Reviewerly/exHarmony huggingface dataset:
An Appraisal of Content-Based Image Retrieval (CBIR) Methods

Background: Content Based Image Retrieval (CBIR) is an aspect of computer vision and image processing that finds images that are similar to a given query image in a large scale database using the visual contents of images such as colour, texture, shape, and spatial arrangement of regions of interest (ROIs) rather than manually annotated textual keywords. A CBIR system represents an image as a feature vector and measures the similarity between the image and other images in the database for the purpose of retrieving similar images with minimal human intervention. The CBIR system has been deployed in several fields such as fingerprint identification, biodiversity information systems, digital libraries, Architectural and Engineering design, crime prevention, historical research and medicine. There are several steps involved in the development of CBIR systems. Typical examples of these steps include feature extraction and selection, indexing and similarity measurement.&#x0D; Problem: However, each of these steps has its own method. Nevertheless, there is no universally acceptable method for retrieving similar images in CBIR.&#x0D; Aim: Hence, this study examines the diverse methods used in CBIR systems. This is with the aim of revealing the strengths and weakness of each of these methods.&#x0D; Methodology: Literatures that are related to the subject matter were sought in three scientific electronic databases namely CiteseerX, Science Direct and Google scholar. The Google search engine was used to search for documents and WebPages that are appropriate to the study.&#x0D; Results: The result of the study revealed that three main features are usually extracted during CBIR. These features include colour, shape and text. The study also revealed that diverse methods that can be used for extracting each of the features in CBIR. For instance, colour space, colour histogram, colour moments, geometric moment as well as colour correlogram can be used for extracting colour features. The commonly used methods for texture feature extraction include statistical, model-based, and transform-based methods while the edge method, Fourier transform and Zernike methods can be used for extracting shape features.&#x0D; Contributions: The paper highlights the benefits and challenges of diverse methods used in CBIR. This is with the aim of revealing the methods that are more efficient for CBIR.&#x0D; Conclusion: Each of the CBIR methods has their own advantages and disadvantages. However, there is a need for a further work that will validate the reliability and efficiency of each of the method.
The Effects of the PORPE Method on Students’ Reading Comprehension and Metacognitive Awareness

This research was conducted to ascertain: (1) whether or not the application of the PORPE method improves the students’ reading comprehension and (2) whether or not the application of PORPE method improves the students’ metacognitive awareness. The research method employed a quasi-experimental design. The population of this study was the second semester students at Makasssar Muhammadiyah University in the 2015/2016 academic year. The population consisted of two group design and Each group consisted of 32 to 33 students. The sample was chosen by applying total sampling technique. The researcher had the students answer an essay test and take a metacognitive awareness inventory questionaire. The research result showed that: (1) the application of the PORPE method improved the students’ reading comprehension and (2) there is no effect of PORPE in regarding the students’ metacognitive awareness.
Episodic Memory Network with Self-attention for Emotion Detection

Accurate perception of emotion from natural language text is key factors to the success of understanding what a person is expressing. In this paper, we propose an episodic memory network model with self-attention mechanism, which is expected to reflect an aspect, or component of the emotion sementics for given sentence. The self-attention allows extracting different aspects of the input text into multiple vector representation and the episodic memory aims to retrieve the information to answer the emotion category. We evaluate our approach on emotion detection and obtains state-of-the-art results comparison with baselines on pre-trained word embeddings without external knowledge.
Examining the practical side channel resilience of ARX-boxes

Implementations of ARX ciphers are hoped to have some intrinsic side channel resilience owing to the specific choice of cipher components: modular addition (A), rotation (R) and exclusive-or (X). Previous work has contributed to this understanding by developing theory regarding the side channel resilience of components (pioneered by the early works of Prouff) as well as some more recent practical investigations by Biryukov et al. that focused on lightweight cipher constructions. We add to this work by specifically studying ARX-boxes both mathematically as well as practically. Our results show that previous works' reliance on the simplistic assumption that intermediates independently leak (their Hamming weight) has led to the incorrect conclusion that the modular addition is necessarily the best target and that ARX constructions are therefore harder to attack in practice: we show that on an ARM M0, the best practical target is the exclusive or and attacks succeed with only tens of traces.
Reconstruction approach to quantum dynamics of bosonic systems

We propose an approach to analytically solve the quantum dynamics of bosonic systems. The method is based on reconstructing the quantum state of the system from the moments of its annihilation operators, dynamics of which is solved in the Heisenberg picture. The proposed method is general in the sense that it does not assume anything on the initial conditions of the system such as separability, or the structure of the system such as linearity. It is an alternative to the standard master equation approaches, which are analytically demanding especially for large multipartite quantum systems. To demonstrate the proposed technique, we apply it to a system consisting of two coupled damped quantum harmonic oscillators.
A New Method of Calculating Squared Euclidean Distance (SED) Using pTree Technology and Its Performance Analysis

One of the advantages of Euclidean distance is that it measures the regular distance between two points in space. For this reason, it is widely used in the applications where the distance between data points are needed to be calculated to measure similarities. However, this method is costly as there involve expensive square and square root operations. One useful observation is that in many data mining applications absolute distance measures are not necessary as long as the distances are used to compare the closeness between various data points. For example, in classification and clustering, we often measure the distances of multiple data points to compare their distances from known classes or from centroids to assign those points in a class or in a cluster. In this regards, an alternative approach known as Squared Euclidean Distance (SED) can be used to avoid the computation of square root to get the squared distance between the data points. SED has been used in classification, clustering, image processing, and other areas to save the computational expenses. In this paper, we show how SED can be calculated for the vertical data represented in pTrees. We also analyze its performance and compared it with traditional horizontal data representation.
Exploration for Ferrous Metals Using Geochemical Soil Survey in Dagbala-Atte District, Southwestern Nigeria

Geochemical survey of residual soils to explore for ferrous metals in the more prospective Dagbala-Atte District of Igarra Schist Belt, South western Nigeria was undertaken.The study involved sampling of residual soils from 49 sites; geochemical analysis of the soil samples for 9 elements, namely, Co, Cr, Fe, Mn, Mo, Ni, Ti, V and W; and subjecting the geochemical analytical data to both qualitative and statistical analyses which include univariate (histograms and box plots), multivariate statistics (correlation matrix and factor analysis) and construction of geochemical distribution maps.By determining the threshold for each element using the univariate plots, it was possible to isolate elements having anomalous values which were compared with their background concentrations in the World's soils.As a result, only four of the ferrous metals subjected to statistical studies, viz, Co, Cr, Ni and V, were found to have significant anomalous values and are suspected to be mineralized in the district.On the basis of correlation matrix and factor analysis, three groups of element association were recognized, viz, Cr-Ni-V-Ti-Fe, Mn-Co-Ni and Mo-Fe-V groups.Only one suspected mineralization, viz, Cr-Ni-V mineralization and a Co-Ni anomaly were inferred to be present in the Dagbala-Atte District.With the aid of the elemental geochemical distribution maps the suspected Cr-Ni-V mineralization was linked to the sheared, silicified rock at the northern and southern parts of the district while the suspected Co-Ni anomaly is linked to the quartz-biotite schist at the southwestern part.Based on the foregoing findings, it is recommended that lithogeochemical survey be carried out in the areas circumscribed for the suspected mineralization and anomaly of Cr, Ni, V and Co in the district.
Random Decision DAG: An Entropy Based Compression Approach for Random Forest

Tree ensembles, such as Random Forest (RF), are popular methods in machine learning because of their efficiency and superior performance. However, they always grow big trees and large forests, which limits their use in many memory constrained applications. In this paper, we propose Random decision Directed Acyclic Graph (RDAG), which employs an entropy-based pre-pruning and node merging strategy to reduce the number of nodes in random forest. Empirical results show that the resulting model, which is a DAG, dramatically reduces the model size while achieving competitive classification performance when compared to RF.
Quasi-subfield Polynomials and the Elliptic Curve Discrete Logarithm Problem

Abstract We initiate the study of a new class of polynomials which we call quasi-subfield polynomials. First, we show that this class of polynomials could lead to more efficient attacks for the elliptic curve discrete logarithm problem via the index calculus approach. Specifically, we use these polynomials to construct factor bases for the index calculus approach and we provide explicit complexity bounds. Next, we investigate the existence of quasi-subfield polynomials.
Practical Experiments on Fabricated TAS-MRAM Dies to Evaluate the Stochastic Behavior of Voltage-Controlled TRNGs

Noises exist in digital circuits can be leveraged as the source of entropy to design true random numbers generators (TRNGs), which is an important primitive component in cryptography and hardware-based security applications. In this paper, we present practical experiments and results of a TRNG implemented on magnetic random-access memory (MRAM) dies, fabricated by the thermally-assisted-switching MRAM (TAS-MRAM) technology. We, first, explain how one can find out a heating voltage value using that writing operations in the TAS-MRAM dies have a stochastic behavior. Then, we propose an improvement based on a feedback loop from being generated random bits. It helps to adjust the founded heating voltage value for writing operation with the aim of reaching the maximum entropy. Finally, we report the results of some post-processing methods, which are usually required in TRNGs to successfully pass the statistical test of NIST SP-800. The results show that one can generate random numbers with a high quality of randomness using the proposed TRNGs besides simple post-processing methods.
Anomaly Detection in Time-Evolving Attributed Networks

Recently, there is a surge of research interests in finding anomalous nodes upon attributed networks. However, a vast majority of existing methods fail to capture the evolution of the networks properly, as they regard them as static. Meanwhile, they treat all the attributes and the instances equally, ignoring the existence of noisy. To tackle these problems, we propose a novel dynamic anomaly detection framework based on residual analysis, namely AMAD. It leverages the small smooth disturbance between time stamps to characterize the evolution of networks for incrementally update. Experiments conducted on several datasets show the superiority of AMAD in detecting anomalies.
Accelerating Real-Time Tracking Applications over Big Data Stream with Constrained Space

Existing approaches are insufficient to provide real-time results for tracking applications against big and fast data streams. In this paper, we leverage freshness sensitive properties of tracking applications and propose an approximate query answering approach, called FS-Sketch, to accelerating real-time temporal queries over big data streams. FS-Sketch constructs its sketch over high-speed data streams via composed online sampling strategies, including sliding-window sampling and space-constrained sampling. Furthermore, FS-Sketch can compress its sketch into constrained space dynamically via utilizing time-decayed mechanism. We evaluate performance of FS-Sketch using real-world and synthetic datasets. FS-Sketch can respond temporal queries within 2 ms from 1.4 billion records with accurate estimates. Meanwhile, FS-Sketch can also outperform the state-of-the-art big data analytical system (Spark) by 5 orders of magnitude on response time when we query over TB-scale real-world datasets.
ВИКОРИСТАННЯ СИСТЕМИ MOODLE ДЛЯ ВДОСКОНАЛЕННЯ ФОНЕТИЧНОЇ КОМПЕТЕНТНОСТІ МАЙБУТНІХ УЧИТЕЛІВ АНГЛІЙСЬКОЇ МОВИ

The article examines the suitability of using learning management system Moodle as tools for organizing pre-service English teachers' self-study work at improving their phonological competency. As a result of analysis of the main advantages and disadvantages of developing, programming and using the existing multimedia computer courses it was concluded that the revealed disadvantages can be solved by their adaptation and reproduction on distance learning platform Moodle. A reflexive model of improving phonological competency in self-study work is suggested as the basis for exercises and tasks arrangement to improve teachers' to be pronunciation. The structure of the multimedia computer course «Sound English» is analyzed in detail, and the ways of its implementation on learning management system Moodle are suggested, in particular, distribution of learning material into modules and their smaller structural units – cycles. The notion «learning objects» is examined; the advantages of their use and the peculiarities of combination on different levels are given. To implement the seven stages of improving phonological competency the available types of activities in learning management system Moodle were analyzed. The following activities suitable for pronunciation improvement in self-study work are picked out: Read aloud, Questionnaire, Workshop, Lesson, Quiz, Glossary, Assignment. These types of activities are correlated with the multimedia computer course «Sound English» cycle headings: Watch Video, Check Pronunciation, Review Pronunciation Rules, Do Exercises, See Progress. To optimize the process of control and self-control of students' self-study work it is suggested to use self-monitoring worksheets, training questionnaires, and learning diaries. An algorithm of doing the exercises and tasks for improving phonological competency with the use of the system of learning management Moodle is suggested.
Transfer Learning Utilization for Banknote Recognition: a Comparative Study Based on Bosnian Currency

Transfer learning introduces the ability to perform deep learning models over a small set of data. This paper investigates the utilization of three fine-tuned Convolutional Neural Networks (CNNs), namely, Alexnet, Googlenet, and Vgg16. Alexnet and Googlenet consider as the state-of-the-art models in deep learning, while Vgg16 preference due to its depth. Each model was fine-tuned, trained, and tested over a dataset contains Bosnian Banknotes (BAM). The dataset covers 11 classes where 10 images were collected through mobile phone camera for each class. Alexnet showed a better performance in terms of completing the training while Vgg16 showed better performance in terms of accuracy as it achieved 100% compared to 95.24% for Alexnet. Googlenet showed less efficient performance by achieving 88.65%.
MDAL: Multi-task Dual Attention LSTM Model for Semi-supervised Network Embedding

In recent years, both the academic and commercial communities have paid great attentions on embedding methods to analyze all kinds of network data. Despite of the great successes of DeepWalk and the following neural models, only a few of them have the ability to incorporate contents and labels into low-dimensional representation vectors of nodes. Besides, most network embedding methods only consider universal representations and the optimal representations could not be learned for specific tasks. In this paper, we propose a Multi-task Dual Attention LSTM model (dubbed as MDAL), which can capture structure, content, and label information of network and adjust representation vectors according to the concrete downstream task simultaneously. For the target node, MDAL leverages Tree-LSTM structure to extract structure, text and label information from its neighborhood. With the help of dual attention mechanism, the content related and label related neighbor nodes are emphasized during embedding. MDAL utilizes a multi-task learning framework that considering both network embedding and downstream tasks. The appropriate loss functions are proposed for task adaption and a joint optimization process is conducted for task-specific network embedding. We compare MDAL with the state-of-the-art and strong baselines for node classification, network visualization and link prediction tasks. Experimental results show the effectiveness and superiority of our proposed MDAL model.
Recipe Recommendation Based on Ingredients using Machine Learning

Thinking of what to cook is also a difficult problem.To attract children liking, parent need to exchange the menu every day.Parents not only think to what recipe to changes, they also need to consider the nutrition that their children taken.Besides that, some people will forget buy ingredients to stock in their kitchen.This will become a problem when they want to prepare meal within short time.It is difficult to think what to cook with limited ingredient that in the kitchen Many people often cook a dish with a cooking recipe on Websites and magazines.The listed ingredients in the recipe sometimes cannot be prepared.This paper propose a recommendation system for different ingredients.The recommendation ingredients based on co-occurrence frequency of ingredients on recipe database and ingredient category stored in a cooking ontology.For object detection open CV is used which is java based.Basically we are using this platform for feature extraction of ingredients.Grey scale of the image is calculated, further histogram is generated which helps in identification of the food items.
Resource Description and Access Adoption and Implementation in Public Libraries in the United States

This study surveyed the current state of knowledge about, and application or use of, Resource Description and Access (RDA) among American public library catalogers. In 2017, an online survey request was e-mailed to four thousand libraries for the person or persons most responsible for cataloging to complete the questionnaire. More than three hundred libraries responded. The data expose serious concerns with RDA adoption within the public library sector. While a majority of catalogers know about RDA, their working knowledge about it differs substantially depending on whether they work in rural or urban library settings. Regardless, 22 percent of respondants still had not heard of the RDA standard until completing this survey. While further training and educational opportunities (along with funds) for catalogers nationwide would help minimize this disparity, LIS schools also can play a role by educating more thoroughly the next generations of catalogers in this newer descriptive standard. Coming on the brink of a shift in the theoretical framework of the RDA standard, from the Functional Requirements for Bibliographic Records (FRBR) model to the IFLA Library Reference Model (LRM), public library catalogers risk falling even farther behind in their knowledge and competency with the RDA standard.
Challenges in Displaying Health Data on Small Smartwatch Screens.

Using smartwatches for self-tracking purposes has become increasingly common. This tracking is possible as a result of the many sensors embedded in modern smartwatches including GPS, heart rate monitor, accelerometer, and gyroscope. The ability to obtain personal health-related data is one of the most compelling reason to purchase such devices. However, form factor limitations create numerous challenges for users hoping to access and interpret the data available. Typically, users rely on a secondary device, such as a smartphone to view health data. The aim of our research is to identify methods to improve user consumption of health-related data directly on a smartwatch. To study and apply novel visualization approaches, several key challenges need to be addressed. We present these here along with their corollary methods of circumvention.
Detecting anti-patterns in SQL Queries using Text Classification Techniques

A major problem with using relational databases, is writing efficient SQL queries.Some common errors known as anti-patterns are frequent in SQL queries and can seriously impact the query execution time and sometimes, the database general performance.This paper shows how ma-chine learning techniques can be leveraged to detect anti-patterns in SQL queries by approaching the problem as a text classification problem.Our result is a model based on a convolutional neural network that can be used to classify a SQL query into zero, one or many anti-patterns classes.
Blockchains - perspectives and challenges

Blockchain is probably the most disruptive and revolutionary technology since the advent of the World Wide Web in the 1990s. Blockchain became known, above all, as the technology behind the bitcoin cryptocurrency. However, its potential still remains largely unexploited and according to experts, opportunities are almost ‘endless’, both in financial and non-financial sectors. Challenges and opportunities in this field vary greatly from person to person depending notably if they are researchers, practitioners or regulators. After a short introduction in order to place the analysis in its context, this chapter will focus on three selected opportunities, namely blockchain as a new payment method (cryptocurrency), as a new method for funding innovation (ICO/TGE) and as a new organizational structure (DAO); it will then focus on three selected challenges, namely regulatory, environmental and governance. The author then questions in a forward-looking manner whether blockchain may be seen as a gateway from the third to the fourth industrial/digital revolution.