Reviewerly/exHarmony huggingface dataset:
Intertextuality and Digital Humanities

Abstract Proceeding from the debate on intertextuality, some considerations are presented here for Literary and Historical Studies that suggest a theory-driven approach applying algorithm-based procedures. It will be shown that methodical tensions between qualitative and quantitative approaches can be solved simultaneously in this way. On this basis, the approach combines intertextuality theory with an algorithm-based procedure (here a search based on Word Mover’s Distance).
Volume and Surface Area-Based Cluster Validity Index

Cluster validity index plays an important role in assessing the quality of clustering results. However, most of the existing validity indices take a trial-and-error strategy, and their correctness depend on not only the measurements of intra- and inter-cluster distances but also the specific clustering algorithms and data structures. Consequently, the applications of these indices are limited in practice. In this paper, we firstly define the total surface area and volume of all clusters in a 2-dimensinal data space, thereby recovering their natural interrelation among various numbers of clusters. On this basis, a novel validity index is proposed to directly assess the clustering results of any dataset, which does not require any trail-and-error process, clustering algorithms, data structures, or the measurements of intra- and inter-cluster distances. In the case of a high-dimensional data space, all clusters are transformed into spherical clusters of normalized size in a 2-dimensinal data space through a multidimensional scaling transformation. Two groups of typical synthetic datasets and real datasets with various characteristics are used to validate the novel validity index.
Exploring Feature Selection Technique in Detecting Sybil Accounts in a Social Network

Machine learning (ML) provides us the techniques to carve out meaningful insights into the useful information embedded in various datasets by making the machine learn from the datasets. There are different machine learning techniques available for various purposes. The general sequence of steps for a typical supervised machine learning technique includes preprocessing, feature selection, building the prediction model, testing and validating the model. Various ML techniques are being used to detect the presence of fake as well as spambot accounts on a number of Online Social Networks (OSNs). These fake/spambot accounts especially the Sybil accounts appear in these networks with malicious intentions to disrupt or highjack the very purpose of these networks. In this paper, we have trained various prediction models using appropriate real-time datasets to detect the presence of Sybil accounts on online social media. Since the data is collected from various sources; it necessitates the preprocessing of the dataset. The preprocessing has mainly been carried out for (a) removing the noise from this data and/or (b) normalizing values of various features. Next, three different feature selection techniques have been used for the selection of the optimal set of features from the superset of features so as to remove the features that are redundant and irrelevant in making accurate predictions. The three feature selection techniques used are Correlation Matrix with Heatmap, Feature Importance and Recursive Feature Elimination with Cross-Validation. Further, K-Nearest Neighbor (KNN), Random Forest (RF) and Support Vector Machine (SVM) classifiers have been deployed to train the proposed prediction models for predicting the presence of Sybil accounts in the OSN dataset. The performances of the proposed prediction models have been analyzed using six standard metrics. We conclude that the prediction model based on the Random Forest classifier provides the best results in predicting the presence of Sybil accounts in the dataset of an OSN.
Optimized SIKE Round 2 on 64-bit ARM

In this work, we present the first highly-optimized implementation of Supersingular Isogeny Key Encapsulation (SIKE) submitted to NIST’s second round of post quantum standardization process, on 64-bit ARMv8 processors. To the best of our knowledge, this work is the first optimized implementation of SIKE round 2 on 64-bit ARM over SIKEp434 and SIKEp610. The proposed library is explicitly optimized for these two security levels and provides constant-time implementation of the SIKE mechanism on ARMv8-powered embedded devices. We adapt different optimization techniques to reduce the total number of underlying arithmetic operations on the filed level. In particular, the benchmark results on embedded processors equipped with ARM Cortex-A53@1.536 GHz show that the entire SIKE round 2 key encapsulation mechanism takes only 84 ms at NIST’s security level 1. Considering SIKE’s extremely small key size in comparison to other candidates, our result implies that SIKE is one of the promising candidates for key encapsulation mechanism on embedded devices in the quantum era.
Language Modeling Using Part-of-speech and Long Short-Term Memory Networks

In recent years, neural networks have been widely used for language modeling in different tasks of natural language processing. Results show that long short-term memory (LSTM) neural networks are appropriate for language modeling due to their ability to process long sequences. Furthermore, many studies are shown that extra information improve language models (LMs) performance. In this research, we propose parallel structures for incorporating part-of-speech tags into language modeling task using both the unidirectional and bidirectional type of LSTMs. Words and part-of-speech tags are given to the network as parallel inputs. In this way, to concatenate these two paths, two different structures are proposed according to the type of network used in the parallel part. We analyze the efficiency on Penn Treebank (PTB) dataset using perplexity measure. These two proposed structures show improvements in comparison to the baseline models. Not only does the bidirectional LSTM method gain the lowest perplexity, but it also has the lowest training parameters among our proposed methods. The perplexity of proposed structures has reduced 1.5% and %13 for unidirectional and bidirectional LSTMs, respectively.
The Use of Book Store GIS Data Warehouse in Implementing the Analysis of Most Book Selling

Data is the part that represents the evidence in presenting the state of the surrounding environment, obtained through research in the form of numbers, sources or scales. with data, conditions can be measured to produce the best solution. GIS Bookstore is one company that needs data every month to be analyzed. However, GIS Bookstore has constraints in processing large data with increasing time. for example GIS bookstore has problems in analyzing bestsellers because of the increasing number of authors and book publishers who work together. In this study, GIS Bookstore found a solution by implementing business intelligence that plays a role in the processing of raw data in large amounts of data into well-structured data information, the results are collected and processed in a data warehouse scheme. Targets stored in the data warehouse are data that will be processed based on the model design method (ERD) and star schema. This paper discusses the GIS Bookstore data warehouse with the aim of analyzing the sales of most books based on a certain time period. The method used in retrieving data uses Pentaho data integration tools, MySQL workbench, report designer, bi server and workbench schema.
FPGA Design and Implementation of an AES Algorithm based on Iterative Looping Architecture

Most applications nowadays require protection of their data from tampering to ensure secure transfer of information. At the same time, they need to be secured quickly and within available resources. In this paper, efficient hardware architecture has been designed and implemented for the Advanced Encryption Standard (AES) algorithm based on the Field Programmable Gate Array (FPGA) using High Level Language (HLL). The design focused on the optimal use of available resources, thus iterative looping architecture is suggested to minimize the hardware resources utilization and power consumption. The design is synthesized and simulated using Xilinx ISE 14.7 and ModelSim software, respectively. The implementation is compared with other previous works in terms of area, and power consumption. The results show that the proposed design achieves significant decrease in area (utilize 423 slices) and power consumption (3.68 W). The throughput reaches the value of 2457 Mbit/Sec implemented on Virtex-6 xc6vlx195t-3 of Xilinx device family. This makes the proposed design suitable for applications, where resources are limited.
Improving Collaborative Filtering Recommender System Results using Optimization Technique

Nowadays, recommender systems are utilized as a suitable solution to facilitate the shopping process and make it faster. Collaborative Filtering (CF) is the most popular recommendation method which generates the recommendation for the Active User (AU) based on like-minded users. Thus, the selected neighbors have a significant impact on the accuracy and quality of recommendation. This paper presents a novel optimization-based recommender system called Opt-Nibors. OptNibors employs an optimization tool to select the best neighbors' list that improves prediction accuracy. Consequently, each individual represents a candidate neighbors list of AU. The proposed method consists of two phases, preprocessing and optimization phases. The preprocessing phase prepares the used seeds for initializing the population in the optimization phase. The executed preprocessing steps differ based on the historical recorded shopping behavior of AU. A set of experiments was conducted to compare OptNibors with alternative methods. On average, OptNibors improved the prediction accuracy and recommendation quality by 31.1% and 7.7%. The results demonstrate the superiority of OptNibors and its capability to achieve high performance regardless of the number of selected neighbors.
Face Analysis: State of the Art and Ethical Challenges

In face analysis, the task is to identify a subject appearing in an image as a unique individual and to extract facial attributes like age, gender, and expressions from the face image. Over the last years, we have witnessed tremendous improvements in face analysis algorithms developed by the industry and by academia as well. Some applications, that might have been considered science fiction in the past, have become reality now. We can observe that nowadays tools are far from perfect, however, they can deal with very challenging images such as pictures taken in an unconstrained environment. In this paper, we show how easy is to build very effective applications with open source tools. For instance, it is possible to analyze the facial expressions of a public figure and his/her interactions in the last 24 h by processing images from Twitter given a hashtag. Obviously, the same analysis can be performed using images from a surveillance camera or from a family photo album. The recognition rate is now comparable to human vision, but computer vision can process thousands of images in a couple of hours. For these applications, it is not necessary to train complex deep learning networks, because they are already trained and available in public repositories. In our work, we show that anyone with certain computer skills can use (or misuse) this technology. The increased performance of facial analysis and its easy implementation have enormous potential for good, and –unfortunately– for ill too. For these reasons, we believe that our community should discuss the scope and limitations of this technology in terms of ethical issues such as definition of good practices, standards, and restrictions when using and teaching facial analysis.
Fusion Based Single Image De-hazing

Digital images captured in hazy outdoor environments, due to particle disturbance and air-light intervention, often suffer from loss of contrast and color distortion. These images are incapable of providing valuable information to viewers. To recover the haze-free counterpart, an image-fusion based algorithm is here proposed. The transmission factor, representing the haze severity, is found closely related to the input image minimum color channel. Furthermore, to ensure the recovered haze-free image does not lose the depth sense within the allowed dynamic range, a threshold is included as the lower bound for the transmission range. Intermediate images generated from varying transmission thresholds are fed into a fusion process, where the weighting matrix is determined by the range filtered image intensity. The resultant image is further processed to maintain the original hue and realize contrast enhancement. Results are analyzed both qualitatively and quantitatively compared to other state-of-the-art image de-hazing algorithms to validate the performance and efficiency of the proposed method.
Context Awareness-Based Accident Prevention During Mobile Phone Use

In recent years, with the continuous development of technologies, people's dependence on smartphones is growing increasingly. Excessive dependence has been causing a considerable number of people to face potential accidents and injuries on account of the use of mobile phones. Based on the above considerations, this paper defines the dangerous context in mathematical forms, using the data collected by sensors in phones and context-aware technology to study the accident prevention methods in the process of using mobile phones. The research involves three parts. Human behavior awareness subproblem refers to the type of behavior currently being taken by the mobile phone holder, which is different from the traditional human behavior recognition problem. The spatial position awareness subproblem refers to the perception of whether the phone holder is in a dangerous area prone to accidental injury. The interactive awareness subproblem refers to how the mobile phone holder interacts with the mobile phone to determine whether the mobile phone holder is in the process of using the mobile phone. The study of accident prevention methods during the use of mobile phones will be based on the output of the above subproblems. After designing and implementing the solutions toward the three subproblems, algorithms are integrated, and the application is designed.
Utilization of the open source datasets for semantic segmentation in automotive vision

Great advancements in deep-learning-based machine vision are boosting the automotive industry potential to accurately recognize vehicle environment. The need for high quality datasets is of critical importance when training neural networks which are used to detect and classify objects in front of the vehicle. Available open source datasets for semantic segmentation can be used by a wide community of researchers to develop next generation self-driving functions. Those datasets have severe limitations such as class imbalance, unobserved objects, erroneous labelling and limited number of covered scenarios. In this paper, we propose a sequence of steps to combine and manipulate existing open source datasets to maximize the inference performance. Those steps include relabeling with outlier removal, class-driven balancing of validation and training datasets, as well as targeted image manipulation for scarce classes. Our evaluation indicates the improved inference accuracy when compared to the usage of most common open source datasets.
Hate Speech Detection in Indonesian Language on Instagram Comment Section Using Deep Neural Network Classification Method

Instagram is the second social media that is often used by people, especially in Indonesia. Instagram has a function to comment on photos or videos uploaded by users. Comments that submitted by a community, some of them are positive, some of them are negative. Which one is hate speech. Based on ITE constitution that prohibited acts are intentionally and without rights to disseminate information intended to incite hatred or hostility of certain individuals and/or groups of people based on ethnicity, religion, race, and intergroup. Authorities find it difficult to handle the hate speech because there are many variations of hate speech. Therefore, the system is made to detect hate speech using Deep Neural Network method. With this system, it can be to help the authorities to handle this case, especially on Instagram comment section. From the research results of this final task in classifying a hate speech from Instagram column comment section obtained accuracy with an average 97.19%.
Refined analysis to the extended tower number field sieve

The hardness of discrete logarithm problem over finite fields is the security foundation of many cryptographic protocols. When the characteristic of the finite field is medium or large, the state-of-art algorithms for solving the corresponding problem are the number field sieve and its variants. In 2016, Kim and Barbulescu presented the extended tower number field sieve, which achieves a new complexity in the medium prime case and imposes a new estimation of the security of concrete parameters in certain cryptosystems such as pairing-based cryptosystems. In this paper, a refined analysis to this algorithm is given as follows. Firstly, a uniform formula is given for the total complexity of the extended tower number field sieve. For a given polynomial selection method, this formula can directly give the complexity in this case. Then, a method is proposed to improve the computation in the smoothing phase by exploring subfield structures when the extension degree is composite. At last, the complexity of the descent phase is analyzed when sieving over degree-one polynomials and high-degree polynomials respectively and it is shown still negligible compared to the improved smoothing phase.
Performance Modelling and Optimisation of NoSQL Database Systems

Salvatore Dipietro is a final-year PhD candidate in Computing at Imperial College London. His current research focus is on performance modelling and optimization of NoSQL database systems. His work is supported by HiPEDS centre for doctoral training, funded by EPSRC. Before the PhD, Salvatore completed his MRes in Advance computing at Imperial College London (2015) and his MSc in computer security and forensics at Bedfordshire University (2013). Earlier he gained his undergraduate degree in computing engineering at Politecnico di Torino (2012). Besides, he also has extensive professional work experience as Cloud and DevOps engineer. His interest lies in performance and optimization, capacity planning, distributed applications and network security.
Improved Answer Selection For Factoid Questions

In recent years, question and answer systems and information retrieval have been widely used by web users. The purpose of these systems is to find answers to users' questions. These systems consist of several components that the most essential of which is the Answer Selection, which finds the most relevant answer. In related works, the proposed models used lexical features to measure the similarity of sentences, but in recent works, the line of research has changed. They used deep neural networks. In the deep neural networks, early, recurrent neural networks were used due to the sequencing structure of the text, but in state of the art works, convolutional neural networks are used. We represent a new method based on deep neural network algorithms in this research. This method attempts to find the correct answer to a given question from the pool of responses. Our proposed method uses wide convolution instead of narrow convolution, concatenates sparse features vector into feature vector and uses dropout in order to rank candidate answers of the user's question semantically. The results show a 1.01% improvement at the MAP and a 0.2% improvement at the MRR metrics than the best previous model. The experiments show using context-sensitive interactions between input sentences is useful for finding the best answer.
A Security Detection Model for Selfish Mining Attack

As a new technology, the “decentralization” of blockchain has been paid more and more attention. With the increasingly wide range of applications of blockchain technology, its security has become a bottleneck restricting the development. In order to solve the problem, the blockchain structure characteristic can be used to build a security detection model. Taking 51% attack detection as an example, the relationship among attack state, attack intensity and attack time was analyzed. However, because of the variety of blockchain attacks, the test of only one attack cannot verify the effectiveness of the detection model. On the basis of the existing work, this paper studies the security of selfish mining attack under the existing detection model and compares it with 51% attack, and finds that the attack block and honest block are related to the ability of the attacker. When the attacker’s ability exceeds half of the total ability, the attack block is more than the honest block. However, for the number of system states, there is a big difference between 51% attack and selfish mining attack. Specifically, in selfish mining attack, when the attacker’s ability exceeds 25%, the number of states tends to stabilize. While in 51% attack, the attacker’s ability needs to exceed 50% in order to stabilize. This shows that the detection model can well distinguish 51% attack and selfish mining attack. According to this characteristic, we can further construct an effective alarm mechanism.
Prediction Method for Lysine Acetylation Sites Based on LSTM Network

Protein lysine acetylation is an important post- translational modification (PTM) that takes an important part in many cellular processes, such as metabolism, differentiation and replication. However, the current lysine acetylation site identification mainly adopts experimental detection methods which are time-consuming and inefficient. Therefore, several shallow machine learning algorithms were applied for protein lysine acetylation site prediction. However, traditional machine learning algorithms are not as accurate as deep learning algorithms. As a special kind of recurrent neural network (RNN), long short-term memory (LSTM) can not only process sequence data but also solve the vanishing gradient problem. Therefore, this paper proposes a method based on LSTM to predict lysine acetylation sites. In addition, two traditional machine learning algorithms k-nearest neighbors (KNN) and support vector machine (SVM) were adopted for training as comparison. Through experimental verification, the prediction method based on LSTM is superior to the other two based on traditional machine learning algorithms, and the final prediction accuracy of LSTM model can reach up to 81.52%.
Cyberbullying and Detection Methods

One of the major problems of our time, cyber bullying is increasing day by day. It is very difficult to identify this type of bullying which causes great difficulties for its victims and sometimes leads them to suicide. The widespread use of the Internet extends the scope of cyberbullying. As a result, social networks, platforms and sharing sites using can apply cyber bullying more than classical bullying with the self-confidence that the crimes they will commit cannot be detected and they will not face any sanctions even if they are detected. Various studies have been carried out in the literature to identify this problem. In this study, firstly, what is cyber bullying, types, prevention and detection of what kind of studies are carried out in the literature and cyber bullying detection methods are presented.