Reviewerly/exHarmony huggingface dataset:
Social Media and Identity Theft Implications on Nigerian Victims and International Economy

This research was conducted with the sole aim of examining the implications of social media identity theft and level of awareness of security measures on social networking platforms. The survey and interview method of data gathering was employed and result revealed that the implication of social media identity theft on victims are rather negative than positive especially as a result of their lack of knowledge on the security measures necessary to employ in avoiding the implications that comes with the use of social media networking sites. The research recommend that the government make policies that will punish perpetrators and social media networking site owners begin to seek our ways to protect their users on those platforms and provide avenues to educate people to stay safe online for the future generation of online social media users.
Self-Driving Car Control Using Visual Ego-Motion Estimation

Unsupervised depth learning and ego-motion estimation of individual objects present in the view of a driverless car is a challenging task. Unsupervised learning removes the need of separate ego-motion using ground truths images and multi view cameras. For ego-motion estimation previous work mostly utilized the local pixel neighborhood and its position in the next frame. In this paper a control algorithm is proposed which utilized visual information for detecting individual objects, calculating estimated depth, and ego-motion estimation of each object detected. The Tensorflow object detection API is used to detect the objects in the view and then this result is used to calculate depth of each object and for ego-motion estimation of with respect to detected object in the view. A prototype car equipped with four cameras is used for acquisition of live video data, and subsequently each camera video is then used for fusion of extracted useful information for object detection and ego-motion estimation.
Low Visibility Street Scenes Recognition with Augmented Image Sets

Recently, deep learning is one of the popular ways of object detection, and the most important thing in object detection is the dataset, which is used in the training process. However, when we look over the dataset, we can find that most of accessible dataset is taken during the good weather (e.g., sunny day) with the simple environmen, most of the testing pictures and videos are also taken in the same environment. When we turn to test the model that is trained by bright and simple dataset during the bad weather (e.g., rainy day, cloudy day stormy, foggy day) or in the dim place (e.g., tunnel, night, dusk, under the object's shade), we will get a bad performance. To solve this problem, we use Photoshop to modify the original dataset, try to make the dataset more versatile and larger in number of images according to the real environment. Our experiments show that we improve the accuracy from 98.6% to 99.3% for the daytime testing dataset. And for the nighttime testing dataset, we improve the accuracy from 22% to 50%.
Significance of Low-Frequent Words in Concept Describing Document

In applications of information retrieval, text mining, and natural language processing, tf-idf (term frequency-inverse document frequency) is a numerical statistic that is intended to reflect how significant a word is to a document in a collection. The value of tf-idf increases proportionally to the number of times a word occurs in the document and is offset by the number of documents in the corpus that contain the word, reflecting the fact that some words appear more frequently in general. Therefore, the value of tf-idf is designed to be more significant in a certain document when a word occurs frequently. In other words, document classification using tf-idf does not care about the role of the infrequent words. In this paper, we focus on words that appear infrequently in a document. Specifically, we examine features that characterize document sets that describe specific knowledge using the SVM (Support Vector Machine) based feature extraction method. As a result, we confirmed that the words appeared only once in some of a document that belong to documents describing specific knowledge and contribute to distinguishing them from the documents that describe general knowledge.
Gray Scale Image Restoration Using Advanced Atmospheric Light Estimation

Visibility of scene is often degraded by different atmospheric phenomena which results in failure of many computer vision applications like outdoor object recognition systems, barrier detection systems, visual surveillance systems, intelligent transportation systems etc. Light coming out from the source and scattered by atmospheric particles towards the camera is the atmospheric light. In many existing image restoration algorithms the results depend on this light. So this paper introduces a image restoration method applied for fog removal which search atmospheric light in a superior way by dividing an image into blocks. Fog decreases slowly from infinite sky regions to the nearer camera regions horizontally downward direction. Consequently atmospheric light is estimated locally in each blocks from upper to lower. The global atmospheric light is the weighted average of all the local atmospheric light. The weight is calculated from histogram of each block. There are many algorithms which suffer from halo effects and edge hammering at the output. In this paper it is rectified by nonlinear filtering which is a pre-processing step. A new edge preserving transmission is also produced using this nonlinear filtering to reduce halo effects. Experimental results are verified and compared qualitatively and quantitatively with the existing haze removal methods. Comparison results shows a better performance in terms of saturated pixels and visual evaluation of different real time scenes.
Scientific Datasets in Archaeological Research Through Pottery Dating and Provenance Cases

Recent trends in archaeological research dictate the incorporation of various analytical methods for dating, chemical/mineralogical characterization, morphological description, etc. of various excavation findings and landscapes to support and corroborate the archaeologists' observations. Each method, when used individually, provides a different feedback, but once single studies are combined, the information produced can significantly contribute to the solution of the archaeological puzzle. Thus, numerous scientific data and metadata are produced creating large and complex scientific datasets. The use of common terminology and definitions to describe these data along with the establishment of a formal standardized structure any cultural heritage information could be mapped to would assure the credibility and durability of such scientific datasets. The chapter provides an overview of the types and formats of the scientific data/metadata produced in the archaeological research through the detailed presentation of the common practices used in pottery dating and provenance studies.
A Modern Review of EDI: Representation, Protocols and Security Considerations

Electronic data interchange (EDI) is a subset of electronic business-to-business (B2B) data exchange. It is one of the technologies that enable businesses to transact in an automated paperless way. Massive amounts of transactions that may take hours or days to process manually require a few seconds in EDI. This paper reviews the current state of EDI and the modern challenges it is facing. It fills a gap in the current literature where existing research on certain aspects of EDI is either absent or deprecated. EDI message representations are discussed with a specific focus on the genre implemented in international EDI standards. Different modern concepts are detailed, including electronic enveloping and translation. The current internet-based EDI protocols are discussed and evaluated for security issues. Finally, the security requirements are summarized along with a possible control mechanisms.
Query Specific Semantic Matcher and Summarization

Creating trained models that semantically represent the corpus and summarizing relevant portions according to the user query remains a challenging task. We propose a semantic matcher based approach for identifying the relevant sentences in the corpus pertaining to the prominent entities in the corpus and their relationships in the user query. Trained models are created from the corpus by leveraging word-embeddings and are semantically searched to retrieve top results of the user query. Top matched sentences are analyzed for coherence based on semantic chains leveraging Semantic Role Labeler and are summarized, where entity relationships are exploited. The approach is applied and tested on user query based Ticketing System where policy documents in IT industry are used as corpus and specific summaries in the form of steps to be followed are created according to the user query.
The Research on Auto-Assignment Method of Service Orders Based on Rule Reasoning

In incident management process, IT service management (ITSM) system usually sets up a dispatch center and full-time dispatchers to assign service orders to engineers. The assigning process has problems such as low efficiency and poor standardization. Aiming at these problems, We propose an auto-assignment model (AMM) of service orders for IT service management systems. Based on the actual business scenarios, the AMM model focuses on several factors such as attributes of service orders, responsibilities of roles, interests of candidates, load of candidates, social relationships between candidates, and attendance information of candidates. In addition, based on the auto-assignment model, we propose an auto-assignment method of service orders based on rule reasoning (AAM-RR). The analysis results of factors such as accuracy, coverage and time show that the AAM-RR can solve the problem of service orders' automatic assignment in IT service management system.
Smart Phone Self Checkout Payments in Super bazaar

With the increasing super bazaar purchases by the customers in cities there is a problem of bottleneck for billing the customers at peak times, to solve this problem we propose a secure way for billing the purchased goods by the customer with their Smartphone's. There are other self checkouts that are present such as ‘Amazon go’, ‘Perpule 1Pay’, and self checkout terminals, some of the solutions uses RFID tags, these solutions are costlier and difficult to implement in countries such as India. ‘Amazon go’ uses computer vision, Deep learning algorithms, Sensor Fusion [1], Our solution is a combination of Smartphone QR Code scanner app and secure website for billing the products. Users are provided with two options to pay their bill either to pay the bill online or to pay by cash. Second verification of the purchased products by the guard will be just weighing the purchased products. Further extensions and security strength are discussed in this paper.
SOA System for Commerce Transaction in Indonesia: I-HEMAT

Service Oriented Architecture (SOA) is a paradigm for organizing and utilizing distributed capabilities, SOA has been adapted as information technology to help selling and shopping activity in separate platform and complicated activity become one place. Customer in Indonesia looking for a best quality product with a lowest price, so we proposed SOA to build a website with a concept one stop shopping for people in Indonesia. It's helps customer to find a product, compare the product, searching for a promotion and also view the review from other people from social media and other platform. And it's help seller to promote their product, so their promotion can reach target customer.
Obstacle Detection Based on Logistic Regression in Unstructured Environment

Obstacles in off-road environments can pose a greater risk to autonomous vehicles, so it is necessary to accurately detect obstacles. This paper proposes an obstacle detection method based on logistic regression. In order to extract the obstacle features better, we first project the discrete point cloud data into the two-dimensional depth map, and then we extract the height difference value and distance difference value between the pixels neighborhoods, after that we use the logistic regression to train and get the corresponding parameters. Combining the training parameters and the extracted effective features, we can obtain the passable probability in the depth map coordinates, and then back-project the depth map pixels into the two-dimensional grid map to obtain the final passable region result. We conduct a number of experiments and the results demonstrate the effectiveness of our method. Furthermore, our method meets the requirements of real-time applications and provides accurate environmental information for unmanned vehicle decision-making and planning.
Implicit Identity Authentication Mechanism based on Smartphone Touch Dynamics

Information security has now become an important area of concern. Considering the shortcomings in traditional unlocking ways of smartphones, this paper proposes an implicit identity authentication mechanism by extracting users' touch dynamic characteristics. 86-dimensional features from two types of sensor data (e.g., acceleration and gyroscope) generated when a user unlocks a smartphone are extracted to characterize the user's behavior. Particularly, we adopt three popular classifiers: support vector machine (SVM), K-nearest neighbor (KNN) and random forest (RF) to perform training. Finally, we verify the accuracy of the classifier. Experimental results show that the RF classifier achieves an ideal accuracy rate for passwords with different levels of repetition, and the average accuracy rate is over 98%.
Research of Pedestrian Re-identification Method Based on Video Surveillance

In order to solve the problem of person recognition in cross-view video sequences of non-overlapping camera, most of the current person re-identification models based on deep learning either need to manually label features as their attributes, or learn the overall single semantic level of feature representation. This paper proposes a person re-identification method based on DNN with multi-level feature fusion, it can automatically learn multi-level discriminative visual factors that are insensitive to viewing condition changes, and identify and utilize them when matching images. Firstly, this paper uses the HOG feature to perform person detection on the video of the two cameras respectively. The person images detected of the camera1 are used as the prob, the person images detected in the camera2 are used as the gallery, and then the two parts are put into the person re-ID model and completed by the training. Finally, the cross-view tracking is implemented for the re-identified persons in combination with the KCF algorithm. The experimental results confirm the accuracy and efficiency of the method.
Certain Answers to a sparql Query over a Knowledge Base

Ontology-Mediated Query Answering (OMQA) is a well-established framework to answer queries over an rdfs or owl Knowledge Base (KB). OMQA was originally designed for unions of conjunctive queries (UCQs), and based on certain answers. More recently, OMQA has been extended to sparql queries, but to our knowledge, none of the efforts made in this direction (either in the literature, or the so-called sparql entailment regimes) is able to capture both certain answers for UCQs and the standard interpretation of sparql over a plain graph. We formalize these as requirements to be met by any semantics aiming at conciliating certain answers and sparql answers, and define three additional requirements, which generalize to KBs some basic properties of sparql answers. Then we show that a semantics can be defined that satisfies all requirements for sparql queries with SELECT, UNION, and OPTIONAL, and for DLs with the canonical model property. We also investigate combined complexity for query answering under such a semantics over $$\textit{DL-Lite}_{\mathcal {R}}$$ KBs. In particular, we show for different fragments of sparql that known upper-bounds for query answering over a plain graph are matched.
Learning Sparse Support Vector Machine with Relaxation and Rounding

A sparse representation of Support Vector Machines (sparse SVMs) is desirable for many applications. However, for large-scale problems with high-dimensional features solving sparse SVMs remains a challenging problem, and most of the existing work are heuristic in that there are no performance guarantees and can't effectively control the trade-off between the sparsity and the accuracy of the decision hyperplane. To address this issue, we propose a new method for via relaxation and rounding, which obtains (ε,δ)-approximate solution in Õ(n/εδ) time with probability at least 1-δ. Such regularization explicitly penalizes parameters different from zero with no further restrictions. We first show that learning sparse SVMs with ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> norm can be reformulated as an exactly Boolean program by introducing Boolean variables to each parameter. With dual and Boolean relaxation, this Boolean problem can be relaxed as a convex programming. For the ε-approximate solution of this convex programming, we get a feasible solution of the original problem without loss accuracy by a determined rounding. We analyze the proposed method in details and give a provable guarantee which is missing from the previous work. Experimental results on both synthetic data and real world data support our theoretical results and verify the validity of the proposed method.
Architecture Design of Data Exchange and Service Platform for Internet of Things

In this paper, the architecture of data exchange and service platform for Internet of Things(IOT) is studied. The architecture mainly includes Data Exchange Platform, Data Service Platform, Device Adaptation Module and Application Adaptation Module. The architecture is mainly used to solve the problems of data interchange and data protocol compatibility between systems and systems, systems and devices, which occurs in the process of access and integration of IOT devices and systems. Thus, it can realize fast and efficient access to IOT devices, as well as unified and standardized support for access systems.
Targeted Sentiment Classification with Knowledge Powered Attention Network

Targeted sentiment classification aims to identify the sentiment expressed towards some targets given context sentences, having great application value in social media, ecommerce platform and other fields. Most of the previous methods model context and target words with RNN and attention mechanism, which primarily do not use any external knowledge. In this paper, we utilize external knowledge from knowledge bases to reinforce the semantic representation of context and target. We propose a new model called Knowledge Powered Attention Network (KPAN), which uses the multi-head attention mechanism to represent target and context and to fuse with conceptual knowledge extracted from external knowledge bases. The experiments on three public datasets revealed that our proposed model outperforms the state-of-the-art methods, which signify the validity of our model.
An Analytical Study on Email Classification Using 10-Fold Cross-Validation

Start-up companies nowadays face main problems in managing a large amount of data, currently called “Big data” [1]. It is possible to say that most crucial data appear in email contents. As a result, email is considered as a valuable database. In order to obtain information from email messages, one possible preprocess is categorizing emails based on their contents. The research proposes a mechanism to classify email based on their contents. In order to evaluate the proposed mechanism, a 10-fold cross-validation method is applied. The empirical results demonstrate that the accuracy of email classification is approximately 61.89%, while the standard deviation is 2.77. The accuracy rate does not meet the research expectation perhaps because of the variance in word frequency in each category. Therefore, future work could weight each word to improve prediction performance.
System on Chip Implementation of a Fuzzy Logic Based Autonomous Robot Navigation System

A System on Chip (SoC) implementation of a fuzzy logic sensor fusion architecture for an autonomous robot navigation system is proposed in this work. The core of the system is based on a zero order Takagi Sugeno fuzzy logic controller, whose structure is optimized to benefit from the specific properties of the Zynq-7000 SoC arithmetic DSP48 slices in terms of computational speed and logic resource consumption.
Suffix Tree Construction based Mapreduce

The genome sequence indexing is a primary step in order to facilitate other further treatments such as patterns search or assembly with a reference genome etc. And the suffix tree is one of the most used data structures for indexing the genome sequence. However, the memory required for running the suffix tree construction algorithms may exceed the amount of available main memory. Despite the efforts made by the researchers, the construction of suffix tree remains very expensive with the use of data centres to ensure optimal parallelization of treatments and reduce the execution time without forgetting the risks of breakdown and the problems that it breeds. The parallelization performed by Hadoop and MapReduce gives solutions to storage and data processing capacity limitations as well as fault tolerance, all that at reasonable costs. The emergence of Hadoop, a framework related to big data and the paradigm MapReduce that allows to model parallel and distributed processing, is investigating many domains of science in order to effectively parallel their treatments. PWOTD (Partition and Write Only Top Down) algorithm, is chosen here as it has proven itself in textual algorithms for genome sequencing. In this paper, an approach to model the parallel construction of the suffix tree using the MapReduce paradigm is designed for implementation in Hadoop with a java API.
A Comparative Study of Clone Detection Approaches

Code resemblance or taking a fragment of code from any place and then regenerate it by writing the same either without or with any alteration is termed as code cloning. It is also called as Duplicate code. Most of the studies show that about 5% to 20% of a software system contains cloned code, this is just the outcome of mimeographing the existing fragments of code. One of the most impediment problem of such copied fragments of code is that all the code chunks need to be checked for the same bug reported in the original chunk. In software maintainance, the other prime concern is code refractoring. The state of the art is explored in clone detection. Firstly, we define the terms used in respect to clone detection and its literature and also specify there correlative mappings for the oftenly used type of clone. Secondly, the review of existing detection approaches, clone taxanomies and experimental evaluations of clone detection tools is provided. How clone detection applications when explored, could be beneficial to other areas of software engineering is also presented here in this paper.
Image Encryption and Reversible Watermarking on Quad and Non-quad Channel Images

In the past few years, several security algorithms such as encryption and watermarking algorithms have been put forward in order to ensure the security of the digital image from attackers. Most of the encryption algorithm make use of relatively small key spaces and hence it provides less security to attackers.The proposed system make use of an image encryption algorithm that is working on the principle of Rubik's cube. Watermarking techniques are added to the image before applying Rubik's cube algorithm so as to provide more security for the digital image. The above algorithm is applied on both RGB images and ARGB images and a comparison evaluation is done between the two image types using various performance metrics.