Reviewerly/exHarmony huggingface dataset:
THE CLASSIFIED CHECKING WITH CIPHER TEXT GRID TRAFFIC IN PORTABLE MESSAGING APPLICATIONS

Fortunately, using the recognition and rapid growth and development of social systems, more and more users like to discuss their encounters, for example, assessments, ratings, photos, and moods. Social systems bring together volumes of knowledge contributed by users around the world. This post is versatile. It always contains descriptions of articles / services. We recommend the diffusion factor of interpersonal skills to fully understand the qualification behaviors of users. We explore the social circle of the user and divide the social networks into three components: direct friends, mutual friends and also indirect friends, to understand in depth the diffusions of the classification behavior of social users. In this paper, we suggest a model of personal service qualification conjecture according to the probabilistic matrix factorization, exploring qualified behaviors. It is suggested a perception of the qualification program to represent the user's daily qualification behavior. The similarity between user rating programs is necessary to represent the similarity of interpersonal skills qualification. We ran several experiments on the Yelp and Douban Movie datasets. The experimental results of our model show a significant improvement.
Blind Spots of Neural Sequence Models

Author(s): Neekhara, Paarth | Advisor(s): Dubnov, Shlomo | Abstract: Deep neural networks (DNNs) serve as a backbone of many image, language and speech processing systems. Such models are being deployed extensively in personal devices, cloud-based applications and automated security services like face recognition, speaker identification etc. While DNNs have shown to achieve state of the art results in their respective domains, recent studies have exposed the vulnerabilities of these models to adversarial attacks. The work on adversarial examples has primarily focused on the domain of images. In this work, we explore the vulnerabilities of neural networks working on sequential data like text and audio.We propose a novel method to repurpose text classification networks for alternate tasks. This gives incentive to adversaries to steal computational resources from a system provider. An adversary in such an attack scenario can potentially train a simple input transformation for discrete sequences for repurposing the victim model for a new classification task. We also study the existence of universal adversarial perturbations for Automatic Speech Recognition (ASR) Systems. We propose an algorithm to find a single quasi-imperceptible perturbation, which when added to any arbitrary speech signal, will most likely fool the victim speech recognition model. Our experiments demonstrate the application of our proposed technique by crafting audio-agnostic universal perturbations for the state-of-the-art ASR system -- Mozilla DeepSpeech. Additionally, we show that such perturbations generalize to a significant extent across models that are not available during training, by performing a transferability test on a WaveNet based ASR system.
[PDF] NCERT Solutions for Class 3 English Unit-7 Poem Puppy and I

NCERT Solutions for Class 3 English Unit-7 Poem Puppy and I are part of NCERT Solutions for Class 3 English. Here we have given NCERT Solutions for Class ...
Activity and Fall Detection in the Habitational Environment: Subsystem: Sensor design and Hardware Abstraction Layer

This report is part of a bigger project to design a fall detection system for elderly and describes the hardware of the system. The importance of this research is that elderly can live longer in their home by alerting the correct instances in case of an accident. The hardware consists of a 1x2 meter sensor grid below the surface of the floor. The sensor grid uses the piezoresistivity of linqstat to measure the pressure and the location of the pressured point on the floor. An ESP8266 microprocessor combined with 74HC595 shift registers and a 74HC4067 demultiplexer is used to gather the data and send it to a server. The design is modular allowing easy installation in differently sized or shaped rooms. Each individual sensor grid has an update frequency of 14Hz and uses wifi to connect to a common server. Each module has an average power consumption of 0,74W and a peak power of 1,6W.
[PDF] NCERT Solutions for class 3 English Unit-2 Poem Bird Talk

NCERT Solutions for Class 3 English Unit-2 Poem Bird Talk are part of NCERT Solutions for Class 3 English. Here we have given NCERT Solutions for Class 3 ...
Non-imaging optics: Using Laplacian magic windows and Zernike polynomials

In this report a semi-analytic solution to the Laplacian magic window is proposed. The Laplacian magic window is a term recently introduced in 2017[2]. When a uniform wavefront hits a refractive surface, it creates an illumination distribution behind the surface. When the curvature of the surface is sufficiently small, it can be related linearly to the target illumination, and thus creates a ‘magic window’. The main idea of the semi-analytic solution is that a target illumination or a surface given in terms of Zernike polynomials can be solved analytically and expressed again in Zernike polynomials. The Zernike polynomials are set of complete and orthogonal polynomials that are already used in the field of optics to describe wavefronts of optical surfaces. The results of the semi-analytic solution agree qualitatively with the numeric results and work for complex and diverse inputs. The implementation is done in 2D, but the method is general enough so it can be extended to 3D.
Density Estimation for RWRE

We consider the problem of nonparametric density estimation of a random environment from the observation of a single trajectory of a random walk in this environment. We build several density estimators using the beta-moments of this distribution. Then we apply the Goldenschluger-Lepski method to select an estimator satisfying an oracle type inequality. We obtain non-asymptotic bounds for the supremum norm of these estimators that hold when the RWRE is recurrent or transient to the right. A simulation study supports our theoretical findings.
REINFORCEMENT OF PYTHON LEARNING THROUGH A PROGRAMMING GYMKHANA

1 Universidad de Oviedo, Department of Computer Science (SPAIN)2 Universidad Politécnica de Madrid, Department of Computer Languages and Systems and Software Engineering (SPAIN)
Ray W. Howard Library: Stable Links: eBook

This guide will cover proxy prefixes and the use of building URLs that will work for both on campus and off campus users within the Shoreline Community College information systems. The information here reflects the changes being made to our certificates Linking to Electronic Books such as ACLS, ProQuest Ebooks Central, Ebsco Ebooks, from our Springshare LibGuides pages require a proxy prefix to authenticate student and faculty users.
Integration Strategy for Standalone Compliant Interactive Systems for Add-on IoT based Electronics

I present the idea and inspiration behind the selection of my Ph.D. topic and elaborate the work done until now with a brief look at the future work. My aim is to enhance the functionality of humans, plants, and things by providing them with add-on low-cost standalone flexible systems capable of integration with IoT based systems to make everything smart. For each subcategory, I chose a research problem to potentially find solutions. For humans, I am working on a foil based acoustic sensor optimized to monitor wheezing by designing its diaphragm. Match filtering will then be used to differentiate between different wheezing sounds. For plants, I am developing a standalone flexible ultra-lightweight platform that monitors the microclimate along plants more accurately from the leaf instead of the soil. For things, I plan to install paper-based humidity and temperature sensors to monitor pill health. The challenge is to figure out an integration strategy for incorporating paper sensors in electronic systems as paper sensors are incompatible with soldering.
On solution of 3D inverse problems of magnetic explorations

The new equations of 3D inverse problems of magnetic explorations with explicit operator have been derived. The author takes into account demagnetization factor. As the primary field it was considered the main magnetic field of the Earth and spreading current magnetic field. Based on these equations, it is possible to use the method, which does not require trial-and-error forward modeling. One can construct the inverse problem solutions for set of physical parameters of the medium. Borehole data can be used for choosing the unique solution.
TEACHER EDUCATORS: FACILITATING TRANSFER OF TRAINING

Appears in: EDULEARN19 Proceedings Publication year: 2019Pages: 10485-10495ISBN: 978-84-09-12031-4ISSN: 2340-1117doi: 10.21125/edulearn.2019.2641Conference name: 11th International Conference on Education and New Learning TechnologiesDates: 1-3 July, 2019Location: Palma, Spain
Predicting bugs using symbolic execution graph*

Many bug prediction algorithms have been developed by academia, and also some algorithms were adopted by the industry. These algorithms help reviewers to better distribute their time among patches, since some of them require more care.Symbolic execution is a static analysis method that can be used to find bugs. In this paper, we introduce a new bug prediction algorithm that is based on symbolic execution. Instead of using the results of this static analysis method we use the shape of the symbolic execution graph and how it can be mapped back to the original source code to predict bugs. We also investigate how this metric correlates with well-known methods such as the Rahman metric or Time Weighted Risk.
End-to-end learning for answering structured queries directly over text

Structured queries expressed in languages (such as SQL, SPARQL, or XQuery) offer a convenient and explicit way for users to express their information needs for a number of tasks. In this work, we present an approach to answer these directly over text data without storing results in a database. We specifically look at the case of knowledge bases where queries are over entities and the relations between them. Our approach combines distributed query answering (e.g. Triple Pattern Fragments) with models built for extractive question answering. Importantly, by applying distributed querying answering we are able to simplify the model learning problem. We train models for a large portion (572) of the relations within Wikidata and achieve an average 0.70 F1 measure across all models. We describe both a method to construct the necessary training data for this task from knowledge graphs as well as a prototype implementation.
Counterfactual Learning from Logs for Improved Ranking of E-Commerce Products.

Improved search quality enhances users' satisfaction, which directly impactssales growth of an E-Commerce (E-Com) platform. Traditional Learning to Rank(LTR) algorithms require relevance judgments on products. In E-Com, gettingsuch judgments poses an immense challenge. In the literature, it is proposed toemploy user feedback (add-to-basket (AtB) clicks, orders etc) to generaterelevance judgments. It is done in two steps: first, query-product pair dataare aggregated from the logs and then order rate etc are calculated for eachpair in the logs. In this paper, we advocate counterfactual risk minimization(CRM) approach which circumvents the need of relevance judgements, dataaggregation and is better suited for learning from logged data, i.e. contextualbandit feedback. Due to unavailability of public E-Com LTR dataset, we provide\textit{Commercial dataset} from our platform. It contains more than 10 millionAtB click logs and 1 million order logs from a catalogue of about 3.5 millionproducts associated with 3060 queries. To the best of our knowledge, this isthe first work which examines effectiveness of CRM approach in learning rankingmodel from real-world logged data. Our empirical evaluation shows that CRMapproach learns effectively from logged data and beats strong baseline ranker($\lambda$-MART) by a huge margin. Our method outperforms full-information loss(e.g. cross-entropy) on various deep neural network models. These findings showthat by adopting CRM approach, E-Com platforms can improve product searchquality without artificially mending the data to fit in the traditional LTRalgorithms.
Storage-Optimization Method for Massive Small Files of Agricultural Resources Based on Hadoop

The main function of Hadoop is the storage and processing of big data, especially the processing of large datasets. However, in practice, there are numerous small files, and Hadoop has many flaws when dealing with these small files. A storage-optimization method for numerous agricultural resource small files based on Hadoop is proposed, using the precursor and subsequent relationship between different small files of agricultural resources to merge small files. By accessing small files and performing metadata caching through an index mechanism, as well as the prefetching mechanism of associated small files, the storage-optimization method improves the reading efficiency. Experimental results show that this method reduces the memory consumption of the Hadoop name node and improves the performance of the system.
User Environment Detection with Acoustic Sensors Embedded on Mobile Devices for the Recognition of Activities of Daily Living

The detection of the environment where the user is located, is of extreme use for the identification of Activities of Daily Living (ADL). ADL can be identified by use of the sensors available in many off-the-shelf mobile devices, including magnetic and motion, and the environment can be also identified using acoustic sensors. The main objective of this study is to recognize the environments and some standing ADL to include in the development of a framework for the recognition of ADL and its environments. The study presented in this paper is divided in two parts: firstly, we discuss the recognition of the environment using acoustic sensors (i.e., microphone), and secondly, we fuse this information with motion and magnetic sensors (i.e., motion and magnetic sensors) for the recognition of standing ADL. The recognition of the environments and the ADL are performed using pattern recognition techniques, in order to develop a system that includes data acquisition,data processing, data fusion, and classification methods. The classification methods explored in this study are composed by different types of Artificial Neural Networks (ANN), comparing the different types of ANN and selecting the best methods to implement in the different stages of the system developed. Conclusions point to the use of Deep Neural Networks (DNN)with normalized data for the identification of ADL with 85.89% of accuracy, the use of Feedforward Neural Networks (FNN) with non-normalized data for the identification of the environments with 86.50% of accuracy, and the use of DNN method with normalized data for the identification of standing activities with 100% of accuracy.
Data Science Challenges @ LinkedIn

I plan to talk about a few big challenges we have at LinkedIn in the space of Data Science. The ones coming to my mind are (1) Measuring long-term impact; (2) Learning while preserving privacy; (3) Fairness. I can also touch upon productivity and efficiency -- which is a very practical challenge I'm sure all DS organizations face.
Reconstruction of a Photonic Qubit State with Reinforcement Learning (Adv. Quantum Technol. 7‐8/2019)

When quantum mechanics meets the machine learning techniques, enhanced protocols and opportunities can be expected. In article number 1800074, Shang Yu, Jian-Shun Tang, Chuan-Feng Li and co-workers consider a reinforcement learning algorithm and apply it on the quantum photonics hardware for reconstructing an unknown quantum state with a limited amount of photon copies. This experiment successfully exhibits the advantages of the combination of optical quantum experiments and reinforcement learning.
Secure Two-Phase Decentralized Data Deduplication with Assured Deletion in Multi User Environment

Use of cloud for storage has acquired great importance as number of users and ever increasing growth in generation of data by these users. Even though existence of multiple replicas of files on multiple nodes leads to better availability, on the other hand it unnecessarily drains off valuable storage space on Storage Cloud Service Provider (S-CSP). Data deduplication is a technique that needs to be applied to get rid of these problems. Data deduplication is widely used by S-CSP to reduce amount of storage and save bandwidth by eliminating duplicate copies of same files. Proposed system provides data deduplication at both client and server side thus eliminates target collision. Intra and Inter user data deduplication technique also reduces workload of S-CSP by allowing Deduplication Proxy (DP) to apply intra user data deduplication check. Data deduplication may leads to problem of uncontrolled deletion of files in multi-user environment is also eliminated in this approach.
[PDF] NCERT Solutions for Class 3 English Unit-6 The story of the road

NCERT Solutions for Class 3 English Unit-6 The story of the road are part of NCERT Solutions for Class 3 English. Here we have given NCERT Solutions for ...
Personalized Virtual Keyboard for Multi-Touch Tables

Multi-touch tables provide a high-level collaborative workspace for multiple users around a physical surface. Users sit and work together to perform different tasks. There is no intermediate device between the users and tablet. Users can select and manipulate digital contents using fingers gestures and virtual keyboards. Virtual keyboard still have limitations that can adversely affect the quality and speed of typing on multi-touch table. For multi-touch tablets, small keys and a lack of tactile feedback make text entry with a virtual keyboard difficult, and the zoom and rotation properties exacerbate the problem. Inserting text quickly and accurately is still a challenge on a multi-touch-table. Our paper introduces a new virtual keyboard design, MT-TYPEHEX, which overcomes some of these limitations, increases the efficiency of human interaction with touch screens, and improves performance and productivity in cooperative work. After describing the MT-TYPEHEX, we will make a comparative study between a conventional virtual keyboard (QWERTY) and the MT-TYPEHEX keyboard by conducting experiments to evaluate their performance and error rates. We will also evaluate the user experience by administering questionnaires. From the final results of this comparative study, we will determine which keyboard is the most effective.
[PDF] NCERT Solutions for class 3 English Unit-4 Poem Sea Song

NCERT Solutions for Class 3 English Unit-4 Poem Sea Song are part of NCERT Solutions for Class 3 English. Here we have given NCERT Solutions for Class 3 ...
Personalization of learning activities within a virtual environment for training based on fuzzy logic theory

The development of computers and multimedia technology has opened up new possibilities for training based on virtual reality.Virtual reality is the most powerful extension of simulation based systems.In virtual reality there is a move to three dimensional, multi-sensory interfaces.A virtual environment for training (VET) can be defined as a computer-generated environment based on virtual reality, to simulate the real world.Learning through a VET can personalize learning needs for learners to promote the quality of learning.However, learners can't be provided with appropriate learning activities because often there is no personalized service to respond to each learner's particular needs.The obvious solution is to generate learning activities based on each learner's profile.Yet it is a complex process, especially with the inaccuracy of data that may contains a learner's profile.The main goal of this paper is to associate suitable learning activities to each learner based on his profile, to do so, we propose to employ fuzzy logic technique, and the fuzzy inference system to handle reasoning under uncertainty and inaccuracy which is one major issue of great concern in learner model design.
Research on False Alarm Removal Method Based on SVM for Small Sample Target Detection

In the target detection technology in the field of computer vision, the small sample target detection technology has a small number of samples and insufficient feature extraction ability, resulting in low detection rate and over-fitting. In this paper, a false alarm removal method for small sample target detection is proposed. The Haar +Adaboost algorithm is used for preliminary detection, and the false alarm target is removed by SVM to improve the accuracy of detection. The experimental results show that the accuracy of the small sample target detection is indeed improved, and the detection speed is also faster.
[PDF] Important Questions for Class 12 Computer Science (Python) Chapter - 9 – Networking and Open Source Concepts

Important Questions for Class 12 Computer Science (Python) Chapter - 9 – Networking and Open Source ...
Kemampuan Berbicara Mahasiswa Program Studi Tadris Bahasa Indonesia Jurusan Tarbiyah STAIN Pamekasan

termasuk dalam kategori (cukup) dengan nilai 63,91.Pada aspek kebahasaan termasuk kategori (kurang) dengan nilai rata-rata 58,75; dan (cukup) pada aspek non-kebahasaan dengan nilai 69,06.Untuk itu diharapkan bagi guru dan mahasiswa untuk dapat meningkatkan kemampuan berbicara mahasiswa karena berbicara merupakan kemampuan berbahasa yang sangat penting.(The ability to speak is the language skills most often used.Lessons learned in schools and in universities are not given enough attention and guidance.Likewise, in the Indonesian Language Tadris Study Program which is closely related to speaking ability.As a result, students lack mastery in this skill.The researcher used a Mixed Method with the Sequential Explanatory Research Design.The research data will be collected in the form of speeches from the population of all these students, while the samples taken were 13% or 16 students.Analyzed data using descriptive statistics and Three Flow Models from Miles and Huberman.The results of this study are the students' speaking ability of the Indonesian Language Study Program, Department of Tarbiyah STAIN Pamekasan 2017/2018 Academic Year included in the category (sufficient) with a value of 63,91.In linguistic aspects including (less) category with an average value of 58,75; and (sufficient) on nonlinguistic aspects with a value of 69,06.For this reason, it is expected that teachers and students can improve students' speaking skills because speaking is a very important language ability.
[PDF] NCERT Solutions for Class 3 English Unit-2 Nina and the baby Sparrow

NCERT Solutions for Class 3 English Unit-2 Nina and the baby Sparrow are part of NCERT Solutions for Class 3 English. Here we have given NCERT Solutions ...
Explaining Deep Neural Networks Using Spectrum-Based Fault Localization

The black-box nature of deep neural networks (DNNs) makes it impossible to understand why a particular output is produced, creating demand for Explainable AI. In this paper, we show that statistical fault localization (SFL) techniques from software engineering deliver high quality explanations of the outputs of DNNs, where we define an explanation as a minimal subset of features sufficient for making the same decision as for the original input. We present an algorithm and a tool called DeepCover, which synthesizes a ranking of the features of the inputs using SFL and constructs explanations for the decisions of the DNN based on this ranking. We compare explanations produced by DeepCover with those of the state-of-the-art tools GradCAM, LIME, SHAP, RISE and Extremal and show that explanations generated by DeepCover are consistently better across a broad set of experiments. On a benchmark set with known ground truth, DeepCover achieves 76.7% accuracy, which is 6% better than the second best Extremal.