Reviewerly/exHarmony huggingface dataset:
Public can learn about youth-crime project on new website

A website has been launched to boost work aiming to steer young people away from the potential of a life of crime.
PENGEMBANGAN MEDIA PEMBELAJARAN BERBASIS ANDROID DI SEKOLAH MENENGAH

Information technology development has reached all aspects of life, including education. The use of technology in the learning process provides a big role and function for the world of education in providing interesting and easy to be accessed learning resources. This study aims to develop Android-based learning media on the lesson matrix for class XI, find the feasibility level of Android-based learning media according to validation results and find the level of acceptance of Android-based learning media in accordance with the results of testing on users. This study uses the Research and Development method. The steps include the analysis, design, development, implementation, and evaluation stages. The results showed that the learning media "SMA Matrix" is very feasible to use, this is based on the results of validation by media experts and material experts with each obtaining an average value of 4.27 (very feasible) and 4.44 (very feasible). The level of acceptance of learning media "SMA Matrix" is very highly accepted by users, this is based on the results of media testing by users who get an average value of 4.63 (very high acceptance). The based on the results of the validation and testing media, it can be concluded that the learning media "Matriks SMA" is very feasible to be used as a learning media.
Multi-Object Tracking with Hallucinated and Unlabeled Videos

In this paper, we explore learning end-to-end deep neural trackers without tracking annotations. This is important as large-scale training data is essential for training deep neural trackers while tracking annotations are expensive to acquire. In place of tracking annotations, we first hallucinate videos from images with bounding box annotations using zoom-in/out motion transformations to obtain free tracking labels. We add video simulation augmentations to create a diverse tracking dataset, albeit with simple motion. Next, to tackle harder tracking cases, we mine hard examples across an unlabeled pool of real videos with a tracker trained on our hallucinated video data. For hard example mining, we propose an optimization-based connecting process to first identify and then rectify hard examples from the pool of unlabeled videos. Finally, we train our tracker jointly on hallucinated data and mined hard video examples. Our weakly supervised tracker achieves state-of-the-art performance on the MOT17 and TAO-person datasets. On MOT17, we further demonstrate that the combination of our self-generated data and the existing manually-annotated data leads to additional improvements.
Watch for the laughs! The music of graphic scores

In this case study, Emilie Capulet discusses the concepts of play and playfulness in music performance by investigating the playful nature of graphic scores and showing how graphic scores can play a central role in a game of musical creativity which may help to restore the confidence of young musicians.
Ordination analyses in sedimentology, geochemistry and paleoenvironment - current trends and recommendations&amp;#160;

&amp;lt;p&amp;gt;Ordination is a family of multivariate exploratory data analysis methods. With the advent of high-throughput data acquisition protocols, community databases, and multiproxy studies, the use of ordination in Earth sciences has snowballed. As data management and analytical tools expand, this growing body of knowledge opens new possibilities of meta-analyses and data-mining across studies. This requires the analyses to be chosen adequately to the character of Earth science data, including pre-treatment consistent with the precision and accuracy of the variables, as well as appropriate documentation. To investigate the current situation in Earth sciences, we surveyed 174 ordination analyses in 163 publications in the fields of geochemistry, sedimentology and palaeoenvironmental reconstruction and monitoring. We focussed on studies using Principal Component Analysis (PCA), Non-Metric Multidimensional Scaling (NMDS) and Detrended Correspondence Analysis (DCA).&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;PCA was the most ubiquitous type of analysis (84%), with the other two accounting for ca. 12% each. Of 128 uses of PCA, only 5 included a test for normality, and most of these cases were not applied or documented correctly. Common problems include: (1) not providing information on the dimensions of the analysed matrix (16% cases); (2) using a larger number of variables than observations (24 cases); (3) not documenting the distance metric used in NMDS (55% cases); and (4) lack of information on the software used (38% cases). The majority (53%) of surveyed studies did not provide the data used for analysis at all and a further 35% provided data sets in a format that does not allow immediate, error-free reuse, e.g. as data table directly in the article text or in PDF appendix. The &amp;amp;#8220;golden standard&amp;amp;#8221; of placing a curated data set in an open access repository was followed only by 6 (3%) of the analyses. Among analyses which reported using code-based statistical environments such as R Software, SAS or SPSS, none provided the code that would allow reproducing the analyses.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Geochemical and Earth science data sets require expert knowledge which should support analytical decisions and interpretations. Data analysis skills attract students to Earth sciences study programmes and offer a viable research alternative when field- or lab-based work is limited. However, many study curricula and publishing process have not yet endorsed this methodological progress, leading to situations where mentors, reviewers and editors cannot offer quality assurance for the use of ordination methods. We provide a review of solutions and annotated R Software code for PCA, NMDA and DCA of geochemical data sets in the freeware R Software environment, encouraging the community to reuse and further develop a reproducible ordination workflow.&amp;lt;/p&amp;gt;
Magnification-independent Histopathological Image Classification with Similarity-based Multi-scale Embeddings

The classification of histopathological images is of great value in both cancer diagnosis and pathological studies. However, multiple reasons, such as variations caused by magnification factors and class imbalance, make it a challenging task where conventional methods that learn from image-label datasets perform unsatisfactorily in many cases. We observe that tumours of the same class often share common morphological patterns. To exploit this fact, we propose an approach that learns similarity-based multi-scale embeddings (SMSE) for magnification-independent histopathological image classification. In particular, a pair loss and a triplet loss are leveraged to learn similarity-based embeddings from image pairs or image triplets. The learned embeddings provide accurate measurements of similarities between images, which are regarded as a more effective form of representation for histopathological morphology than normal image features. Furthermore, in order to ensure the generated models are magnification-independent, images acquired at different magnification factors are simultaneously fed to networks during training for learning multi-scale embeddings. In addition to the SMSE, to eliminate the impact of class imbalance, instead of using the hard sample mining strategy that intuitively discards some easy samples, we introduce a new reinforced focal loss to simultaneously punish hard misclassified samples while suppressing easy well-classified samples. Experimental results show that the SMSE improves the performance for histopathological image classification tasks for both breast and liver cancers by a large margin compared to previous methods. In particular, the SMSE achieves the best performance on the BreakHis benchmark with an improvement ranging from 5% to 18% compared to previous methods using traditional features.
A Knowledge-based Approach for Answering Complex Questions in Persian

Research on open-domain question answering (QA) has a long tradition. A challenge in this domain is answering complex questions (CQA) that require complex inference methods and large amounts of knowledge. In low resource languages, such as Persian, there are not many datasets for open-domain complex questions and also the language processing toolkits are not very accurate. In this paper, we propose a knowledge-based approach for answering Persian complex questions using Farsbase; the Persian knowledge graph, exploiting PeCoQ; the newly created complex Persian question dataset. In this work, we handle multi-constraint and multi-hop questions by building their set of possible corresponding logical forms. Then Multilingual-BERT is used to select the logical form that best describes the input complex question syntactically and semantically. The answer to the question is built from the answer to the logical form, extracted from the knowledge graph. Experiments show that our approach outperforms other approaches in Persian CQA.
Probabilistic analysis of block Wiedemann for leading invariant factors

We determine the probability, structure dependent, that the block Wiedemann algorithm correctly computes leading invariant factors. This leads to a tight lower bound for the probability, structure independent. We show, using block size slightly larger than r, that the leading r invariant factors are computed correctly with high probability over any field. Moreover, an algorithm is provided to compute the probability bound for a given matrix size and thus to select the block size needed to obtain the desired probability. The worst case probability bound is improved, post hoc, by incorporating the partial information about the invariant factors.
WORKSHOP PENYUSUSNAN LAPORAN BEBAN KERJA DOSEN (BKD) DAN LAPORAN KINERJA DOSEN (LKD) SEKOLAH TINGGI ILMU EKONOMI INDRAGIRI (STIE-I) RENGAT

Lecturer workload is a lecturer performance report which includes components of implementing education, carrying out research and carrying out community service as well as supporting tridharma activities and / or additional assignments within a certain period of time. The lecturer workload must be reported every semester at the assignment college. The process of assessing the workload of lecturers is focused on the implementation of the main duties of the lecturer and supporting activities of tridharma, BKD reports are data and facts of all activities carried out by lecturers every semester.
Hate versus Politics: Detection of Hate against Policy makers in Italian tweets

Accurate detection of hate speech against politicians, policy making and political ideas is crucial to maintain democracy and free speech. Unfortunately, the amount of labelled data necessary for training models to detect hate speech are limited and domain-dependent. In this paper, we address the issue of classification of hate speech against policy makers from Twitter in Italian, producing the first resource of this type in this language. We collected and annotated 1264 tweets, examined the cases of disagreements between annotators, and performed in-domain and cross-domain hate speech classifications with different features and algorithms. We achieved a performance of ROC AUC 0.83 and analyzed the most predictive attributes, also finding the different language features in the anti-policymakers and anti-immigration domains. Finally, we visualized networks of hashtags to capture the topics used in hateful and normal tweets.
Yair (Yeshiva Academic Institutional Repository): How Rethinking an Open-Source Institutional Repository is Changing the Visibility of Faculty, Students, and Administration

The director of libraries conceived of Yeshiva University’s institutional repository (IR) in 2018 in part as a cost-effective alternative to Digital Measures, a scholarly productivity tracking program used to determine faculty eligibility for tenure. It was mandated in Yeshiva University’s first Strategic Plan 2016-2010, under Strategic Imperative 2: Advance Faculty Development and Excellence in Teaching and Research. The IR would be a secure, prestigious, university-sanctioned platform for showcasing, documenting, and sharing intellectual output across the globe. It was important that most of the work would be open access, with accompanying Creative Commons Non-Commercial No-Derivatives licenses. In addition to faculty, undergraduate and graduate students would be given a platform to self-archive their intellectual output. Both faculty and students would have the option to opt-out from making their work public, or at least limiting the visibility to the university public only.
Language Curriculum in America and Indonesia: A Comparative Analysis for Improvement of Indonesian Character Education Practice

This article explores the U.S. English Language Arts Curriculum and Indonesian Language and Literature Curriculum. A comparative analysis is drawn to investigate ideological, cultural, and political economy factors that influence the development of both curricula. In addition, the educational purposes and philosophies that support both curricula are also exposed. Specifically, the discussion of the unstated purposes of the curricula shows an interesting finding. Language and literature curriculum of both countries highlight the importance of citizenship and character education and are integrated in the teaching of the curricula. However, the US language curriculum places a premium on fostering American identity, with an emphasis on increasing learning for professional workforce and career-ready individuals, which will benefit the global economy in the future. Meanwhile, Indonesian language curriculum gives priority to moral and religious instruction. The language and literature teaching are integrated in nation’s character building. After analyzing, comparing, and contrasting both curricula, the author found that America successfully instills character education to shape the worldview and characters of its citizens extracted from their clear ideology, while Indonesia is somewhat inexplicable and vague. Therefore, this article shows room for improvement for educators and educational leaders in Indonesia to be clearer in defining which character is best exposed in the curriculum.
RESEARCH ON EMPLOYEES’ CAREER DEVELOPMENT: CASE OF STARBUCKS CORPORATION

The current study is designed to research employees’ career development and employee retention rate, and the Starbucks Corporation as a case reference. Thirty participants who are having working experience 3 months and above have been recruited for the current study. As a section of participants, purposive sampling has been used as a sampling technique. Mixed method design means collects and analyses the data quantitatively and also qualitatively. The current study would have Cronbach Alpha greater than 0.70 to ensure the strength of reliability. Data would be analyzed quantitively by running IBM SPSS in a systematic way while the data would be analyzed qualitatively by forming themes. Future studies should be larger the sample size and make arrangements for break time. The current study would contribute to the HRM, especially in the employee retention part.
Perancangan dan Pembangunan Aplikasi Electronic Menu Restoran

Electronic Menu or abbreviated as eMenu is an application that designed to improve the performance of service processes in a business sector, especially restaurants. eMenu aims to change thefood ordering service process that was originally traditional using paper, into web-based digital media. Not only handling food ordering services, eMenu also handles almost all business processes in a restaurant such as menu management, managing information on orders received by waiters and chef, payment transactions, to sales reports. To handle payment transaction, eMenu using third party application payment gateway, called Duitku. eMenu is built using Single-Page Application concept that use AJAX calls which allows time efficiency in displaying the required data. By using the Single-Page Application, the browser does not reload the entire requested page but only part of data is displayed dynamically. The Single-Page Application uses Vue.js as Front-end stack, and Laravel to manage Back-end. eMenu application is responsive design, so that user can access via smartphone or desktop computer with properly.
Fluid system influence on geochemical differentiation of noble metals in Mid-Atlantic Ridge lithosphere

In a result of investigation of noble metals and volatile component contents in the rocks from Mid-Atlantic Ridge (Fracture Zone 15°20’) it was found that there is a good correlation of metal concentration and fluid characteristics among various rock types. The data support the concept that fluid systems greatly influence geochemical differentiation of noble metals in oceanic lithosphere.
Machines and humans. The war will not take place

Medicine, biology, economics, finance, sociology, sport, the fields to mobilize machine learning are increasingly numerous. Their interest has grown steadily in recent years as they have seen “artificial intelligence” methods as technical tools for processing large databases with a high degree of accuracy rather than as a competitive science. Belatedly, lawyers are exploring artificial intelligence, but not without resistance. Sometimes considered as the dawn of a new era of “big data,” others denounce artificial intelligence in which they foresee a mechanization of the law where the machine would supplant the human. Yet, as with all other sciences, algorithms are powerful tools for knowledge production. Cooperation between artificial machine intelligence and natural human intelligence will strengthen our understanding of legal mechanisms and their application by legal professionals.
COPER: a Query-Adaptable Semantics-based Search Engine for Persian COVID-19 Articles

With the surge of pretrained language models, a new pathway has been opened to incorporate Persian text contextual information. Meanwhile, as many other countries, including Iran, are fighting against COVID-19, a plethora of COVID-19 related articles has been published in Iranian Healthcare magazines to better inform the public of the situation. However, finding answers in this sheer volume of information is an extremely difficult task. In this paper, we collected a large dataset of these articles, leveraged different BERT variations as well as other keyword models such as BM25 and TF-IDF, and created a search engine to sift through these documents and rank them, given a user's query. Our final search engine consists of a ranker and a re-ranker, which adapts itself to the query. We fine-tune our models using Semantic Textual Similarity and evaluate them with standard task metrics. Our final method outperforms the rest by a considerable margin.
Part2Word: Learning Joint Embedding of Point Clouds and Text by Matching Parts to Words.

It is important to learn joint embedding for 3D shapes and text in different shape understanding tasks, such as shape-text matching, retrieval, and shape captioning. Current multi-view based methods learn a mapping from multiple rendered views to text. However, these methods can not analyze 3D shapes well due to the self-occlusion and limitation of learning manifolds. To resolve this issue, we propose a method to learn joint embedding of point clouds and text by matching parts from shapes to words from sentences in a common space. Specifically, we first learn segmentation prior to segment point clouds into parts. Then, we map parts and words into an optimized space, where the parts and words can be matched with each other. In the optimized space, we represent a part by aggregating features of all points within the part, while representing each word with its context information, where we train our network to minimize the triplet ranking loss. Moreover, we also introduce cross-modal attention to capture the relationship of part-word in this matching procedure, which enhances joint embedding learning. Our experimental results outperform the state-of-the-art in multi-modal retrieval under the widely used benchmark.
Smaller ACC0 Circuits for Symmetric Functions

What is the power of constant-depth circuits with $MOD_m$ gates, that can count modulo $m$? Can they efficiently compute MAJORITY and other symmetric functions? When $m$ is a constant prime power, the answer is well understood: Razborov and Smolensky proved in the 1980s that MAJORITY and $MOD_m$ require super-polynomial-size $MOD_q$ circuits, where $q$ is any prime power not dividing $m$. However, relatively little is known about the power of $MOD_m$ circuits for non-prime-power $m$. For example, it is still open whether every problem in $EXP$ can be computed by depth-$3$ circuits of polynomial size and only $MOD_6$ gates. We shed some light on the difficulty of proving lower bounds for $MOD_m$ circuits, by giving new upper bounds. We construct $MOD_m$ circuits computing symmetric functions with non-prime power $m$, with size-depth tradeoffs that beat the longstanding lower bounds for $AC^0[m]$ circuits for prime power $m$. Our size-depth tradeoff circuits have essentially optimal dependence on $m$ and $d$ in the exponent, under a natural circuit complexity hypothesis. For example, we show for every $\varepsilon &gt; 0$ that every symmetric function can be computed with depth-3 $MOD_m$ circuits of $\exp(O(n^{\varepsilon}))$ size, for a constant $m$ depending only on $\varepsilon &gt; 0$. That is, depth-$3$ $CC^0$ circuits can compute any symmetric function in \emph{subexponential} size. This demonstrates a significant difference in the power of depth-$3$ $CC^0$ circuits, compared to other models: for certain symmetric functions, depth-$3$ $AC^0$ circuits require $2^{Ω(\sqrt{n})}$ size [Håstad 1986], and depth-$3$ $AC^0[p^k]$ circuits (for fixed prime power $p^k$) require $2^{Ω(n^{1/6})}$ size [Smolensky 1987]. Even for depth-two $MOD_p \circ MOD_m$ circuits, $2^{Ω(n)}$ lower bounds were known [Barrington Straubing Thérien 1990].
LibGuides: TigerHow: Set Up Email, Blackboard and Banner Web (Distance Education): Helpdesk for Blackboard/Email

Learn how to set up and log into email, Blackboard and Banner Web. Helpdesk for Blackboard/Email
PROTOTYPES OF LEARNING PHYSICS USING COMPUTER ASSISTED INSTRUCTION METHOD

The learning that is being carried out now is still conventional in that the teacher only stands in front of the class and records the material on the board while the students copy the notes written on the board into their notes. This results in the reduced achievement of students who tend to prefer playing rather than learning. An alternative solution is to create learning applications using computer media with the hope of being able to improve classroom learning such as increasing student achievement. CAI stands for Computer Assisted Instruction which means it is a computer system that can deliver instruction directly to recipients of information by interacting with programmed subjects. Computer-Assisted Instruction (CAI) has been developed recently and has proven its benefits to assist teachers in teaching and assisting students in learning.
Why we use Django rather than Flask in Asset Management System

Python provide number of frameworks for web development and other applications by Django, Flask, Bottle, Web2py, CherryPy and many more. Frameworks are efficient and versatile to build, test and optimize software. A web framework is a collection of package or module which allows us to develop web applications or services. It provides a foundation on which software developers can built a functional program for a specific platform. The main purpose of this study about python framework is to analyze which is better framework among Django or flask for web development. The study implement a practical approach on PyCharm. The result of this study is - “Django is better than flask”.