Reviewerly/exHarmony huggingface dataset:
A Scalable Framework for Acceleration of CNN Training on Deeply-Pipelined FPGA Clusters with Weight and Workload Balancing.

Deep Neural Networks (DNNs) have revolutionized numerous applications, but the demand for ever more performance remains unabated. Scaling DNN computations to larger clusters is generally done by distributing tasks in batch mode using methods such as distributed synchronous SGD. Among the issues with this approach is that to make the distributed cluster work with high utilization, the workload distributed to each node must be large, which implies nontrivial growth in the SGD mini-batch size. In this paper, we propose a framework called FPDeep, which uses a hybrid of model and layer parallelism to configure distributed reconfigurable clusters to train DNNs. This approach has numerous benefits. First, the design does not suffer from batch size growth. Second, novel workload and weight partitioning leads to balanced loads of both among nodes. And third, the entire system is a fine-grained pipeline. This leads to high parallelism and utilization and also minimizes the time features need to be cached while waiting for back-propagation. As a result, storage demand is reduced to the point where only on-chip memory is used for the convolution layers. We evaluate FPDeep with the Alexnet, VGG-16, and VGG-19 benchmarks. Experimental results show that FPDeep has good scalability to a large number of FPGAs, with the limiting factor being the FPGA-to-FPGA bandwidth. With 6 transceivers per FPGA, FPDeep shows linearity up to 83 FPGAs. Energy efficiency is evaluated with respect to GOPs/J. FPDeep provides, on average, 6.36x higher energy efficiency than comparable GPU servers.
Building a text corpus for automatic biographical facts extraction from Russian texts

The tasks of computer linguistics and machine learning related to natural language processing (NLP) often require the use of text corpora. Text corpora are specially prepared collection of documents equipped with text markup containing morphological, syntactic, semantic or other information. The data received from the text corpora is used in supervised machine learning for building classifiers of texts written in natural language and in other tasks associated with natural language processing and computer linguistics. The specificity of the information presented in the corpus, as well as the type of texts, is determined by the aim and tasks of the particular study. This article presents a tool for building a corpus of biographical texts in Russian. The process of building a text corpus includes two stages: the collection of texts and their markup. At the first stage we collected texts suitable for markup. Thus, we included in the corpus biographical articles placed in Wikipedia in free access. For this purpose, we developed an automatic parser based on open Python libraries. The second stage is the semantic markup of the text sentences and the selection of biographical facts. This stage took place in a semi-automatic mode. The article describes the features of the process of building the corpus of biographical facts, taxonomy of biographical facts using in our work, software implementation for text collecting and markup, text representation in the corpus and the characteristics of the prepared corpus.
Enhanced Security for Dynamic Multi Keyword Ranked Search Using Greedy Best First Search

Today, due to the enormous growth of data technology in cloud computing, the data owners are stimulated to outsource their data in data management to reduce cost and for the convenient. Data confidentiality, in general, can be obtained by encrypting the data before it is outsourced. The client stores the encrypted data to the cloud using Searchable encryption schemes and applies keyword search techniques over cipher text domain. But the main problem in outsourcing is the lack of security and privacy for the sensitive data. So, to overcome this, for privacy requirement, the sensitive data can be encrypted before it is outsourced. Various methods were proposed to preserve the privacy and to provide security to the cloud data which are encrypted. Here in this paper, we proposed a tree-based search method over the encrypted datain the cloud that supports dynamic operation and multi-keyword ranked search. Clearly, the commonly used “inverse document frequency (IDF) term frequency (TF)” model and the vector space method are joined in the query generation and index creation to give multi-keyword ranked search. To get high search efficiency, a tree-type index structure, “Greedy Best-first Search” algorithm is proposed based on the tree- index.
Adaptive Activity Monitoring with Uncertainty Quantification in Switching Gaussian Process Models

Emerging wearable sensors have enabled the unprecedented ability to continuously monitor human activities for healthcare purposes. However, with so many ambient sensors collecting different measurements, it becomes important not only to maintain good monitoring accuracy, but also low power consumption to ensure sustainable monitoring. This power-efficient sensing scheme can be achieved by deciding which group of sensors to use at a given time, requiring an accurate characterization of the trade-off between sensor energy usage and the uncertainty in ignoring certain sensor signals while monitor- ing. To address this challenge in the context of activity monitoring, we have designed an adaptive activity monitoring framework. We first propose a switching Gaussian process to model the observed sensor signals emitting from the underlying activity states. To efficiently compute the Gaussian process model likelihood and quantify the context prediction uncertainty, we propose a block circulant embedding technique and use Fast Fourier Transforms (FFT) for inference. By computing the Bayesian loss function tailored to switching Gaussian processes, an adaptive monitoring procedure is developed to select features from available sensors that optimize the trade-off between sensor power consumption and the prediction performance quantified by state prediction entropy. We demonstrate the effectiveness of our framework on the popular benchmark of UCI Human Activity Recognition using Smartphones.
CANDI TINGKIP DAN LINGKUNGANNYA

Environment and humans are two variables that are interrelated and influence each other, as well as their culture and environment. The environment chosen as a place to live and the construction of religious buildings need to consider the potential and resources they have. In building sacred buildings Hindu-Buddhist religions have special consideration for the environment. Tingkip Temple is one of the temples in the Musi Rawas area. The purpose of this paper is to determine the relationship between the establishment of Tingkip temple buildings and natural resources in the Musi Rawas area. This research uses qualitative methods, with inductive reasoning, by collecting library and field data, as well as data processing by conducting environmental analysis. The results of the research show that Musi Rawas has natural potential that is suitable as a place for the establishment of sacred buildings, because it has the type of soil that is suitable for organic farming, besides being surrounded by rivers and creeks, and the vegetation around it in the form of agricultural and plantation crops. Musi Rawas natural resource potential affects the establishment of the Tingkip Temple.
Efficient Clustering Techniques on Hadoop and Spark

Clustering is an essential data mining technique that divides observations into groups where each group contains similar observations. K-means is one of the most popular clustering algorithms that has been used for over 50 years. Due to the current exponential growth of the data, it became a necessity to improve the efficiency and scalability of K-means even further to cope with large-scale datasets known as big data. This paper presents K-means optimisations using triangle inequality on two well-known distributed computing platforms: Hadoop and Spark. K-means variants that use triangle inequality usually require caching extra information from the previous iteration, which is a challenging task to achieve on Hadoop. Hence, this work introduces two methods to pass information from one iteration to the next on Hadoop to accelerate K-means. The experimental work shows that the efficiency of K-means on Hadoop and Spark can be significantly improved by using triangle inequality optimisations.
Teaching Reform of Object-oriented Programming Course Based on OBE

In view of the shortcomings of teacher-centered and curriculum content-centered in Higher Vocational education, this paper adopts the results-oriented and student-centered teaching reform model, takes the course of "Object-oriented Programming" in Higher Vocational Computer Specialty as an example, and advances from the aspects of curriculum objectives, teaching design, teaching implementation and teaching evaluation. In order to carry out reform and exploration, we design a set of relatively perfect curriculum achievement indicators system and make continuous improvement. Through the teaching practice of the course, the achievement degree of the course has been greatly improved, and the expected teaching objectives have been achieved, which provides a new way to improve the teaching quality.
The Technology Acceptance Model on Vein Recognition Payment and Unmanned Store

This article may be used for research, teaching and private study purposes.Any substantial or systematic reproduction, re-distribution, re-selling, loan or sublicensing, systematic supply or
IMPLEMENTASI KOMPILASI ALGORITMA KRIPTOGRAFI TRANSPOSISI COLUMNAR DAN RSA UNTUK PENGAMANAN PESAN RAHASIA

Data security is important in maintaining the confidentiality of certain data that can only be known by those who have rights. If the data transmission is done by using network, there is a big possibility the data to be known by unauthorized parties.The columnar transposition algorithm is one simple transposition password. Columnar transposition is one of the classic cryptographic algorithms. Columnar transposition is one part of the transposition cipher with cryptographic method where the message is written in a row from a specified length, and then the column per column is read again with a reading sequence based on a keyword. Series length is determined by the length of the keyword. The order of column readings is based on column order.RSA Algorithm is one of asymmetric cryptography, which is a type of cryptography that uses two different keys: public key and private key. Thus, there is one key, namely the public key, which can be sent through a free channel, without any particular security. In this case there are two keys arranged so that they have a relationship in modulo arithmetic equation. In this case, combining both columnar transposition algorithms and RSA algorithms allows security in the form of message data to be very effective for locking the data even better.Keywords: Cryptography, Columnar Transposition, RSA, Java
Study on Factors Influencing Job Perception and Job Performance of PDOs: An Empirical Evidence in Case of Karnataka, India

The present study was undertaken to analyze the Job perception and job performance of Panchaya Development officers in Karnataka State during the year 2013-2014.The study area includes Dharwad, Haveri, Bagalkot and Bijapur districts of northern Karnataka.The Ex-post facto" research design was employed for the study.Further, the PDOs working in Dharwad, Haveri.Bagalkot and Bijapur districts were selected as the population for the study.From each districts 38 PDOs were selected by simple random sampling to form total sample of 152 respondents".The results revealed that coefficients of determination (R 2 ) were 0.671 and 0.583 for job perception and job performance, respectively.The results further imply that all the selected eight independent variables put together contributed 67.10 per cent and 58.30 per cent of variation in job perception and job performance of PDOs respectively.In addition, study also reveals that, the constraints in job performance of PDOs shows that, all most 96 per cent of PDOs perceived that they come across with interference of local representatives in performing job activities this coupled with Over workload was the second major constraint as perceived by 83.55 per cent of PDOs.Further, Majority of PDOs perceived co-workers are not co-operating (69.74%), lack of recognition or appreciation for good work (61.84%), lack of time to implement the programmes (56.58%) and lack of promotion opportunities (56.58%) as other constraints in effective job performance.
Evaluation of breast lump using elastography, histopathology and its diagnostic accuracy

Background: Current era has brought with it a dramatic climb in the incidence of malignancies especially breast carcinoma among females. It is known to be top most diagnosed life-threatening malignancy in females across the world. Thus, there is need for an imaging modality that helps in minimizing any delay in treatment initiation and avoids unnecessary biopsy. Elastography is a technique based on the concept that hardness of tissue increases proportionately with pathology developing within it compared to the tissue parenchyma surrounding it.Methods: A prospective cross-sectional study was carried out at JSS Hospital, Mysuru enrolling 90 patients who consented for the study. Patients who gave history of breast lump were admitted and evaluated with detailed history, clinical examination, followed by radiological and pathological investigation. Statistical methods were applied and sensitivity and specificity of B-Mode USG and elastography were determined. The diagnosis was confirmed by histopathology of the specimen.Results: Out of 90 patients, 46 lesions were benign and 44 were malignant. The sensitivity, specificity and diagnostic accuracy of B-mode USG was calculated to be 71.74%, 90.91% and 81.11% and that for elastography was 95.65%, 68.18% and 82.22% respectively.Conclusions: In this study, elastography may complement conventional B-mode USG to improve the diagnostic performance.
A new class of searchable and provably highly compressible string transformations

The Burrows-Wheeler Transform is a string transformation that plays a fundamental role for the design of self-indexing compressed data structures. Over the years, researchers have successfully extended this transformation outside the domains of strings. However, efforts to find non-trivial alternatives of the original, now 25 years old, Burrows-Wheeler string transformation have met limited success. In this paper we bring new lymph to this area by introducing a whole new family of transformations that have all the myriad virtues of the BWT: they can be computed and inverted in linear time, they produce provably highly compressible strings, and they support linear time pattern search directly on the transformed string. This new family is a special case of a more general class of transformations based on context adaptive alphabet orderings, a concept introduced here. This more general class includes also the Alternating BWT, another invertible string transforms recently introduced in connection with a generalization of Lyndon words.
Skill Model Estimation of Ability for Reading Drawings

3D recognition power function learning curve A B S T R A C TIn Japan, students study various ways of understanding 3D drawings in junior high school-level technical education, high schoollevel industrial education, and university-level engineering education.However, without an understanding of trigonometry, reading the drawings is difficult.Moreover, drawing the assembly parts is equally difficult.To address these concerns, many research studies have been conducted.These examinations have used before-after analysis, but none has used time-series data analysis.Therefore, this study aims to develop a new quantitative evaluation method of 3D recognition ability through the use of time-series analysis.
Essential Technical Concepts

Conducting a digital forensics investigation requires a thorough understanding of some of the main technical concepts of computing. Knowing how data is stored in computers, number theory, how digital files are structured, and the types of storage units and the difference between them are essential areas to know how to locate and handle digital evidence. While this book is intended for those with working knowledge of using computers in general (especially Windows OS), there are some technical theories that first must be discussed because of their importance in conducting digital forensics examinations. This chapter will cover those basic concepts.
Pengembangan E-modul Interaktif Berbasis Android dan Nature of Science Pada Materi Ikatan Kimia dan Gaya Antar Molekul Untuk Menumbuhkan Literasi Sains Siswa

Chemical bond sand intermolecular forces have abstract characteristics. Thepresence of technology in learning media can help students understand theconcept. The learning media in question can be in the form of an androidbasedinteractive e-module that provides convenient accessibility for users. Topresent the atmosphere of science learning, the e-module can be structuredwith the orientation of the Nature Of Science. This research and developmentaims to produce an android-based interactive e-module prototype with thenature of science oriented on chemical bonding and intermolecular forcessubject material to foster students' scientific literacy. This type of researchand development study was carried out with the Nieven development modelwhich consisted of 4 stages: (1) preliminary research stage, (2) prototypingstage ,(3) summative evaluation stage, and (4) systematic reflection anddocumentation stage. However, due to limited resources, this study wascarried out until summative evaluation. With the presentation formula andwith the categories, the results of expert validation obtained an averagepresentation of 90% with very feasible categories, and practical test resultson chemistry teachers obtained avalue of 95% with very feasible categoriesand the results of limited group trials obtained 85% with very decentcategory. Therefore, it can be concluded that the interactive e-moduleprototype that developed is very feasible and can be proceed to abroaderstage in fostering students' literacy skills.
Bootstrapping a Neural Morphological Generator from Morphological Analyzer Output for Inuktitut

We present a method for building a morphological generator from the output of an existing analyzer for Inuktitut, in the absence of a two-way finite state transducer which would normally provide this functionality. We make use of a sequence to sequence neural network which “translates” underlying Inuktitut morpheme sequences into surface character sequences. The neural network uses only the previous and the following morphemes as context. We report a morpheme accuracy of approximately 86%. We are able to increase this accuracy slightly by passing deep morphemes directly to output for unknown morphemes. We do not see significant improvement when increasing training data set size, and postulate possible causes for this.
AN IOT DATA ANOMALY RESPONSE MODEL FOR SMART FACTORY PERFORMANCE MEASUREMENT

The recent rapid proliferation of Internet of Things increases the possibility of IoT failure due to complexity of the network and large data volumes. The demand for clear acquisition of data as an important source of performance measurement presents manufacturing companies with a major challenge. To cope with IoT fault problem, we developed the performance measurement and IoT anomaly response model. Development comprises three steps: (1) configuration of the IoT-based performance measurement process using an Overall Equipment Effectiveness Key Performance Indictor and IoT anomaly response model architecture; (2) implementation of an IoT fault case classification and data anomaly detection and mitigation algorithm, using K-means and statistical inference methods; and (3) validation of the proposed algorithm through experimental simulation. The experimental simulation results show that the proposed algorithm has a positive impact on IoT data anomaly detection and mitigation, thus enabling a response to the IoT fault problem.
The Screen Object Design Pattern in Android UI Tests

The Screen Object Design Pattern in mobile UI tests is equivalent to the well known Page Object Design Pattern in web tests, which is the abstraction layer representing an interface that allows its users to operate page elements or validate the page state. Since Page Object takes its name from the web page, it is hard to name a mobile application View or Screen, which is represented to the users as a page. This chapter demonstrates how the Screen Object Design Pattern can be applied to Android UI tests using Kotlin. You will learn to create a screen object that represents a single Android application activity or fragment (i.e., a screen) and then use these objects or their methods in tests that represent real user flows.
Connective intelligence to stay safe while shopping online for e-products and e-services on business-2-business and business-2-consumer websites

Electronic commerce markets provide benefits for both buyers and sellers; however, there are risks involved. Various assurance models have attempted to mitigate online risks, but are limited when it comes to compliance. The aim of this study is to propose a connective intelligent model for safe online shopping called the page ranking analytical hierarchy process (PRAHP). PRAHP uses complementary techniques of the analytical hierarchy process (AHP) and page ranking (PR) to evaluate the trustworthiness of web attributes. PRAHP assesses five important attributes from ten selected websites. PRAHP's paradigms were evaluated on business-to-business (B2B) and business-to-consumer (B2C) sites and validated by customer comments. The inbound links were adjusted to test their effect on PRAHP's reliability. 50%, 40% and 10% of the sites were found to be partially trustworthy, untrustworthy and fully trustworthy, respectively. Based on the results, the model is very reliable in providing customers guidance on purchasing decisions.
Outlier Detection for Line Matching

Finding counterparts for straight lines over multiple images is a fundamental task in image processing, and the base for 3D reconstruction methods using segments. This paper introduces novel insights to improve the state-of-the-art unsupervised line matching over groups of images, aimed to source geometrical relations for 3D reconstruction algorithms. Most of the line-based 3D reconstruction methods published are ballasted as a consequence of sourcing the correspondences from matching methods that are not designed for this purpose. The repetitive line patterns present in many man-made structure turns difficult to came up with an outliers-free set of segment correspondences. The presented approach integrates an outliers detector based on 3D structure into a state of the art line matching algorithm.