective value. In addition, we try to avoid oscillations of the objective function. We introduce Scope Sizing Subgradient algorithm, denoted by SSSA, which is an adaptive algorithm, that implements this idea. We experimentally show the advantage of this approach by considering different search strategies or additional constraints. A gain of a factor of two in time is observed compared to the state of the art.
Feeling scarcity

We describe the results of a design project about making scarce resources perceptible: a campfire-inspired lamp, a router, and a knife block. All three are `designed for discomfort' and intended to serve as thought-provoking objects.
Bidirectional Scene Text Recognition with a Single Decoder

Scene Text Recognition (STR) is the problem of recognizing the correct word or character sequence in a cropped word image. To obtain more robust output sequences, the notion of bidirectional STR has been introduced. So far, bidirectional STRs have been implemented by using two separate decoders; one for left-to-right decoding and one for right-to-left. Having two separate decoders for almost the same task with the same output space is undesirable from a computational and optimization point of view. We introduce the Bidirectional Scene Text Transformer (Bi-STET), a novel bidirectional STR method with a single decoder for bidirectional text decoding. With its single decoder, Bi-STET outperforms methods that apply bidirectional decoding by using two separate decoders while also being more efficient than those methods, Furthermore, we achieve or beat state-of-the-art (SOTA) methods on all STR benchmarks with Bi-STET. Finally, we provide analyzes and insights into the performance of Bi-STET.
Stacked Dense Optical Flows and Dropout Layers to Predict Sperm Motility and Morphology.

In this paper, we analyse two deep learning methods to predict sperm motility and sperm morphology from sperm videos. We use two different inputs: stacked pure frames of videos and dense optical flows of video frames. To solve this regression task of predicting motility and morphology, stacked dense optical flows and extracted original frames from sperm videos were used with the modified state of the art convolution neural networks. For modifications of the selected models, we have introduced an additional multi-layer perceptron to overcome the problem of over-fitting. The method which had an additional multi-layer perceptron with dropout layers, shows the best results when the inputs consist of both dense optical flows and an original frame of videos.
LionForests: local interpretation of random forests.

Towards a future where machine learning systems will integrate into every aspect of people's lives, researching methods to interpret such systems is necessary, instead of focusing exclusively on enhancing their performance. Enriching the trust between these systems and people will accelerate this integration process. Many medical and retail banking/finance applications use state-of-the-art machine learning techniques to predict certain aspects of new instances. Tree ensembles, like random forests, are widely acceptable solutions on these tasks, while at the same time they are avoided due to their black-box uninterpretable nature, creating an unreasonable paradox. In this paper, we provide a methodology for shedding light on the predictions of the misjudged family of tree ensemble algorithms. Using classic unsupervised learning techniques and an enhanced similarity metric, to wander among transparent trees inside a forest following breadcrumbs, the interpretable essence of tree ensembles arises. An interpretation provided by these systems using our approach, which we call LionForests, can be a simple, comprehensive rule.
Sparse Interpolation With Errors in Chebyshev Basis Beyond Redundant-Block Decoding

We present sparse interpolation algorithms for recovering a polynomial with $\le B$ terms from $N$ evaluations at distinct values for the variable when $\le E$ of the evaluations can be erroneous. Our algorithms perform exact arithmetic in the field of scalars $\mathsf{K}$ and the terms can be standard powers of the variable or Chebyshev polynomials, in which case the characteristic of $\mathsf{K}$ is $\ne 2$. Our algorithms return a list of valid sparse interpolants for the $N$ support points and run in polynomial-time. For standard power basis our algorithms sample at $N = \lfloor \frac{4}{3} E + 2 \rfloor B$ points, which are fewer points than $N = 2(E+1)B - 1$ given by Kaltofen and Pernet in 2014. For Chebyshev basis our algorithms sample at $N = \lfloor \frac{3}{2} E + 2 \rfloor B$ points, which are also fewer than the number of points required by the algorithm given by Arnold and Kaltofen in 2015, which has $N = 74 \lfloor \frac{E}{13} + 1 \rfloor$ for $B = 3$ and $E \ge 222$. Our method shows how to correct $2$ errors in a block of $4B$ points for standard basis and how to correct $1$ error in a block of $3B$ points for Chebyshev Basis.
Wearable Full-Body Inertial Measurement with Task Classification Using Deep Learning

In this work, an embedded system is developed for the non-invasive sensing and storage of biomechanical variables of people. It takes advantage of wearable technology, distributing sensors in strategic points of the body, ergonomically and functionally. The results are verified by recording and analysing tasks performed by six subjects to form a database. These tasks include being stood up, sitting down or standing up from a chair, going upstairs and downstairs and walking. Additionally, a convolutional neural network is tested for offline task classification. This work aims to initiate a process that ends in assistance-oriented applications, for the development of better injury rehabilitation techniques and support for elder people, among others. In this way, it seeks to open a path towards an improvement in the living conditions of people with and without reduced activities of daily living capacity.
Moving Objects Detection and Tracking with Camera Motion Compensation

The background subtraction is a widely used approach for Detecting Moving Objects (DMO) by a static camera using a simple algorithm; however, it is very sensitive to the local gradual changes of illumination, shadows, non-rigid moving objects and partial or full target occlusion. In order to overcome these issues, and to bring more potential to the solution, we propose an effective method for object detection based on active contour with camera motion compensation. The principle of active contours is to evolve an initial curve towards the object of interest that corresponds to the boundaries of the moving objects. Once the object has been detected, then it can be tracked by the Kalman filter. This latter requires many preassumptions about models and noise characteristics. As an alternative, a new method based on the Smooth Variable Structure Filter (SVSF) is implemented. The SVSF was introduced in an effort to provide a more robust estimation strategy. Detection and tracking algorithms require stable images to recognize a real moving target position. Therefore, the captured image from cameras which are placed on the moving platform, have undesired jitters, shakes and blurs. Video stabilization becomes an indispensable technique which is focusing on removing unnecessary camera vibrations from image sequences using a homographic matrix by extracting features using FAST corner detector and FREAK feature descriptor. The proposed algorithm is validated in real-world and the obtained results confirm the efficiency and robustness of our approach.