Reviewerly/exHarmony huggingface dataset:
Pengembangan Media Pembelajaran Dengan Aplikasi Google Sites Dengan Aplikasi Google Sites Berbasis Ensiklopedia Materi Teks Eksplanasi Kelas VIII SMP

Researchers have a strong desire to carry out the development of learning media with the Google Sites application based on the encyclopedia of explanatory text material for class VIII SMP. The aims of this study are to 1) describe each stage in implementing the development of explanatory text learning media on Google Sites based on encyclopedia class VIII SMP/MTs, and 2) describe the feasibility of explanatory text learning media on Google Sites based on encyclopedia class VIII SMP/MTs. The type of research used in this research is the type of development research with the ADDIE model which has five steps, namely: 1) analysis; 2) design (design); 3) development (development); 4) implementation (implementation); 5) evaluation (evaluation). The subjects of this study were students of class VIII of MTs Negeri 7 Bantul and the object of the research was the material of explanatory text for class VIII. The method of data collection is done by interview and instrument. The research instrument was in the form of questionnaire and interview guidelines. The data analysis technique used quantitative descriptive techniques. The results of this study stated that the media in developing learning media with the Google Sites application based on the encyclopedia of explanatory text material for class VIII SMP obtained Very Eligible results, namely with a score of 88.85. The average score obtained from the processing of material expert validation scores is 96, media experts are 76, teaching experts are 89 and the results of small-scale product trials are 94.4. Based on the results of the assessment obtained, it can be concluded that the learning media with the Google Sites application based on the encyclopedia of explanatory text material for class VIII SMP Very Eligible is used as a learning medium.
The Development of Matching Game as Media to Teach Vocabulary for Elementary School Students

This research aimed at developing a learning media that can be used in the learning process at class V SDIT Syahiral Ilmi Bukittinggi. The media that developed was a matching game by using Adobe Flash CS6 to teaching vocabulary to young learners at the fifth grade Of SDIT Syahiral Ilmi Bukitinggi. This research was done because most students still encounter problems in learning English especially vocabulary mastery. Most of the students were difficult to memorize the new word, then the atmosphere of the classroom was not conducive and the students tend to be sleepy and bored in the classroom. Difficulties of students in understanding the material in learning English becomes a consideration for the researcher to develop the matching game as media to teach vocabulary to young learners. The research design was Research and Development (R D), by using ADDIE models that consist of: (1) analysis, (2) design, (3) development, (4) implementation, and (5) Evaluation. The researchers have limited this research because in the Covid-19 pandemic time, therefore this research was done until only the Implementation stage. The data collection was obtained by validation results from learning media experts. The result of two media experts showed the percentage score was 94% in the “valid” category, while from the teacher as material experts showed 84% which was “valid” criteria as well as from language experts obtained percentage to 86% include “valid” category. Based on the result of analyzing data the media of matching game was feasible to be used in teaching and learning English.
Decision Tree Heuristics Can Fail, Even in the Smoothed Setting

Greedy decision tree learning heuristics are mainstays of machine learning practice, but theoretical justification for their empirical success remains elusive. In fact, it has long been known that there are simple target functions for which they fail badly (Kearns and Mansour, STOC 1996).Recent work of Brutzkus, Daniely, and Malach (COLT 2020) considered the smoothed analysis model as a possible avenue towards resolving this disconnect. Within the smoothed setting and for targets f that are k-juntas, they showed that these heuristics successfully learn f with depth-k decision tree hypotheses. They conjectured that the same guarantee holds more generally for targets that are depth-k decision trees. We provide a counterexample to this conjecture: we construct targets that are depth-k decision trees and show that even in the smoothed setting, these heuristics build trees of depth 2^{Ω(k)} before achieving high accuracy. We also show that the guarantees of Brutzkus et al. cannot extend to the agnostic setting: there are targets that are very close to k-juntas, for which these heuristics build trees of depth 2^{Ω(k)} before achieving high accuracy.
EVALUASI TATA KELOLA TEKNOLOGI INFORMASI MENGGUNAKAN FRAMEWORK COBIT 5 STUDI KASUS: CV. BATARA JAYA TRANSPORTASI (YOJOL)

Currently, information technology is an important part for companies to meet their needs and support the achievement of the company's strategic plans. Providing competitive advantages, increasing effectiveness, time and reducing expenses is the role of information technology that is very vital in today's business area, as well as CV Batara Jaya Transportasi, which has prioritized information technology in daily activities. By examining information technology governance using the COBIT 5 framework in a company, it can be seen whether the company has met the indicator requirements. In this study, an audit of the governance framework and maintenance was conducted at CV. Batara Jaya Transportasi (YoJol) to find out the ability indicators and the results have been obtained by using a questionnaire distributed to the company. In this study, it was found that CV. Batara Jaya Transportasi (YoJol) has achieved the results expected.
Triple Uncertainties: Credence Goods, Deceptive Counterfeits, and Fake Product Reviews

Worried about unwittingly buying counterfeits, online customers often resort to “the wisdom of crowds” (e.g., product reviews) before purchasing. However, unethical sellers may game the review system through buying fake reviews. In this paper, we use a two-period game theoretical model to study a market that consists of an authentic seller and a deceptive counterfeiter; they sell credence goods (e.g., nutritional supplements and N95 masks) whose qualities are difficult to evaluate, so customers cannot differentiate genuine products from counterfeits even after consumption. In the beginning, the authentic seller decides the level of selling effort. In the first period, customers make purchasing decisions and leave either positive or negative product reviews. In the second period, the counterfeiter decides whether to buy fake positive product reviews. Then, each seller may receive a product badge that is granted based on the fraction of reviews being positive. Customers update their beliefs about sellers’ authenticity depending on badge status and then make purchasing decisions. We find that the authentic seller can benefit, while the counterfeiter can be worse off, from the counterfeiter having an option to buy fake product review. We also show that the fake review option can increase consumer surplus. Lastly, even though the counterfeiter bears the cost of fake review, the counterfeiter can be better off from a higher price of fake review, whereas the authentic seller is worse off.
Estimation of Object Position And Movement by Bayesian Inference

A method of measuring the position and movement of an object in a non-contact manner using Bayesian inference is considered. The position and movement of an object can be measured by processing the movement of the object captured as a moving image by image recognition by Bayesian inference. Since it can produce features different from those of conventional measurement methods, it can be applied in various ways. This paper describes the development of basic theories and algorithms for that purpose. Bayesian inference can provide learning and inference that is completely different from neural networks. Therefore, it may be possible to overcome the problems of neural networks.
Penerapan Model Pembelajaran Two Stay Two Stray untuk Meningkatkan Keaktifan dan Prestasi Belajar Siswa Kelas VA SDN 74 Kota Bengkulu

This study aims to improve the activeness and learning achievement of VA class students at SDN 74 Bengkulu City by applying the Two Stay Two Stray learning model. The subjects of the study were students of class VA SDN 74 Bengkulu City. The research instruments were observation sheets and test sheets. The observation data analysis technique uses the formula of the average score, the highest score, the lowest score, the difference in score and the range of values for each criterion. Test data were analyzed using the formula of the average value and the percentage of classical learning completeness. Thus the application of the Two Stay Two Stray learning model can increase the activeness and learning achievement of class VA students at SDN 74 Bengkulu City. Keywords: Two Stay Two Stray Model, Active, Learning Achievement.
Aquedücte

In smart city scenarios, a plethora of data is generated from multiple, decentralized sources, e.g., systems, applications, citizen devices, sensors, etc. However, these data may be not readily available for use due to the high heterogeneity of types, formats, and protocols, thereby giving rise to vertical silos that hamper sharing, integrating, and exploiting these data. This paper introduces Aquedücte, a data integration service for smart cities. Aquedücte offers functionalities for (i) handling data from heterogeneous sources such as files, Web services, systems, etc., (ii) converting these data to a common protocol, and (iii) making them further available to platforms aimed to support the development of smart city applications.
Intellectual talent of higher education seekers: theoretical principles

From the theme: a retrospective analysis of the concepts of "gifted and intellectually talented personality"; we analyzed the experience of working with gifted young people abroad. In particular, a set of theoretical methods was used: comparison, analysis, synthesis and generalization; empirical methods: analysis of documentation and results of pedagogical activity, observation . The characteristics of working with talented young students were revealed through analysis of teaching, methodological and practical work with gifted students. An empirical study was carried out to determine the attitude of gifted young people towards organizational ways of working with them and a scientific circle was developed. The results revealed that the organization of pedagogical work is a fundamental point to provoke the development of their competencies, the explicit ones and those that are latent.
AI: Artificial Intelligence or Accidentally Intrusive

Drawn out from the science fiction movies decades ago, Artificial Intelligence (“AI”) in the last few years has become our reality. While this technology is constantly and rapidly progressing, we need to evaluate whether our existing legal system is equipped to deal with this new facet. This paper aims to look at AI’s potential to impact our daily lives and assess how existing legal framework attempts to deal with the encounter of the thinning line between “real” and “artificial”. The Paper discusses some of the recent incidents making AI the subject matter of concern and dispute, and how certain jurisdictions attempt to ascertain liability in such matters. Through this paper we aim to highlight the challenges that use of AI brings to the legal fraternity.Artificial Intelligence
Modeling Personality Effect in Trust Prediction

Trust among users in online social networks is a key factor in determining the amount of information that is perceived as reliable. Compared to the number of users in online social networks, user-specified trust relations are very sparse. This makes the pair-wise trust prediction a challenging task. Social studies have investigated trust and why people trust each other. The relation between trust and personality traits of people who established those relations, has been proved by social theories. In this work, we attempt to alleviate the effect of the sparsity of trust relations by extracting implicit information from the users, in particular, by focusing on users' personality traits and seeking a low-rank representation of users. We investigate the potential impact on the prediction of trust relations, by incorporating users' personality traits based on the Big Five factor personality model. We evaluate the impact of similarities of users' personality traits and the effect of each personality trait on pair-wise trust relations. Next, we formulate a new unsupervised trust prediction model based on tensor decomposition. Finally, we empirically evaluate this model using two real-world datasets. Our extensive experiments confirm the superior performance of our model compared to the state-of-the-art approaches.
PENERAPAN MODEL PEMBELAJARAN COOPERATIVE INTEGRATED READING AND COMPOSITION (CIRC) UNTUK MENINGKATKAN KEMAMPUAN BERPIKIR KRITIS DAN MENULIS TEKS BERITA (Studi pada mata pelajaran Bahasa Indonesia Kelas VIII SMPNegeri 1 Pulau Pinang)

This study aims to describe the application of Cooperative Integrated Reading and Composition (CIRC) learning models to improve students' critical thinking skills, write news texts. This research was conducted using Classroom Action Research (CAR) and quasi-experimental. The subjects of the study were students of class VIII in semester one of the 2019/2020 school year at SMPN 1 Pulau Pinang. For quasi-experimental samples of classes 8.1 and 8.3. Data collection methods in this study used observation sheets for the CIRC model and critical thinking skills, as well as tests to measure the ability to write student news texts. For data analysis the average score analysis and t-test were used. From the results of the study, it shows that the application of the CIRC learning model can improve the ability to think critically and write news texts for Grade VIII students of SMPN 1 Pulau Pinang.
Framework for Accountability in Criminal Investigations

Investigators in criminal investigation units are given great latitude with time management. They are expected to work cases thoroughly. However, it has been found that across the country cases occasionally fall through the cracks and are either worked inadequately or not worked at all. This has led to disciplinary actions including criminal indictments or termination of veteran officers. The public questions the integrity of investigation divisions, sometimes leading to costly lawsuits or not receiving the justice they count on. Many of these issues could have been avoided by requiring interaction between supervisors anFd investigators to keep abreast of open cases in the unit, setting goals, and identifying overburdened or problem employees. Identifying these issues leads to additional training or resources to help with larger and more demanding cases. Law enforcement agencies can avoid law suits, increase trust with the public, have better relationships with their employees, and have a more productive criminal investigation unit by adopting an accountability framework. Law enforcement should create procedures for accountability in criminal investigations that create communication every month between investigators and supervisors.
One seismic source and receiver couple to detect steam effects on legacy data in Surmont, Canada.

Summary 4D seismic surveys have been completed every 6 months over the years to monitor the development of the steam chamber at Surmont, a heavy oil field located in northeast Alberta, Canada. Using only one source and one receiver optimally placed in the field, a novel light seismic monitoring has been “blind-tested” on non-migrated data from 2010 and 2015 for 3 spot locations chosen on the pad by ConocoPhillips. The objective was to see if the steam had reached one zone and not another by plotting the evolution of time-shift in these spots without knowing previous history. The time-shift changes obtained after processing were successfully fact-checked with temperature data from observation wells, confirming the qualitative variations attributed to the effects of the steam chamber evolution in 2 spots out of 3. This demonstrated the viability of this focused seismic approach to monitor the evolution of the steam chamber in Surmont. This also paves the way for other applications including offshore reservoir monitoring, inspired from this light and agile seismic acquisition system.
[Automatic Segmentation of Digital Pathology Slides Based on Unsupervised Learning].

To segment images through an unsupervised method as an alternative to manual labeling.A total of 100 whole slide image (WSI) data of HE stained and Pap stained slides were selected as the research and test objects, including 70 breast slides, 20 lung slides and 10 thyroid slides. In order to ensure the diversity of data, the breast slides included those of normal tissue, inflammation and tumor, the lung slides were mainly neoplasms in the lower lobe, including those of inflammation and tumor, and the thyroid slides were of cells, all benign, obtained through fine needle aspiration. The maximum total magnification (original magnification) of each image was 400 times, and the file format was NDPI. Each WSI was manually labeled, and the labeled area of each WSI was more than 10 fields of vision. The labeled information was to be used for validity verification. An unsupervised image segmentation technique based on superpixel and fully convolution neural network algorithms was constructed and used to segment any region of interest (ROI) of unlabeled WSI. In comparison with the region adjacency graph merging method, the segmentation effect of the two methods was assessed with the under segmentation error, the boundary recall and the mean Intersection-over-Union, and the efficiency of the two methods was also compared. In the comparison of execution efficiency, the test process included the preprocessing time of superpixel, and excluded the time of loading the deep learning engine.Unsupervised automatic segmentation was implemented for any ROI region of WSI according to the texture and color. The results of the breast slides, lung slides and thyroid slides showed slight differences, and multiple tests yielded stable results. However, the performance of this method in differentiating inflammation and tumor was average. The under-segmentation error, the boundary recall and the mean Intersection-over-Union were 19.10%, 82.06% and 45.06%, respectively. The under segmentation error, the boundary recall and the mean Intersection-over-Union for the region adjacency graph merging method were 21.52%, 78.39% and 44.81%, respectively. The average time consumption of the whole process was 0.27 s in GPU mode and 1.30 s in CPU mode. The average time consumption of the region adjacency graph merging method was 10.5 s in CPU mode because the method of region adjacency graph merging was not realized in the GPU mode.This method produced ideal pixel level labeling results through simple human-computer interaction, which could effectively reduce the cost of digital pathology slide data labeling. Compared with the region adjacency graph merging method, this method had better performance in processing image texture and had faster processing speed.
Schneemann Sounding: Embodied Sonic Systems

Abstract This essay examines the central position the medium of sound played in the work of the artist Carolee Schneemann (US, 1939–2019). By exploring a few key early works such as Glass Environment for Sound and Motion (1962), Chromelodeon (1963), and Noise Bodies (1965), it traces how her exposure to emergent forms in experimental music informed important translations she made of complex sonic structures into expanded, layered, constantly evolving visual systems. The essay argues that Schneemann transformed the body into a soft recording system that broadcast sonic testimonies of everyday encounters with the excesses of conspicuous consumption, the inequalities of gendered relations, and the disturbing encounters with state-mandated violence against other cultures, in particular the US invasion of Vietnam.
Deterministic Algorithms for the Hidden Subgroup Problem

We consider deterministic algorithms for the well-known hidden subgroup problem ($\mathsf{HSP}$): for a finite group $G$ and a finite set $X$, given a function $f:G \to X$ and the promise that for any $g_1, g_2 \in G, f(g_1) = f(g_2)$ iff $g_1H=g_2H$ for a subgroup $H \le G$, the goal of the decision version is to determine whether $H$ is trivial, and the goal of the search version is to find $H$. An algorithm for the problem should query $f(g)$ for $g\in G$ at least as possible. Nayak \cite{Nayak2021} asked whether there exist deterministic algorithms with $O(\sqrt{\frac{|G|}{|H|}})$ query complexity for $\mathsf{HSP}$. We answer this problem completely by proving the following results, which also extend the main results of Ref. \cite{Ye2019query}, since here the algorithms do not rely on any prior knowledge of $H$. \begin{itemize} \item [(i)] When $G$ is a general finite Abelian group, there exists an algorithm with $O(\sqrt{\frac{|G|}{|H|}})$ queries to decide the triviality of $H$ and an algorithm to find $H$ with $O(\sqrt{\frac{|G|}{|H|}\log |H|}+\log |H|)$ queries. Â \item [(ii)] In general, there is no deterministic algorithm for the search version of $\mathsf{HSP}$ with query complexity of $O(\sqrt{\frac{|G|}{|H|}})$, since there exists an instance of $\mathsf{HSP}$ that needs $\omega(\sqrt{\frac{|G|}{|H|}})$ queries to find $H$.\footnote{$f(x)$ is said to be $\omega(g(x))$ if for every positive constant $C$, there exists a positive constant $N$ such that for $x>N$, $f(x)\ge C\cdot g(x)$, which means $g$ is a strict lower bound for $f$.} On the other hand, there exist instances of $\mathsf{HSP}$ with query complexity far smaller than $O(\sqrt{\frac{|G|}{|H|}})$, whose query complexity is $O(\log \frac{|G|}{|H|})$ and even $O(1)$.
Decoding, Fast and Slow: A Case Study on Balancing Trade-Offs in Incremental, Character-level Pragmatic Reasoning

Recent work has adopted models of pragmatic reasoning for the generation of informative language in, e.g., image captioning. We propose a simple but highly effective relaxation of fully rational decoding, based on an existing incremental and character-level approach to pragmatically informative neural image captioning. We implement a mixed, ‘fast’ and ‘slow’, speaker that applies pragmatic reasoning occasionally (only word-initially), while unrolling the language model. In our evaluation, we find that increased informativeness through pragmatic decoding generally lowers quality and, somewhat counter-intuitively, increases repetitiveness in captions. Our mixed speaker, however, achieves a good balance between quality and informativeness.
Pliancy and Weakness (Character Examinations)

This chapter is an in-depth analysis on each of the individual characters in the film <italic>Stalker</italic> and their relations with one another and the plot. This includes an elongated comparison to Dostoyevsky’s <italic>The Idiot,</italic> as well as comparisons with some western films, <italic>Raiders of the Lost Ark</italic> and <italic>The Matrix</italic>, as well as a look at some of Tarkovsky’s other films, including, notably, <italic>Solaris</italic>, his other Science-fiction film, made a couple of years previously to <italic>Stalker</italic>. The chapter also takes a look at Tarkovsky’s tendency to underwrite female characters in his films, through the lens of an uncomfortable interview he gave in 1984, on the subject.