Reviewerly/exHarmony huggingface dataset:
Segmentation of Touching Arabic Characters in Handwritten Documents by Overlapping Set Theory and Contour Tracing

Segmentation of handwritten words into characters is one of the challenging problem in the field of OCR.In presence of touching characters, make this problem more difficult and challenging.There are many obstacles/challenges in segmentation of touching Arabic handwritten text.Although researches are busy in solving the problem of segmentation of these touching characters but still there exist unsolved problems of segmentation of touching offline Arabic handwritten characters.This is due to large variety of characters and their shapes.So in this research, a new method for segmentation of touching Arabic Handwritten character has been developed.The main idea of the proposed method is to segment the touching characters by identifying the touching point by overlapping set theory and ending points of the Arabic word by applying some standard morphology operation methods.After identifying all the points, segmentation method is applied to trace the boundaries of characters to separate these touching characters.Experiments were conducted on touching characters taken from different data sets.The results show the accuracy of the proposed method.
In-service Science Teachers' Readiness of Integrating Augmented Reality

This paper investigates the readiness degree among in-service science teachers in Ma'an Governorate for integratingaugmented reality in teaching according to Technological, Pedagogical and Content Knowledge TPACK frameworkand its domains. The study sample consisted of (60) in-service science teachers enrolled voluntary in the study. Theresearcher used descriptive statistics, ANCOVA and T-test. The data analyses revealed significant differences amongteachers' readiness in a high degree. Paired sample t-test showed female teachers' readiness higher than male teachersat all of the TPACK the domains. Results of ANCOVA revealed that experience of teachers' readiness do not differsignificantly. The study recommended the focus on integrating augmented reality within learning environments.
FUSE: A Microservice Approach to Cross-domain Federation using Docker Containers

In crisis situations, it is important to be able to quickly gather information from various sources to form a complete and accurate picture of the situation.However, the different policies of participating companies often make it difficult to connect their information sources quickly, or to allow software to be deployed on their networks in a uniform way.The difficulty in deploying software is exacerbated by the fact that companies often use different software platforms in their existing networks.In this paper, Flexible federated Unified Service Environment (FUSE) is presented as a solution for joining multiple domains into a microservice based ad hoc federation, and for deploying and managing container-based software on the devices of a federation.The resource requirements for setting up a FUSE federation are examined, and a video streaming application is deployed to demonstrate the performance of software deployed on an example federation.The results show that FUSE can be deployed in 10 minutes or less, and that it can support multiple video streams under normal network conditions, making it a viable solution for the problem of quick and easy cross-domain federation.
Barriers to VR use in HE

VR promises revolutionary changes in the levels of immersion that users can experience, and if applied successfully in educational contexts this deep immersion could have significant effects on both teaching and learning.To utilise VR effectively in the higher education (HE) space, there must be some consideration given to what might prevent the use of VR in this sector and why these barriers exist-and how they can be mitigated against.Based on an extensive research project involving qualitative interviews with 21 VR makers and designers in autumn 2017, following a thematic analysis of the interview data, this paper identifies 5 major barriers to the uptake of VR in a wider cultural sense and in a specific, educational context.These identified barriers are: the materiality of VR and issues with headsets and cables; interfaces within VR and issues with haptic technology; the 'language of VR' and the difficulty in communicating the benefits of VR; cybersickness and gender issues with VR use, and, the cost of VR.The preparation of educational VR materials requires an acknowledgement of these sometimes-concealed barriers to VR use, and it is proposed that through knowledge-transfer and sharing of best practice the use of VR in higher education could become a model of best practice for designing inclusive VR experiences that avoid major barriers to participation in VR.
Discrete Signals with Special Correlation Properties

The methods of synthesis of discrete signals are analyzed with the special cross-correlation properties: m-sequences, signals of Legendre, Barker, Paley-Plotkin, Gold, small and large set of Kasami.Comparative researches of properties of the formed discrete signals are conducted.Separate direction develops in development of methods of forming of discrete signals, that is based on the use of algebraic and structural properties of circular shifts of group codes.It is shown that offered approach allows forming the great numbers of discrete sequences, ensemble and cross-correlation properties of that are set by the properties of the corresponding group controlled from distance, structural and cyclic.
Further results on Euclidean and Hermitian linear complementary dual codes

By using the cogredience theories of an alternate matrix, a symmetric matrix and a Hermitian-symmetric matrix, we will find a special family of generator matrices for any linear code, and then using the special family of generator matrices, we will provide a general method to construct a linear complementary dual (LCD) code (resp. a Hermitian LCD code) from any given linear code. Still using the special family of generator matrices for LCD codes (resp. Hermitian LCD codes), we will present the enumeration of all [n,k] LCD codes (resp. Hermitian LCD codes).
HUMAN DETECTION BASED ON A SEQUENCE OF THERMAL IMAGES USING DEEP LEARNING

Abstract. Human detection has been playing an increasingly important role in many fields in recent years. Human detection is a still challenging task because, for the group of people, each individual has his unique appearance and, body shape.Compared with the traditional method, the deep learning neural network has the advantages of shorter computing time, higher accuracy and easier operation. Therefore, deep learning method has been widely used in object detection. The current state of art in human detection is RetinaNet. Among all the deep learning approaches, RetinaNet gives the highest accuracy of human detection (Lin, Goyal, Girshick, He, &amp;amp; Piotr Dollar, 2018).The temporal component of video provides additional and significant clues as compared to the static image. In this paper, the temporal relationship of the images is utilized to improve the accuracy of human detection. Compared to using only an image, the accuracy of human detection is 21.4% higher when a sequence of images is applied.
Near-Optimal Multicast Tree Construction in Leaf-Spine Data Center Networks

In this paper, we revisit the optimization problem of constructing multicast trees for cloud applications in the context of leaf-spine data center network (DCN). On one hand, we find that, the maximum multicast rate a network can provide equals to the minimum of the maximum unsplit throughput it can provide to each of the receivers. On the other hand, by taking the characteristic of leaf-spine DCN into account, we prove that the optimal construction of multicast tree in leaf-spine DCNs is equivalent to the well-known problem of minimum set cover, distinguished from the conventional wisdom that models the problem as a general-purpose directed minimum Steiner. Based on these findings, we develop simple yet near-optimal algorithms to construct multicast trees and deal with network failures.
Optimizing Answer Set Computation via Heuristic-Based Decomposition

Abstract Answer Set Programming (ASP) is a purely declarative formalism developed in the field of logic programming and non-monotonic reasoning: computational problems are encoded by logic programs whose answer sets, corresponding to solutions, are computed by an ASP system. Different, semantically equivalent, programs can be defined for the same problem; however, performance of systems evaluating them might significantly vary. We propose an approach for automatically transforming an input logic program into an equivalent one that can be evaluated more efficiently. One can make use of existing tree-decomposition techniques for rewriting selected rules into a set of multiple ones; the idea is to guide and adaptively apply them on the basis of proper new heuristics, to obtain a smart rewriting algorithm to be integrated into an ASP system. The method is rather general: it can be adapted to any system and implement different preference policies. Furthermore, we define a set of new heuristics tailored at optimizing grounding, one of the main phases of the ASP computation; we use them in order to implement the approach into the ASP system DLV , in particular into its grounding subsystem ℐ-DLV , and carry out an extensive experimental activity for assessing the impact of the proposal.
Statistical modeling and process optimization of coagulation-flocculation for treatment of municipal wastewater

In this study coagulation-flocculation method was evaluated to treat municipal wastewater using aluminum sulphate and calcium hydroxide as coagulants, and polyacrylamide as flocculant by varying pH from 5 to 7 and coagulant dosage from 100 to 300 mg L -1 at constant flocculant dosage (10 mg L -1 ), time (1 min for rapid mixing followed by 30 min for slow mixing), temperature (30°C ± 2°C), agitation speed (150 rpm for rapid mixing followed by 50 rpm for slow mixing) and settling time (2 h) by jar test method.The responses were percentages reduction in turbidity, chemical oxygen demand (COD) and biochemical oxygen demand (BOD).The sample was real-time wastewater from sewage treatment plant of Salalah Sanitary Drainage Services (SSDS) Company, Salalah, Sultanate of Oman.The trials were performed with single and mixed or combined coagulants.Response surface methodology (RSM) based central composite design (CCD) was used for the optimization of pH and coagulant dosage to maximize percentage reduction in turbidity, COD and BOD.The maximum of 99.7%, 98%, and 95.5% were achieved for turbidity removal using aluminum sulphate, calcium hydroxide and combined coagulants respectively.However, maximum of 60%, 58.1%, and 42.5% were achieved for COD removal using calcium hydroxide, combined and aluminum sulphate respectively.Moreover, maximum of 79.5%, 78.5%, and 54.5% were achieved for BOD removal using aluminum sulphate, calcium hydroxide and combined coagulants respectively.The obtained results indicate that treatment using combined coagulant is effective for removal of turbidity and COD, and could be used for the treatment of municipal wastewater.
MV-C3D: A Spatial Correlated Multi-View 3D Convolutional Neural Networks

As the development of deep neural networks, 3D object recognition is becoming increasingly popular in the computer vision community.Many multi-view-based methods are proposed to improve the category recognition accuracy.These approaches mainly rely on multi-view images that are rendered with the whole circumference.In real-world applications, however, 3D objects are mostly observed from partial viewpoints in a less range.Therefore, we propose a multi-view-based 3D convolutional neural network that takes only part of contiguous multi-view images as input and can still maintain high accuracy.Moreover, our model takes these view images as a joint variable to better learn the spatially correlated features using 3D convolution and 3D max-pooling layers.The experimental results on ModelNet10 and ModelNet40 datasets show that our MV-C3D technique can achieve outstanding performance with multi-view images that are captured from partial angles with less range.The results on 3D-rotated real image dataset MIRO further demonstrate that MV-C3D is more adaptable in real-world scenarios.The classification accuracy can be further improved with the increasing number of view images.
The index lift in data mining has a close relationship with the association measure relative risk in epidemiological studies

Data mining tools have been increasingly used in health research, with the promise of accelerating discoveries. Lift is a standard association metric in the data mining community. However, health researchers struggle with the interpretation of lift. As a result, dissemination of data mining results can be met with hesitation. The relative risk and odds ratio are standard association measures in the health domain, due to their straightforward interpretation and comparability across populations. We aimed to investigate the lift-relative risk and the lift-odds ratio relationships, and provide tools to convert lift to the relative risk and odds ratio.We derived equations linking lift-relative risk and lift-odds ratio. We discussed how lift, relative risk, and odds ratio behave numerically with varying association strengths and exposure prevalence levels. The lift-relative risk relationship was further illustrated using a high-dimensional dataset which examines the association of exposure to airborne pollutants and adverse birth outcomes. We conducted spatial association rule mining using the Kingfisher algorithm, which identified association rules using its built-in lift metric. We directly estimated relative risks and odds ratios from 2 by 2 tables for each identified rule. These values were compared to the corresponding lift values, and relative risks and odds ratios were computed using the derived equations.As the exposure-outcome association strengthens, the odds ratio and relative risk move away from 1 faster numerically than lift, i.e. |log (odds ratio)| ≥ |log (relative risk)| ≥ |log (lift)|. In addition, lift is bounded by the smaller of the inverse probability of outcome or exposure, i.e. lift≤ min (1/P(O), 1/P(E)). Unlike the relative risk and odds ratio, lift depends on the exposure prevalence for fixed outcomes. For example, when an exposure A and a less prevalent exposure B have the same relative risk for an outcome, exposure A has a lower lift than B.Lift, relative risk, and odds ratio are positively correlated and share the same null value. However, lift depends on the exposure prevalence, and thus is not straightforward to interpret or to use to compare association strength. Tools are provided to obtain the relative risk and odds ratio from lift.
RANCANG BANGUN GAME EDUKASI PENGENALAN WARNA UNTUK PENDIDIKAN ANAK USIA DINI MENGGUNAKAN ADOBE FLASH BERBASIS ANDROID

Early childhood education (PAUD) is the level of education before the level of basic education.Where education is a coaching effort aimed at children aged two to five.In this age one aspect of intelligence to be developed is the knowledge of color.This is done to train the level of creativity of children and prepare children to enter the next education level.In addition, color recognition for early childhood can also detect and identify color blindness in children.In this education the child will be guided to learn while playing or vice versa, so that children do not get bored with learning materials received.In the delivery of the material must be done easily and interesting, then it is required an interactive learning media.In this study will be made the introduction of color patterns in the form of Android phone games where today many children who are smart operate this phone, so it can be used for education.This research uses Research and Development method.The result of this study is an Educational Game designed to help teachers or parents to further increase their interest in learning color recognition and can be installed in Android phones..
SCHEDA: Lightweight euclidean-like heuristics for anomaly detection in periodic time series

Detecting anomalies in time series in real time can be challenging, in particular when anomalies can manifest themselves at different time scales and need to be detected with minimal latency. The need for lightweight real-time algorithms has risen in the context of Cloud computing, where thousands of devices are monitored and deviations from normal behaviour must be detected to prevent incidents. However, this need has yet to be addressed in a way that actually scales to the size of today’s network infrastructures. Typically, time series generated by human activity often exhibit daily and weekly patterns creating long-term dependencies that are difficult to process. In such cases, the euclidean distance between subsequences of the time series, or euclidean anomaly score, can be a very effective tool to achieve good detection within constrained latency; however, this computation has a quadratic complexity and a computational footprint too high for any realistic application. In this paper, we propose SCHEDA (Sampled Causal Heuristics for Euclidean Distance Approximation), a collection of three heuristics designed to approximate the euclidean anomaly score with a low computational footprint in time series with long-term dependencies. Our design goals are a low computational cost, the possibility of real-time operation and the absence of tuning parameters. We benchmark SCHEDA against ARIMA and the euclidean distance and show that in typical monitoring scenarios, it outperforms both at only a fraction of the computational cost.
Critical perspectives on provable security: Fifteen years of "another look" papers

We give an overview of our critiques of 'proofs' of security and a guide to our papers on the subject that have appeared over the past decade and a half. We also provide numerous additional examples and a few updates and errata.
Rationally Reappraising ATIS-based Dialogue Systems

The Air Travel Information Service (ATIS) corpus has been the most common benchmark for evaluating Spoken Language Understanding (SLU) tasks for more than three decades since it was released.Recent state-of-the-art neural models have obtained F1-scores near 98% on the task of slot filling.We developed a rule-based grammar for the ATIS domain that achieves a 95.82% F1-score on our evaluation set.In the process, we furthermore discovered numerous shortcomings in the ATIS corpus annotation, which we have fixed.This paper presents a detailed account of these shortcomings, our proposed repairs, our rulebased grammar and the neural slot-filling architectures associated with ATIS.We also rationally reappraise the motivations for choosing a neural architecture in view of this account.Fixing the annotation errors results in a relative error reduction of between 19.4 and 52% across all architectures.We nevertheless argue that neural models must play a different role in ATIS dialogues because of the latter's lack of variety.
Combining Knowledge Hunting and Neural Language Models to Solve the Winograd Schema Challenge

Winograd Schema Challenge (WSC) is a pronoun resolution task which seems to require reasoning with commonsense knowledge.The needed knowledge is not present in the given text.Automatic extraction of the needed knowledge is a bottleneck in solving the challenge.The existing state-of-the-art approach uses the knowledge embedded in their pretrained language model.However, the language models only embed part of the knowledge, the ones related to frequently co-existing concepts.This limits the performance of such models on the WSC problems.In this work, we build-up on the language model based methods and augment them with a commonsense knowledge hunting (using automatic extraction from text) module and an explicit reasoning module.Our end-to-end system built in such a manner improves on the accuracy of two of the available language model based approaches by 5.53% and 7.7% respectively.Overall our system achieves the state-of-theart accuracy of 71.06% on the WSC dataset, an improvement of 7.36% over the previous best.
An Adaptive and Asymmetric Residual Hash for Fast Image Retrieval

Hashing algorithm has attracted great attention in recent years. In order to improve the query speed and retrieval accuracy, this paper proposes an adaptive and asymmetric residual hash (AASH) algorithm based on residual hash, integrated learning, and asymmetric pairwise loss. The specific description of the AASH algorithm is as follows: 1) the integrated learning model is proposed based on transfer learning and multi-feature fusion strategy to learn the database hash code; 2) the residual hash model is proposed based on ResNet-50 to learn the query image hash code; 3) the asymmetric pairwise loss is proposed and the parameters of the residual hash model is optimized based on the database hash code; 4) the algorithm learns the database hash code and the query image hash code in an asymmetric manner, and integrates the feature learning part and the hash-coded part in one frame. The experimental results on three different datasets fully demonstrate that the proposed AASH method has better performance than most symmetric and asymmetric deep hash algorithms. Specifically, the optimal result of the AASH algorithm is 0.971 on Cifar10 when the hyperparameter is 100 and the hash code length is 32. The optimal result of the AASH algorithm is 0.945 on ceil images when the hyperparameter is 10 and the hash code length is 24. The optimal result of the AASH algorithm is 0.945 on FD-XJ when the hyperparameter is 15 and the hash code length is 32. In addition, the algorithm verifies convergence, time loss, and effectiveness.
Spade

RDF data is complex; exploring it is hard, and can be done through many different metaphors. We have developed and propose to demonstrate Spade, a tool helping users discover meaningful content of an RDF graph by showing them the results of aggregation (OLAP-style) queries automatically identified from the data. Spade chooses aggregates that are visually interesting , a property formally based on statistic properties of the aggregation query results. While well understood for relational data, such exploration raises multiple challenges for RDF: facts, dimensions and measures have to be identified (as opposed to known beforehand); as there are more candidate aggregates, assessing their interestingness can be very costly; finally, ontologies bring novel specific challenges but also novel opportunities, enabling ontology-driven exploration from an aggregate initially proposed by the system. Spade is a generic, extensible framework , which we instantiated with: ( i ) novel methods for enumerating candidate measures and dimensions in the vast space of possibilities provided by an RDF graph; ( ii ) a set of aggregate interestingness functions; ( iii ) ontology-based interactive exploration, and ( iv ) efficient early-stop techniques for estimating the interestingness of an aggregate query. The demonstration will comprise interactive scenarios on a variety of large, interesting RDF graphs.
AutoML Strategy Based on Grammatical Evolution: A Case Study about Knowledge Discovery from Text

The process of extracting knowledge from natural language text poses a complex problem that requires both a combination of machine learning techniques and proper feature selection.Recent advances in Automatic Machine Learning (AutoML) provide effective tools to explore large sets of algorithms, hyperparameters and features to find out the most suitable combination of them.This paper proposes a novel AutoML strategy based on probabilistic grammatical evolution, which is evaluated on the health domain by facing the knowledge discovery challenge in Spanish text documents.Our approach achieves state-ofthe-art results and provides interesting insights into the best combination of parameters and algorithms to use when dealing with this challenge.Source code is provided for the research community.