Reviewerly/exHarmony huggingface dataset:
Color Image Encryption and Compression Using DCT in Joint Process

Color image compression and encryption are two basic steps for the secure transmission of large size images over the channels. The compression process is needed because of the limitation of storage and transmission bandwidth. Encryption is the study of encoding and decoding the provided information for the security purpose through the Internet. In general, compressed data and encrypted data are transmitted separately so time consumption and computation become expensive. In this paper, separately and simultaneously compression and encryption processes have been simulated in a single program to reduce entire operation time. The proposed work also comprises of the solution of image security by encryption and color image compression using discrete cosine transform (DCT).
Sensuality, AR/VR, and the Virtual Sublime

This article investigates the body, embodiment, augmented, virtual reality (AR/VR) and the virtual sublime. Through AR/VR one negotiates virtual worlds, often with a feeling of endless possibility and sublimity. This experience can lead to the danger of being swallowed up by the sublime. However, instead of being confronted by nature and the immensity of the skies, the virtual sublime references technology, infinitely zooming into microscopic and atomic structures, yet still shaking our sense of our world. The concepts of virtuality, digital materiality, the analogue/digital divide, an AR/VR spectrum, essentialism, sensorial sensuality and avatar instantiation will be explored, concluding with an analysis of the senses and the natural extension of sensorial engagement—affect. This article proposes that the heightened sensations of an AR/VR encounter lend themselves to the sublime. However, the deficit of AR/VR sensuality due to truncated sensorial input leads to feelings of disaffection and disconnection. The residual effect translates into a longing for a heightened engagement and becomes a yearning for the sensual input of physicality. Yearning therefore becomes a defining attribute of the virtual sublime. These ideas are considered in light of the philosopher Henri Bergson’s concepts of the absolute and the relative.
The Future of Information Retrieval Evaluation

Oard, Douglas W.Looking back over the storied history of NTCIR that is recounted in this volume, we can see many impactful contributions. As we look at the future, we might then ask what points of continuity and change we might reasonably anticipate. Beginning that discussion is the focus of this chapter.
Object Detection Based on Multi-Source Information Fusion in Different Traffic Scenes

In practical scenarios, object detection performance is largely affected by the current environment. For example, in traffic object detection scenes, object conglutination caused by the influence of light, occlusion and other negative factors brings difficulties to accurate multi-target detection. And the accuracy of object detection in traffic scenes directly impacts the safety of automatic driving. In this paper, a novel object detection method based on multi-source information fusion is proposed to deal with the low accuracy of object detection in different traffic scenes. The method first adopts a traditional two-stage detection network to extract visual features, then extracts the semantic features and relation features among objects, finally designs a multi-source information fusion module to integrate these features above. The fusion module actually enhances the learning ability of the network and improves detection accuracy. Moreover, the proposed network can achieve adaptability in various scenarios according to different geographic, environmental and weather characteristics. The simulation results show that the proposed approach has better performance in object detection than other traditional methods in different traffic scenes.
Constructions of several classes of linear codes with a few weights

Recently, linear codes with a few weights have been constructed and extensively studied due to their applications in secret sharing, authentication codes, association schemes, and strongly regular graphs. In this paper, we construct several classes of linear codes with a few weights over Fp, where p is an odd prime. The weight distributions of these constructed codes are also settled by applications of the theory of quadratic forms and Gauss sums over finite fields. Some of the linear codes obtained are optimal or almost optimal. The parameters of these linear codes are new in most cases. Moreover, two classes of MDS codes are obtained.
Intelligent Intrusion Detection Using TensorFlow

Detection of human beings accurately in a visual system like in images, videos, live stream videos is critical for diverse application which includes elderly abnormality detection, violent detection, human gait characterization, congestion analysis, person recognition and gender classification. Object detection is the process of distinguishing the presence of predefined types of objects in a frame. This process includes both identification of the presence of the objects with a rectangular bounding surrounding it. The application utilizes MSCOCO model trained from ImageNet database with TensorFlow to detect human beings or objects with a greater accuracy. For greater speed in detection, single shot detectors (SSD) have been used for detection. There is a tradeoff between faster detection and accuracy. The test observations results show the detection as well as the recognition of the human beings with a greater speed and accuracy that indicates the effectiveness of the proposed approach.
Feature Analysis for characterization of Phased Array images based on Hilbert Transform

Phased array is one of the most promising ultrasonic testing methods. Non-destructive testing finds its application in various industries, that includes online monitoring purpose, defect identification and so on. Both conventional and non-conventional testing methods are used in industries to carry on the detection process. Ultrasonic phased array testing finds its application in almost all the areas, which is a counterpart for radiography. In this paper, an automated testing method is proposed, which deals with weld flaw segmentation technique using hybrid clustering and Hilbert transform. In order to improve the quality of the obtained image, preprocessing steps are required and adaptive diffusion filter is proposed for removal of noise and pixel correction. In this proposed work, 2D-Hilbert Transform is implemented for the calculation of analytic images and satisfy the properties as in the case of 1D method. This technique provides a 2D spectrum equal to zero in one quadrant. After the process of segmenting the region of interest, the unique features are estimated using the Gray level co-occurrence GLCM. These unique features are fed as the input to deep learning for best classification of the defects in weldments.
Super-Resolution Reconstruction Algorithm of Target Image Based on Learning Background

In the realistic video monitoring environment, the traditional super-resolution reconstruction technique based on prior knowledge is not suitable for monitoring the super-resolution reconstruction of the image. In this paper, a super-resolution reconstruction algorithm of target image based on learning background is proposed. The first part of the algorithm is to design a non-manifolds consistency algorithm for super-resolution reconstruction of the whole video surveillance image. The second part of the algorithm, from video surveillance images in the background, to select the characteristics significantly, and the relatively fixed background. And then to study the background, study a mapping function can improve image quality. Finally, the mapping function to restoration image of interested target, so that we can better recover the structure and texture of target image details. The experimental results show that the proposed algorithm improves both the objective evaluation index and the subjective visual effect.
Video Frame Encryption using Symmetric Algorithm

Security of the data in the internet is very important. Everything we use in our life is digitalized. Every data crawling on the internet is not safe. So Cryptography is the encryption technique to secure the data. Most importantly video is a huge data type but encryption of the video data is little more difficult. So video is extracted into RGB frames then Rijndael algorithm is used for encrypting the RGB frames. Finally the encrypted RGB frames recreate into video again. The reverse process is used for decryption as well. Time is very important to our busy world so the encryption speed is compared between the existing and the proposed technique.
Case study: Factors that hinder and support the adoption of Pair Programming in an agile software development company

Pair programming is the XP practice that is the least adopted in software development departments or companies; although previous research suggests there are numerous benefits that can be realised from its use. It is suggested that this practice, which involves two programmers working collaboratively on the same task and following a prescribed approach, produces high quality code, in shorter timeframes, with increased knowledge-sharing and improved developer productivity. This paper investigates the factors that support, and deter, pair programming adoption in a case study at a software development company in the eThekwini (Durban) region, South Africa. The survey and interviews indicate that the most positive influences are the support from senior and peer staff and the company's willingness to provide the necessary hardware, software and physical layout to enable the use of pair programming. The biggest challenge was the personality mix in a pair, for example introvert/extrovert pairs, where extroverts can dominate pair collaboration. This company has specific managerial guidelines and practices which are used to mitigate the challenges and provide support to, not only make pair programming a more operationally feasible option, but also reap its benefits.
Simulating integrated photonic gates using FDTD

Quantum technologies, such as quantum communication, sensing and imaging, need a platform which is flexible, miniaturizable and works at room temperature. Integrated photonics is a promising and fast-developing platform. This requires to develop the right tools to design and fabricate arbitrary photonic quantum devices. Here we present an algorithm which, starting from a $n$-mode transformation $U$, designs a photonic device implementing $U$. Using this method we design integrated photonic devices which implement quantum gates with high fidelity. Apart from quantum computation, future applications include the design of photonic subroutines or embedded quantum devices. These custom-designed photonic devices will implement in a single step a given algorithm and will be small, robust and fast compared to a fully-programmable processor.
Humanoid Robotics: Guidelines for Usability Testing

In the macrofield of human–computer interaction, human–robot interaction (HRI) is a relatively new and unexplored field of study. As several efforts have been made in robotics to expand the range of functionalities of hardware and software, perhaps the hardest challenge is to ensure good usability and fluid interaction for users. With regard to remote operated robots, these aspects are unrelated and the interaction features may be taken into account at an advanced design stage. Nevertheless, when it comes to humanoid robots, interaction features are the keypoint, therefore the design of the latter should be driven by UX and UCD practices. The actual fact is that too little attention is paid to these issues or, at best, the latter are approached by IT’s point of view, who are in charge of the software development. In conjunction, a team of engineers take care of the mechanical parts and, in the most highly developed project, the appearance is curated by industrial designers. Given the importance of the interaction features, the design process should be rearranged in favour of the last-mentioned phase. However, the team in charge of this task should be composed of industrial designers, that we can consider as UI designers, along with UX expert. This study applies UX practices to the design process of a humanoid robot, providing practical guidance and guidelines to put in place usability testing during the various stages of development. Regarding testing, only user satisfaction is often investigated. Instead, the present approach includes an adaptation of typical elements of the web design area, such as learnability, efficiency, memorability and errors.
The problem with AI music: song and cyborg creativity in the digital age

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Concurrent error detection revisited

Fault Injection Analysis (FIA) and Side-Channel Analysis (SCA) are considered among the most serious threats to cryptographic implementations and require dedicated countermeasures to ensure protection through the entire life-cycle of the implementations.
Multilayer Fuzzy Extreme Learning Machine Applied to Active classification and Transport of objects using an Unmanned Aerial Vehicle

Based on hierarchical Multilayer Extreme Learning Machine (ML-ELM) and Fuzzy Logic theory (FL), in this paper a Multilayer Fuzzy Extreme Learning Machine (ML-FELM) has been developed with an application to active classification and transport of objects using an indoors Unmanned Aerial Vehicle (UAV). The learning approach that follows the proposed ML-FELM is a forward two-step hierarchical methodology. First, by stacking a number of Fuzzy Autoencoders (FAEs), input data is projected into a feature representation space. Each FAE is functionally equivalent to a Mamdani Fuzzy Logic System of type-1 (T1 FLS). Finally, in the second stage, features achieved by stacking a number of FAEs are classified by using a Fuzzy ELM (FELM) based on T1 FLS theory and ELM. To evaluate the effectiveness of the proposed ML-FELM, a number of other existing machine learning approaches were employed for the active classification and transport of four different geometrical objects. To further ensure the efficiency of the ML-ELM, a number of popular benchmark data sets for classification problems are also suggested. Based on our experimental results and compared to other deep learning strategies, the ML-FELM not only represents a fast machine learning approach, but also produces a high model accuracy for image classification.
TSSweb: a Web Tool for Training Set Selection

Supervised learning methods aimed at performing precise predictions by learning from labeled training data. Unfortunately, training data can contain noisy or wrong information, specially when they come from real-world applications. In this scenario, applying a so-called training set selection procedure on data can lead to improve the performance of the supervised learning methods used for classification or regression tasks. In literature, several training set selection techniques have been proposed, but, to the best of our knowledge, few software tools implement this procedure. Moreover, all of them require programming capabilities or software package installation what makes their use difficult for people without specific computer skills. This paper proposes the first web-based tool, named TSSweb, for performing an accurate selection of the training instances. Thanks to its web nature, TSSweb enables all researchers, coming from several and heterogeneous scientific backgrounds, to reduce own datasets so as to improve their analysis and reduce the execution time of their supervised learning models. As shown in the experimental session, TSSweb produces reduced datasets with a good quality as well as being user-friendly.
Reverse Engineering of Android Applications: REiMPAcT

Reverse engineering may be helpful for extracting information from existing apps to understand them better and ease their maintenance. Reverse engineering may be performed by a static analysis of the apps’ code but, when the code is not available, a dynamic approach may be useful. This paper presents a tool that allows extracting dynamically, in a complete black-box approach, the explored activities of Android applications. It is an extension of iMPAcT testing tool that combines reverse engineering, dynamic exploration, and testing. The extracted information is later used to construct an HFSM (Hierarchical Finite State Machine) with three distinct levels of abstraction. The top-level shows the interactions needed to traverse the activities of the mobile application. The middle level shows the screens traversed while in a specific activity. The bottom level shows all screens traversed during exploration. This information helps to understand better the application which facilitates its maintenance and errors fixing. This paper provides a complete description of the tool, its architecture and the results of some case studies conducted on mobile apps publicly available on the Google Store.
HP: A light-weight hybrid algorithm for accurate data partitioning

This paper introduces a hybridization of the k-means and k-medoids paradigms. The new algorithms is named HP (hybrid partitioning) algorithm. Specifically, we improve on a recently developed scalable version of k-means (k-means-lite), by introducing the PAM algorithm into it in such a way that the high accuracy of the latter is absorbed without inheriting its high inefficiency. K-means-lite runs standard k-means on the combination of intermediate centroids obtained by initially feeding n samples into k-means. In HP, instead of k-means, PAM is used to cluster the combination of centroids obtained from the samples. This PAM component is fast because it is run on very small data, precisely of size nk. Experiments show that this modification improves not only the accuracy of k-means-lite but also outperforms the accuracy of k-means, without losing much k-means-lite's efficiency.
Design of Fuzzy Controller for Simple Inverted Pendulum

This paper deals with the design of a fuzzy controller for the stabilization of a simple inverted pendulum on the cart in the upper equilibrium. The Mamdani type fuzzy logic controller was used for this purpose. The paper is aimed at the design of the control structure based on a normalized controller with individually determined scale factors. The paper lists possible methods of calculation of the scale factors. Also, it gives a detailed description of the particular chosen method. The method bases on LQR gains and represents a key part of the proposed approach. The controller functionality was verified in offline simulation and compared with the LQR controller.
Classification of Eyes Based on Fuzzy Logic

The systems of eye classification in an image are indispensable in several domains. To better find the class of membership of the eye in a minimal time, the classic methods of detection are inadequate. Fuzzy logic is considered to be an effective technique for solving an eye classification problem. This article proposes a fuzzy approach for eye classification. The tasks of classification are realized in two steps. In the first step, the characteristic points of the image are extracted in order to locate the eye. These characteristic points allow generating a representative model of the eye. In the second step, the detected eyes have to pass by a fuzzy controller containing several parts: Fuzzification, inference rules, and defuzzification. Finally, the system gives the degree of membership of the detected eyes to each class in the database.
Tool for image annotation based on gaze

Supervised learning on image data demands availability of large amounts of annotated image data. Annotation is predominantly a tool assisted manual activity and increasingly accounts for a large share of budget in machine learning systems development. This is due to the time involved and the need for large manpower to annotate large databases. Instead of the predominantly bounding box drawing using mouse cursor, we propose a more natural human computer interface - the human gaze. We hereby propose a technique of image annotation by using a novel protocol for acquiring gaze data to create a polygon around the object rather than bounding boxes. In this study the method is outlined and the results are compared with manually created annotations. The technique can be used to annotate existing image databases or create new annotated databases by simultaneous image acquisition and annotation.
Hyper-Laplacian Regularized Low-Rank Collaborative Representation Classification

Face recognition is an important branch of computer vision. Domestic and foreign scholars have proposed many algorithms to improve the face recognition rate. However, when the training sample and the test sample are exposed to light, occlusion or contamination, the performance of the proposed algorithm will decrease. The recently proposed low-rank constrained collaborative representation classification algorithm (LCRC) has been proven to have superior performance in face recognition. The model is a global clustering method that can effectively recover the global subspace structures of the data, but does not consider the local geometric manifold structures of the original data. This will cause it to break the manifold structures of the original data while restoring the data, thereby losing the local geometric information of the recovered data. For the flaws of the algorithm, this paper proposes a hyper-Laplacian regularized low-rank collaborative representation classification (HLCRC). The hyper-Laplacian regularizer is introduced into the low-rank collaborative representation model to maintain the multivariate geometric manifold structures between data. Experiments on public face database show that the proposed algorithm is superior to many existing algorithms in face recognition rate.
Classification of Social Signals Using Deep LSTM-based Recurrent Neural Networks

Non-linguistic speech cues aid expression of various emotions in human communication. In this work, we demonstrate the application of deep long short-term memory (LSTM) recurrent neural networks for frame-wise detection and classification of laughter and filler vocalizations in speech data. Further, we propose a novel approach to perform classification by incorporating cluster information as an additional feature wherein the clusters in the dataset are extracted via a k-means clustering algorithm. Extensive simulation results demonstrate that the proposed approach achieves significant improvement over the conventional LSTM-based classification methods. Also, the performance of deep LSTM models obtained by stacking LSTMs, is studied. Lastly, for classification of the temporally correlated speech data considered in this work, a comparison with popular machine learning-based techniques validates the superiority of the proposed LSTM-based scheme.