Reviewerly/exHarmony huggingface dataset:
An Exponential Lower Bound for Proofs in Focused Calculi

In [7], Iemhoff introduced a special form of sequent-style rules and axioms, which she called focused, and studied the relationship between the focused proof systems, the systems only consisting of this kind of rules and axioms, and the uniform interpolation of the logic that the system captures. Subsequently, as a negative consequence of this relationship, she excludes almost all super-intuitionistic logics from having these focused proof systems. In this paper, we will provide a complexity theoretic analogue of her negative result to show that even in the cases that these systems exist, their proof-length would computationally explode. More precisely, we will first introduce two natural subclasses of focused rules, called $$\mathrm {PPF}$$ and $$\mathrm {MPF}$$ rules. Then, we will introduce some $$\mathbf {CPC}$$-valid ($$\mathbf {IPC}$$-valid) sequents with polynomially short tree-like proofs in the usual Hilbert-style proof system for classical logic, or equivalently $$\mathbf {LK} + \mathbf {Cut}$$, that have exponentially long proofs in the systems only consisting of $$\mathrm {PPF}$$ ($$\mathrm {MPF}$$) rules.
KEEFEKTIVAN PENGGUNAAN METODE THINK TALK WRITE DALAM PEMBELAJARAN MENULIS TEKS ULASAN PADA SISWA MTS

This research is based on the curiosity of researchers in the use of think talk write method towards review text learning in class VIII Mts Asaasuttarbiyah Mande. From the research background, researchers can draw a problem statement, namely Is the think talk write method effectively used in learning to write review texts? and thus the researcher aims to determine the ability to write review texts on class VIII Mts Asaasuttarbiyah this study also intends to determine the effectiveness of the think talk write method in learning to write review texts. Review texts are chosen because they can help students train students in the language fluently and develop student writing. The method that the researcher uses is the experimental method. In this study, the researchers used one group design pretest-posttest. Data taken by researchers is quantitative data, namely data analyzed by passing statistical analysis techniques. The class that the researcher chooses is class VIII A with 30 students being sampled for the research material. For this reason the results of the study state that the initial test (pretest) and the final test (posttest) change or increase with the results of the initial test 63 and the final test average 81 results. For this reason, the researcher can conclude that the writers' talk method is effective (influential) to be used in learning to write review texts.
On the judgement of full-period sequences and a novel congruential map with double modulus on Z(p n )

This paper studies the judgement problem of full-period maps on Z(pn) and proposes a novel congruential map with double modulus on Z(pn). The full-period properties of the sequences generated by the novel map are studied completely. We prove some theorems including full-period judgement theorem on Z(pn) and validate them by some numerical experiments. In the experiments, full-period sequences are generated by a full-period map on Z(pn). By the binarization, full-period sequences are transformed into binary sequences. Then, we test the binary sequences with the NIST SP 800-22 software package and make the poker test. The passing rates of the statistical tests are high in NIST test and the sequences pass the poker test. So the randomness and statistic characteristics of the binary sequences are good. The analysis and experiments show that these full-period maps can be applied in the pseudo-random number generation (PRNG), cryptography, spread spectrum communications and so on.
Facial Expression Recognition via Kernel Sparse Representation

For seeking sparse representation coefficients in the kernel feature space, we propose a novel method of facial expression recognition based on kernel sparse representation classification to improve the recognition rate of facial expression. The 1 l -norm minimization problem was solved in this paper. The coefficient vector was obtained and the local binary patterns features were extracted as the facial representation features. kernel sparse representation classification was used to perform facial expression classification and compared with sparse representation-based classification, the nearest subspace, support vector machines, K-nearest neighbor, and radial basis function neural network. Test results based on the universal JAFFE database show that the presented kernel sparse representation classification method is more effective than other methods in facial expression recognition.
Accelerating ELM Training over Data Streams

As a machine learning method, extreme learning machine (ELM) has the characteristics of fast learning speed and high accuracy. With the explosive growth of data volume, running machine learning algorithms on distributed computing platforms is an unstoppable trend. Apache Flink is an open-source stream-based distributed platform for massive data processing with good scalability, high throughput, and fault-tolerant ability. In this paper, we first research the characteristics of ELM and distributed computing platforms, then propose a distributed ELM framework (FL-ELM) which is based on Flink. Then we evaluate this framework with synthetic data on a 5-node distributed cluster. In summary, the advantages of the proposed framework is highlighted as follows: (1) The training speed of FL-ELM is always faster than that in Spark; (2) The scalability of FL-ELM behave better than that in Spark.
An Event Recommendation Model Using ELM in Event-Based Social Network

In recent years, Event Based Social Networks (EBSNs) platforms have increasingly entered people’s daily life and become more and more popular. In EBSNs, event recommendation is a typical problem which recommend interested events to users. Different from traditional social networks, both online and offline factors play an important role in EBSNs. However, the existing methods do not make full use of the online and offline information, which may lead a low accuracy, and they are also not efficient enough. In this paper, we propose a novel event recommendation model to solve the shortcomings talked above. At first, a feature extraction phase is constructed to make full user of the EBSN information. And then, we regard the recommendation problem as a classification problem and ELM is extended as the classifier in the model. Extensive experiments are conducted on real EBSN datasets. The experimental results demonstrate that our approach is efficient and has a better performance than some existing methods.
Classification of Similarity-Looking Drugs by Human Perception

The aim of this study is to investigate the similarities of drug shapes for preventing medical accidents due to similar appearances. According to the Medical Error Corrective Guidance published by the Korea Ministry of Health and Welfare’s policy team, a number of pairs of medicine that should be treated carefully in order to prevent confusion. However, systematic investigation on which morphological factors cause confusion is not enough yet. In this study, investigated human’s perception of similar drugs experimentally. As the first step, 15,000 tablet images were collected from the Korea Food & Drug Administration’s online medicine library. Among them, 100 randomly selected images were used for card sorting experiment to construct a similarity matrix. The tablets were classified into several groups based on the similarity score analyzed by multidimensional scaling (MDS). The result showed that people have a tendency to classify tablets by shape, color, size and material.
Is Adaptivity a Core Property of Intelligent Systems? It Depends

Autonomous systems rely upon intelligence to enable them to function without the need for human intervention. In many cases, these systems must adapt to properly react to numerous scenarios. This adaptivity could be a simple action such as activating an additional server, or could be incredibly complex in creating a completely new adaptation strategy. In the following work, we address the question Is Adaptability a Core Property of Intelligent Systems? We determine that due to the ambiguity of the term `intelligent system' and the wide range of such systems, that there is no clear and definitive answer to this question. This paper details the deliberation of three AI/Self-Adaptive researchers in addressing this question. We use several scenarios of intelligent systems ranging from a simple motion detector to a complex `humanoid' futuristic artificial system to demonstrate the complexity of this question.
Stability Strategy and its Direct Role in Achieving Competitive Advantage at Jordanian Communication Companies

The study aimed to identify the direct effect of the Stability Strategy in achieving the competitive advantage of Jordan Telecom Companies by surveying the opinions of the executives and middle level managers in these companies. The researchers conducted an exploratory survey of all companies operating in the Jordanian telecom sector (34). The researchers also distributed the questionnaires the study community which totaled 160 members. Questionnaires were distributed, of which, 130 were retrieved and were valid for analysis. There was a statistically significant effect at 0.05 level of significance of the stability strategy in achieving competitive advantage of cost reduction, quality improvement, flexibility and innovation combined. The study showed an absence of significant statistical effect at 0.05 level of significance in achieving the competitive edge of innovation and quality. The study recommends that companies working in the telecommunications sector reduce direct production costs to provide a competitive advantage, and recommends that researchers undertake further studies of stability strategy in different industry sectors.
From Query Variations To Learned Relevance Modeling

Thinking in terms of an information need instead of simply queries provides a rich set of new opportunities in improving the effectiveness of search [6]. User queries may vary a lot for a single information need [3, 9], as a query is often under-specified. Many techniques have been proposed to enrich a single query, for example relevance modeling [8]. These techniques focus on improving overall system performance but may fail in some occasions. Instead of optimizing for a single query, another direction is to use multiple query variations to represent an information need. With fusion techniques, query variations can improve system performance while failing fewer queries [2, 6].
Implementation of key phrase audio detection using binary neural network inference in a low power field programmable gate array device

The emergence of smart factories, cities, homes and mobile are driving shifts in system architectures and new applications that require intelligence at the edge. Artificial Intelligence/Machine Learning solutions are essential to meeting the requirements for this new generation AI-based edge computing applications. Convolutional Neural Network (CNN) has been widely used in image classification and object recognition. Low power, low latency and mobile applications is pushing further research on reducing the computational complexity and memory requirements. One breakthrough involves the use of binary weights and activation, which is known as Binary Neural Network (BNN). Binarized neural network replaces all multiply-accumulate operations by simple XNOR-addition operations. This architecture is implemented on a low power Field Programmable Gate Array (FPGA) for Speech Recognition application using the proprietary Mobile Development Platform with CNN accelerator cores.
통신사실확인자료 제공 제도의 문제점과 개선방향

On June 28, 2018, the Korean Constitutional Court has held the collection of cell tower metadata and the collection of real-time location data by criminal investigative authority based on Article 13 (1) of Protection of Communications Secrets Act(PCSA) as unconstitutional and ordered reform enactment until 31 March, 2020.This article analyzes the general problems of the communication confirmation data(so-called ‘metadata’) provision system and proposes a reform direction of Article 13 (1) of PCSA.Although communication metadata is not information about communication contents, comprehensive analysis of metadata can not only indirectly deduce the contents of communication but also infer internal activities related to the privacy of the information subject. Therefore, the traditional theoretical premise that the intensity of infringement of fundamental rights by the collection of communication metadata is lower than the collection of contents data is no longer valid today. Moreover, it is also possible to provide real-time metadata in the future according to Article 13 (1) of PCSA, which should in fact cause the infringement of the fundamental rights at the same level as the contents data interception. Nevertheless, the provision of communication metadata is very broadly and easily permitted under the comprehensive requirement of necessity for criminal and without limitation of applicable crimes or target persons.Therefore, fundamental reform of the system for providing the metadata is indispensable. First, the applicable crimes should be limited to a level similar to that of the eavesdropping. Second, the target persons should be limited to the person who is suspect, mediator for the suspect, or the owner of the telecommunication account for the suspect. Third, the procedural requirements shall specify the proportionality and complementarity requirements as well as the specific crime charges of target crimes. This article emphasizes that it is appropriate to strictly limit the collection of metadata to exceptional and supplementary investigation methods.As a result of the improvement of the communication data provision system as a whole, more strengthened requirements should be set up on the collection cell tower metadata and collection of location data.
Publication Topic Selection Algorithm Based on Association Analysis

In the process of traditional education publishing, publication topic selection is completed by subjective experience of editorial team, which is difficult for editorial team to take into account complex factors such as needs of readers, knowledge update and market change. A large number of teaching materials are unsalable and publications can hardly meet actual needs of reader and market. Theme and content of traditional textbooks are lagging behind, which are difficult to meet the needs of today’s education development. To solve these problems, this paper focuses on Publication topic selection algorithm based on association analysis. First of all, an algorithm for automatically acquiring data and information from web pages is designed. Then, this paper designs similarity degree calculation method, score prediction algorithm and prediction score updating algorithm. Finally, effectiveness of the algorithm is verified by experiments.
Steady geometric quantum discord in driven coupled quantum spin systems

A geometric measure of the quantum discord in driven coupled quantum spin systems is investigated. In the rotating wave and Born–Markov approximation, by calculating the quantum master equation, the effects of noise strength and spin-coupling constant on quantum discord have been discussed in detail. The results show that we need to reduce the noise strength to get a higher value of geometric quantum discord. We also suggest a more effective way to approach the steady geometric quantum discord by controlling the noise strength and coupling constant.
Penerapan Metode JIGSAW untuk Meningkatkan Hasil Belajar Bahasa Indonesia Siswa Kelas V SD Negeri 010 Beringin Jaya

The author discussed the Indonesian language learning problem by incorporating the cooperative type jigsaw method in the teaching and learning process. The purpose of this study was to improve the learning outcomes of fifth-grade students at Beringin Jaya Elementary School using the jigsaw cooperative method. This research was conducted on the fifth-grade students of SDN 010 Beringin Jaya, with a total number of 20 students consisting of 12 female students and 8 male students. This research was conducted in 2 cycles starting from the end of March to May 2016. The results of the study showed that each cycle of teacher activity increased. It is seen that the teacher activity score in the first cycle got a score of 19 in the good category, and in the second cycle got a score of 23 categorized very well. Student activities from activities, in the first cycle, got a score of 20 categorized very well, and in the second cycle got a score of 22 very good categories. Additionally, the improvement of student learning outcomes was seen clearly, in the first cycle was 83.33%, while in the second cycle 91.66% of students completed. Thus, the use of the jigsaw cooperative method in Indonesian language learning can improve the learning outcomes of fifth-grade students of SDN 010 Beringin Jaya.
Hierarchical Behaviour for Object Shape Recognition Using a Swarm of Robots

A hierarchical cognitive architecture for robot exploration and recognition of object shape is presented. This cognitive architecture proposes the combination of multiple robot behaviours based on (1) Evolutionary, (2) Fuzzy Logic and (3) Bayesian approaches. First, the Evolutionary approach allows a swarm of robots to locate and reach an object for exploration. Second, Fuzzy Logic is used to control the exploration of the object shape. Third, the Bayesian approach allows the robot to detect the orientation of the walls of the object being explored. Once the exploration process finishes, the swarm of robots determine whether the object has a rectangular or circular shape. This work is validated in a simulated environment and MATLAB using a swarm of E-puck robots. Overall, the experiments demonstrate that simple robots are capable of performing complex tasks through the combination of simple collective behaviours while learning from the interaction with the environment.
A Flexible Categorial Formalisation of Term Graphs as Directed Hypergraphs

Term graphs are the concept at the core of important implementation techniques for functional programming languages, and are also used as internal data structures in many other symbolic computation setting, including in code generation back-ends for example in compilers. To our knowledge, there are no formally verified term graph manipulation systems so far; we present an approach to formalising term graphs, as a relatively complex example of graph structures, in the dependently-typed programming language and proof system Agda in a way that both the mathematical theory and useful executable implementations can be obtained as instances of the same abstract definition.
Effects Of A 13-week Yoga Class On College Aged Student's Flexibility, Body Image, And Mood

PURPOSE: Yoga has been practiced as a low to moderate intensity physical activity in recent years and evidence suggests that yoga practices can bring beneficial effects on physical and mental health. The present study examined whether a 13-week yoga practice of postures, breathing, and relaxation techniques can improve the flexibility, body image and mood of college aged students. METHODS: A sample of 60 students was recruited from university to participate in a 13-week yoga class. A pre-post test design was used for this study. Measurement on the physical aspects included the flexibility test and whereas the mental aspects included body image questionnaire (Body Appreciation Scale-2) and mood questionnaire (The Positive and Negative Affect Scale). Data were analyzed at the significance level of p<.05 for one group pre- and post-test of two data sets. RESULTS: The Mann-Whitney showed significance at the p<.01 for the sit and reach flexibility test and significance at the p<.05 for the body image and mood questionnaire. Sixty students (19 ± 2.1years; 1.60 ± 0.15 m; 52 ± 8.4kg;) reported improved flexibility (ranged from 3% to 9%) , increased body appreciation (mean value from 3.8 to 4.2) and positive mood (mean value from 3.9 to 4.3) after the 13-weeks class. The majority students desired to participate in more yoga classes in the future. CONCLUSIONS: This study suggests that a 13-week yoga class showed improvements of flexibility, increasing positive body image and enhancing mood who are novice yoga practitioners. Since the participants in this study were all girls and future study can examine the gender difference on the topic.
Image Retrieval and Classification in Relational Databases

Relational databases are used to store information in every kind of life and business. They are suited for storing structured data and binary large objects (BLOBs). Unfortunately, BLOBs and multimedia data are difficult to handle, index, query and retrieve. Usually, relational database management systems are not equipped with tools to retrieve multimedia by their content.
Is artificial data useful for biomedical Natural Language Processing

A major obstacle to the development of Natural Language Processing (NLP) methods in the biomedical domain is data accessibility. This problem can be addressed by generating medical data artificially. Most previous studies have focused on the generation of short clinical text, and evaluation of the data utility has been limited. We propose a generic methodology to guide the generation of clinical text with key phrases. We use the artificial data as additional training data in two key biomedical NLP tasks: text classification and temporal relation extraction. We show that artificially generated training data used in conjunction with real training data can lead to performance boosts for data-greedy neural network algorithms. We also demonstrate the usefulness of the generated data for NLP setups where it fully replaces real training data.
Categorization of Bangla Medical Text Documents Based on Hybrid Internal Feature

This paper aims to develop an automatic text categorization system that classifies Bangla medical and non-medical text documents based on two primary features, that is, word length and the presence of English equivalent words in the text documents. To start with, it has been shown that based on the word length and the number of English equivalent words present in a particular text, Bangla medical text documents can be identified among other text documents of any domain. SGD (Stochastic Gradient Descent) classification algorithm is used and an accuracy of 97.75% has been achieved. Comparisons have also been done with other commonly used classifiers to test the system from which it has been observed that SGD performs better than those classifiers.
Psychological Analysis On Mind Controlling Of The Main Character In “Coraline” Novel

This study analysis the mind controlling of the main character and the main character’s rejection of mind controlling in Coraline by Neil Gaiman using Jacques Lacan theory of desire in psychoanalysis. This study has the purpose to know how the concept of mind controlling is applied in this novel. This research used the theory of desire taken from Jacques Lacan (1966 and 2007). This theory illustrates how this aspect participates in mind controlling. The result of this analysis is that desire that can actually affect someone to control the thoughts of others with suggestions, for example television advertising and hipnotic. The existence of mind controlling to influence someone also has negative effects on the main character.Keywords: mind controlling, desire, rejection, Lacan