Reviewerly/exHarmony huggingface dataset:
Conservation and Literacy Values in Reading Lectures of Indonesian Language with Problem Based Learning Model

The main problems of learning are understanding the implementation of learning in schools, the lack of knowledge, or the weak of literacy, as well as the weakness of prospective teacher students ability in the development of learning.The realistic solution offered is the implementation of student-centered learning models, namely problem based learning.In addition, the strengthening of literacy awareness is also an important part in the steps of implementing a learning model that is strengthened by the conservation value as a characteristic of Semarang State University.This research used descriptive research, which prioritizes the description of exist events and take place at the time of learning.The research subjects were six reading learning classes in 2017 and 2018.The results of the study included three things, the effectiveness of reading lectures measured by the achievement of students average score who reached 83.75 for reading comprehension skills.The implementation of conservation value is seen in all steps of learning with a different of conservation value, from inspirational, humanist, caring, creative, and innovative values.Literacy implementation was described by lecturing assignments in the form of reading assistance by applying approaches, models, methods, and reading techniques.
CATS-CAE Reflective Middleware Framework for Adapting Context-Aware Transactional Services

Pervasive environments are characterized by limited computing resources and wireless connectivity. In parallel, current application domains have variable transactional requirements that do not fit the traditional ACID model. As a result, the pervasive environment characteristics are compelling and cannot be supported by conventional solutions that are typically dedicated to a specific application domain and support a limited set of context parameters. This article aims at providing a complete solution that addresses the challenges of the adaptability of context-aware transactional services “CATS” in pervasive environments. Thus, a new framework CATS-CAE was designed, which offers a comprehensive structure of multiple component chains. The adaptation strategy in CATS-CAE is based on a hybrid approach combining the use of adaptation policies, alternative strategy and behavioral adaptation of composite services through the “Profiled Task Class” concept. A probabilistic model is also presented to support the efficiency of the proposed approach.
PENYUSUNAN MEDIA AUDIOVISUAL GREEN LEARNING BERBASIS ESTIMASI KANDUNGAN KARBON HUTAN RAKYAT DESA KARE KECAMATAN KARE KABUPATEN MADIUN

Audiovisual media for green learning to show number percentage media feasibility of 91.4% then could used as learning media for support process learning to be more interesting. This media used on material global warming. Taking potency existing local in districts Madiun in the form of Forest people so that could improve students in keep environment and reduce impact global warming. The method used that is method Quantitative obtained from results score testing the feasibility of learning media. Fill in video material load results vegetation vegetation compiler Forest people influence womb carbon. Results vegetation and womb carbon use method quadrant for measure Index Value Important (INP) as well formula allometric and Biomass Expansion Factor for measure biomass. Analysis Index Value Important (INP) obtained from density relative, frequency relative, and relative dominance whereas analysis savings carbon use formula carbon. Kare Community Forest save carbon amounting to 73.44 tons/ha with details tree amounting to 31.53 tons/ha, plants under amounting to 35.75 tons/ha, and litter amounting to 6.16 tons/ha. Physico-chemical parameters shows the whole parameter average plot area . Results of environmental parameters to show that condition physico chemistry community forest curry quite normal so many there is type plant as compiler vegetation community forest. Keyword :Audiovisual Media, Green Learning, Kare Community Forest ,
The Word, Phrase, and Sentence Productivity, and Average Length of Utterances among Children with Mild Mental Retardation

This study aims to describe and explain the patterns of words, phrases, sentences, and the average length of utterances in the CMMR speech based on the minimum and maximum ability.This research approach is a qualitative one that is observationally controlled through a cross-sectional design.The research data were in the form of written stories, conversations, and records of field data.The source of the data was the MMRC of Year 5.The technique of collecting data was recording and observation.The findings of this study were as follows: (1) based on the maximum capacity, the productivity of the word productivity was that the noun increased by 86%, the verbs by 81%, function words by 75%, adjectives by 76%, pronouns by 78%, numerals by 83%, and adverbs by 75%.(2) Based on the maximum capacity, the productivity of phrases was that the prepositional phrases increased by 72%, nominal phrases by 88%, verbal phrases by 70%, numeral phrases by 76%, pronominal phrases by 50%, conjunctive phrases by 100%, and adverbial phrases by 100%.(3) Based on the number of clauses related to the minimal capabilities, the sentence productivity found in the form of simple sentences and can be found in neither compound nor complex sentences and based on the maximum capabilities productivity of simple sentence increased by 86% and it was found that there was neither compound nor complex sentences.Based on the completeness of the sentence elements, it was found that according to the minimal ability there were 15% of complete sentences and 85% of incomplete sentences and based on the maximum capabilities, there was a 25% increase of complete sentences and a decrease by 75% of incomplete sentences.(4) Based on the minimum capability, the average length of utterances (ALU) was5,69 and based on the maximum capacity; it increased by 6,24.
The Application of Current Antitrust Law to Explicit Collusion by Autonomously Acting Pricing Algorithms

This paper aims to answer the question whether the current (EU) antitrust law is well-equipped to deal with explicit collusion by pricing algorithms that act independently from the companies using them. The paper unfolds in three main parts: first, it outlines the exact circumstances of the later discussed scenario and the differentiation to other ways in which algorithms can be related to infringements of antitrust law. Then it investigates whether the use of algorithms specified in the first part falls under the scope of antitrust law in general, that is whether the term 'agreement' according to Art. 101 (1) TFEU is applicable to independently acting algorithms or whether any other tool of current antitrust law (here: concerted practices) enables for the application of Art. 101 (1) TFEU to algorithmic price-setting. Finally, it assesses if the company using the algorithm can be held responsible for any breach of antitrust law that the algorithm is engaged in. It does so by applying the concept of attribution of liability for acts of employees to the employing firm. My hypothesis is that there is no fundamental difference between the conduct of deep learning algorithms and human employees and that as a result the same principles of attribution of liability to firms can be applied.
Survey on Cyber Security and Defensive Measures

The progression of advanced data is developing a regular routine making it gradually hard to oversee and structure it or even to isolate what is significant based on what is pointless. Looked with this test, new encouraging achievement advancements are being created to bring 'information examination's to the following developmental level. Man-made reasoning (AI), specifically, is required to wind up huge in numerous fields. A few types of AI empower AI like profound learning can be utilized to perform prescient scrutiny. Their possible for the defense domain is huge as AI solutions are expected to develop in serious fields such as cyber defense, decision-support systems, risk management, pattern recognition, cyber situation awareness, projection, malware detection and data relationship to name but a few. One of the potential uses of AI in digital protection might be to empower the setting up of self-designing systems. It would imply that AI agendas could recognize vulnerabilities (programming bugs) and perform reaction activities such as self-fixing. This opens better approaches to fortifying correspondences and data frameworks security by giving system strength, avoidance and insurance against digital dangers.
Uncertainty based Class Activation Maps for Visual Question Answering

Understanding and explaining deep learning models is an imperative task. Towards this, we propose a method that obtains gradient-based certainty estimates that also provide visual attention maps. Particularly, we solve for visual question answering task. We incorporate modern probabilistic deep learning methods that we further improve by using the gradients for these estimates. These have two-fold benefits: a) improvement in obtaining the certainty estimates that correlate better with misclassified samples and b) improved attention maps that provide state-of-the-art results in terms of correlation with human attention regions. The improved attention maps result in consistent improvement for various methods for visual question answering. Therefore, the proposed technique can be thought of as a recipe for obtaining improved certainty estimates and explanations for deep learning models. We provide detailed empirical analysis for the visual question answering task on all standard benchmarks and comparison with state of the art methods.
Causal structure learning from time series: Large regression coefficients may predict causal links better in practice than small p-values

In this article, we describe the algorithms for causal structure learning from time series data that won the Causality 4 Climate competition at the Conference on Neural Information Processing Systems 2019 (NeurIPS). We examine how our combination of established ideas achieves competitive performance on semi-realistic and realistic time series data exhibiting common challenges in real-world Earth sciences data. In particular, we discuss a) a rationale for leveraging linear methods to identify causal links in non-linear systems, b) a simulation-backed explanation as to why large regression coefficients may predict causal links better in practice than small p-values and thus why normalising the data may sometimes hinder causal structure learning. For benchmark usage, we detail the algorithms here and provide implementations at https://github.com/sweichwald/tidybench . We propose the presented competition-proven methods for baseline benchmark comparisons to guide the development of novel algorithms for structure learning from time series.
an Analysis of Students’ Error in Constructing the Negative and Interrogative Sentence by Using Simple Present Tense

This research aims to find out the errors made by the students in constructing the negative and interrogative sentences by using the simple present tense. Those errors were classified into four types, namely omission, addition, misformation, and misordering based on surface strategy taxonomy from Daulay. The researchers used a descriptive qualitative method to find the result. The subject of this research was the VIII-A class of SMP Negeri 40 Medan that consisted of 30 students. The instrument for gathering the data was the essay test, which included 20 affirmative sentences, which asked to form those sentences into the negative and interrogative sentences. The findings indicated that the students' errors in this study were 318 (50,4%) case of mistakes on misformation; 175 (27,8%) case of errors on addition; 100 (15,9%) case of errors on misordering; and 37 (5,9%) case of errors on omission. It can be concluded that misformation in the most frequent errors, while failure is the rarest error in this research.&#x0D; Keywords: error analysis, constructing negative and interrogative sentence
Global Horizontal Irradiance Prediction Using Deep Neural Network Framework

Due to uncertainty in solar radiation caused by cloud cover and other environmental conditions, it is necessary to accurately predict solar irradiance for time ahead planning and scheduling. In this paper, a comparative analysis of 1 h ahead forecasting of global horizontal irradiance using various popular machine learning technique and deep neural network is determined. The forecasting accuracy is analyzed using various error metrics such as mean absolute error (MAE)and mean square error (MSE). The number of time lags influencing 1 h ahead forecasting is determined using autocorrelation and partial autocorrelation function. The hyper-parameters of various machine learning techniques are tuned using random search method. It is observed the deep neural network provides the least prediction error as compared to other machine learning techniques.
Time delay reservoir computing with VCSEL

Neural networks in which the interconnections between the nodes are randomly assigned are promising for the realization of neuromorphic devices in which the resource requirements for training are lower than for a fully deterministic system. Reservoir computing is a class of recurrent network for which the input and internal weights are random and fixed over time, and only the output weights are trained via a linear regression. In this work, we review the recent work on photonic reservoirs and describe our recent results on the implementation of a single node system based on multi-mode optical interconnect technology developed for high channel density and low power data transfer applications. We discuss the potential advantages of this approach for the realization of a photonic cluster of reservoirs.
Aspects of hydrothermal alteration assemblages around Bakwani-Bagwa, Northwestern Nigeria; Insights from multifractal analysis of measured thresholds for mineral exploration.

This study is aimed at identifying highly altered zones by applying concentration area (C-A) fractal model technique on Landsat ETM+ image of area around Bakwani-Bagwa, Northwestern Nigeria. Multifractal analysis was used to quantify degree of alteration on satellite images processed using crosta technique. A loglog plot was used to display the multifractal characteristics for clay and iron altered images and identify threshold values. Maximum and minimum threshold values obtained from fractal analysis are 1.74 and 4.01 for clay altered images and 0.607 and 3.04 for iron altered images. High clay and iron alteration zones accounts for 5.89% and 7.84%, covering an area of 22.28km2 and 29.65km2 respectively. Delineating highly altered zones from these images reveals higher alteration largely within silicified sheared rocks and areas around faults. The current study proved the multifractal processing techniques to be valuable mapping tools for geological reconnaissance which can be extended to a large area of the Nigerian basement and provide preliminary hydrothermal alteration maps that are accurate as well as cost and time effective.
PRAVNI OKVIR ZA REGULISANJE ODNOSA NASTALIH POD UTICAJEM ČETVRTE INDUSTRIJSKE REVOLUCIJE UNUTAR GLOBALNIH EKONOMSKIH KRETANJA

The emergence of the fourth industrial revolution of intellectual property rights, especially the rights of industrial property, has taken on the greatest importance in the development of the global economy, as the rights that have become the driving force of production, thanks to constant innovations from the fields of digitialization and thus devoting the greatest attention to it. Computer software is one of the intellectual property rights. The controversial question is whether they are protected by copyright and related rights or industrial property rights. Whether their transfer is done through an author's contract or through a license agreement. Domestic practice and legislation have taken a mixed position, believing that computer software enjoy copyright protection and part of industrial property rights. Depending on the way in which it is observed, foreign legal practice considers that the transfer of rights to computer software is transmitted through a license agreement, although it is deemed to be a copyright work.
Longitudinal Support Vector Machines for High Dimensional Time Series

We consider the problem of learning a classifier from observed functional data. Here, each data-point takes the form of a single time-series and contains numerous features. Assuming that each such series comes with a binary label, the problem of learning to predict the label of a new coming time-series is considered. Hereto, the notion of {\em margin} underlying the classical support vector machine is extended to the continuous version for such data. The longitudinal support vector machine is also a convex optimization problem and its dual form is derived as well. Empirical results for specified cases with significance tests indicate the efficacy of this innovative algorithm for analyzing such long-term multivariate data.
A Review on H-Mining-(High Utility Itemsets of Mining)

High utility pattern mining is a rising information science task, which comprises of finding patterns having a high significance in databases. The utility of a pattern can be estimated regarding different target criteria's, for example, its benefit, recurrence, and weight. Among the different sorts of high utility patterns that can be found in databases, high utility itemsets are the most considered. A high utility itemset is a lot of qualities that shows up in a database and has a high significance to the client, as estimated by an utility capacity. High utility itemset mining sums up the issue of successive itemset mining by thinking about thing amounts and loads. A well known use of high utility itemset mining is to find all arrangements of things bought together by clients that return a high benefit. This paper gives a prologue to high utility itemsets mining, audits the best in class calculations
Eye Contact Detection from Third Person Video

Eye contact is fundamental for human communication and social interactions; therefore much effort has been made to develop automated eye-contact detection using image recognition techniques. However, existing methods use first-person-videos (FPV) that need participants to equip wearable cameras. In this work, we develop an novel eye contact detection algorithm taken from normal viewpoint (third person video) assuming the scenes of conversations or social interactions. Our system have high affordability since it does not require special hardware or recording setups, moreover, can use pre-recorded videos such as Youtube and home videos. In designing algorithm, we first develop DNN-based one-sided gaze estimation algorithms which output the states whether the one subject looks at another. Afterwards, eye contact is found at the frame when the pair of one-sided gaze happens. To verify the proposed algorithm, we generate third-person eye contact video dataset using publicly available videos from Youtube. As the result, proposed algorithms performed 0.775 in precision and 0.671 in recall, while the existing method performed 0.484 in precision and 0.061 in recall, respectively.
Lyle, TR - Biographical entry - Index to the AMA archive

Lyle, TR - Biographical entry - Index to the AMA archive - NAME is a biographical, bibliographical and archival database of SUBJECT with links to related articles and images. ADD MORE DESCRIPTION AS REQUIRED
Gli studi contrastivi dell’italianistica macedone: sviluppi e recenti prospettive

The paper aims to present contrastive Italian-Macedonian studies, giving a general overview of the development of these studies, with particular attention to the context in which the first studies between these two languages appeared.A corpus of 60 studies published in the last two decades is analysed in the second part of the paper.The classification of the studies based on the topics covered reveals a prevalence of morphosyntactic, semantic, and translation studies.Contrastive studies related to teaching Italian as a foreign language and studies that introduce other topics are also present in the corpus.The final part of the paper reflects upon the future of this type of contrastive study, especially in light of the recent changes related to the interest in studying Italian in Macedonia.
Evaluation of Citizen Partner Program in State High School 13 Surabaya

The development of education in Indonesia must use at least four basic strategies. Namely, The first is equitable distribution of opportunities for education; the second is relevance; the third is quality improvement, and the fourth is efficiency. Generally, this strategy can be divided into two dimensions, namely education improvement and equity. Development of quality improvement is expected to increase the efficiency, effectiveness and productivity of education. "Dimensions of educational equity are expected to provide equal opportunities in obtaining education for all school ages" (Amri, 2013: 68). In realizing distribution of opportunities to obtain education, Surabaya implements a program for acceptance of new students through the path of community partners. The emergence of this policy is also a proof of the government's commitment in carrying out the mandate of the Constitution regarding 20% of the budget for education. This study uses an evaluation research with a qualitative approach. The main data source in this study was obtained by snowball sampling. Data collection techniques in this study used three techniques, namely observation, interviews, and documentation. State High School 13 Surabaya is one of the schools implementing a citizen partner program. The conclusion from this study states that the implementation of the citizen partner program in State High School 13 Surabaya has not yet been reached because of the discovery of elements of nepotism and lack of socialization resulting in one student having not been affected by the community partner program.
AccQOC: Accelerating Quantum Optimal Control Based Pulse Generation

In the last decades, we have witnessed the rapid growth of Quantum Computing. In the current Noisy Intermediate-Scale Quantum (NISQ) era, the capability of a quantum machine is limited by the decoherence time, gate fidelity and the number of Qubits. Current quantum computing applications are far from the real "quantum supremacy" due to the fragile physical Qubits, which can only be entangled for a few microseconds. Recent works use quantum optimal control to reduce the latency of quantum circuits, thereby effectively increasing quantum volume. However, the key challenge of this technique is the large overhead due to long compilation time. In this paper, we propose AccQOC, a comprehensive static/dynamic hybrid workflow to transform gate groups (equivalent to matrices) to pulses using QOC (Quantum Optimal Control) with a reasonable compilation time budget. AccQOC is composed of static pre-compilation and accelerated dynamic compilation. With the methodology of AccQOC, we reached a balanced point of compilation time and overall latency. The results show that accelerated compilation based on MST achieves 9.88x compilation speedup compared to the standard compilation of each group while maintaining an average 2.43x latency reduction compared with gate-based compilation.