Reviewerly/exHarmony huggingface dataset:
Normalization by Evaluation for Typed Weak lambda-Reduction

Weak reduction relations in the lambda-calculus are characterized by the rejection of the so-called xi-rule, which allows arbitrary reductions under abstractions. A notable instance of weak reduction can be found in the literature under the name restricted reduction or weak lambda-reduction.In this work, we attack the problem of algorithmically computing normal forms for lambda-wk, the lambda-calculus with weak lambda-reduction. We do so by first contrasting it with other weak systems, arguing that their notion of reduction is not strong enough to compute lambda-wk-normal forms. We observe that some aspects of weak lambda-reduction prevent us from normalizing lambda-wk directly, thus devise a new, better-behaved weak calculus lambda-ex, and reduce the normalization problem for lambda-w to that of lambda-ex. We finally define type systems for both calculi and apply Normalization by Evaluation to lambda-ex, obtaining a normalization proof for lambda-wk as a corollary. We formalize all our results in Agda, a proof-assistant based on intensional Martin-Lof Type Theory.
Path Planning With Naive-Valley-Path Obstacle Avoidance and Global Map-Free.

In this paper, we present a complete Path Planning approach divided into two main categories: Global Path Planning (GPP) and Local Path Planning (LPP). Unlike most other works, the GPP layer, instead of complex and heavy maps, uses road and intersections graphs obtained directly from internet applications like OpenStreetMaps (OSM). This map-free GPP frees us from the common area-size restrictions. In the LPP layer, we use a novel Naive-Valley-Path method (NVP) to generate a local path avoiding obstacles in the road in an extremely-low execution time period. This approach exploits the concept of valley areas around local minima, i.e., the ones always away from obstacles. We demonstrate the robustness of the system in our research platform BLUE, driving autonomously across the University of Alicante Scientific Park for more than 20 km in a 12.33 ha area. Our vehicle avoids different static persistent and non-persistent obstacles in the road and even dynamic ones, such as vehicles and pedestrians. Code is available at this https URL.
Price of the college admission scam

With the hopes of securing his son’s spot at USC, Bizzack allegedly donated $200,000 to a fake charity run by admissions center associate Rick Singer and sent a separate donation check in the sum of $50,000 to the school’s sports center as a recruit for the USC volleyball team. Bizzack’s son started at USC in the fall of 2018.
Multi-Attributed and Structured Text-to-Face Synthesis

Generative Adversarial Networks (GANs) have revolutionized image synthesis through many applications like face generation, photograph editing, and image super-resolution. Image synthesis using GANs has predominantly been uni-modal, with few approaches that can synthesize images from text or other data modes. Text-to-image synthesis, especially text-to-face synthesis, has promising use cases of robust face-generation from eye witness accounts and augmentation of the reading experience with visual cues. However, only a couple of datasets provide consolidated face data and textual descriptions for text-to-face synthesis. Moreover, these textual annotations are less extensive and descriptive, which reduces the diversity of faces generated from it. This paper empirically proves that increasing the number of facial attributes in each textual description helps GANs generate more diverse and real-looking faces. To prove this, we propose a new methodology that focuses on using structured textual descriptions. We also consolidate a Multi-Attributed and Structured Text-to-face (MAST) dataset consisting of high-quality images with structured textual annotations and make it available to researchers to experiment and build upon. Lastly, we report benchmark Frechet's Inception Distance (FID), Facial Semantic Similarity (FSS), and Facial Semantic Distance (FSD) scores for the MAST dataset.
Generation of Spin Cat States in an Engineered Dicke Model

We study trajectories of collective spin states of an ensemble of spinors. The spinors considered here are either trapped ions in free space or atoms confined in a cavity, both systems of which are engineered through their interactions with light fields to obey an effective Dicke model. In an appropriate limit of the Dicke model, one obtains one-axis twisting dynamics of the collective spin and evolution after a finite time to a spin cat state, or, in the long-time limit, the Dicke state $|S,0\rangle_x$, conditioned upon there being no photon emissions from the system (i.e., no quantum jumps). If there is a jump, however, the system evolves probabilistically into one of a finite number of entangled-state cycles, where the system then undergoes a persistent sequence of jumps between two Dicke state superpositions in a rotated basis. The different cycles can be distinguished by the frequency at which jumps occur.
R Markdown Cookbook

Journal of the Royal Statistical Society: Series A (Statistics in Society)Volume 184, Issue 4 p. 1613-1613 BOOK REVIEW R Markdown Cookbook , Xie, Yihui, Dervieux, Christophe, Riederer, Emily, Chapman and Hall/CRC Press, pp. xxix + 329 ISBN 9780367563837 Shalabh, Corresponding Author Shalabh shalab@iitk.ac.in Department of Mathematics & Statistics, Indian Institute of Technology, Kanpur, IndiaSearch for more papers by this author Shalabh, Corresponding Author Shalabh shalab@iitk.ac.in Department of Mathematics & Statistics, Indian Institute of Technology, Kanpur, IndiaSearch for more papers by this author First published: 23 August 2021 https://doi.org/10.1111/rssa.12743Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume184, Issue4October 2021Pages 1613-1613 RelatedInformation
A framework for a Secure IoT based EHR system using Blockchain Technology

IoT based Electronic Healthcare system is an emerging trend in the healthcare system. It aims for quick diagnosis and accelerated treatment, provided the patient's case history is already known. It is a well-known fact that nearly thirty million people do not live within an hour of trauma care; this is because of the poor access to the link of a trauma centre. The problems of such sort need an immediate address. The annual mortality rate can significantly reduce if the healthcare history of the patient is maintained correctly. The existing method has solved these problems using IoT techniques with Machine learning techniques; this paper attempts to propose a framework for a secure IoT-based EHR using Blockchain Technology. Blockchain for EHR is used to meet all the security concerns of medical record sharing and accessing like Authentication, privacy, security, scalability & confidentiality.
The 4th Workshop on Heterogeneous Information Network Analysis and Applications (HENA 2021)

The 4th Workshop on Heterogeneous Information Network Analysis and Applications (HENA 2021) is co-located with the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. The goal of this workshop is to bring together researchers and practitioners in the field and provide a forum for sharing new techniques and applications in heterogeneous information network analysis. This workshop has an exciting program that spans a number of subtopics, such as heterogeneous network embedding and graph neural networks, data mining techniques on heterogeneous information networks, and applications of heterogeneous information network analysis. The workshop program includes several invited speakers, lively discussion on emerging topics, and presentations of accepted research papers.
Adaptive 1-D Polynomial Coding of C621 Base for Image Compression

The optimal solution to the difficult issues associated with bytes consumption of digital images is to utilize image compression techniques that essentially based on exploiting redundancy(s) efficiently to minimize image size for storage requirements and /or fast transmitted. 1-D polynomial coding is a simple form of the common 2D- polynomial coding that based on modeling spatial image block information using the 1-D nature, which implicitly diminished the extra coefficients of deterministic part and leads to improved compression performance. In this paper, Adaptive 1-D Polynomial Coding for grayscale image compression is proposed with adopting a new compress scheme of six to one data (C621) base for probabilistic part (residual image) effectively, The experimental results tested on six standard gray square images of medical and natural bases, the results showed elegant performance in terms of CR and PSNR compared to the traditional   1-D coding techniques and the well-known standard JPEG, that the compression ratio increase more than three times compared to the traditional 1-D and with higher quality compared to JPEG for the images converged to the same compression performance.
Towards Vivid and Diverse Image Colorization with Generative Color Prior

Colorization has attracted increasing interest in recent years. Classic reference-based methods usually rely on external color images for plausible results. A large image database or online search engine is inevitably required for retrieving such exemplars. Recent deep-learning-based methods could automatically colorize images at a low cost. However, unsatisfactory artifacts and incoherent colors are always accompanied. In this work, we aim at recovering vivid colors by leveraging the rich and diverse color priors encapsulated in a pretrained Generative Adversarial Networks (GAN). Specifically, we first retrieve matched features (similar to exemplars) via a GAN encoder and then incorporate these features into the colorization process with feature modulations. Thanks to the powerful generative color prior and delicate designs, our method could produce vivid colors with a single forward pass. Moreover, it is highly convenient to obtain diverse results by modifying GAN latent codes. Our method also inherits the merit of interpretable controls of GANs and could attain controllable and smooth transitions by walking through GAN latent space. Extensive experiments and user studies demonstrate that our method achieves superior performance than previous works.
From embodying tool to embodying alien limb: sensory-motor modulation of personal and extrapersonal space

Years ago, it was demonstrated (e.g., Rizzolatti et al. in Handbook of neuropsychology, Elsevier Science, Amsterdam, 2000) that the brain does not encode the space around us in a homogeneous way, but through neural circuits that map the space relative to the distance that objects of interest have from the body. In monkeys, relatively discrete neural systems, characterized by neurons with specific neurophysiological responses, seem to be dedicated either to represent the space that can be reached by the hand (near/peripersonal space) or to the distant space (far/extrapersonal space). It was also shown that the encoding of spaces has dynamic aspects because they can be remapped by the use of tools that trigger different actions (e.g., Iriki et al. 1998). In this latter case, the effect of the tool depends on the modulation of personal space, that is the space of our body. In this paper, I will review and discuss selected research, which demonstrated that also in humans: 1 spaces are encoded in a dynamic way; 2 encoding can be modulated by the use of tool that the system comes to consider as parts of the own body; 3 body representations are not fixed, but they are fragile and subject to change to the point that we can incorporate not only the tools necessary for action, but even limbs belonging to other people. What embodiment of tools and of alien limb tell us about body representations is then briefly discussed.
Development of a Decision Support System for Clustering Scientific Publications Using K-Means

To improve the quality of departments within a university, a decision support system ( DSS ) that maps the number of scientific publications published is needed urgently today. This is because clusters and ranks for a university are determined by these scientific publications. In DSS , there is a crawler bot to recap publication data from Google Scholar and Scopus, as well as the K-means algorithm for clustering departments. DSS will produce 2 clusters with a silhouette score of 0.6245660527900866. A positive value indicates that the data in one cluster are similar. The sample used data from faculty members at the Faculty of Teacher Training and Education at Sebelas Maret University in Indonesia. The results showed that cluster one consisted of 18 departments. The average results were Scopus publication of 0.4669, Google scholar publications of 1.8955, Scopus citations of 0.8328, Google scholar citations of 18.3519. Cluster two consists of six departments., The average results were Scopus publication of 3.1727, Google Scholar publication of 4.2636, Scopus Citation of 9.0636, Google Scholar Citation of 67.6455.
Classification of Medical Images using Deep and Handcrafted Visual Feature-based Algorithm

Though the medical images contain a lot of information, there is a problem in retrieving that information and make it useful for further diagnosis. Information from these medical images can be utilized effectively by implementing classification and retrieval. Features of the image are the most important factors for the image classification. Different kinds of handcrafted features are available which include single descriptors for colour, texture, and shape and combined descriptors. Different feature extraction algorithms like LBP and BOF are used to extract these features. We also have many deep learning techniques that extract deep learned features and are widely acknowledged as a powerful tool for image classification. But due to lack of not large enough dataset, over-fitting may occur. To address these problems, a combined deep and handcrafted visual feature-based algorithm is implemented in this work.
Automatic Keyword Extraction from Persian short Text Using word2vec

With the growing number of Persian electronic documents and texts, the use of quick and inexpensive   methods to access desired texts from the extensive collection of these documents becomes more important. One of the effective techniques to achieve this goal is the extraction of the keywords which represent the main concept of the text. For this purpose, the frequency of a word in the text can not be a proper indication of its significance and its crucial role. Also, most of the keyword extraction methods ignore the concept and semantic of the text. On the other hand, the unstructured nature of new texts in news and electronic        documents makes it difficult to extract these words. In this paper, an automated, unsupervised method for keywords extraction in the Persian language that does not have a proper structure is proposed. This method not only takes into account the probability of occurrence of a word and its frequency in the text, but it also understands the concept and semantic of the text by learning word2vec model on the text. In the proposed method, which is a combination of statistical and machine learning methods, after learning word2vec on the text, the words that have the smallest distance with other words are extracted. Then, a statistical equation is proposed to calculate the score of each extracted word using co-occurence and frequency. Finally, words which have the highest scores are selected as the keywords. The evaluations indicate that the efficiency of the method by the F-measure is 53.92% which is 11% superior to other methods.
Engaging Families with Accelerated Reader and myON

This is part of our webinar series of blogs: covering topics that are particularly useful during the coronavirus pandemic while schools are closed and the vast majority of students and educators are working from home. This blog covers some key resources that you can share with your students and their families to help get everyone engaged with students' reading progress.
The Phantom Alignment Strength Conjecture: Practical use of graph matching alignment strength to indicate a meaningful graph match.

The alignment of a graph matching is a quantity that gives the practitioner a measure of the correlation of the two graphs, and it can also give the practitioner a sense for whether the graph matching algorithm found the true matching. Unfortunately, when a graph matching algorithm fails to find the truth because of weak signal, there may be alignment strength from meaningless matchings that, by random noise, have fewer disagreements than average (sometimes substantially fewer); this alignment may give the misleading appearance of significance. A practitioner needs to know what level of alignment may be phantom alignment and what level indicates that the graph matching algorithm obtained the true matching and is a meaningful measure of the graph correlation. The {\it Phantom Alignment Strength Conjecture} introduced here provides a principled and practical means to approach this issue. We provide empirical evidence for the conjecture, and explore its consequences.
The correlation between Bitcoin and conventional currencies

In today's technology-oriented world, electronic devices are becoming increasingly important for everyone. Because of this, the emergence of cryptocurrency seems natural. Thus, there is an urgent need to understand the impact brought by cryptocurrency to the world financial system. As a type of currency, how will the cryptocurrency influence the other types of traditional currency? We use data related to Bitcoin to illustrate the connection between those two types of currency. By applying machine learning to the data, we found out that there is scarcely any correlation between Bitcoin value and conventional currency value, with the exception of the USD. With this result, we hope to contribute to the establishment of a better currency system and therefore, provide the world with a healthier economic environment. At the same time, the economic society may get a clearer understanding of the interrelationship between the new currency---Bitcoin and several typical examples of conventional currency. Meanwhile, throughout the research, we can acknowledge the main difference on investing strategies between conventional currencies and cryptocurrencies, which will therefore, help us make a wiser decision when it comes to the purchases of money power.
SYSTEM FUNCTIONALITY CONTROL BY AIR MOUSE

This paper puts forward the usage of a camera based hand detection system which uses live stream from a webcam. This system allows the user to perform basic mouse operations like left click, right click, scroll up, scroll down and drag and also feature additional operations like volume adjustment, brightness adjustment, tab switching and desktop switching and even capturing screenshots in your laptop/desktop. This system is cost efficient as it only uses a built in webcam to capture or track the user’s hands having 3D landmarks in two dimensions. The library we use in our proposed system is Mediapipe and it is implemented using Python and OpenCV. Keeping in mind the importance of easy and effortless communication, we have implemented this system. Each operation has its own input gesture and the corresponding output is shown on the screen.
Hybrid Data Mining with the Combination of K-Means Algorithm and C4.5 to Predict Student Achievement

Getting academic achievement is the dream of every student who studies at higher education, especially undergraduate level. Undergraduate students aspire to the highest achievement (champion) at the last achievement of their studies. However, students cannot predict whether these students with the habits that have been done and the current conditions will make them excel or not. Apart from that, of course, students also want to know what factors and conditions influence the achievement the most. The objective to be achieved in this research is how to predict which number of students among them are predicted to excel (champion) at the end of the semester with a combination of the K-Means and C4.5 methods. Besides, the purpose of this study reveals how the K-Means algorithm performs data clustering of student data who will excel or not and how the C4.5 algorithm predicts students who have been grouped. Data processing in this study uses the Rapid Miner software version 9.7.002. The result of this research is that it is easier to group data in numerical form than data in polynomial form. Other results in this study were that out of 100 students, 27 students (27%) were predicted to excel (champions) and 73 (73%) did not achieve (not champions).
New Algorithm For Multi-Robot Path Planning In Known Environment

In this paper, solved a robot path planning problem in known environment using anew algorithm called the Fuzzy Probalistic Road Map Star PRM* algorithm. In this algorithm, we have modeled the workplace including obstacles As fuzzy. Here, we develop PRM algorithm, so that it can run in a fuzzy environment.We assign a weight for each obstacle (fuzzy number) which represents the obstaclemeasures, meaning that whenever this weight is larger, the obstacle becomes more impassable (or more dangerous). This algorithm has been designed for using by robot's movement in difficult areas (such as areas full with obstacles in which the robot requiresto crossover these obstacles to reach its goal). By compare the FPRM* withnormal PRM we found that modeling the environment as fuzzy helps to improve the path.
Sixth Sense Technology using gesture control

Sixth Sense Technology is based on a system consisting a projector which is coupled with a camera and a computer is produced on this paper. Sixth Sense Technology consists a Web-Camera which continuously tracks the hand gestures and produces live output. The camera recognizes the hand gestures instantly and operates the function which is set for that particular gesture. The information is projected onto any surface, including the object or the hand. It can also use its fingers to obtain and manipulate data. It captures a photo by simply making a hand gesture with fingers, in this case a rectangle, indicating the frame of image that you wish to capture, and the system captures the shot—which may then be organised with others using one's hands in the air. The recorded snapshot is subsequently saved in the computing device's memory, where it can be retrieved for modification. Advanced image viewing, which includes zooming in and out, can also be done from anywhere. The goal of this initiative is to bridge the gap between the digital and physical worlds. This is accomplished by creating a system that continuously monitors one's actions and, when a gesture is detected, performs it as a function specific to that gesture. For eg. Capturing an image, sliding and zooming in-out etc. The camera helps in recognizing the gestures, capturing images and projector projects the image on the surface. By this you are carrying a digital world with you and can capture images anytime anywhere you want by just doing simple gestures.