Reviewerly/exHarmony huggingface dataset:
Subcovers and Codes on a Class of Trace-Defining Curves

In this paper, we construct some class of explicit subcovers of the curve $\mathcal {X}_{n,r}$ defined over $\mathbb {F}_{q^{n}}$ by affine equation $y^{q^{n-1}}+\cdots +y^{q}+y=x^{q^{n-r}+1}-x^{q^{n}+q^{n-r}}$ . These subcovers are defined over $\mathbb {F}_{q^{n}}$ by affine equation $g_{s}(y)=x^{q^{n}+q^{n-r}}-x^{q^{n-r}+1}$ , where $g_{s}(y)$ is a $q$ -polynomial of degree $q^{s}$ . The Weierstrass semigroup $H(P_\infty)$ , where $P_\infty $ is the only point at infinity on such subcovers, is determined for $1 \leq s \leq 2r-n+1$ , and the corresponding one-point AG codes are investigated. Codes establishing new records on the parameters with respect to the previously known ones are discovered, and 108 improvements on MinT tables are obtained.
On the Minimum Perceptual Temporal Video Sampling Rate and Its Application to Adaptive Frame Skipping

Media technology, in particular video recording and playback, keeps improving to provide users with high-quality real and virtual visual content. In recent years, increasing the temporal sampling rate of videos and the refresh rate of displays has become one focus of technical innovation. This raises the question, how high the sampling and refresh rates should be? To answer this question, we determine the minimum temporal sampling rate at which a video should be presented to make temporal sampling imperceptible to viewers. Through a psychophysical study, we find that this minimum sampling rate depends on both the speed of the objects in the image plane and the exposure time of the recording camera. We propose a model to compute the required minimum sampling rate based on these two parameters. In addition, state-of-the-art video codecs employ motion vectors from which the local object movement speed can be inferred. Therefore, we present a procedure to compute the minimum sampling rate given an encoded video and camera exposure time. Since the object motion speed in a video may vary, the corresponding minimum frame rate is also varying. This is why the results of this paper are particularly applicable when used together with adaptive frame rate computer generated graphics or novel video communication solutions that drop insignificant frames. In our experiments, we show that videos played back at the minimum adaptive frame rate achieve an average bit rate reduction of 26% compared to constant frame rate playback, while perceptually no difference can be observed.
Mathematics research by Indians in British India: a bibliometric analysis

The study analyses the publication pattern of Indian researchers in mathematics during the first half of the twentieth century. Authorship pattern, Collaborative Coefficient, Prolific researchers, the appropriateness of Lotka’s law and Bradford’s law and Geographical distributions of publications have been studied. The Quinquennial distribution of publications indicates that there is a gradual growth in the number of publications. The Lotka’s law seems to be accepted for the present data set, but not Bradford’s law.The study analyses the publication pattern of Indian researchers in mathematics during the first half of twentieth century. Authorship pattern, Collaborative Coefficient, Prolific researchers, the appropriateness of Lotka’s law and Bradford’s law and Geographical distributions of publications  have been studied. The Quinquennial distribution of publications indicates that there is a gradual growth in the number of publications. The Lotka’s law seems to be accepted for the present data set, but not the Bradford’s law.
Reducing Regression Time in Neural Network us-ing Nth Absolute Root Mean Error

Neural network training process takes long time when the size of training data is huge, without the large set of training values the neural network is unable to learn features. This dilemma between time and size of data is often solved using fast GPUs, but we present a better solution for a subset of those problems. To reduce the time for training a regression model using neural network we introduce a loss function called Nth Absolute Root Mean Error (NARME). It helps to train regression models much faster compared to other existing loss functions. Experiments show that in most use cases NARME reduces the required number of epochs to almost one-tenth of that required by other commonly used loss functions, and also achieves great accuracy in the small amount of time in which it was trained.
Obras raras: Inep recupera coleção do seu acervo

Sintetiza a evolução da imprensa e dos fatores determinantes da formação do público leitor no Brasil. Conceitua "obra rara" e aponta os motivos pelos quais a expressão deve ser aplicada com maior flexibilidade na avaliação dos acervos bibliográficos brasileiros. Informa sobre a coleção de obras raras do Instituto Nacional de Estudos e Pesquisas Educacionais (Inep), destacando a importância de alguns de seus títulos, segundo os critérios empregados pelos bibliófilos. Relata o trabalho de restauração dessas obras, realizado em 1998; revela as condições em que se encontra a coleção e como os interessados podem ter acesso às obras que a integram. Palavras-Chave: obra rara; conceituação; acervo bibliográfico; Inep. Abstract The article synthesizes the evolution of printing press and the determinant factors of the reading public formation in Brazil. It appraises "rare work" and points out the motives at which the expression should be applied with greater flexibility in the evaluation of the brazilian bibliographical lot. It also informs on the rare work collection of the National Institute for Educational Studies and Research – INEP , emphasizing the importance of some of its titles, according to the criterion employed by the bibliophiles. The article finally reports on the restoration of these works, finished in 1998; reveals the collection's condition and how the public can have access to the works that integrate it. Key-Words: rare book; concept; bibliographical lot; Inep.
A flexible space-time tradeoff on hybrid index with bicriteria optimization

Inverted indexes are widely adopted in the vast majority of information systems. Growing requirements for efficient query processing have motivated the development of various compression techniques with different space-time characteristics. Although a single encoder yields a relatively stable point in the space-time tradeoff curve, flexibly transforming its characteristic along the curve to fit different information retrieval tasks can be a better way to prepare the index. Recent research comes out with an idea of integrating different encoders within the same index, namely, exploiting access skewness by compressing frequently accessed regions with faster encoders and rarely accessed regions with succinct encoders, thereby improving the efficiency while minimizing the compressed size. However, these methods are either inefficient or result in coarse granularity. To address these issues, we introduce the concept of bicriteria compression, which aims to formalize the problem of optimally trading the compressed size and query processing time for inverted index. We also adopt a Lagrangian relaxation algorithm to solve this problem by reducing it to a knapsack-type problem, which works in O(n log n) time and O(n) space, with a negligible additive approximation. Furthermore, this algorithm can be extended via dynamic programming pursuing improved query efficiency. We perform an extensive experiment to show that, given a bounded time/space budget, our method can optimally trade one for another with more efficient indexing and query performance.
PENGARUH METODE PEMBELAJARAN INDEX CARD MATCH TERHADAP MOTIVASI DAN HASIL BELAJAR KOGNITIF SISWA SEKOLAH MENENGAH PERTAMA (SMP)

This study aims to find out how the influence of the use of learning methods of Index Card Match on the motivation and cognitive learning outcomes of students SMPN 4 Jonggat. Learning by using the method of learning Index Card Match expected high motivated students in learning allows to obtain high learning results as well. Through this application can also make it easier for teachers to improve thinking and increase student involvement in teaching and learning. The type of research used in this research is quasi experiment, this design has control group, but not fully function to control the variables that influence the experiment implementation. The experimental class was treated using Index Card Match learning method while control using conventional method. To obtain the necessary data, then in this study using t-test analysis techniques. From the results of the study, based on the results of experimental class hypothesis testing and control class using t-test obtained t count of 0.13 and t table of 2.12. This means that the value of t is smaller than the value of t table or 0.13 < 2.12. This indicates that Ha is rejected and H0 is accepted, hence there is influence of Index Card Match learning method toward motivation and student cognitive learning result. Normal 0 false false false EN-US X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable{mso-style-name:Table Normal;mso-tstyle-rowband-size:0;mso-tstyle-colband-size:0;mso-style-noshow:yes;mso-style-priority:99;mso-style-qformat:yes;mso-style-parent:;mso-padding-alt:0cm 5.4pt 0cm 5.4pt;mso-para-margin-top:0cm;mso-para-margin-right:0cm;mso-para-margin-bottom:10.0pt;mso-para-margin-left:0cm;line-height:115%;mso-pagination:widow-orphan;font-size:11.0pt;font-family:Calibri,sans-serif;mso-ascii-font-family:Calibri;mso-ascii-theme-font:minor-latin;mso-fareast-font-family:Times New Roman;mso-fareast-theme-font:minor-fareast;mso-hansi-font-family:Calibri;mso-hansi-theme-font:minor-latin;mso-bidi-font-family:Times New Roman;mso-bidi-theme-font:minor-bidi;}
Leveraging Concepts in Open Access Publications

This paper addresses the integration of a Named Entity Recognition and Disambiguation (NERD) service within a group of open access (OA) publishing digital platforms and considers its potential impact on both research and scholarly publishing. The software powering this service, called entity-fishing, was initially developed by Inria in the context of the EU FP7 project CENDARI and provides automatic entity recognition and disambiguation using the Wikipedia and Wikidata data sets. The application is distributed with an open-source licence, and it has been deployed as a web service in DARIAH's infrastructure hosted by the French HumaNum. In the paper, we focus on the specific issues related to its integration on five OA platforms specialized in the publication of scholarly monographs in the social sciences and humanities (SSH), as part of the work carried out within the EU H2020 project HIRMEOS (High Integration of Research Monographs in the European Open Science infrastructure). In the first section, we give a brief overview of the current status and evolution of OA publications, considering specifically the challenges that OA monographs are encountering. In the second part, we show how the HIRMEOS project aims to face these challenges by optimizing five OA digital platforms for the publication of monographs from the SSH and ensuring their interoperability. In sections three and four we give a comprehensive description of the entity-fishing service, focusing on its concrete applications in real use cases together with some further possible ideas on how to exploit the annotations generated. We show that entity-fishing annotations can improve both research and publishing process. In the last chapter, we briefly present further possible application scenarios that could be made available through infrastructural projects.
The Polynomial Hierarchy Collapses

This paper illustrates the power of Gaussian Elimination by adapting it to the problem of Exact Satisfiability. For 1-in-3 SAT instances with non-negated literals we are able to obtain considerably smaller equivalent instances of 0/1 Integer Programming restricted to equality only. Thus we obtain an upper bound for the complexity of its counting version of O(2κr2 (1−κ)r) for number of variables r and clauses-to-variables ratio κ. Combining this method with previous results gives a time and space complexity for the counting problem of O(4/3|V |2 3|V |/8) and O(4/3|V |2 3|V |/16). Our method shows that Positive instances of 1-in-3 SAT may be reduced to significantly smaller instances of I.P. in the following sense: any such instance of |V | variables and |C| clauses can be polynomial-time reduced to an instance of 0/1 Integer Programming with equality only, of size at most 2/3|V | variables and at most |C| clauses. We then proceed to define formally the notion of a non-trivial kernel. For this, we define the problems considered as Constraint Satisfaction Problems. Considering recent advances in Computational Complexity relating to sparsification and existence of non-trivial kernels, we conclude by showing that the method presented here, giving a non-trivial kernel for positive 1-in-3 SAT, implies the existence of a non-trivial kernel for 1-in-3 SAT. Our proof shows the structure known as the Polynomial Hierarchy collapses to the level above P = N P .
LibGuides: Education: Search

This Education LibGuide provides a list of resources for research purposes. Major categories include elementary education, secondary education, and education theory.
Improving signature quality for network application identification

Network application identification is one of the core elements in network operations and management to provide enhanced network service and security. For accurate identification, an approach using common patterns called “signatures” is widely used to compensate the limitations of the traditional transport-layer port-based classification. However, our simulation results indicate that using the signatures generated from a set of well known algorithms may lead to very poor identification performance, with less than 60% of true positives even in an optimal case. To improve the quality of signatures, we present a technique in this paper, which consists of two steps: (i) pairwise merging to consider every possible combination of the initially collected signatures to reduce their specificity that causes the signatures to be less common; and (ii) signature reduction to identify effective signatures with greater importance from a large set of signatures produced in the merging step, so as to manage the space/time complexity in the identification process for greater scalability. Our experimental results show that the proposed technique can dramatically improve the performance, even with a small number of signatures (e.g., 95% true positives rate with 30 signatures per application) which is more compact than the initial signature set.
The Implementation of Comic Strips to The 8th Graders Junior High School Students to Improve Reading Comprehension Skill of Narrative Text

Reading comprehension as one of the language skills which is taught at Junior High School is regarded as a skill which has a complex process. In this skill students have to master the text by recognizing the written words and understanding the meaning. In a reading session, students felt it was difficult to find information of the texts. It was because the texts are too long and complex to be understood, and the media used or the text is also not interesting. Their reading comprehension skill was still low. The average score of reading comprehension task was only 68.19%. in fact, there were 18 students out of 23 or 78,26% students were not able to achieve the minimum passing grade 75. To overcome this problem this research was conducted by employing comic strips as a media to improve students reading comprehension skill. The research questions are (1). How is the implementation of comic strips in the reading comprehension skill of narrative text to the 8th graders Junior High School? (2). How can comic strips improve the reading comprehension skill of the 8th graders Junior High School? The research is designed in Classroom Action Research (CAR). The subjects were the 23 students of the 8th graders Al-Hidayah Junior High School. There were two kinds of data gained; First teacher and students activities in the teaching and learning process, and students reflection on the use of comic strips in the class. Second is students score on reading comprehension task on the implementation of comic strips. A collaborator helped the researcher to observe the class and collect the data. The data were collected by observation and reading comprehension task. The implementation of comic strips was running well as on the planning stage. The observation showed that the students were easier on comprehending the text, so they could do their work and discuss their reading comprehension task. They got motivated and enjoyed the teaching learning process, and interest in reading was increased. The students reading comprehension skill was also improved. It can be seen by looking at the students score on the first meeting around 86.96% pass the minimum criteria with the mean score 78.48. Than on the second meeting all of the students or 100% got score more than 75 with the mean score 85.65. It means that all of them pass the minimum criterion that is 75. The result of this study showed that comic strips is applicable and practical to be applied in teaching reading comprehension.
Special issue on advances in graph algorithm and applications

With the rapid development of social network platforms, more and more people are using them to search for material related to their interests. As the texts of social media messages are usually so short, when traditional existing document modeling methods are used in social network search tasks, the problem of semantic sparsity arises, leading to low-quality semantic representation and low-precision social network search results. Fortunately, besides of short text, social media data also has other features, such as timestamps, locations, and its user information. In light of this, to realize precise social network search, we propose a multi-feature probabilistic graphical model (MFPGM), which can generate high-quality semantic representation. To deal with the problem of semantic sparsity, we exploit two strategies in MFPGM. First, we propose a concept named special region and utilize location information to aggregate short text into long text. Second, we introduce the biterm pattern that can generate dense semantic space by supposing that a biterm occurring in the same context has the same topic. In order to generate high-quality semantic representations, we simultaneously model multiple features (i.e., biterm, user, location and timestamp) of social network data to enhance the semantic learning process of MFPGM. We conduct a lot of experiments on real-word datasets, and the comparisons with several state-of-art baseline methods have demonstrated the superiority of our MFPGM on topic quality and search performance. Additionally, with the help of the generated semantic representations, MFPGM allows people to analyze the relationships between time and the popularities of topics.
Extractive and Abstractive Sentence Labelling of Sentiment-bearing Topics

This paper tackles the problem of automatically labelling sentiment-bearing topics with descriptive sentence labels. We propose two approaches to the problem, one extractive and the other abstractive. Both approaches rely on a novel mechanism to automatically learn the relevance of each sentence in a corpus to sentiment-bearing topics extracted from that corpus. The extractive approach uses a sentence ranking algorithm for label selection which for the first time jointly optimises topic--sentence relevance as well as aspect--sentiment co-coverage. The abstractive approach instead addresses aspect--sentiment co-coverage by using sentence fusion to generate a sentential label that includes relevant content from multiple sentences. To our knowledge, we are the first to study the problem of labelling sentiment-bearing topics. Our experimental results on three real-world datasets show that both the extractive and abstractive approaches outperform four strong baselines in terms of facilitating topic understanding and interpretation. In addition, when comparing extractive and abstractive labels, our evaluation shows that our best performing abstractive method is able to provide more topic information coverage in fewer words, at the cost of generating less grammatical labels than the extractive method. We conclude that abstractive methods can effectively synthesise the rich information contained in sentiment-bearing topics.
Language Errors in Public Advertisement

Advertisement is the media that have strong power to convey any message. The use of English inappropriately makes their message unclear and fail to be perceived by people as the reader. This article shows the levels of error based on James in his Errors in Language Learning and Use. The errors found in advertisements mostly are in lexical level, morphological level, and syntax level. The advertisements publisher should understand the English grammar and vocabulary before they create the message on the board in English.
The EFFECT OF USING 3-2-1 STRATEGY ON STUDENTS’ READING COMPREHENSION ACHIEVEMENT

Reading needs comprehension to form understanding or meaning of the information provided in the reading text. Reading comprehension is influenced by some various aspects. This research focused on the use of reading strategy as one of many aspects that affect reading comprehension. The 3-2-1 strategy was determined to be investigated its effectiveness when employed in reading comprehension especially on narrative text. This research aimed at investigating the effect of using 3-2-1 strategy students’ reading comprehension achievement. The design of this research was quasi-experimental research. The participants were the eighth grade students of junior high school. The result in the form of post-test scores was analyzed by using Independence sample t-test in SPSS computing system. Based on the calculation of the data analysis, the significant value was 0.046 which was less than 0.05 (p&lt;0.05). The result indicated that there was a significant difference. Therefore, it can be concluded that the 3-2-1 strategy gave a significant effect on the students’ reading comprehension achievement.
PENGEMBANGAN ALGORITMA PENGENALAN JENIS KALIMAT PADA BAHASA ARAB DENGAN METODE AFFIX MAPPING

Arabic is one of the most popular languages in Indonesia which is Muslim population being majority. Ability to recognize the types of sentence in Arabic Language is one of basic topic in Arabic language studying. The type of sentence in Arabic is known from the first word in the sentence, but often the first word of a sentence is not the original words, so it can not be found directly in the dictionary. In this reseach, the Arabic Stemming algorithm was modified to find the type of the word using affixes include to the word, this method refers to the rules in Sharf science, therefore this method is named as Affix Mapping Algorithm. The algorithm that has been prepared is tested using Cyclomatic Complexity method and the result shows that the independent path obtained is 5, from the result it can be concluded that the algorithm is structured well, very easy to test, and last long.