Reviewerly/exHarmony huggingface dataset:
The Overview of Multi-person Pose Estimation Method

Research on Multi-person pose estimation is partly improved by deep learning and the computer vision. Multi-person pose estimation is expected to be involved in many applications, such as fitness training, pedestrian recognition, military training, and so on. The prospect of multi-person estimation development is promising and challenging. This paper provides a brief survey on four major multi-person pose estimation methods – DeepCut, DeeperCut, OpenPose and AlphaPose, and presents the advantages and disadvantages of these methods.
AWSQ: an approximated web server queuing algorithm for heterogeneous web server cluster

With the rising popularity of web-based applications, the primary and consistent resource in the infrastructure of World Wide Web are cluster-based web servers. Overtly in dynamic contents and database driven applications, especially at heavy load circumstances, the performance handling of clusters is a solemn task. Without using efficient mechanisms, an overloaded web server cannot provide great performance. In clusters, this overloaded condition can be avoided using load balancing mechanisms by sharing the load among available web servers. The existing load balancing mechanisms which were intended to handle static contents will grieve from substantial performance deprivation under database-driven and dynamic contents. The most serviceable load balancing approaches are Web Server Queuing (WSQ), Server Content based Queue (QSC) and Remaining Capacity (RC) under specific conditions to provide better results. By Considering this, we have proposed an approximated web server Queuing mechanism for web server clusters and also proposed an analytical model for calculating the load of a web server. The requests are classified based on the service time and keep tracking the number of outstanding requests at each webserver to achieve better performance. The approximated load of each web server is used for load balancing. The investigational results illustrate the effectiveness of the proposed mechanism by improving the mean response time, throughput and drop rate of the server cluster.
An Approach for Detection and Correction of Missing Word in Bengali Sentence

Auto-correction for missing word in a sentence is not so easy. Also, it is found more challenging for the Bengali language. Our rigorous study reveals the fact that no significant research works have been done for the Bengali Language on this very topic. In this paper, we proposed a method that can detect the missing word and provide a suggestion list correspond to missed word with 82.82% accuracy. We have used n-gram model to find whether a word is missing between two words from a sentence or not. Then, we have used probability scoring to rank the suggestion list after finding the probable words for the missed word. We have used a corpus for making the decision which is the collection of bigram and another corpus is used for preferable word for missed word which is a collection of the trigram. Finally, we have used another six corpora to evaluate our proposed method. All corpora are created by us using the data collected from the web.
M7 subtype leukemic cell edge detection techniques with threshold value comparison and noise filters

The aim of this paper is to study and identify various threshold values for two prevalently used edge detection techniques, which are Sobel and Canny. The purpose is to determine which value gives an accurate result for identifying a leukemic cell. Moreover, evaluating suitability of edge detectors is also essential as feature extraction of cell depends greatly on image segmentation (edge detection). Firstly, an image of M7 subtype of Acute Myelocytic Leukemia (AML) is selected due to its diagnosing which were found lacking. Next, apply noise filters for the best of image quality. Thus by comparing image with no filter, median and average filters, useful information can be acquired. Each edge detectors is fixed with threshold value of 0-0.5 but for Cann edge detection the value can increase until 0.9. From the research, it is found that Canny edge with no filter and a threshold value of 0.7 gives a clearer image with less noise reduction.
A video watermark algorithm based on tensor decomposition

Since most of the previous video watermark algorithms regard a video as a series of consecutive images, the embedding and extraction of watermark are performed on these images, and the correlation and redundancy among frames of a video are not considered. Such algorithms are weak in protecting against frame attacks. In order to improve the robustness, we take into consideration the correlation and redundancy among the frames of a video to propose a blind video watermark algorithm based on tensor decomposition. First, a grayscale video is represented as a 3-order tensor, and the core tensor is obtained by tensor decomposition. Second, the watermark embedding position is selected based on the stability of the maximum value in the core tensor because the core tensor represents the main energy of a video. Then, the watermark is embedded by quantifying the maximum value in the core tensor. Finally, the watermark is uniformly distributed across frames of a video by inverse tensor decomposition. The experiments show that our algorithm based on tensor decomposition has better imperceptibility and robustness against common video attacks.
Improvement of binarization performance using local otsu thresholding

&lt;p&gt;&lt;span&gt;Ancient document usually contains multiple noises such as uneven-background, show-through, water-spilling, spots, and blur text. The noise will affect the binarization process. Binarization is an extremely important process in image processing, especially for character recognition. This paper presents an improvement to Nina binarization technique. Improvements were achieved by reducing processing steps and replacing median filtering by Wiener filtering. First, the document background was approximated by using Wiener filter, and then image subtraction was applied. Furthermore, the manuscript contrast was adjusted by mapping intensity of image value using intensity transformation method. Next, the local Otsu thresholding was applied. For removing spotting noise, we applied labeled connected component. The proposed method had been testing on H-DIBCO 2014 and degraded Jawi handwritten ancient documents. It performed better regarding recall and precision values, as compared to Otsu, Niblack, Sauvola, Lu, Su, and Nina, especially in the documents with show-through, water-spilling and combination noises.&lt;/span&gt;&lt;/p&gt;
Influence of NAFDAC Mobile Drugs Authentication Service on the Use of Fake Drugs Among Consumers in Southeast Nigeria

OBJECTIVE: This paper investigates the influence of Mobile Authentication Service (MAS) in eliminating the consumption of fake/counterfeit drugs in South-east Nigeria. &#x0D; &#x0D; METHODS: 1000 respondents were randomly selected in the five states of the South-east as study sample, while a structured questionnaire was used for collecting data. SPSS version 20.0 was used to analyse the data. &#x0D; &#x0D; RESULTS: Analysis results reveal that there is a low level of MAS scheme awareness while few persons operate the MAS scheme. It further reveals that MAS scheme made no impact on the distribution and consumption of fake/counterfeit drugs. It also reveals people&amp;rsquo;s inability to procure appropriate phone technology, poor network services, and low level of MAS scheme media awareness campaign was responsible for MAS scheme ineffectiveness. &#x0D; &#x0D; CONCLUSION: NAFDAC MAS scheme has not reduced or eliminated the production, distribution and consumption of fake/counterfeit drugs in the South-east Nigeria. This is because of inappropriate media awareness campaign, people&amp;rsquo;s inability to procure the appropriate phone technology and access good network services required to operate the MAS scheme. This paper recommends appropriate massive media awareness campaign on the MAS Scheme, modernisation of MAS scheme software to accommodate lower level of phone technology, and improved network access as panacea for MAS scheme effectiveness.
Exploring Concurrent and Stateless Evolutionary Algorithms

Creating a concurrent and stateless version of an evolutionary algorithm implies changes in its algorithmic model. From the performance point of view, the main challenge is to balance computation with communication, but from the evolutionary point of view another challenge is to keep diversity high so that the algorithm is not stuck in local minima. In a concurrent setting, we will have to find the right balance so that improvements in both facets do not cancel out. In this paper we address such an issue, by exploring the space of parameters of a population based concurrent evolutionary algorithm that yields to find out the best combination for a particular problem.
Adopting and Implementing a Government Cloud in Saudi Arabia, an Integral Part of Vision 2030

Cloud computing is a relatively mature and robust technology that has promised its users with several proven advantages, such as cost reduction, immediate scalability, and resource sharing. The Cloud is built based on providing resources as services, such as providing Infrastructure, Platform, and Software as a Service. Such approach enables Cloud users to access these services based on their demand. In the government sector of Saudi Arabia, adoption and utilization of the Cloud is minimal. Despite being adopted officially, the Cloud has not been yet implemented properly. In our work we introduce how the government sector in Saudi Arabia can adopt and implement a Cloud Solution through utilizing its services and while considering issues related to its security.
Off-Line character recognition using an integrated DBSCAN-ANN scheme

&lt;p&gt;Handwriting character recognition involves a high degree of variability and imprecision. For that, the main factor to judge the recognition accuracy is the technique that is used to extract the features. This paper developed a novel method for handwritten Arabic characters by combining the Density-Based Clustering method with statistical and morphological features. The first stage in recognition of handwritten character image has been done by binarization the image then applies noise removal techniques. The Density-Based Algorithm used to categorize and find any shape of clusters based on pixel information positions. This technique divided the image into characters. Each character will be decomposing into four regions from the centroid followed by feature extraction. These features include vertical and horizontal projections, upper and lower profile, rectangularity and orientation. The results of the present process will transfer to the Neural Network (NN) stage which generates a high level of correctness and accuracy by training. The testing results compared with two of state-of-art researches. The total accuracy of this proposed work observes a better recognition of characters.&lt;/p&gt;
Review on mathematical models for the prediction of solar radiation

Global Solar Radiation (GSR) data is important for all solar energy based applications, mainly to forecast the output power of solar PV system in case of renewable energy integration in to the existing grid. The solar radiation components are measured using pyranometer, solarimeter, pyroheliometer and so on. It is not practically possible to install this radiation measuring instruments at all the locations due to the cost and difficulty in measurements. Hence the availability of solar radiation data is limited to few meteorological stations especially in the developing country like India. Therefore, it is necessary to develop mathematical models to predict the solar radiation to eliminate the costly pyranometer. In this paper, the review of mathematical models using trigonometric functions for the prediction of global solar radiation is presented. The mathematical models are applicable wherever the radiation data is unavailable. From the review results, it is concluded that mathematical model with both sine and cosine wave equation gives good prediction accuracy with correlation coefficient of 0.95
Quantum eigenlogic observables applied to the study of fuzzy behaviour of Braitenberg vehicle quantum robots

Purpose The purpose of this paper is to apply the quantum “eigenlogic” formulation to behavioural analysis. Agents, represented by Braitenberg vehicles, are investigated in the context of the quantum robot paradigm. The agents are processed through quantum logical gates with fuzzy and multivalued inputs; this permits to enlarge the behavioural possibilities and the associated decisions for these simple vehicles. Design/methodology/approach In eigenlogic, the eigenvalues of the observables are the truth values and the associated eigenvectors are the logical interpretations of the propositional system. Logical observables belong to families of commuting observables for binary logic and many-valued logic. By extension, a fuzzy logic interpretation is proposed by using vectors outside the eigensystem of the logical connective observables. The fuzzy membership function is calculated by the quantum mean value (Born rule) of the logical projection operators and is associated to a quantum probability. The methodology of this paper is based on quantum measurement theory. Findings Fuzziness arises naturally when considering systems described by state vectors not in the considered logical eigensystem. These states correspond to incompatible and complementary systems outside the realm of classical logic. Considering these states allows the detection of new Braitenberg vehicle behaviours related to identified emotions; these are linked to quantum-like effects. Research limitations/implications The method does not deal at this stage with first-order logic and is limited to different families of commuting logical observables. An extension to families of logical non-commuting operators associated to predicate quantifiers could profit of the “quantum advantage” due to effects such as superposition, parallelism, non-commutativity and entanglement. This direction of research has a variety of applications, including robotics. Practical implications The goal of this research is to show the multiplicity of behaviours obtained by using fuzzy logic along with quantum logical gates in the control of simple Braitenberg vehicle agents. By changing and combining different quantum control gates, one can tune small changes in the vehicle’s behaviour and hence get specific features around the main basic robot’s emotions. Originality/value New mathematical formulation for propositional logic based on linear algebra. This methodology demonstrates the potentiality of this formalism for behavioural agent models (quantum robots).
Towards a Robust Deep Neural Network in Text Domain A Survey

Deep neural networks (DNNs) have achieved remarkable success in various tasks (e.g., image classification, speech recognition, and natural language processing). However, researches have shown that DNN models are vulnerable to adversarial examples, which cause incorrect predictions by adding imperceptible perturbations into normal inputs. Studies on adversarial examples in image domain have been well investigated, but in texts the research is not enough, let alone a comprehensive survey in this field. In this paper, we aim at presenting a comprehensive understanding of adversarial attacks and corresponding mitigation strategies in texts. Specifically, we first give a taxonomy of adversarial attacks and defenses in texts from the perspective of different natural language processing (NLP) tasks, and then introduce how to build a robust DNN model via testing and verification. Finally, we discuss the existing challenges of adversarial attacks and defenses in texts and present the future research directions in this emerging field.
More cyclotomic constructions of optimal frequency-hopping sequences

In this paper, some general properties of the Zeng-Cai-Tang-Yang cyclotomy are studied. As its applications, two constructions of frequency-hopping sequences (FHSs) and two constructions of FHS sets are presented, where the length of sequences can be any odd integer larger than 3. The FHSs and FHS sets generated by our construction are (near-) optimal with respect to the Lempel–Greenberger bound and Peng–Fan bound, respectively. By choosing appropriate indexes and index sets, a lot of (near-) optimal FHSs and FHS sets can be obtained by our construction. Furthermore, some of them have new parameters which are not covered in the literature.
Indonesian sign language recognition using kinect and dynamic time warping

Sign Language Recognition System (SLRS) is a system to recognise sign language and then translate them into text. This system can be developed by using a sensor-based technique. Some studies have implemented various feature extraction and classification methods to recognise sign language in the different country. However, their systems were user dependent (the accuracy was high when the trained and the tested user were the same people, but it was getting worse when the tested user was different to the trained user). Therefore in this study, we proposed a feature extraction method which is invariant to a user. We used the distance between two users’ skeleton instead of using the users’ skeleton positions because the skeleton distance is independent to the user posture. Finally, forty-five features were extracted in this proposed method. Further, we classified the features by using a classification method that is suitable with sign language gestures characteristic (time-dependent sequence data). The classification method is Dynamic Time Wrapping. For the experiment, we used twenty Indonesian sign languages from different semantic groups (greetings, questions, pronouns, places, family and others) and different gesture characteristic (static gesture and dynamic gesture). Then the system was tested by a different user with the user who did the training. The result was promising, this proposed method produced high accuracy, reach 91% which shows that this proposed method is user independent.
Generating Pseudo-relevant Representations for Spoken Document Retrieval

Spoken document retrieval (SDR) has become an important research subject due to the immenseness of multimedia data along with speech have spread around the world in our daily life. One of the fundamental challenge facing SDR is that the input query usually contains only a few words, which is too short to convey the information need of a user. In order to mitigate the problem, a well-practiced strategy is to reformulate the original query by performing a pseudo-relevance feedback process. Although several studies have evidenced its ability and capability for enhancing the retrieval performance, the time-consuming problem makes it hard to be used in reality. Motivated by the observations, in this paper, we concentrate on proposing a novel framework, which targets at generating a set of pseudo-relevant representations for a given query automatically, and eliminating the time-wasting problem. On top of the generated representations, we further investigate a novel query reformulation mechanism so as to improve the retrieval performance. A series of empirical SDR experiments conducted on a benchmark collection demonstrate the good efficacy of the proposed framework, compared to several existing strong baseline systems.
Mineralogy, geochemistry, and fluid inclusion studies in Zaveh copper mineralization occurrence, southeast of Torbat-e-Hydarieh

Mineralogy, geochemistry, and fluid inclusion studies in Zaveh copper mineralization occurrence, southeast of Torbat-e-Hydarieh
Optimizing Software Effort Estimation Models Using Back-Propagation Versus Radial Base Function Networks

Software development effort estimation becomes a very important and vital tool for many researchers in different fields.Software estimation used in controlling, organizing and achieving projects in the required time and cost to avoid the financial punishments due to the time delay and other different circumstances that may happen.Good project cost estimation will lead to project success and reduce the risk of project failure.In this paper, two neural network models are used, the Back-propagation algorithm versus the redial base algorithm.A comparison is done between the suggested models to find the best model that can reduce the project risks related to time and increase the profit by achieving the demands of the required project in time.The two models are implemented on a 60 of NASA public dataset, divided into 45 data samples for training and 15 data samples for testing.From the result obtained we can clearly say that the performance of the back-propagation neural network in training and testing cases is actually better than the radial base function, so the backpropagation algorithm can be recommended as a useful tool in the software effort and cost estimation.
Research on Disaster Communications

From our experience the Great East Japan Earthquake in March 2011 to help out the people in the affected area in terms of IT support such as internetworking and providing PCs, we came across an interesting issue concerning collaboration with people including supporters as well as the victims. We call this problem disaster communications. We also came to know the needs for disaster information processing as there was no such systems to manage shelters, goods distribution and volunteers. On the other hand such systems have been researched in the United States of America and Europe in terms of information processing for emergency management to a great extent. While we have had many natural disasters in Japan, only a very few of the researchers in computer science and information systems have been working on this issue. From this perspective, we try to identify the information required at disaster in this research. In particular, we explored the needs and seeds for disaster information processing in Iwate, Japan. We interviewed some supporters at Iwate in March 2011 such as local government officials, doctors and university administrators and found the situation awareness was required desperately in the beginning of the disaster. This paper reports our project of interviewing people who worked on disaster relief during the emergency response as well as our trial of a system for situation awareness based on the results from our interviews.