Reviewerly/exHarmony huggingface dataset:
Goblins and Demons

The belief that there were no more than seven metals persisted for hundreds of years, and it was not until the seventeenth century that the inconvenient, inescapable realization came that there were probably many more. I’ve already mentioned Barba’s report from 1640 about the new metal bismuth; it was one of a number of metals or metal-like species that began to be noticed in the sixteenth and seventeenth centuries. In his History of Metals from 1671, Webster begins Chapter 27: ‘Having now ended our Collections and Discourse of the seven Metals, vulgarly accounted so; we now come to some others, that many do also repute for Metals; and if they be not so, at least they are semi-Metals, and some of them accounted new Metals or Minerals, of that sort that were not known to the Ancients.’ In the chapter Webster speaks of antimony, arsenic, bismuth, cobalt, and zinc. While we now understand these as distinct elements, earlier on there was great confusion, with the names being used for compounds rather than the elements themselves—and, furthermore, the different compounds and elements often being mistaken for each other. This makes unravelling their history all the more complicated. We’ll start with Barba’s ‘Mettal between Tin and Lead, and yet distinct from them both’: bismuth. The first mention of bismuth predates Barba’s reference by more than one hundred years. The name appears in its variant spelling, ‘wissmad’, in what is probably the very first book on mining geology. This was published around the turn of the sixteenth century and attributed to one Ulrich Rülein von Calw, the son of a miller who entered the University of Leipzig in 1485. Ulrich mentions in passing that bismuth ore can be an aid to finding silver, since the latter is often found beneath it. Consequently, miners called bismuth ‘the roof of silver’. As Webster later put it in his History of Metals, ‘The ore from whence it is drawn . . . is also more black, and of a leaden colour, which sometimes containeth Silver in it, from whence in the places where it is digged up, they gather that Silver is underneath, and the Miners call it the Cooping, or Covering of Silver.’
Learning Graph Structure With A Finite-State Automaton Layer

Graph-based neural network models are producing strong results in a number of domains, in part because graphs provide flexibility to encode domain knowledge in the form of relational structure (edges) between nodes in the graph. In practice, edges are used both to represent intrinsic structure (e.g., abstract syntax trees of programs) and more abstract relations that aid reasoning for a downstream task (e.g., results of relevant program analyses). In this work, we study the problem of learning to derive abstract relations from the intrinsic graph structure. Motivated by their power in program analyses, we consider relations defined by paths on the base graph accepted by a finite-state automaton. We show how to learn these relations end-to-end by relaxing the problem into learning finite-state automata policies on a graph-based POMDP and then training these policies using implicit differentiation. The result is a differentiable Graph Finite-State Automaton (GFSA) layer that adds a new edge type (expressed as a weighted adjacency matrix) to a base graph. We demonstrate that this layer can find shortcuts in grid-world graphs and reproduce simple static analyses on Python programs. Additionally, we combine the GFSA layer with a larger graph-based model trained end-to-end on the variable misuse program understanding task, and find that using the GFSA layer leads to better performance than using hand-engineered semantic edges or other baseline methods for adding learned edge types.
Viewpoint-aware Progressive Clustering for Unsupervised Vehicle Re-identification

Vehicle re-identification (Re-ID) is an active task due to its importance in large-scale intelligent monitoring in smart cities. Despite the rapid progress in recent years, most existing methods handle vehicle Re-ID task in a supervised manner, which is both time and labor-consuming and limits their application to real-life scenarios. Recently, unsupervised person Re-ID methods achieve impressive performance by exploring domain adaption or clustering-based techniques. However, one cannot directly generalize these methods to vehicle Re-ID since vehicle images present huge appearance variations in different viewpoints. To handle this problem, we propose a novel viewpoint-aware clustering algorithm for unsupervised vehicle Re-ID. In particular, we first divide the entire feature space into different subspaces according to the predicted viewpoints and then perform a progressive clustering to mine the accurate relationship among samples. Comprehensive experiments against the state-of-the-art methods on two multi-viewpoint benchmark datasets VeRi and VeRi-Wild validate the promising performance of the proposed method in both with and without domain adaption scenarios while handling unsupervised vehicle Re-ID.
Learning with Limited Supervision for Static and Dynamic Tasks

Author(s): Paul, Sujoy | Advisor(s): Roy-Chowdhury, Amit K | Abstract: The recent successes in computer vision have been mostly around using a huge corpus of intricately labeled data for training recognition models. But, in real-world cases, acquiring such large datasets will require a lot of manual annotation, which may be strenuous, out of budget, or even prone to errors. Whereas, a lot of real data that are generated daily can be acquired at low to no annotation cost. Such data can be unlabeled or contain tag/meta-data information, termed as weak annotation. Our goal is to develop methods that can learn recognition models from such data involving limited manual supervision. In this thesis, we explore two dimensions of learning with limited supervision - first, reducing the number of manually labeled data required to learn recognition models, and second, reducing the level of supervision from strong to weak which can be mined from the web, easily queried from an oracle, or imposed as rule-based labels derived from domain knowledge.In the first dimension of learning with limited supervision, we show that context information, often present in natural data, can be used to reduce the number of annotations required. We take an information-theoretic approach considering the relationship in data points, to select them for labeling, unlike works in literature which only use the uncertainty of individual samples. In the next dimension of learning with limited supervision, i.e., reducing the level of supervision, we use weak labels instead of dense strong labels, for learning dense prediction tasks. We develop frameworks to learn using weak labels for action detection in videos and domain adaptation of semantic segmentation models on images. In action detection, unlike using frame-wise annotations as in the literature, we use only video-level annotations, which is much easier to obtain from the annotator and can also be mined from the web. In domain adaptation of semantic segmentation models, we use weak image-level labels in two forms - pseudo weak labels, which are estimated using the source segmentation model, incurring no annotation cost, or oracle weak labels, which are obtained from the human annotator and incurring a very low cost. In spite of using such weak labels, our methods perform close to frameworks using strong supervision. Continuing in the direction of learning from weak labels, we explore sequential decision-making problems. We learn robotics tasks with a small set of expert human demonstrations. Traditional imitation learning methods can only be as good as the expert, with a lot of human demos. We devise a strategy that divides a complex task into subgoals and solves them sequentially with reinforcement learning. We learn the subgoal partitions just from the human demos without any partition labels from the human annotator, by imposing only a temporal ordering based weak constraint among the subgoals, often arising in most real-world tasks. Our method is able to solve tasks with a low number of demos which other methods in the literature are not able to solve.
The Application of Cooperative Learning Model With Teams Games Tournament Type On the Seventh Grade Students In Social Arithmetic

The purpose of this study was to determine the results of the application of the Teams Games Tournament (TGT) type of cooperative learning model as seen from the teacher's ability to manage learning, student activities, learning outcomes and student response results. This research design uses One-Shot Case Study. The sample used in this study was class VII C, amounting to 28 students. The data analysis technique was carried out using the method of obesity, test methods, and student questionnaire responses. Data analysis techniques that describe the results of the teacher's ability to manage learning, student activity data, learning outcome data and student response results data. Based on the results of research on the implementation of the Teams Games Tournamnet (TGT) type of cooperative learning model on social arithmetic material, 1) the ability of the teacher to manage the cooperative learning model of the Teams Games Tournament (TGT) gets an overall score of 4.1 with a very good category, 2) student activities with the application of learning models using the Teams Games Tournament (TGT) obtained an overall score of 55, 5 with a fairly active category, 3) overall student learning outcomes reached 78.57% complete student grade scores, 4) the results of student responses using the TGT learning model overall reached positive with a value of 83.21% Keywords: teams games tournament, aritmetika sosial
Four-Dimensional Sparse Data Structures for Representing Text Data

This paper focuses on a string encoding algorithm, which produces sparse distributed representations of text data.A characteristic feature of the algorithm described here, is that it works without tokenizing the text and can avoid other data preparation steps, such as stemming and lemmatization.The text can be of arbitrary size, whether it is a single word or an entire book, it can be processed in the same way.Such approaches to text vectorization are not common in the machine learning literature.This sets the presented encoder apart from conventional text vectorizers.Two versions of the encoding algorithm are described and compared -the initial one and an improved version.The goal is to produce a robust data preparation procedure, capable of handling highly corrupted texts.
Sketch and Scale: Geo-distributed tSNE and UMAP

Running machine learning analytics over geographically distributed datasets is a rapidly arising problem in the world of data management policies ensuring privacy and data security. Visualizing high dimensional data using tools such as t-distributed Stochastic Neighbor Embedding (tSNE) and Uniform Manifold Approximation and Projection (UMAP) became common practice for data scientists. Both tools scale poorly in time and memory. While recent optimizations showed successful handling of 10,000 data points, scaling beyond million points is still challenging. We introduce a novel framework: Sketch and Scale (SnS). It leverages a Count Sketch data structure to compress the data on the edge nodes, aggregates the reduced size sketches on the master node, and runs vanilla tSNE or UMAP on the summary, representing the densest areas, extracted from the aggregated sketch. We show this technique to be fully parallel, scale linearly in time, logarithmically in memory, and communication, making it possible to analyze datasets with many millions, potentially billions of data points, spread across several data centers around the globe. We demonstrate the power of our method on two mid-size datasets: cancer data with 52 million 35-band pixels from multiple images of tumor biopsies; and astrophysics data of 100 million stars with multi-color photometry from the Sloan Digital Sky Survey (SDSS).
Mode Penalty Generative Adversarial Network with adapted Auto-encoder

Generative Adversarial Networks (GAN) are trained to generate sample images of interest distribution. To this end, generator network of GAN learns implicit distribution of real data set from the classification with candidate generated samples. Recently, various GANs have suggested novel ideas for stable optimizing of its networks. However, in real implementation, sometimes they still represent a only narrow part of true distribution or fail to converge. We assume this ill posed problem comes from poor gradient from objective function of discriminator, which easily trap the generator in a bad situation. To address this problem, we propose a mode penalty GAN combined with pre-trained auto encoder for explicit representation of generated and real data samples in the encoded space. In this space, we make a generator manifold to follow a real manifold by finding entire modes of target distribution. In addition, penalty for uncovered modes of target distribution is given to the generator which encourages it to find overall target distribution. We demonstrate that applying the proposed method to GANs helps generator's optimization becoming more stable and having faster convergence through experimental evaluations.
Decoupled Appearance and Motion Learning for Efficient Anomaly Detection in Surveillance Video

Automating the analysis of surveillance video footage is of great interest when urban environments or industrial sites are monitored by a large number of cameras. As anomalies are often context-specific, it is hard to predefine events of interest and collect labelled training data. A purely unsupervised approach for automated anomaly detection is much more suitable. For every camera, a separate algorithm could then be deployed that learns over time a baseline model of appearance and motion related features of the objects within the camera viewport. Anything that deviates from this baseline is flagged as an anomaly for further analysis downstream. We propose a new neural network architecture that learns the normal behavior in a purely unsupervised fashion. In contrast to previous work, we use latent code predictions as our anomaly metric. We show that this outperforms reconstruction-based and frame prediction-based methods on different benchmark datasets both in terms of accuracy and robustness against changing lighting and weather conditions. By decoupling an appearance and a motion model, our model can also process 16 to 45 times more frames per second than related approaches which makes our model suitable for deploying on the camera itself or on other edge devices.
Implementasi DNS Clustering Dengan BIND Dan PowerDNS Berbasis Linux

DNS (Domain Name System) is technology standart that organize public naming of a website. DNS has a very important role for the internet. Everytime you use the internet, at the moment DNS will be used. Many of request that must be served, the server is expected to provide maximum services. If the server fails or get disruption, then the request can not be served. DNS clustering is one of solution that must be implemented. DNS clustering is a group of name servers that provide the same data record for domains in the group. The function of DNS Clustering is to prevent data access to server from failing. BIND and PowerDNS is open source application and usually use to build DNS server. The result of average failover testing that obtained by BIND is 4,2 msec, then PowerDNS is 2,2 msec. After that, at the failback testing obtained the average time by BIND is 9,4 msec, and then the PowerDNS is 7,2 msec. Next, the propogation time is 1 hour 3 minutes 23 seconds, then PowerDNS is 3 hours 4 minutes 55 seconds. Then delay of query time of BIND is 1027,4 msec, and PowerDNS is 1015,4 msec.Keywords: DNS, Server, Clustering, BIND, PowerDNS.
The system of personalized instruction in school education: problems of implementation and adaptation

In the context of global trends in the humanization of education, issues of differentiation, individualization and personalization of education are actively discussed. At the same time, the key question remains – how to preserve the individual capabilities, interests and needs of each student while maintaining collective learning formats? How to take into account the personal characteristics and capabilities of each when passing and mastering general education programs? One such solution was the PCBL personalized learning platform developed in the USA. Currently, the Khoroshevskaya school is introducing and adapting this platform to the Russian conditions of education. The article examines the specific case of implementing a system of personalized competency-based education in a separate school – what problems, barriers and difficulties are encountered in its implementation. The study is built in the logic of qualitative research on the basis of high-quality research interviews with the main participants in the educational process in the context of introducing a personalized learning system.
ADD, TDA, Cıvıltı-z ve AÜ Ayrıştırma kullanarak Sağlam Görüntü Filigranı Oluşturma

The exponential increase of multimedia technologies usage has made connectivity simpler, more convenient and quicker, but has also contributed to a number of infringements of digital content.Digital watermarking has been known to offer a solution to compliance issues related to copyright infringements in multimedia technology.This paper proposes a robust approach to image watermarking by integrating the benefits of all four algorithms.The proposed method is based on discrete wavelet transform (DWT), chirp z-transform (CZT), lower and upper (LU) decomposition and singular decomposition value (SVD).In this method, using 1-level DWT, the image is broken down into its frequency sub-bands.The LL sub-band is then converted into a z domain using CZT.LU decomposition was then used to further decompose the image into two matrixes, each of which was used to insert the watermark.SVD has been applied to each of the matrixes and the unique value of the watermark is inserted to the matrixes of the decomposed image.The robustness and imperceptibility of the proposed solution was tested by checking the watermarked image for various image processing operations.Experimental findings show that the proposed technique has high imperceptibility capability and it shows a reasonable level of robustness against signal processing operations such as filtering, scaling, JPEG, rotating, gamma correction, blurring, cropping, gaussian noise, contrast enhancement, histogram equalization, and salt & pepper noise.
Metrics also Disagree in the Low Scoring Range: Revisiting Summarization Evaluation Metrics

In text summarization, evaluating the efficacy of automatic metrics without human judgments has become recently popular. One exemplar work concludes that automatic metrics strongly disagree when ranking high-scoring summaries. In this paper, we revisit their experiments and find that their observations stem from the fact that metrics disagree in ranking summaries from any narrow scoring range. We hypothesize that this may be because summaries are similar to each other in a narrow scoring range and are thus, difficult to rank. Apart from the width of the scoring range of summaries, we analyze three other properties that impact inter-metric agreement - Ease of Summarization, Abstractiveness, and Coverage. To encourage reproducible research, we make all our analysis code and data publicly available.
LibGuides: Supplemental Teaching Materials for Transitioning from Face-to-Face Teaching to Online: Streaming Videos

This guide provides information about providers & vendors who are working with universities & libraries to provide their resources for free or at a reduced cost. If there is a specific resource you need, please reach out to Kate Moore (kabmoore@ius.edu).
Personalized Mobile Information Recommendation Based on Fine-Grained User Behaviors

At present, the enthusiasm of users to score actively in mobile information recommendation system is generally poor. Moreover, the existing research works rarely start with the analysis of fine-grained reading behaviors of mobile terminal users, but mostly based on the analysis of reading content and the improvement of model. It is difficult to find out the objective, short-term and local behavioral preferences of users. To solve the above problems, we propose six kinds of explicit fine-grained reading behaviors and integrate them into the user reading interest model to form the SVR-ALL model. The effectiveness of these six fine-grained behaviors is verified by ablative experiments. On the basis of SVR-ALL model, four implicit fine-grained reading behaviors are further mined by considering the difference of user reading habits, and then propose the user reading preference model called F-AFC. The updating mechanism for user preference designed in F-AFC can fully reflect the changes of users’ reading habits in different periods. Experiments show that the accuracy of the user interest model considering user’s reading preference and its update can be improved to some extent.
结合深度学习和语言难度特征的句子可读性计算方法(The method of calculating sentence readability combined with deep learning and language difficulty characteristics)

本文提出了可读性语料库构建的改进方法,基于该方法,构建了规模更大的汉语句子可读性语料库。该语料库在句子绝对难度评估任务上的准确率达到0.7869,相对前人工作提升了0.15以上,证明了改进方法的有效性。将深度学习方法应用于汉语可读性评估,探究了不同深度学习方法自动捕获难度特征的能力,并进仛步探究了向深度学习特征中融入不同层面的语难度特征对模型整体性能的影响。实验结果显示,不同深度学习模型的难度特征捕获能力不尽相同,语言难度特征可以不同程度地提高深度学习模型的难度表征能力。
LibGuides: Student Resources: Interlibrary Loan

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LibGuides: The Fourth Industrial Revolution: Virtual Reality & Augmented Reality

Professor Klaus Schwab is convinced that we are at ‘the beginning of a revolution that is fundamentally changing the way we live, work and relate to one another.’ He calls this revolution the Fourth Industrial Revolution.
Almost optimal classical approximation algorithms for a quantum generalization of Max-Cut

Approximation algorithms for constraint satisfaction problems (CSPs) are a central direction of study in theoretical computer science. In this work, we study classical product state approximation algorithms for a physically motivated quantum generalization of Max-Cut, known as the quantum Heisenberg model. This model is notoriously difficult to solve exactly, even on bipartite graphs, in stark contrast to the classical setting of Max-Cut. Here we show, for any interaction graph, how to classically and efficiently obtain approximation ratios 0.649 (anti-ferromagnetic XY model) and 0.498 (anti-ferromagnetic Heisenberg XYZ model). These are almost optimal; we show that the best possible ratios achievable by a product state for these models is 2/3 and 1/2, respectively.
Causal inference from data. On some inadequacy problems of structures with hidden causes

The reliability of causal inference from data (by independence-based methods) is analyzed. We uncover some mechanisms which may result in model inadequacy due to sample bias and hidden variables. We detect some specific problems in recognition of direction of influence when some causes are hidden. Incorrectness of known rule for edge orientation (under causal insufficiency) is revealed. We suggest the correction to the rule aiming to retain model adequacy.