Reviewerly/exHarmony huggingface dataset:
Computer Based Diagnosis of Malaria in Thin Blood Smears Using Thresholding Based Approach

Millions of people worldwide are diagnosed with malaria every year and a lot of them results in the death of the infected person. Malaria in caused by a plasmodium infected anopheles genus mosquito. The conventional method of detecting the plasmodium parasite through the microscope requires a significant amount of time and is still prone to errors. Thus, making it inefficient for analysis where a large number of samples needs to be checked for malaria. This paper aims to propose an efficient automated system which uses image processing methods to identify the presence of plasmodium parasite in red blood cells. This whole process consists of four parts namely preprocessing, segmentation, feature extraction and classification of the parasite. The whole project was carried out in Matlab 2019a environment. After the preprocessing that included greyscale conversion and application of adaptive mean filter for reduction of noise from the images, we used Zach Thresholding process for segmentation of thin blood slide images. The database for images that was used was MP-IDB which consisted of total of 229 images classified in each category of species. We applied this process on all 229 images and got an accuracy of about 99.49%, 92.48% sensitivity and 99.79% specificity showing that this procedure is very efficient and with high accuracy.
Deep Learning Based Response Generation using Emotion Feature Extraction

Neural response generation is to generate human-like response given human utterance by using a deep learning. In the previous studies, expressing emotion in response generation improve user performance, user engagement, and user satisfaction. Also, the conversational agents can communicate with users at the human level. However, the previous emotional response generation model cannot understand the subtle part of emotions, because this model use the desired emotion of response as a token form. Moreover, this model is difficult to generate natural responses related to input utterance at the content level, since the information of input utterance can be biased to the emotion token. To overcome these limitations, we propose an emotional response generation model which generates emotional and natural responses by using the emotion feature extraction. Our model consists of two parts: Extraction part and Generation part. The extraction part is to extract the emotion of input utterance as a vector form by using the pre-trained LSTM based classification model. The generation part is to generate an emotional and natural response to the input utterance by reflecting the emotion vector from the extraction part and the thought vector from the encoder. We evaluate our model on the emotion-labeled dialogue dataset: DailyDialog. We evaluate our model on quantitative analysis and qualitative analysis: emotion classification; response generation modeling; comparative study. In general, experiments show that the proposed model can generate emotional and natural responses.
Multi-Level Steganography Aided with Compression

Steganography is one of the information hiding Techniques that hide a message inside another message without drawing any suspicion. In this research, multilevel steganography techniques proposed with two levels, level one text steganography, and level two is image steganography. In level, one uses a new approach that is based on the Pseudo-Random Number Generation (PRNG) to embed the secret message into a Cover-text that generated randomly. Level Two uses a cover image to embed the message by using the LSB insertion technique that produces the Stego-image. Between these levels, the compression process (Huffman Compression Algorithm) reduces the size of level one output and passed to level two. The model uses two secret keys at both ends for authentication. At the receiver side, different reverse operations have been carried out to get back the original information. A lot of experiment to test a variety of measurements such as the capacity and quality of the proposed model showed.
Efficient Mining Closed k-Mers from DNA and Protein Sequences

Counting frequent k-mers as patterns in a long sequence is a typical data-mining problem and contributes to broad applications in bioinformatics. Existing methods of k-mer counting usually produce all the possible contiguous subsequences of length k that are contained in a given sequence. However, these methods often spawn numerous redundant and possibly meaningless k-mers. This inherently gives rise to excessive runtime and space overhead at sequence analysis. Moreover, most of the k-mer counting algorithms consider only single-length contiguous subsequences, which limits the diversity of data mining and knowledge discovery. Previous studies have demonstrated that closed patterns contain the complete information regarding the corresponding patterns. Inspired by the application of the upperclosure property of sequential patterns, we propose an efficient algorithm, called CloKmer, to find closed k-mers of various lengths that are compact yet lossless representation of k-mers. CloKmer utilizes the inverted index to project the original sequence onto an equivalent set and then mines closed k-mers by exploiting space pruning and upper-closure checking. Our experimental results demonstrate that CloKmer generates average 44% less patters but still achieves a comparable accuracy when classifying transcription factor binding sites (TFBSs), compared to traditional (k-mer-based classification) methods.
Implementation of cryptanalysis based on FPGA hardware using AES with SHA-1

The meaning of security is to protect something important from not authorized peoples. Cryptography is one of the security systems. Many applications could be used in the security systems like military applications, email, banking, information technology of database. The proposed system uses advanced encryption standard(AES) with secure hash algorithms(SHA-1) to produce a strong ciphertext. Moreover, this paper make the combination of (SHA-1) which is a one way encryption, as a key with (AES) algorithm which is a two way encryption as a ciphering generator to obtain confidentiality and integrity, In this paper presented implementation of Advanced Encryption Standard (AES) and Secure Hash (SHA-1) Algorithm by the use of FPGA Hardware card, However implementation of the AES algorithm and SHA-1 using LABVIEW environment with MATLAB system generator to generate stream data to FPGA hardware target. The output of the hash function is used as an input key for AES in order to improve data security, From the algorithms result of AES and SHA-1 obtained in FPGA, the system obtained how it is simple in modeling hashing and AES algorithms, generating hash codes and plain texts in (small and capital letters), symbols and numbers. The FPGA card used in this system (Spartan 3AN)with frequency (50%), the complete system take (1,147)size from the available space of (11,776) and accuracy (90%),the system work properly.
Quantum Computing and Deep Learning Working Together to Solve Optimization Problems

This paper explores the use of quantum computing for solving machine learning problems more efficiently. Today's quantum computers are rather primitive, so only relatively small machine learning problems can be solved at this time. Nevertheless, the machine learning problems described here that have been solved on quantum simulators or actual quantum computers indicates the potential power of quantum computers for solving computationally intensive machine learning problems such as the deep learning multi-layer neural networks.
Improved Text Classification using Long Short-Term Memory and Word Embedding Technique

Text classification is an important problem for spam filtering, sentiment analysis, news filtering, document organizations, document retrieval and many more.The complexity of text classification increases with some classes and training samples.The main objective of this paper is to improve the accuracy of text classification with long short-term memory with word embedding.Experiments were conducted on seven benchmark datasets namely IMDB, Amazon review full score, Amazon review polarity, Yelp review polarity, AG news topic classification, Yahoo!Answers topic classification, DBpedia ontology classification with the different number of classes and training samples.Different experiments are conducted to evaluate the effect of each parameter on LSTM.Results show that 100 batch size, 50 epochs, Adagrad optimizer, 5 hidden nodes, 100-word vector length, 2 LSTM layers, 0.001 L2 regularizations, 0.001 learning rate give the higher accuracy.The results of LSTM are compared with the literature.For IMDB, Amazon review full score, Yahoo!Answers topic classification dataset the results obtained are better than literature.Results of LSTM for Amazon review polarity, Yelp review polarity, AG news topic classification are close to bestknown results.For the DBpedia ontology classification dataset the accuracy is more than 91% but less than best known.
How Does Stakeholders Pressure Affect Organization Performance and Employee Wellbeing? Study of Multiple Mediating Roles

Employee satisfaction and high performance is every organization’s goal. When seen through the lens of stakeholder theory, these goals could be achieved by CSR practices through certain ways. Stakeholder theory affords an initial point to assimilate stakeholder pressure and corporate social responsibility practices. This research studies the effect of pressure from stakeholders over organization performance and wellbeing of employees by including the mediating effect of CSR oriented culture, CSR practices, organizational citizenship behavior, organizational commitment, and organizational trust in Pakistan’s banking sector. The study employs quantitative and cross sectional research design. A sample of 180 banks was studied through employing SmartPLS3.0 software using mediation analyses.The findings reveal that CSR oriented culture mediates between stakeholder pressure and CSR practices; CSR practices affect organization performance through organization citizenship behavior. Further, trust is a partial mediator among CSR practices and wellbeing of employees. Serial mediators play their role between stakeholder pressure and organization performance, and also play a role between stakeholder pressure and employee wellbeing. Implications/Originality/Value: The findings suggest Pakistani banks to acknowledge pressure from all stakeholders, and concentrate on CSR practices for their bank performance and staff wellbeing.
Learning-Based Human Segmentation and Velocity Estimation Using Automatic Labeled LiDAR Sequence for Training

In this paper, we propose an automatic labeled sequential data generation pipeline for human segmentation and velocity estimation with point clouds. Considering the impact of deep neural networks, state-of-the-art network architectures have been proposed for human recognition using point clouds captured by Light Detection and Ranging (LiDAR). However, almost all conventional datasets are either a collection of single LiDAR scanning with label information or sequential LiDAR scanning without label information. This limitation has disturbed the progress of research to date. Therefore, we have developed an automatic labeled sequential data generation pipeline, in which we can control any parameter or data generation environment with pixel-wise and per-frame ground truth segmentation and pixel-wise velocity information for human recognition. Our approach uses a precise human model and reproduces a precise motion to generate realistic artificial data. We present more than 7K sequences, where each sequence consists of 32 frames generated by the proposed pipeline. With the proposed sequence generator, we confirm that human segmentation performance is improved when using the sequential data compared to when using the data from a single LiDAR scan. We also evaluate our data by comparing with data generated under different conditions. In addition, we estimate pedestrian velocity with LiDAR by only utilizing data generated by the proposed pipeline.
Boosting Cross-Modal Retrieval With MVSE++ and Reciprocal Neighbors

In this paper, we propose to boost the cross-modal retrieval through mutually aligning images and captions on the aspects of both features and relationships. First, we propose a multi-feature based visual-semantic embedding (MVSE++) space to retrieve the candidates in another modality, which provides a more comprehensive representation of the visual content of objects and scene context in images. Thus, we have more potential to find a more accurate and detailed caption for the image. However, captioning concentrates the image contents by semantic description. The cross-modal neighboring relationships start from the visual and semantic sides are asymmetric. To retrieve a better cross-modal neighbor, we propose to re-rank the initially retrieved candidates according to the ${k}$ nearest reciprocal neighbors in MVSE++ space. The method is evaluated on the benchmark datasets of MSCOCO and Flickr30K with standard metrics. We achieve highe accuracy in caption retrieval and image retrieval at both R@1 and R@10.
Learning deep networks with crowdsourcing for relevance evaluation

Abstract In this paper, we propose a novel relevance evaluation method using labels collected from crowdsourcing. The proposed method not only predicts the relevance between query texts and responses in information retrieval systems but also performs the label aggregation tasks simultaneously. It first merges two kinds of heterogeneous data (i.e., image and query text) and constructs a CNN-like deep neural network. Then, on the top of its softmax layer, an additional layer was built to model the crowd workers. Finally, classification models for relevance prediction and aggregated labels for training examples can be simultaneously learned from noisy labels. Experimental results show that the proposed method significantly outperforms other state-of-the-art methods on a real-world dataset.
The Effect of Extensive Reading on Students Reading Comprehension

This research investigated the effect of extensive reading on students reading comprehension. The writer used the quantitative as the researched method and the writer used the queasy experiment as the research design. For collecting the data the writer gave the test for experimental and control group. For experimental group the writer used the extensive reading method as the reading class room and the other method for the control group. After the post- test gave to both classes, the writer analyzed it by using SPPS 20 program to found out the improvement on student reading comprehension after extensive reading implied on reading classroom. Based on the finding and discussion. It can be concluded that hypothesis was accepted. It meant that there was effect on extensive reading on student reading comprehension. Based on the resulted of this research. The writer suggested the teacher to used extensive reading on reading process to improved student reading comprehension.
A novel shilling attack detection model based on particle filter and gravitation

With the rapid development of e-commerce, the security issues of collaborative filtering recommender systems have been widely investigated. Malicious users can benefit from injecting a great quantities of fake profiles into recommender systems to manipulate recommendation results. As one of the most important attack methods in recommender systems, the shilling attack has been paid considerable attention, especially to its model and the way to detect it. Among them, the loose version of Group Shilling Attack Generation Algorithm (GSAGen <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</sub> ) has outstanding performance. It can be immune to some PCC (Pearson Correlation Coefficient)-based detectors due to the nature of anti-Pearson correlation. In order to overcome the vulnerabilities caused by GSAGen <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</sub> a gravitation-based detection model (GBDM) is presented, integrated with a sophisticated gravitational detector and a decider. And meanwhile two new basic attributes and a particle filter algorithm are used for tracking prediction. And then, whether an attack occurs can be judged according to the law of universal gravitation in decision-making. The detection performances of GBDM, HHT-SVM, UnRAP, AP-UnRAP Semi-SAD, SVM-TIA and PCA-P are compared and evaluated. And simulation results show the effectiveness and availability of GBDM.
Algorithm for quickly improving quantitative analysis of risk assessment of large-scale enterprise information systems

Information system security is a systematic project with integrity, complexity, and uncertainty. Relying solely on technical means cannot fundamentally eliminate information system security issues. It is necessary to consider the establishment of comprehensive safeguards, comprehensive management and monitoring. The information security risk assessment can solve the problem that is difficult to quantify in the security of the information system. Through the quantitative analysis of the asset risk, the original difficult-to-qualify risk value becomes easy to confirm and assign. After extensive research on large-scale enterprise information systems and multiple risk assessment projects, the in-depth study of the general computing model and quantitative computing model of information security risk assessment, a quantitative information security risk assessment model with a risk correction value is proposed. The algorithm of this model makes the risk analysis and risk value calculation in the information security risk assessment process faster, more accurate, and more practical. At the same time, it can be quickly applied to the information system risk assessment of large enterprises.
Social Media Ecosystem: Review on Social Media Profile’s Security and Introduce a New Approach

Social media is a platform for maintaining a connection among like-minded people. It provides business opportunities, alliances, career opportunities, friendships, relationships, developing social skills, online communication, virtual community and many more. From the last one decade, social media is popular medium for communication among cardinal online users. They provide communication mechanism that enables users to be connected among family and friends. Social media plays a vital role for users because it cringes geographical borders. While the popularity and usefulness of social media make a speculative toward users with regards to data security. In this paper, we have analyzed different social network attack techniques with its subsequences. On the basis of analysis, we propose the machine learning-based system for better security arrangement and data security.
Improving Classification Accuracy of Random Forest Algorithm Using Unsupervised Discretization with Fuzzy Partition and Fuzzy Set Intervals

It is known that certain classification algorithm requires continuous data to be discretized for it to produce better classification accuracy. Hence, many works have explored the pairing of classification algorithm and discretization techniques, yet tree-based classifier especially Classification and Regression Trees (CART) still have an issue with classification accuracy regardless of different pairing with existing discretization techniques. The role of fuzzy partition and fuzzy sets interval are not something new in data discretization but none yet to explore the pairing of fuzzy discretization with tree-based algorithm. This paper will be discussing on an approach of using fuzzy based discretization and a member of tree-based algorithm known as Random Forest, a better version of CART. In this study, continuous data are identified from a dataset and discretized through the fuzzy discretization. Then, 10-fold cross validation is done on the transformed dataset and seven well-known classifiers are used including the proposed approach. Based on the results, better classification accuracy is achieved when fuzzy discretization is paired with Random Forest algorithm compared to CART. On top of that, with the present of fuzzy discretization technique, an increased in the classification accuracy has been obtained compared to other classification algorithms.
Generative Adversarial Networks in Digital Pathology: A Survey on Trends and Future Potential

(PATTER 1, 100089-1–100089-11; September 11, 2020) Due to an error in production, the following equation had a typographical error. The equation was published as (MI(M11 (x1i ),M12 (xij))) and the correct equation should be (MI(M11(x1i),M12(x1j))). This change does not affect the conclusions of the paper and has been corrected online. We sincerely apologize to the readers for any confusion that may have resulted from these errors. Generative Adversarial Networks in Digital Pathology: A Survey on Trends and Future PotentialTschuchnig et al.PatternsSeptember 11, 2020In BriefWhole-slide scanners digitize microscopic tissue slides and thereby generate a large amount of digital image material. This advocates for methods facilitating (semi-)automated analysis. In this paper, we investigate generative adversarial networks, which are a powerful class of deep-learning-based approaches, useful in, for example, histological image analysis. The most important publications in the field of digital pathology are collected, summarized, and categorized according to the technical approaches employed and the aspired application scenarios. We identify the main findings and furthermore provide an outlook. Full-Text PDF Open Access
An Empirical Analysis of the Maintainability Evolution of Open Source Systems

Maintainability is a key factor for the evolution of an open source system due to the highly distributed development teams that contribute to many projects. In the literature there are a number of different approaches that has been developed to evaluate the maintainability of a product but almost each method has been developed in an independent way without leveraging on the existing work and with almost no independent evaluation of the performance of the models. In most of the cases, the models are only validated through a limited set of projects only by the people that propose the specific approach. This paper is a first step towards a different direction focusing on the independent application of the existing models to popular open source projects.
Neural Machine Translation: Fine-Grained Evaluation of Google Translate Output for English-to-Arabic Translation

The neural machine translation (NMT) revolution is upon us. Since 2016, an increasing number of scientific publications have examined the improvements in the quality of machine translation (MT) systems. However, much remains to be done for specific language pairs, such as Arabic and English. This raises the question whether NMT is a useful tool for translating text from English to Arabic. For this purpose, 100 English passages were obtained from different broadcasting websites and translated using NMT in Google Translate. The NMT outputs were reviewed by three professional bilingual evaluators specializing in linguistics and translation, who scored the translations based on the translation quality assessment (QA) model. First, the evaluators identified the most common errors that appeared in the translated text. Next, they evaluated adequacy and fluency of MT using a 5-point scale. Our results indicate that mistranslation is the most common type of error, followed by corruption of the overall meaning of the sentence and orthographic errors. Nevertheless, adequacy and fluency of the translated text are of acceptable quality. The results of our research can be used to improve the quality of Google NMT output.