Reviewerly/exHarmony huggingface dataset:
A Study of Machine Learning Algorithms on Email Spam Classification

Despite the fact that different techniques have been developed to filter spam, due to the spammer’s rapid adoption of new spam detection techniques, we are still overwhelmed with spam emails. Currently, machine learning techniques are the most effective ways to classify and filter spam emails. In this paper, a comprehensive comparison and analysis of the performance of various classification models on the 2007 TREC Public Spam Corpus are exhibited in various cases of without or with N- Grams as well as using separate or combined datasets. It is shown that the inclusion of the N-Grams in the pre-processing phase provides high accuracy results for classification models in most of the cases, and the models using the split approach with combined datasets give better results than models using the separate dataset.
SMIFD: Novel Social Media Image Forgery Detection Database

Image forgery or manipulation changes the contents of a set of original images to create a new image. Unfortunately, manipulated images become a growing concern with respect to spreading misinformation via image sharing in the social media. Despite the availability of a large number of automatic Image Forgery Detection (IFD) methods, their evaluation in real-world benchmarks seems to be limited due to the lack of diverse datasets. Moreover, the motifs behind the manipulation remains unclear. This research aims to address these issues by proposing a novel social media IFD database, called SMIFD-500, to evaluate the efficiency and generalizability of the IFD methods. The unique property of this dataset is the availability of the technical and social attributes in its ground truth annotations. These will benefit the scientific community to develop efficient methods by exploiting such annotations. Moreover, it provides interesting statistics, which highlights the motifs of image manipulation from social science perspective.
APLIKASI PENGENALAN SITUS BERSEJARAH DI KOTA PEKANBARU DENGAN AUGMENTED REALITY MARKERLESS BERBASIS ANDROID

Historical sites are places or areas where there is information about historical relics. But ingeneral there are not many people who know the historical sites in Indonesia, especially in the city ofPekanbaru. The lack of information media also lies behind the public's ignorance. Therefore we needmedia publications to introduce these historic sites, namely by using Android-based Augmented RealityMarkerless technology. Making this application uses Blender software to create 3D objects andUnity3D software to create Augmented Reality applications. The purpose of this study is to help thepublic get to know the historical sites in the city of Pekanbaru by building interactive introductionmedia that can display photos and 3D objects from the site, it is hoped that people can more easilyrecognize and learn about historical sites in the city of Pekanbaru
Leveraging Blockchain as a Solution for Security Issues and Challenges of Paperless E-Governance Application

DigiLocker, a government of India initiative, provides a free platform to the citizens of India to store and access important personal documents. This platform is implemented by using various open source technologies to deliver a mass solution and contributes back to the ever-growing Indian community. DigiLocker is a weapon for paperless E-governance. It is a platform to issue and verify all types of certificates and documents digitally. Thus gradually eliminate the use of physical documents. In this paper, we have proposed a framework to secure individual information using secured verification method of blockchain-based validation technique. The proposed framework ensures proper validation and verification with a unique hash key. Additionally, Argon2 encryption mechanism is used for securing the hash key in the proposed framework. This will enable a timeline for visibility of the uploaded documents by any authorized person/organization and reduce fraudulent use of documents.
A Scheme to Create Simulated Test Items for Facilitating the Assessment in Web Security Subject

Network security practice learning can be regarded as the hands-on learning, so how to efficiently evaluate the learning outcome of students is an important issue. Accordingly, this study proposes a scheme to create the Simulated Test Items for online assessing the learning outcome of students in Web Security subject, called SimTI-WS scheme. The created Simulated Test Item based on SimTI-WS scheme is able to allow students to virtually operate and interact with the simulated Web Security scenario, e.g., WebGoat. Therefore, the SimTI-WS scheme is workable and the performance of the evaluation concerning the Web Security Subject can thus be expected to be improved.
Deep Learning Method for Generalized Modulation Classification under Varying Noise Condition

Modulation signal classification (MSC) is an indispensable technique to make the possible applications of non-cooperative communications. Currently, convolutional neural network (CNN) based MSC techniques can achieve an outstanding performance at a fixed noise regime. However, they are hard to generalize to all of noise scenarios. Because these conventional methods are trained on specific signal samples with fixed SNR and they only perform well under corresponding noise condition. Unlike the conventional methods, in this paper, we propose a robust CNN based generalized MSC (GMSC) method with powerful generality capability. This capability stems from the mixed dataset, containing in-phase and quadrature (IQ) samples under various SNR regimes. Experimental results show that the proposed method is robust under varying noise conditions, while merely losing a slight performance with comparing with conventional methods.
Indexed and Fibred Structures for Hoare Logic

Indexed and fibred categorical concepts are widely used in computer science as models of logical systems and type theories. Here we focus on Hoare logic and show that a comprehensive categorical analysis of its axiomatic semantics needs the languages of indexed category and fibred category theory. The structural features of the language are presented in an indexed setting, while the logical features of deduction are modeled in the fibred one. Especially, Hoare triples arise naturally as special arrows in a fibred category over a syntactic category of programs, while deduction in the Hoare calculus can be characterized categorically by the heuristic deduction = generation of cartesian arrows + composition of arrows.
High-Performance Deep Learning Classification for Radio Signals

The ability to classify different types of signal modulations in radio transmissions is an important task with applications in defense, networking, and communications. This process has traditionally been done manually by human analysts. Recent advances have shown that applying deep learning methods to this task is feasible. But existing recognition networks are complex, with heavy computational requirements, and poor accuracy on some modulation types and in noisy environmentsWe have built a robust radio frequency signal classifier with a hybrid approach that uses images derived from signal constellation and spectrogram data, combined with an efficient convolutional neural network. Compared to the state-of-the-art deep learning classifier, our system obtains better accuracy, with lower computational requirements.
Profession Identification Using Handwritten Text Images

A writer handwriting depicts various information and it gives the insights into the physical, mental and emotional state of the writer. This art of analyzing and studying handwriting is graphology. The prime features of handwriting such as margins, slanted, the baseline can tell the characteristics of a writer. The writer handwriting analysis reveals strokes and patterns through which identification and understanding the personality of a writer is possible. The writing of a person molds into various shapes and styles, starting from school until the struggle for his/her career. If we examine the writings of a person from different stages of his/her life then we will see that there are many differences in the shapes, styles, and sizes of the characters. The proposed work analyze the handwriting data written by the writer’s from different professions and classify them based on the top features that characterize their profession. In this paper, the profession of a writer is identified by analyzing the features of writer’s offline handwritten images. The previous work mostly includes determining various traits like honesty, emotional stability of a writer. The Proposed work uses the CNN based model for the feature extraction from the writer’s offline handwritten images.
Higher rank matricial ranges and hybrid quantum error correction

We introduce and initiate the study of a family of higher rank matricial ranges, taking motivation from hybrid classical and quantum error correction coding theory and its operator algebra framework. In particular, for a noisy quantum channel, a hybrid quantum error correcting code exists if and only if a distinguished special case of the joint higher rank matricial range of the error operators of the channel is non-empty. We establish bounds on Hilbert space dimension in terms of properties of a tuple of operators that guarantee a matricial range is non-empty and hence additionally guarantee the existence of hybrid codes for a given quantum channel. We also discuss when hybrid codes can have advantages over quantum codes and present a number of examples.
Traffic Sign Classification Using Embedding Learning Approach for Self-driving Cars

Image classification is one of the most popular and important problems in computer vision. In self-driving cars image classification is used to classify detected traffic signs. Modern state-of-the-art algorithms based on deep neural networks use softmax function to interpret the output of the network as the probability that the input data belongs to a certain class. This approach works well, however it has several disadvantages. More precisely, it is necessary to know the number of classes in advance, and if one wants to add a new class, then it is necessary to retrain the network. Moreover, a large number of images of each class are required. In the case of road signs, datasets may contain only the most frequent signs while ignoring rarely used ones. Thus, the traffic signs recognition module in autonomous cars will not recognize traffic signs not included into training dataset, which can lead to accidents. In this paper we put forward another approach that does not have disadvantages of networks with softmax. The approach is based on learning image embeddings in which models are trained to bring closer objects of one class and to move away objects of other classes in embeddings space. Therefore, having even a small number of images of rare classes it becomes possible to create a working classification system. In this work, we test the applicability of these algorithms in the traffic signs classification problem, and also compare its accuracy with neural networks with softmax and with networks pre-trained on softmax. We developed publicly available toolbox for training and testing embedding networks with different loss functions, backbone models, training strategies and other configuration parameters and embedding space visualization tools. All our experiments were carried out on the russian road signs dataset. To simplify the process of conducting training experiments, a framework for embedding learning based neural networks making was created. The framework can be found at https://github.com/RocketFlash/EmbeddingNet .
Building A Bi-objective Quadratic Programming Model for the Support Vector Machine

A support vector machine (SVM) learns the decision surface from two different classes of the input points, in many applications there are misclassifications in some of the input points.In this paper a biobjective quadratic programming model is utilized and different feature quality measures are optimized simultaneously using the weighting method for solving our bi-objective quadratic programming problem.An important contribution will be added for the proposed bi-objective quadratic programming model by getting different efficient support vectors due to changing the weighting values.The experimental results, give evidence of the effectiveness of the weighting parameters on reducing the misclassification between two classes of the input points.
Single Image Super-Resolution by Residual Recovery Based on an Independent Deep Convolutional Network

In this paper, we propose an independent neural network for single image super-resolution by residual recovery. The network is inspired by the observation that there still exists image residuals between the low-resolution image and the downsampled high-resolution output obtained by a previously proposed super-resolution network. Based on this observation, we design a simple but effective deep convolutional neural network to train the mapping between the image residuals and the corresponding ground-truth residuals. Furthermore, we combine the high-resolution output generated by the previous super-resolution network and the high-resolution residual output by the proposed neural network to yield the final high-resolution image. Extensive experiments on simulated natural images and real time-of-flight (ToF) images demonstrate the effectiveness of the proposed method from the aspects of visual and quantitative performance.
Influence of local climate on the tilt and orientation angles in fixed flat surfaces to maximize the capture of solar irradiation : A case study in Cuenca-Ecuador

Solar energy use is spreading rapidly in the world. Determining adequate tilt and orientation angles of flat panels represent environmental, economic and energy benefits. In this study, the influence of local climate on the optimal fixed flat surfaces position is analyzed to maximize the capture of solar irradiation. Five mathematical models are used to estimate the total radiation received by a flat surface. These models use meteorological variables such as: global, direct, diffuse and reflected radiation, as well as incidence angles of the solar radiation for each of the 8760 hours of a year. The data were taken from 2 meteorological stations located in Cuenca-Ecuador city (2.88 ° S, 78.99W). The five used models: diffuse isotropic, Lui & Jordan, Klucher, Temps & Coulson, and Perez et al., concur in the results. The authors recommend in Cuenca-Ecuador, that the flat solar panels must be oriented 35 ° with respect to the North (NNE) and inclined 15° with respect to the horizontal. The obtained results suggest that, in areas close to the equator, the local climate influence is not dramatic. However, a further analysis ensures greater energy capture, so the tilt and orientation angles should not be established only with the geographical position information.
Model-Based Design of Service-Oriented Architectures for Reliable Dynamic Reconfiguration

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Service-oriented architectures (SOAs) are well-established solutions in the IT industry. Their use in the automotive domain is still on the way. Up to now, the automotive domain has taken advantage of service-oriented architectures only in the area of infotainment and not for systems with hard real-time requirements. However, applying SOA to such systems has just started but is missing suitable design and verification methodologies. In this context, we target to include the notion of model-based design to address fail-operational systems. As a result, a model-based approach for the development of fail-operational systems based on dynamic reconfiguration using a service-oriented architecture is illustrated. For the evaluation, we consider an example function of an automatically controlled braking system and analyze the reconfiguration time when the function fails. The reconfiguration time, together with the worst-case execution time (WCET), was determined by a model-based calculation and by simulation. In summary, the proposed approach applied to dynamically reconfigurable systems can meet the design requirements of the ISO 26262.&lt;/div&gt;&lt;/div&gt;
RETRACTED: Research on Remote Control Method of English Multimedia Online Teaching System In Big Data Environment

Abstract In order to ensure the effectiveness and security of remote control of the teaching system, the remote control method of English multimedia online teaching system based on large data environment is proposed. Firstly, the remote control module of online teaching system is optimized, configuration parameters are improved, the remote control module is operated effectively, and the remote control rules of multimedia online teaching system are designed, which further ensures the security of remote control. According to the control rules, the remote control steps are simplified, and the optimization of remote control method for English multimedia online teaching system is finally realized. Finally, the experiment proves that the remote control method of English multimedia online teaching system in large data environment is more effective and safe than the traditional method.
3D vibration studies of large rotating structures using DIC

The present study is based on performing 3D vibrations studies of large structures using digital image correlation (DIC) technique. A 3D-DIC setup is developed for measuring in-plane and out-of-plane 3D displacements as well as natural frequencies of a vibrating structures (small to large scale). The DIC setup is first validated with laboratory experiments. The first indoor lab experiment is based on the application of the developed DIC setup in measuring pure in-plane and out-of-plane displacements. The in-plane and out-of-plane translations have been carried using a micrometer and a Vernier caliper, respectively. The developed DIC setup is able to accurately measure these in-plane and out-of-plane static displacements. With this success, in another indoor experiment, the developed DIC setup is used for performing vibration study of a PVC pipe (length 1m, inner diameter 56.6mm, outer diameter 60.6mm). The DIC setup is able to measure the dynamic displacements of the pipe in all three axes. Apart from this, the natural frequency measurement is also accurate. The first fundamental frequency of the pipe is 4.167 Hz. After successful validation and application in the indoor experiments, the developed DIC method is used for a field experiment. In the field experiment, the DIC technique is applied to perform vibration study of a light pole (length 3m, diameter 75.5mm, made of iron). The cameras are placed at a distance of 14.6m from the pole, whereas the distance between the cameras is 8.5m. A large size calibration board is fabricated and used for calibrating the stereovision system. The developed DIC method is able to produce 3D displacements of the vibrating pole as well as accurately measure its natural frequency. The fundamental natural frequency of the light pole is 4.9Hz. An accelerometer was also used during the field experiment for the validation of the developed DIC-based non-contact 3D vibration testing method.
An AI‐based virtual simulation experimental teaching system in space engineering education

Abstract In recent years, cognitive computing has represented an artifical intelligence (AI)‐based application of the next‐generation interaction among human beings. This study analyses the cognitive internet of things and proposes a Soft K‐Means Fuzzy Clustering approach. The paper presents the data collection procedure from the space applications, the data analysis by cognitive computing, and the ways of overcoming problems of scalability and flexibility. The interactions between robot team members and people become necessary for many real‐life applications. This study mainly focuses on the precision and complexity of the model, time of computation, and scalability. Furthermore, it demonstrates the AI's ability to enhance the communication skills of the multirobot team by making complex activities to carry out all tasks more effectively.
The Arithmetic Coding and Hybrid Discrete Wavelet and Cosine Transform Approaches in Image Compression

Image compression is one of the data compression types applied to digital images in order to reduce their high cost for storage and/or transmission. Image compression algorithms may take the benefit of visual sensitivity and statistical properties of image data to deliver superior results in comparison with generic data compression schemes, which are used for other digital data. In the first approach, the input image is divided into blocks, each of which is 16 x 16, 32 x 32, or 64 x 64 pixels. The blocks are converted first into a string; then, encoded by using a lossless and dictionary-based algorithm known as arithmetic coding. The more occurrence of the pixels values is codded in few bits compare with pixel values of less occurrence through the sub intervals between the range 0 and 1. Finally, the stream of compressed tables is reassembled for decompressing (image restoration). The results showed a compression gain of 10-12% and less time consumption when applying this type of coding to each block rather than the entire image. To improve the compression ratio, the second approach was used based on the YCbCr colour model. In this regard, images were decomposed into four sub-bands (low-low, high-low, low-high, and high-high) by using the discrete wavelet transform compression algorithm. Then, the low-low sub-band was transmuted to frequency components (low and high) via discrete wavelet transform. Next, these components were quantized by using scalar quantization and then scanning in a zigzag way. The compression ratio result is 15.1 to 27.5 for magnetic resonance imaging with a different peak signal to noise ratio and mean square error; 25 to 43 for X-ray images; 32 to 46 for computed tomography scan images; and 19 to 36 for magnetic resonance imaging brain images. The second approach showed an improved compression scheme compared to the first approach considering compression ratio, peak signal to noise ratio, and mean square error.
Human-Library Interaction: A Self-Service Library Management System Using Sequential Multimodal Interface

An ideal library makes every last patron feels what it is like to have their own library where one can traverse a world of knowledge, and then build their personal learning environment. Parallel to this conception is the intensification of fine-tuning the conventional librarianship to transform it into a center for new digital learning. As a contribution to this digital revolution, this paper presents an innovative way of renovating the house of dusty books into the center of creativity, research, and partnership through the fusion of traditional librarianship, self-service solutions, and human-computer interaction. The self-service system employs technologies and modalities such as touch screen-assistive technology for the kiosk terminal with the inclusion of a built-in camera, speaker, microphone, and lights, Automatic Speech Recognition, Radio-Frequency Identification and Content-Based Image Retrieval for holdings circulation and monitoring. In conformance with the ISO 9241-210 (Human-centred design for interactive systems), a series of user-centered evaluations were accomplished to obtain early feedback, and to validate that the user requirements have been satisfied at a later stage of the project cycle. To integrate a humanistic approach, the HCSDLC, or Human-Centered Systems Development Life Cycle Methodology, was utilized to complement the four main user-centered design activities specified in the ISO 9241-210 standard. The prototype designs and final self-service library system were assessed in terms of efficiency, effectiveness, and user satisfaction using metrics defined in the ISO/IEC 25022.