Reviewerly/exHarmony huggingface dataset:
A Fast Finite Field Multiplier for SIKE

Various post-quantum cryptography algorithms have been recently proposed. Supersingluar isogeny Diffie-Hellman key exchange (SIKE) is one of the most promising candidates due to its small key size. However, the SIKE scheme requires numerous finite field multiplications for its isogeny computation, and hence suffers from slow encryption and decryption process. In this paper, we propose a fast finite field multiplier design that performs multiplications in GF(p) with high throughput and low latency. The design accelerates the computation by adopting deep pipelining, and achieves high hardware utilization through data interleaving. The proposed finite field multiplier demonstrates 4.48 times higher throughput than prior work based on the identical fast multiplication algorithm and 1.43 times higher throughput than the state-of-the-art fast finite field multiplier design aimed at SIKE.
Yes! The Kid Lit Collective has pubslished ...

Yes! The Kid Lit Collective has pubslished the 2020 Summer Reading List: https://wtpsite.com/2020/05/28/2020-summer-reading-list-2/ #literacies #edu407 #engchat #nctechat #nctevillage #elachat
LPar - A Distributed Multi Agent platform for building Polyglot, Omni Channel and Industrial grade Natural Language Interfaces.

The goal of serving and delighting customers in a personal and near human like manner is very high on automation agendas of most Enterprises. Last few years, have seen huge progress in Natural Language Processing domain which has led to deployments of conversational agents in many enterprises. Most of the current industrial deployments tend to use Monolithic Single Agent designs that model the entire knowledge and skill of the Domain. While this approach is one of the fastest to market, the monolithic design makes it very hard to scale beyond a point. There are also challenges in seamlessly leveraging many tools offered by sub fields of Natural Language Processing and Information Retrieval in a single solution. The sub fields that can be leveraged to provide relevant information are, Question and Answer system, Abstractive Summarization, Semantic Search, Knowledge Graph etc. Current deployments also tend to be very dependent on the underlying Conversational AI platform (open source or commercial) , which is a challenge as this is a fast evolving space and no one platform can be considered future proof even in medium term of 3-4 years. Lately,there is also work done to build multi agent solutions that tend to leverage a concept of master agent. While this has shown promise, this approach still makes the master agent in itself difficult to scale. To address these challenges, we introduce LPar, a distributed multi agent platform for large scale industrial deployment of polyglot, diverse and inter-operable agents. The asynchronous design of LPar supports dynamically expandable domain. We also introduce multiple strategies available in the LPar system to elect the most suitable agent to service a customer query.
THE PRINCIPLE OF CLARITY AS A METHOD OF TEACHING SPECIAL DISCIPLINES IN HIGHER EDUCATION

The article analyzes the application of the principle of clarity as a means of activating the attention of students, increasing the level of perception of educational material when conducting classes in higher educational institutions of Ukraine. It is proposed to use the principle of clarity when conducting training sessions in special (technical) disciplines. The application of the principle of clarity in teaching is substantiated.
Teaching the Discipline «Pedagogy and Psychology of Higher Education» to Foreign Post-Graduate Students: Problems and Tasks

The role of the discipline «Pedagogy and Psychology of Higher Education» in the formation of pedagogical, academic and research competences of post-graduate students is analyzed. The analysis is focused on the organizational, methodical and didactic problems encountered in teaching this discipline to foreign post-graduate students. The study has been carried out by generalizing the experience gained from teaching the course to foreign post-graduate students at the Moscow Power Engineering Institute. Based on this experience, the following key problems have been identified: the peculiarities of teaching a humanity discipline to foreign students, the need to set up a modern teaching and methodical complex, consideration of social-cultural features, and strengthening the practical orientation of the course. Practical guidelines helping to overcome the vulnerabilities encountered in teaching the above-mentioned course have been formulated.
BREAST CANCER PREDICTION USING MACHINE LEARNING

Women are seriously threatened by breast cancer with high morbidity and mortality. The lack of robust prognosis models results in difficulty for doctors to prepare a treatment plan that may prolong patient survival time. Hence, the requirement of time is to develop the technique which gives minimum error to increase accuracy. Four algorithm SVM, Logistic Regression, Random Forest and KNN which predict the breast cancer outcome have been compared in the paper using different datasets. All experiments are executed within a simulation environment and conducted in JUPYTER platform. Aim of research categorises in three domains. First domain is prediction of cancer before diagnosis, second domain is prediction of diagnosis and treatment and third domain focuses on outcome during treatment. The proposed work can be used to predict the outcome of different technique and suitable technique can be used depending upon requirement. This research is carried out to predict the accuracy. The future research can be carried out to predict the other different parameters and breast cancer research can be categorises on basis of other parameters.
Research on the Evaluation Method of Students' Classroom Performance Based on Artificial Intelligence

In order to solve the problem of time-consuming and low reliability of the traditional evaluation method for students' classroom performance, an artificial intelligence-based evaluation method for students' classroom performance is proposed. This paper establishes the evaluation standard of students' classroom performance and quantifies the evaluation of students' classroom performance. Classroom monitoring device is driven by program code. The initial acquired image is processed by image enhancement and recognition, and the result is transmitted to the upper computer. This paper analyses the evaluation indexes of students' classroom performance, such as attendance, participation, attention and abnormal behaviour, and finally outputs the results of teachers' classroom performance. The experimental results show that compared with the traditional evaluation method, the artificial intelligence-based evaluation method saves 69 seconds on average and improves its reliability by 6.25%.
ANALYSIS OF MULTIPLE CORRELATIONADMISSION OF NEW STUDENT ZONATIONSYSTEM (PPDB) ON LEARNING MOTIVATION OFCLASS X OF SMAN 1 KARANGANYAR

Education is a process of teaching and learning activities as a whole to educate the nation's life. Insimple terms, the education of basic words of students in a large Indonesian dictionary which means is to maintainand give teachings, guidance on morals and intelligence of the mind. Therefore, in accepting students must bekept away from the content of ethnicity, race, and religion. This encourages the government to implement therules for student admission using the Zoning system as stipulated in Permendikbud Number 51 the Year 2018.Asa Favorite School in Karanganyar Regency, SMAN 1 Karanganyar admits students with a zoning system. In thecourse of the consequences of the Zoning, system can be seen in class X both the Science and Social Sciencesclass of 2019/2020. Midterm Assessment Results (PTS) for Mathematics subjects as much as 71% of 430 studentswhose grades are below KKM (Minimum completeness criteria).The purpose of this study is to describe the effectof acceptance of new students through the zoning system on the learning motivation of class X students of SMAN1 Karanganyar in the Academic Year 2019/2020. The research method used in this study is a descriptive methodwith a quantitative approach. The sample in this study amounted to 128 respondents. Data collection techniquesusing an android-based questionnaire and analysis of multiple correlations.Based on the results of research thathas been done, it is known that there is a positive or significant influencewith the high closeness category betweenthe admission of new students through the zoning system to the learning motivation of class X students of SMAN1 Karanganyar in the Academic Year 2019/2020.
Voting with Random Classifiers (VORACE)

We propose an innovative ensemble technique that uses voting rules over a set of randomly-generated classifiers. Given a new input sample, we interpret the output of each classifier as a ranking over the set of possible classes. We then aggregate these output rankings using a voting rule, which treats them as preferences over the classes. We show that our approach obtains good results compared to the state-of-the-art, both providing a theoretical analysis and an empirical evaluation of the approach on several datasets.
Perfect Zero Knowledge: New Upperbounds and Relativized Separations

We investigate the complexity of problems that admit perfect zero-knowledge interactive protocols and establish new unconditional upper bounds and oracle separation results. We establish our results by investigating certain distribution testing problems: computational problems over high-dimensional distributions represented by succinct Boolean circuits. A relatively less-investigated complexity class $$\mathrm{ SBP}$$ emerged as significant in this study. The main results we establish are: Results (1) and (3) imply an oracle separating $$\mathrm{ PZK}$$ from $$\mathrm{ NIPZK}$$ . Our results refine the landscape of perfect zero-knowledge classes in relation to traditional complexity classes.
Reinforcement Learning Algorithms for Autonomous Adaptive Agents

Intelligent agents are being designed to automate many tasks - for e.g., traffic signal control, vehicle driving, inventory control and are also being used in improving lives of people - like in healthcare, agriculture, wildlife protection etc. The widespread deployment of intelligent agents requires that we minimize the bottlenecks which affect their performance and utility. Motivated by this challenge, my thesis proposes new algorithms and methods which helps the agent in efficiently operating in the real-world and also during interaction with humans. My work has shown significant improvements in the performance of deployed agents, when operating in real world.
Making DensePose fast and light

DensePose estimation task is a significant step forward for enhancing user experience computer vision applications ranging from augmented reality to cloth fitting. Existing neural network models capable of solving this task are heavily parameterized and a long way from being transferred to an embedded or mobile device. To enable Dense Pose inference on the end device with current models, one needs to support an expensive server-side infrastructure and have a stable internet connection. To make things worse, mobile and embedded devices do not always have a powerful GPU inside. In this work, we target the problem of redesigning the DensePose R-CNN model's architecture so that the final network retains most of its accuracy but becomes more light-weight and fast. To achieve that, we tested and incorporated many deep learning innovations from recent years, specifically performing an ablation study on 23 efficient backbone architectures, multiple two-stage detection pipeline modifications, and custom model quantization methods. As a result, we achieved $17\times$ model size reduction and $2\times$ latency improvement compared to the baseline model.
B. Peningkatan Hasil Belajar Matematika Menggunakan Metode Demonstrasi Dalam Pembuktian Luas Bangun Datar Siswa Kelas V SDN 2 Banyuurip Ujungpangkah Gresik Tahun Ajaran 2018 / 2019

Abstract Learning activities are low because the learning method applied is not appropriate, therefore researchers are interested in conducting classroom action research with the title Improving Mathematics Learning Outcomes Using Demonstration Methods In Proving the Area of ??Flat Building is the result of classroom action research that ultimately results can be developed in school institutions, so that a little can help teachers overcome mathematics learning which is expected to improve the quality of learning through Demonstration methods. The results of the analysis that have been performed are analyzed. on the first and second cycles of learning with the use of Demonstration media. Students who got a score of 70 and above were 3 students, while those who scored less than 70 there were 4 students. Thus students who complete their study are 43%, so that classically it has not been completed. While from the daily repetition analysis in the second cycle towards increasing classical completeness from 43% in the first cycle to 100% and the average value from 65 to 88. Based on the results the research that has been done can be concluded that the demonstration method can improve the results and mathematics learning activities of class V students on extensive evidence of flat building material. Keywords: Demonstration Method, Proof of Build Flat Area.
The Effects of Professional Background Knowledge and Text Simplification on the Reading Comprehension of Adult Learners of English

Despite numerous studies on each topic individually, background knowledge and text simplification have rarely been looked at together in one study review. Furthermore, background knowledge tends to be looked at in cultural, and not professional, terms. This study looks at the reading comprehension of adults in an EFL context. It sought to find whether possession of background knowledge was more important to comprehension than the simplification of reading materials. As such, 160 Korean professionals were scored on their reading comprehension of both implicit and explicit information, using texts that were either simplified or from an authentic source, and either related to their area of professional expertise or on an unrelated, general topic. The study
Active Community Detection with Maximal Expected Model Change

We present a novel active learning algorithm for community detection on networks. Our proposed algorithm uses a Maximal Expected Model Change (MEMC) criterion for querying network nodes label assignments. MEMC detects nodes that maximally change the community assignment likelihood model following a query. Our method is inspired by detection in the benchmark Stochastic Block Model (SBM), where we provide sample complexity analysis and empirical study with SBM and real network data for binary as well as for the multi-class settings. The analysis also covers the most challenging case of sparse degree and below-detection-threshold SBMs, where we observe a super-linear error reduction. MEMC is shown to be superior to the random selection baseline and other state-of-the-art active learners.
Biometrics in the Quran Perspective: Definition, History, and Type

The Qur’an is the holy book which is inexhaustibly studied by many people. It has inspired a lot of thoughts, research and studies. The discussions of human in the Qur’an are numerous, ranging from the creation, life, naming, relationships of one another, and etc.. The Qur’an mentions that man is created in the perfect form, which is given advantages over other creatures. With his intellectual power, human tries to explore knowledge about him and understand his nature as a creature of God. Biometrics views human as a unique being. Biometrics sees that parts of the human body can be used as a security device because each human has his special uniqueness different from one another. Because of this specificity, human invents devices that refer to all materials, types of equipment, labours, and man-made systems to replicate the existing systems in nature. At present, the scientific community really needs such a device, especially in the field of nanotechnology, robot technology, artificial intelligence, medicine and military. Biometric normally used in the form of authentification, including Introduction to Fingerprint, Face Recognition, Recognition Retina or Iris, Geometry Arm, Geometry Finger, introduction of Palms, Voice Recognition, Introduction to Signatures, DNA (Deoxyribonucleic Acid), Thermal Imaging (Body Temperature), Shape Ear, Body Odor, Body Movement. On some types of biometric authentication on top of the al-Qur’an gives a signal on Surah Fussilat [41]: 20-22.
8 Boolean Atoms Spanning the 256-Dimensional Entanglement-Probability Three-Set Algebra of the Two-Qutrit Hiesmayr-Loffler Magic Simplex of Bell States

We obtain formulas (bot. p. 12)--including $\frac{2}{121}$ and $\frac{4 \left(242 \sqrt{3} \pi -1311\right)}{9801}$--for the eight atoms (Fig.~\ref{fig:Venn}), summing to 1, which span a 256-dimensional three-set (P, S, PPT) entanglement-probability boolean algebra for the two-qutrit Hiesmayr-L{\o}ffler states. PPT denotes positive partial transpose, while P and S provide the Li-Qiao necessary {\it and} sufficient conditions for entanglement. The constraints ensuring entanglement are $s> \frac{16}{9} \approx 1.7777$ and $p> \frac{2^{27}}{3^{18} \cdot 7^{15} \cdot13} \approx 5.61324 \cdot 10^{-15}$. Here, $s$ is the square of the sum (Ky Fan norm) of the eight singular values of the $8 \times 8$ correlation matrix in the Bloch representation, and $p$, the square of the product of the singular values. In the two-{\it ququart} Hiesmayr-L{\o}ffler case, one constraint is $s>\frac{9}{4} \approx 2.25$, while $\frac{3^{24}}{2^{134}} \approx 1.2968528306 \cdot 10^{-29}$ is an upper bound on the appropriate $p$ value, with an entanglement probability $\approx 0.607698$. The $S$ constraints, in both cases, prove equivalent to the well-known CCNR/realignment criteria. Further, we detect and verify--using software of A. Mandilara--pseudo-one-copy undistillable (POCU) negative partial transposed two-qutrit states distributed over the surface of the separable states. Additionally, we study the {\it best separable approximation} problem within this two-qutrit setting, and obtain explicit decompositions of separable states into the sum of eleven product states. Numerous quantities of interest--including the eight atoms--were, first, estimated using a quasirandom procedure.
Research Guides: Remote Support and Access for SJU Library Users: Textbooks and Publisher Access

Whenever, wherever you are working or studying, SJU Library provides online support and access. Check here for changes to library access and services during the COVID-19 pandemic.
Dependent randomized rounding for clustering and partition systems with knapsack constraints

Clustering problems are fundamental to unsupervised learning. There is an increased emphasis on fairness in machine learning and AI; one representative notion of fairness is that no single demographic group should be over-represented among the cluster-centers. This, and much more general clustering problems, can be formulated with knapsack and partition constraints. We develop new randomized algorithms targeting such problems, and study two in particular: multi-knapsack median and multi-knapsack center. Our rounding algorithms give new approximation and pseudo-approximation algorithms for these problems. One key technical tool, which may be of independent interest, is a new tail bound analogous to Feige (2006) for sums of random variables with unbounded variances. Such bounds can be useful in inferring properties of large networks using few samples.
Zero-Knowledge to the Rescue: Consistent Redundant Backup of Keys Generated for Critical Financial Services

We present the work on HADKEG: a protocol for Highly Available Distributed Key Generation. The context is a highly sensitive redundant generation for use and redundant recovery of a set of symmetric cryptography keys. These keys need to be trusted (random) and secure against failures of randomness employment and leakages, and be available via a recovery procedure which needs to be redundant (high availability constraints) yet secure and consistent (i.e., the correct recovery has to be assured regardless of recovery server availability). The working environment allows for distributed key generating parties initiating the system, and a set of recovery and operating agents that hold the key and may be at time off-line. These very practical concrete security, redundancy (availability), and integrity requirements, that typify real world highly sensitive services, operate in a special environment where, as we said, not all recovery agents are available at all times, yet where transfers of encrypted information is semi-synchronous and globally available to parties that become on-line. In this architecture, it turned out, that the usually considered theoretical and costly transferable Zero-Knowledge proofs, actually help overcome the operational and integrity constraints. We present a protocol we implemented called HADKEG: Highly Available Distributed Key Generation. It combined distributed key generation, special encryption and transferable zero-knowledge proofs to achieve the practical goal in the working environment.