Reviewerly/exHarmony huggingface dataset:
AN ANALYSIS OF STUDENTS’ DIFFICULTIES IN READING COMPREHENSION THROUGH GOOGLE DOCS

Reading is an activity to gain some information from the text through written text. The objective of this research is to to find out the students’ difficulties reading comprehension of eleventh class of SMK Santika Jiken academic year of 2021/2022, to find out the factors of causing the students’ difficulty in reading comprehension through google docs at the Eleventh Class of SMK Santika Jiken. This research is qualitative research. The research subjects were class XI SMK Santika Jiken which consisted of 7 females and 13 males. The techniques of collecting the data are questionnaire and test. The researcher uses the technique of analyzing the data from Miles and Huberman. The steps in analyzing the data for the first is data collecting, the researcher collecting the data from the students, such as questionnaire and test of students. The second is data reduction, the researcher classifies and reduces the data based on focus. The researcher deletes several data that did not relate to the difficulties in reading comprehension. Third is data display, the researcher arranging the information described in order to draw the conclusion. The researcher presents the data from the difficulties in Reading Comprehension. The result of the research shows that there are 18% students who have difficulties in understanding to look for the main idea of the text with the total of incorrect is 14. Then, there are 13% students who have difficulties in understanding vocabulary of the text with the total of incorrect is 10. Then, there are 45% students who have difficulties in understanding making inference of the text with the total of incorrect is 35. Next there are 24% students who have difficulties in understanding to look for the detail information of the text with the total incorrect is 18. The factor of causing the students difficulties in comprehending English reading through Google Docs there are difficulty in understanding long sentence in the text, difficulty in understanding vocabulary, house environment, and less preferred subjects.Keywords: reading, reading comprehension, difficulties, google docs
Linear Block and Convolutional MDS Codes to Required Rate, Distance and Type

Algebraic methods for the design of series of maximum distance separable (MDS) linear block and convolutional codes to required specifications and types are presented. Algorithms are given to design codes to required rate and required error-correcting capability and required types. Infinite series of block codes with rate approaching a given rational $R$ with $0<R<1$ and relative distance over length approaching $(1-R)$ are designed. These can be designed over fields of given characteristic $p$ or over fields of prime order and can be specified to be of a particular type such as (i) dual-containing under Euclidean inner product, (ii) dual-containing under Hermitian inner product, (iii) quantum error-correcting, (iv) linear complementary dual (LCD). Convolutional codes to required rate and distance and infinite series of convolutional codes with rate approaching a given rational $R$ and distance over length approaching $2(1-R)$ are designed. The designs are algebraic and properties, including distances, are shown algebraically. Algebraic explicit efficient decoding methods are referenced.
DESIGN SOUND CLASSIFICATION IOT SYSTEM WITH GENETIC ALGORITHMS

This paper discusses the models and methods of machine learning used in IoT (Internet of Things) systems. Particularly some issues of methods development for sound data classifying of various nature, such as speech, music, environmental sounds etc. A sound classification subsystem could be implemented in the various Smart City, Smart Farming systems etc. Development of IoT software includes following challenges: the lack of computational resources, the relevant small RAM memory size etc. Basically, an automated sound classification system could be decomposed into four parts: the audio representation, the features extraction, the machine learning algorithm and the accuracy estimation. This paper deals with a machine learning algorithms. We use a convolutional neural network for sound classification and the Snapshot method for ensemble learning. A genetic algorithm is a typical strategy for both neural network and ensembles structural optimization. Various methods of the solution representation and crossover functions have been studied in order to find optimal configuration of genetic operators. The objective of the paper is to develop the optimal classifier for embedded sound classification system. The solution representation – genotype – is the set of neural network hyper-parameters includes the number and the type of neural network layers, the type of activation functions, the initial values of weights, the learning rate, etc. Both the Snapshot ensemble method and combination of different neural networks are used for ensemble learning. The key idea of this paper is the optimization with genetic algorithms both neural networks and the ensemble construction method. We compare different genetic operators in order to obtain optimal solution for IoT system.
Continuous Homeostatic Reinforcement Learning for Self-Regulated Autonomous Agents

Homeostasis is a prevalent process by which living beings maintain their internal milieu around optimal levels. Multiple lines of evidence suggest that living beings learn to act to predicatively ensure homeostasis (allostasis). A classical theory for such regulation is drive reduction, where a function of the difference between the current and the optimal internal state. The recently introduced homeostatic regulated reinforcement learning theory (HRRL), by defining within the framework of reinforcement learning a reward function based on the internal state of the agent, makes the link between the theories of drive reduction and reinforcement learning. The HRRL makes it possible to explain multiple eating disorders. However, the lack of continuous change in the internal state of the agent with the discrete-time modeling has been so far a key shortcoming of the HRRL theory. Here, we propose an extension of the homeostatic reinforcement learning theory to a continuous environment in space and time, while maintaining the validity of the theoretical results and the behaviors explained by the model in discrete time. Inspired by the self-regulating mechanisms abundantly present in biology, we also introduce a model for the dynamics of the agent internal state, requiring the agent to continuously take actions to maintain homeostasis. Based on the Hamilton-Jacobi-Bellman equation and function approximation with neural networks, we derive a numerical scheme allowing the agent to learn directly how its internal mechanism works, and to choose appropriate action policies via reinforcement learning and an appropriate exploration of the environment. Our numerical experiments show that the agent does indeed learn to behave in a way that is beneficial to its survival in the environment, making our framework promising for modeling animal dynamics and decision-making.
English as a Foreign Language (EFL) teachers teach English for deaf students

This research is motivated by the fact that deaf students have the samerights to get education and access to learn English. However, they needsupport and specific accommodation designs according to their abilities.Teaching English to deaf students is still a problem and varies based onthe situation. Therefore, this research aims at describing how Englishteachers teach deaf students, what problems teachers face in teachingEnglish for deaf students, and the strategies they use. The method usedin this research was qualitative, while the subject was English teachers.The instruments used to collect the data for this research wereobservation, interview and documentation. Besides, the data wereanalyzed using the qualitative data analysis procedures by Yin (2011).The results indicated that there were several problems faced by EFLteachers in teaching the deaf students. It can be categorized into linguisticand nonlinguistic. Likewise, each strategy is implemented based on theproblems and depends on the teachers’ teaching experiences. It issuggested to provide an extensive training program for English teachersand facilitate the learning media to get the success of teaching andlearning process for the deaf students.
An eigenfunction expansion formula for one-dimensional two-state quantum walks

The purpose of this paper is to give a direct proof of an eigenfunction expansion formula for one-dimensional 2-state quantum walks, which is an analog of that for Sturm-Liouville operators due to Weyl, Stone, Titchmarsh and Kodaira. In the context of the theory of CMV matrix it had been already established by Gesztesy-Zinchenko. Our approach is restricted to the class of quantum walks mentioned above whereas it is direct and it gives some important properties of Green functions. The properties given here enable us to give a concrete formula for a positive-matrix-valued measure, which gives directly the spectral measure, in a simplest case of the so-called two-phase model.
PENGUATAN LITERASI PEREMPUAN PADA KADER MASYARAKAT KOTA MOJOKERTO MELALUI PENERBITAN TABLOID SUHITA

Strengthening women's literacy through the publishing of the Suhita tabloid was carried out for community activist in the Mojokerto city. Literacy strengthening is done to support their duties as community activist. The name Suhita is taken from the name of Dyah Suhita who is the queen of the Majapahit Kingdom. In strengthening this literacy the participants were equipped with various journalistic and writing techniques to support their oral competence. Learning activities carried out with the jigsaw adaptation strategy. Participants were divided into groups according to the type of writing and the writing assignments were changed at each meeting. Such learning makes the atmosphere of life learning because learners are treated as adults who are full of initiative and independence. The results of activities in the form of writings that are ready to be published because editing is also done together in each lesson. Editing was done in a new group. The new group is formed by the way the original group divides itself into new groups. Learning outcomes are published in a 16-page tabloid. Keywords: women's literacy, community cadres, Suhita
Global Word Sense Disambiguation of Polysemous Words in Telugu Language

Word Sense Disambiguation (WSD) is a significant issue in Natural Language Processing (NLP). WSD refers to the capacity of recognizing the correct sense of a word in a given context. It can improve numerous NLP applications such as machine translation, text summarization, information retrieval, or sentiment analysis. This paper proposes an approach named ShotgunWSD. Shotgun WSD is an unsupervised and knowledgebased algorithm for global word sense disambiguation. The algorithm is motivated by the Shotgun sequencing technique. Shotgun WSD is proposed to disambiguate the word senses of Telugu document with three functional phases. The Shotgun WSD achieves the better performance than other approaches of WSD in the disambiguating sense of ambiguous words in Telugu documents. The dataset is used in the Indo-WordNet.
Interactive E-book Development for Graphic Design Subjects at Multimedia Department of Vocational School in Malang

This research is based on learning media that is not suitable enough to the needs of students. Most of students don’t understand about the using of e-book in the class because of incomplete learning media material. The development of e-book is used for printing graphic design subjects for grade XI in SMKN 5 Malang. The purpose of this research is: (1) to develop an e-book as printing graphic design learning media for grade XI in multimedia department SMKN 5 Malang. (2) to increase students’ understanding of printing graphic design material by using an e-book. (3) to know the efficiency level of e-book on printing graphic design subjects in even semester. The method of this research is true experimental and pretest-posttest control group as the design model. Data analysis uses 4D e-book development model. There are four important steps in 4D model, that are: (1) Define, (2) Design, (3) Development, and (4) Disseminate. The validation of e-book is done by material experts, media experts, small-scale test, and wide-scale test. The average results of the validation percentage of media experts and material experts is 91,5 percent. The average percentage of small-scale test media validation is 87,75 percent and the average percentage of wide-scale test media validation is 86,42 percent. Based on validation, the total average percentage of learning media is 88,55 percent. On the students learning outcomes show that there is an increasing of average value from pre-test value to post-test value. The average pre-test value is 46,67 percent and the average post-test value is 88,09 percent. These results show that the using of e-book is efficient in learning process for printing graphic design subjects.
Face-NMS: A Core-set Selection Approach for Efficient Face Recognition

Recently, face recognition in the wild has achieved remarkable success and one key engine is the increasing size of training data. For example, the largest face dataset, WebFace42M contains about 2 million identities and 42 million faces. However, a massive number of faces raise the constraints in training time, computing resources, and memory cost. The current research on this problem mainly focuses on designing an efficient Fully-connected layer (FC) to reduce GPU memory consumption caused by a large number of identities. In this work, we relax these constraints by resolving the redundancy problem of the up-to-date face datasets caused by the greedily collecting operation (i.e. the core-set selection perspective). As the first attempt in this perspective on the face recognition problem, we find that existing methods are limited in both performance and efficiency. For superior cost-efficiency, we contribute a novel filtering strategy dubbed Face-NMS. Face-NMS works on feature space and simultaneously considers the local and global sparsity in generating core sets. In practice, Face-NMS is analogous to Non-Maximum Suppression (NMS) in the object detection community. It ranks the faces by their potential contribution to the overall sparsity and filters out the superfluous face in the pairs with high similarity for local sparsity. With respect to the efficiency aspect, Face-NMS accelerates the whole pipeline by applying a smaller but sufficient proxy dataset in training the proxy model. As a result, with Face-NMS, we successfully scale down the WebFace42M dataset to 60% while retaining its performance on the main benchmarks, offering a 40% resource-saving and 1.64 times acceleration. The code is publicly available for reference at https://github.com/HuangJunJie2017/Face-NMS.
The clinical effects of IABP pumps combined with tirofiban in the treatment of acute myocardial infarction and on patients' serum levels.

OBJECTIVE To explore the clinical effects of the intra-aortic balloon pump (IABP) combined with tirofiban in the treatment of acute myocardial infarction (AMI) and to analyze the combination's influence on patient serum levels. METHOD 106 patients with AMI admitted to our hospital from February 2017 to February 2018 were recruited as the research cohort. The patients were randomly placed into a control group and an experimental group according to their order of admission, with 53 patients in each group. The patients in the control group were treated with IABP, while the experimental group was treated with IABP combined with tirofiban. The two groups' clinical efficacy and serum levels were compared. RESULTS The clinical efficacy in the experimental group was significantly higher than the clinical efficacy in the control group. After the treatment, both groups' serum indexes were significantly better, and the experimental group's indexes were comparatively better than the control group's indexes. The experimental group's thrombolysis and thrombin myocardial infarction (TIMI) glow grades were much better than the glow grades in the control group. The experimental group's left ventricular ejection fraction (LVEF) index was higher than the control group', while the left ventricular end-diastolic dimension (LVEDD) index and the left ventricular end-systolic dimension (LVESD) index in the experimental group exhibited lower levels when compared to the control group. The hemorheological parameters in the experimental group were much lower than the hemorheological parameters in the control group, and the difference between the two groups was statistically significant (P < 0.05). CONCLUSION The clinical effects of an IABP pump combined with tirofiban in treating AMI are significant. The patients' clinical symptoms were alleviated drastically, and their serum levels and cardiac and cardiovascular functions improved significantly. Therefore IABP combined with tirofiban in the treatment of AMI is worthy of clinical application and promotion.
A Real-Time Photogrammetric System for Acquisition and Monitoring of Three-Dimensional Human Body Kinematics

Real-time acquisition and analysis of three-dimensional (3D) human body kinematics are essential in many applications. In this paper, we present a real-time photogrammetric system consisting of a stereo pair of red-green-blue (RGB) cameras. The system incorporates a multi-threaded and graphics processing unit (GPU)-accelerated solution for real-time extraction of 3D human kinematics. A deep learning approach is adopted to automatically extract two-dimensional (2D) human body features, which are then converted to 3D features based on photogrammetric processing, including dense image matching and triangulation. The multi-threading scheme and GPU-acceleration enable real-time acquisition and monitoring of 3D human body kinematics. Experimental analysis verified that the system processing rate reached ∼18 frames per second. The effective detection distance reached 15 m, with a geometric accuracy of better than 1% of the distance within a range of 12 m. The real-time measurement accuracy for human body kinematics ranged from 0.8% to 7.5%. The results suggest that the proposed system is capable of real-time acquisition and monitoring of 3D human kinematics with favorable performance, showing great potential for various applications.
Neural Network Based Lidar Gesture Recognition for Realtime Robot Teleoperation

We propose a novel low-complexity lidar gesture recognition system for mobile robot control robust to gesture variation. Our system uses a modular approach, consisting of a pose estimation module and a gesture classifier. Pose estimates are predicted from lidar scans using a Convolutional Neural Network trained using an existing stereo-based pose estimation system. Gesture classification is accomplished using a Long Short-Term Memory network and uses a sequence of estimated body poses as input to predict a gesture. Breaking down the pipeline into two modules reduces the dimensionality of the input, which could be lidar scans, stereo imagery, or any other modality from which body keypoints can be extracted, making our system lightweight and suitable for mobile robot control with limited computing power. The use of lidar contributes to the robustness of the system, allowing it to operate in most outdoor conditions, to be independent of lighting conditions, and for input to be detected 360 degrees around the robot. The lidar-based pose estimator and gesture classifier use data augmentation and automated labeling techniques, requiring a minimal amount of data collection and avoiding the need for manual labeling. We report experimental results for each module of our system and demonstrate its effectiveness by testing it in a real-world robot teleoperation setting.
Rancang Bangun Visual Docking Guidance System (VDGS) Sebagai Pendeteksi Arah Gerak Longitudinal Pesawat Pada Sistem Parkir Pesawat Terbang

The growth of air transportation and technological developments is getting faster every year. This causes airport services to exceed the ability to provide facilities to meet growth adequately. In this case, it cannot be denied that there are always undesirable things that happen unexpectedly such as airplane accidents which are not only always in the air when flying, there are also many cases of plane accidents at the ground whether it's at the landing or parking process. Therefore, a Visual Docking Guidance System (VDGS) tool was designed using the TF Mini LiDAR sensor and programmed for the aircraft parking system at the airport to identify and guide pilots to find the right position when parking. This tool is able to give guidance information on the aircraft position. A &lt;&lt;&lt;&lt; sign that is, the plane must move to the left, the &gt;&gt;&gt;&gt; sign, the plane must move to the right and the // \\ sign as an indicator the aircraft is in the middle position of parking stand. The results showed that the Visual Docking Guidance System (VDGS) using the LiDAR sensor with a distance specification of 10m is a fairly good level of accuracy and the error obtained from testing the distance measurement tool and actual distance has an error of 0-0,7368% with an average error of 0.0972%.
Regularized Training of Nearest Neighbor Language Models

Including memory banks in a natural language processing, architecture increases model capacity by equipping it with additional data at inference time.In this paper, we build upon kNN-LM (Khandelwal et al., 2020), which uses a pre-trained language model together with an exhaustive kNN search through the training data (memory bank) to achieve state-of-the-art results.We investigate whether we can improve the kNN-LM performance by instead training a LM with the knowledge that we will be using a kNN post-hoc.We achieved significant improvement using our method on language modeling tasks on WIKI-2 and WIKI-103.The main phenomenon that we encounter is that adding a simple L2 regularization on the activations (not weights) of the model, a transformer (Vaswani et al., 2017), improves the post-hoc kNN classification performance.We explore some possible reasons for this improvement.In particular, we find that the added L2 regularization seems to improve the performance for high-frequency words without deteriorating the performance for low-frequency ones.
Diophantine Quadruples and Ideal Solutions of the Prouhet–Tarry–Escott Problem of Size Four

A rational Diophantine m-tuple is a set of m rational numbers such that the product of any two is one less than a square. The Prouhet-Tarry-Escott problem seeks two different multisets of n integers such that the sums of like powers of each set are equal for all exponents up to some k < n. Here a new connection is established between rational Diophantine quadruples (m=4) and ideal solutions of the Prouhet–Tarry–Escott problem of size 4 (n=4, k=3) Both problems are shown to be related to finding 3 by 3 singular matrices of integers whose 9 elements are all square.
Composing the Spectrum

This chapter is dedicated to the exploration of spectral music composition. It begins with a general introduction to fundamental aspects of spectral music-making. This discusses the role of metaphors in supporting the realisation of musical ideas, as well as concepts such as space, both physical and virtual. This is followed by a complete analysis of the processes in the composition of Mouvements, a piece of pure spectrally-oriented computer music. The discussion is fully illustrated with code examples taken from the composition, and spectrograms detailing important elements of the piece. The volume concludes with a summary of what the term spectrum signifies from both a musical and a technical point of view.