Reviewerly/exHarmony huggingface dataset:
Lightning Talk - Ruuh: A Conversational Social Agent

Dialogue systems and conversational agents are becoming increasingly popular in the modern society but building an agent capable of holding intelligent conversation with its users is a challenging problem for artificial intelligence. In this talk, we share challenges and learnings from our journey of building a deep learning based conversational social agent called ”Ruuh” (m.me/Ruuh) developed by a team at Microsoft India to converse on a wide range of topics. The authors are co-creators of Ruuh and the original paper was presented in NeurIPS 2018 Demonstration Track by two of the authors. As a social agent, Ruuh needs to think beyond the utilitarian notion of merely generating ”relevant” responses and meet a wider range of user social needs. The agent also needs to detect and respond to abusive language, sensitive topics and trolling behavior of users. Some of the above objectives pose significant research challenges in the areas of NLP, IR and AI. Our agent has interacted with over 2 million real world users till date which has generated over 150 million user conversations. We intend to walk the audience through our journey of overcoming several research challenges to become the most popular social agent in India.
Effect of Derivative Action on Back-Propagation Algorithms

Multilayer neural networks using supervised training try to minimize the error between a given correct answer and the ones produced by the network. The weights in the neural network are adjusted at each iteration and after adequate epochs, adjusted weights give results close to correct answers. Besides the current error, accumulated errors from past iterations are also used for updating weights. This resembles the integral action in control theory, but the method took the name momentums in machine learning. Control theory uses one more technique for achieving faster tracking: the derivative action. In this research, we added the missing derivative action to the training algorithm and obtained promising results. The training algorithm with derivative action achieved 3.8 times speedup comparing to the momentum method.
The Effect of Video in Teaching Writing Skill across Different Personality

This research aims to investigate: firstly, whether there is the effect of videos as the authentic materials on the student’s achievement writing recount paragraph between two groups. Secondly, conducted to know whether there is different in the achievement writing recount paragraph of students’ introvert and extrovert and the thirdly, the basic interaction form of learning tough using video and students personality in writing recount paragraph. This study uses quasi-experimental research design. The population of the research was the eighth grade students of PLUS ISYHAR Junior High. The students of class 8-1 consist of 30 students as the experimental group, and the students of class 8-3 consist of 32 students as the control group. The result of the research showed several results. First, there is significant difference in the students’ writing recount paragraph taught by using video as the authentic materials and conventional technique. Second, the result of the research showed that extrovert and introvert students’ had the same result of writing paragraph recount. Third, there is no interaction between students taught using video and students’ personality in writing recount paragraph.
A Research on SOA in the IT Industry of Pakistan

Many architectural patterns and styles are used for bringing in quality in software's such as client-server, component-based, event-driven and data-centric architectures. Service-oriented architecture (SOA) is a new trend for improving the quality attributes of software such as interoperability and availability. Although, research studies claim that SOA adaption is widely used in practice, it is not very clear that is it used in the same way in under developed countries such as Pakistan. This research study analyzes the usage of SOA by the IT industry of Pakistan. In this paper, several opinions of professionals are collected from IT industry who belongs to different companies from all over the Pakistan. Questionnaires and semi-structured interviews helped as investigation tools for this research and the outcomes are analyzed on their responses. Results indicate that 76 % of the IT companies in Pakistan are using SOA. Most of the companies are using tool-based approach for development. Results also indicate a rising tendency of SOA adoption by Pakistan IT industry in upcoming years.
What Value-at-Risk and Expected Shortfall Metrics Tell a Risk Averse Investor in Cryptocurrencies

The article aims to quantify relative attractiveness of investing into cryptocurrencies for a risk averse investor with standard measures of risk. As market capitalization of cryptocurrencies reached a record $823 billion in January 2018 they could be considered to represent a new investable asset class. Introduction of Bitcoin futures contracts on the Chicago Board Options Exchange (Cboe) and the Chicago Mercantile Exchange (CME) in December 2017 gave large institutional investors such as hedge funds and mutual funds opportunities to enter the cryptocurrencies market in order to diversify investment portfolios and/or to gain exposure to potentially undervalued assets. CFTC’s qualification of cryptocurrencies as commodities makes analysis of market risk associated with them even more necessary. Having compared non-parametric historical one-day Value-at-Risk and Expected Shortfall metrics for 283 cryptocurrencies with that of traditional asset classes (equities, bonds, currencies and commodity futures) we show that the least volatile cryptocurrency Bitcoin is almost twice as risky as the most volatile traditional asset, i.e. natural gas. Risk averse investors can lower market risk of Ethereum and Ripple by investing in portfolio of most valuable digital currencies. By tightness of returns correlations 8 most valuable cryptocurrencies form three clusters. Combining Bitcoin, which with Litecoin comprises a separate cluster, with Dash and Stellar from other clusters could reduce the capitalization-weighted portfolio’s VaR below Bitcoin’s VaR level. However, none of the possible combinations of Bitcoin with cryptocurrencies from two other clusters could reduce ES metric below that of Bitcoin itself. Stellar and Monero have the most prominent footprint in the crypto-currencies space after Bitcoin. Bitcoin – Monero – Stellar capitalization-weighted portfolio is less risky than Bitcoin in terms of VaR.
Towards building actionable indicators of compromise based on a collaboration model

In cyber and threat intelligence areas, Indicators of Compromise (IOC) can be used as inputs to security controls to guide defense and mitigation activities. We propose a collaboration model in certain attributes in IOC model related to the (1) seriousness of the threat that the IOC triggers and (2) the confidence in the IOC detection or prediction are built based on a community or collaborative model. In this model, users can subscribe or introduce new IOCs based on their own/systems’ exposures or analysis. They can also assess IOCs created by others and vote to continuously change IOC seriousness and confidence values.
Every generalized cat state achieves the ultimate scaling sensitivity in quantum metrology

We show a general relationship between a superposition of macroscopically distinct states and sensitivity in quantum metrology. Generalized cat states are defined by using an index which extracts the coherence between macroscopically distinct states, and a wide variety of states, including a classical mixture of an exponentially large number of states, has been identified as the generalized cat state with this criterion. We find that, if we use the generalized cat states for magnetic field sensing without noise, we achieve the Heisenberg limited sensitivity. Moreover, we even show that sensitivity of generalized cat states achieves the ultimate scaling sensitivity beyond the standard quantum limit under the effect of dephasing. As an example, we investigate the sensitivity of a generalized cat state that is attainable through a single global manipulation on a thermal equilibrium state and find an improvement of a few orders of magnitude from the previous sensors. Clarifying a wide class that includes such a peculiar state as metrologically useful, our results significantly broaden the potential of quantum metrology.
The Role of Studying Discrete Mathematics in the Formation of the Logic of Thinking for Computer Science Students

The famous Swiss psychologist and the founder of the theory of cognitive development Jean Piaget in his works [12, 13] on intelligence formation, expresses the idea that the last (highest) stage of intelligence development is the mastery of formal operations. In his opinion, this usually happens by the age of 12. However, it can be continued in the future. As teachers of Higher School, we have made a number of observations that even at student age a person improves in mastering formal operations, i.e. his logical argumentation becomes clear as a result of learning Mathematics. An interest was aroused to the Falsehood Logic as a stage in the logical development of the individual after reading some of Piaget's mathematical works [11, 14] and the Biryukovs' articles [2]. This work is an attempt to confirm Piaget's thesis that a transition from the Falsehood Logic to the Classical Logic occurs in logical thinking due to growing up. In this regard, the role of studying Discrete Mathematics in this transition is in the focus of our attention.
A Semi-local Method for Image Retrieval

The visual content of an image is expressed by global or local features. Global features describe some properties of the image such as color, texture and shape. Local features were successfully used for object category recognition and classification to extract the local information from a set of interest points or regions. In this paper, we propose a semi-local method to extract the features based on the previous features extraction methods. Our technique is called the “Spatial Pyramid Matching: SPM”. It works by partitioning the image into increasingly fine sub-regions (or blocs) and computing histograms of global features found inside each bloc. The results obtained by the proposed method are illustrated through some experiments on Wang and Holidays Dataset. The obtained Results show the simplicity and efficiency of our proposal.
A Lightweight System to Achieve Proactive Risk Management for Household ASIC-Resistant Cryptocurrency Mining

Nowadays, many household computers are used to mine ASIC-resistant cryptocurrency, which brings serious safety risks. In this letter, a light weight system is put forward to achieve proactive risk management for the kind of mining. Based on the system requirement analysis, a brief system design is presented and furthermore, key techniques to implement it with open source hardware and software are given to show its feasibility.
Prosody Correction Preserving Speaker Individuality for Chinese-Accented Japanese HMM-Based Text-to-Speech Synthesis

This article proposes a prosody correction method based on partial model adaptation for Chinese-accented Japanese hidden Markov model (HMM)-based text-to-speech synthesis. Although text-to-speech synthesis built from non-native speech accurately reproduces the speaker's individuality in synthetic speech, the naturalness of the synthetic speech is strongly degraded. In the proposed model, to improve the naturalness while preserving the speaker individuality of Chinese-accented Japanese text-to-speech synthesis, we partially utilize HMM parameters of native Japanese speech to synthesize prosody-corrected synthetic speech. Results of an experimental evaluation demonstrate that duration and F0 correction are significantly effective for improving naturalness.
MIDAS: Multilinear detection at scale

We focus on two classes of problems in graph mining: (1) finding trees and (2) anomaly detection in complex networks using scan statistics. These are fundamental problems in a broad class of applications. Most of the parallel algorithms for such problems are either based on heuristics, which do not scale very well, or use techniques like color coding, which have a high memory overhead. In this paper, we develop a novel approach for parallelizing both these classes of problems, using an algebraic representation of subgraphs as monomials—this methodology involves detecting multilinear terms in multivariate polynomials. Our algorithms show good scaling over a large regime, and they run on networks with close to half one billion edges. The resulting parallel algorithm for trees is able to scale to subgraphs of size 18, which has not been done before, and it significantly outperforms the best prior color coding based method (FASCIA) by more than two orders of magnitude. Our algorithm for network scan statistics is the first such parallelization, and it is able to handle a broad class of scan statistics functions with the same approach.
Proposal of Education Teaching Material Using a Steam Locomotive Model for Acquiring Manufacturing Technologies

In this study, a new education teaching material using a steam locomotive model to acquire technical skills of high accuracy and high-density assembly is proposed. The proposed teaching material is processed by hand finishing, a turning process, and welding. In this teaching material, the machining and assembling accuracy are evaluated by pressure values, which are required for running, as well as the travel distance of the steam locomotive model. Thus, this teaching material can be used for introductory education for acquiring manufacturing technologies, because the importance of the machining and assembling accuracy can be understood.
SMT-Based Encoding of Argumentation Dialogue Games

The aim of this work is to explore possibilities of performing semantic verification of dialogue game argumentation protocols. To this end, we analyse a chosen dialogue system which allows for the simulation of a parent-child discourse. The system under analysis is enriched by the addition of elements of emotional reasoning. In this paper, we show only the first stage of applying the verification technique. It consists of encoding a mathematical game model as an SMT formula. Then, we compute the satisfiability of the conjunction of this formula with another formula that encodes a feature of the model. This technique can be used to verify the correctness of the protocol in the sense of it satisfying the expected properties.
Introductory Mechanical Engineering Education Applying Experience-Based Learning in National Institute of Technology

This paper describes introductory education for mechanical engineering with application of experience-based learning at a Japanese National Institute of Technology. For first-year students, a cross-discipline class designated as ‘Introduction to Engineering’ was introduced. In classes managed by the Department of Mechanical Engineering and Robotics, students learned experience-based lessons that included experiments and manufacturing in addition to conventional lectures. Responses to questionnaires administered to students indicated that almost all had enhanced interest in mechanical engineering. This report describes that experience-based learning gives National Institute of Technology students opportunities to learn proactively in pursuit of their goal.
Research on Frequency Table Storage Method Based on Least Square Method

In this paper, the storage method of frequency table in universal combinatorial coding is researched. According to frequency characteristics of the data which the content is irregular, the trend of the total frequency value of each character is analyzed, and the information of frequency table of each segment data is predicted, then the storage rules of frequency table are determined. By calculating the frequency distribution information of each character, the least square method was used to calculate the frequency value. The optimal frequency value was first obtained by linear fitting of the average frequency value, and then the difference between the frequency value of each segment and the obtained optimal frequency value was calculated. Experimental results show that the method proposed in this paper can improve the efficiency of frequency table storage and save the storage space required by coding.
Design of a True Random Number Generator Based on Low Power Oscillator with Increased Jitter

This paper presents the design of an oscillator-based true random number generator. The operation of the presented TRNG architecture is based on sampling a high-frequency oscillator output with a clock generated by a low-frequency noisy oscillator. The recycling folded cascode architecture was used for low power noise amplifier. A new method to achieve higher jitter in the low frequency oscillator is presented. The bit rate of the designed TRNG is 1.02 Mb/s. The circuit power consumption is 67 μW. The results of the simulations and statistical tests of the designed random number generator are also presented in this paper.
A Privacy-Preserving Framework for Collaborative Association Rule Mining in Cloud

Collaborative Data Mining facilitates multiple organizations to integrate their datasets and extract useful knowledge from their joint datasets for mutual benefits. The knowledge extracted in this manner is found to be superior to the knowledge extracted locally from a single organization's dataset. With the rapid development of outsourcing, there is a growing interest for organizations to outsource their data mining tasks to a cloud environment to effectively address their economic and performance demands. However, due to privacy concerns and stringent compliance regulations, organizations do not want to share their private datasets neither with the cloud nor with other participating organizations. In this paper, we address the problem of outsourcing association rule mining task to a federated cloud environment in a privacy-preserving manner. Specifically, we propose a privacy-preserving framework that allows a set of users, each with a private dataset, to outsource their encrypted databases and the cloud returns the association rules extracted from the aggregated encrypted databases to the participating users. Our proposed solution ensures the confidentiality of the outsourced data and also minimizes the users' participation during the association rule mining process. Additionally, we show that the proposed solution is secure under the standard semihonest model and demonstrate its practicality.
Towards a data-driven framework for realistic self-organized virtual humans

Driven by significant investments from the gaming, film, advertising, and customer service industries among others, efforts across many different fields are converging to create realistic representations of humans that look like (computer graphics), sound like (natural language generation), move like (motion capture), and reason like (artificial intelligence) real humans. The ultimate goal of this work is to push the boundaries even further by exploring the development of realistic self-organized virtual humans that are capable of demonstrating coordinated behaviors across different modalities. Eye movements, for example, may be accompanied by changes in facial expression, head orientation, posture, gait properties, or speech. Traditionally however, these modalities are captured and modeled separately and this disconnect contributes to the well-known uncanny valley phenomenon. We focus initially on facial modalities, in particular, coordinated eye and head movements (and eventually facial expressions), but our proposed data-driven framework will be able to accommodate other modalities as well. transfer [Laine et al. 2017]. Despite these advances, the resulting renderings or animations are often still distinguishable from a real human, sometimes in unsettling ways - the so called uncanny valley phenomenon [Mori et al. 2012]. We argue that the traditional approach of capturing and modeling various human modalities separately contributes this effect. In this work, we focus on capturing, transferring, and generating realistic coordinated facial modalities (eye movements, head movements, and eventually facial expressions). We envision a flexible framework that can be extended to accommodate other modalities as well.
Use of robotics during special education for students with ntellectual disability (mentally defective)

This article is about the ways of using robotics in education, special education, to be precise. The author lists ways of using robotics on the basis of Lego Education More To Math platform. In the conclusion author gives a summary of his experiments and speaks of effectiveness of this particular method of studying.
Mobile Object Detection Using 2D and 3D Basic Geometric Figures in Colour and Grayscale

Abstract In this paper, we use TensorFlow Mobile Lite for Object Detection with datasets of basic geometric figures on iOS mobile devices. Additionally, we trained 4 datasets in 2D and 3D and we compare the accuracy of detection between using colour and grayscale image data. Also, we evaluate the detection rate using 2D and 3D for some kind of normal objects in precision and label output. We used Convolutional Neural Networks (CNN) for build the datasets and OPENCV for convert into grayscale. We value the result relation between flat and volume datasets in way of label and numeric detection, also the affectation of TensorFlow Mobile with this kind of datasets. We make comparisons based on the results of the different experiments object the detection and, in this work, TensorFlow Mobile Lite implementation does not have pseudo boxes and the reason is explained for detection purposes and accuracy adjusted to this kind of experiments.
The Experimental Demonstration of Correcting the Atmospheric Dispersion Using Image Processing Based on Edge Extension

We present an image processing algorithm based on edge extension to correct the influence of atmospheric dispersion. The Elden model is used to estimate the image dispersion index caused by atmospheric dispersion and the image affected by the atmospheric dispersion is regarded as the results of the original image convolution operation. When the direct convolution is used to compensate the blur of star, border effect and ill-posed problem make the result unacceptable. To solve these problems, we use image preprocessing and perform an edge extension method for images before the convolution. The simulated analysis and experimental results from a 300 mm telescope system show that the proposed method can effectively correct the influence of atmospheric dispersion even under relatively low signal to noise ratio (SNR&lt;2). Compared with the traditional prism correction and fiber correction methods, this technique can greatly reduce the complexity of the optical system.
Affective Context-Aware Systems: Architecture of a Dynamic Framework

Affective computing gained a lot of attention from researchers and business over the last decade. However, most of the attempts for building systems that try to predict, or provoke affective state of users were done for specific and narrow domains. This complicates reusing such systems in other, even similar domains. In this paper we present such a solution, that aims at solving such problem by providing a general framework architecture for building affective-aware systems. It supports designing and development of affective-aware solutions, in a holistic and domain independent way.
How to Create a Twitter Account: Easily Make a Page Today!

If you’re one of those folks who aren’t familiar with Twitter at all, I have your back. In this tutorial on how to create a Twitter account, I'll teach you everything you need to know including some important Twitter slang that will be very useful to you.