Reviewerly/exHarmony huggingface dataset:
Effects of Image Filters on Various Image Datasets

Image classification is a very common research area, on which researchers work with various classification techniques. The aim of this study is to apply different filters on four different datasets and evaluate their performances in image classification. The study was performed in WEKA environment with Random Forest algorithm and image filters are applied to the datasets one by one and as a combination. Filter combinations got better performance than applying single filter on data. Filter combinations got the worst result on artworks with a percentage of 83.42%. However they were very successful on classifying the images in natural images dataset with a performance of 99.76%.
Bayesian recommender system for social information sharing: Incorporating tag-based personalized interest and social relationships

Personal information management enables users to manage and classify information via the social tagging. The personal information management platform has recently successfully adopted social networks, enabling users to conveniently share their preferences of information with each other. The emergin g social networks generate new concepts for designing modern recommender systems in personal information management and sharing platforms. To design a recommender mechanism for the personal information management and sharing platforms, this work incorporates tag-based personalized interest and social network relationships into a modified Bayesian probability model. The proposed system is demonstrated with experimental datasets obtained from a popular social resource sharing website. The performances of the proposed system are evaluated based on the word2vec word embedding model. Experimental results indicate that incorporating social network information and personalized tag-based preference with the Bayesian model can improve the recommendation quality for social information sharing websites.
Generating image captions through multimodal embedding

Caption generation requires best of both Computer Vision and Natural Language Processing. Due to recent improvements in both of them many efficient models have been developed. Automatic Image Captioning can be utilized to provide descriptions of website content or to engender frame-by-frame descri ptions of video for the vision-impaired and in many such applications. In this work, a model is described which is utilized to generate novel image captions for a previously unseen image by utilizing a multimodal architecture by amalgamation of a Recurrent Neural Network (RNN) and a Convolutional Neural Network (CNN). The model is trained on Microsoft Common Objects in Context (MSCOCO), an image captioning dataset that aligns captions and images in the same representation space, so that an image is close to its relevant captions in that space and far away from dissimilar captions and dissimilar images. ResNet-50 architecture is used for extracting features from the images and GloVe embeddings are used along with Gated Recurrent Unit (GRU) in Recurrent Neural Network (RNN) for text representation. MSCOCO evaluation server is used for evaluation of the machine generated caption for a given image.
Multiple Image Encryption with Fractional Hartley Transform and Robust Chaotic Mapping

In this paper, we present a simple yet highly robust mechanism for multiple image encryption with optical transformation. A fractional order Hartley transform framework along with secret reversible integer transform (RIT) is used to decorrelate the images. The selection of fractional orders is based on a robust chaotic mapping. Optical transforms support fast and parallel processing but have limitation in digital implementation due to complex coefficients. We present a paradigm where complexity is completely eliminated at each security level to overcome the need of time consuming phase retrieval algorithms. The amalgamation of chaos further make it a highly sensitive and secure technique. This method gives optimum results with almost uniform histogram and lossless recovery of data which is otherwise limitation of optical transforms whose energy is concentrated at the center.
A camera model for cameras with hypercentric lenses and some example applications

We propose a camera model for cameras with hypercentric lenses. Because of their geometry, hypercentric lenses allow to image the top and the sides of an object simultaneously. This makes them useful for certain inspections tasks, for which otherwise multiple images would have to be acquired and stitched together. After describing the projection geometry of hypercentric lenses, we derive a camera model for hypercentric lenses that is intuitive for the user. Furthermore, we describe how to determine the parameter values of the model by calibrating the camera with a planar calibration object. We also apply our camera model to two example applications: in the first application, we show how two cameras with hypercentric lenses can be used for dense 3D reconstruction. For an efficient reconstruction, the images are rectified such that corresponding points occur in the same image row. Standard rectification methods would result in perspective distortions in the images that would prevent stereo matching algorithms from robustly establishing correspondences. Therefore, we propose a new rectification method for objects that are approximately cylindrical in shape, which enables a robust and efficient reconstruction. In the second application, we show how to unwrap cylindrical objects to simplify further inspection tasks. For the unwrapping, the pose of the cylinder must be known. We show how to determine the pose of the cylinder based on a single camera image and based on two images of a stereo camera setup.
Who is Responsible for a Cryptocurrency?

Cryptocurrencies have entered the economy as alternative money, speculation objects, and as utility tokens for digital platforms. Cryptocurrencies are based on cryptography-based asset disposals broadcasted peer-to-peer to be validated in a decentralized way according to consented protocols. The organization and governance of cryptocurrencies disrupts the way things have been done by centralized institutions. This has consequences for responsibility. A centralized institution can be held responsible for its actions; however, in a cryptocurrency scheme, those actions are taken by a crowd of distributed participants not individually necessary for the outcome. This paper explores the participants' causal links to harm produced by a cryptocurrency scheme, and discusses how these causal links translate into responsibility. It is found that despite the decentralized nature of cryptocurrencies, participants cannot conceptually, legally, or morally use lack of causal links as a justification to evade responsibility.
NLP commercialisation in the last 25 years

Abstract The Journal of Natural Language Engineering is now in its 25th year. The editorial preface to the first issue emphasised that the focus of the journal was to be on the practical application of natural language processing (NLP) technologies: the time was ripe for a serious publication that helped encourage research ideas to find their way into real products. The commercialisation of NLP technologies had already started by that point, but things have advanced tremendously over the last quarter-century. So, to celebrate the journal’s anniversary, we look at how commercial NLP products have developed over the last 25 years.
Intrinsic color correction for stereo matching

The improvement of color similarity between stereo images can bring better performance to stereo matching algorithms. For this purpose, we present a color correction method to alleviate the color discrepancy between a pair of stereo images, so that the color appearance of one image, i.e., the target image, is consistent with the other image, i.e., the source image. Our method starts with decomposing both the target image and the source image into two intrinsic layers, i.e., the reflectance layer and the shading layer, using intrinsic decomposition. The purpose of intrinsic decomposition is to distinguish and then process different color discrepancies caused by different factors (shading and reflectance) separately and appropriately. Then, a practical and effective consistent segmentation algorithm, which applied to the original stereo images, is proposed to establish the region correspondences. Next, luminance correction method and color correction method, based on the local region correspondences, are adopted to correct the shading layer and the reflectance layer in the target image, respectively, making them as similar as possible to those of the source image. Eventually, the two corrected layers of the target image are combined to yield the final corrected image. The experimental results demonstrate that the proposed method, which using intrinsic decomposition to handle color discrepancy caused by different factors, not only enhances the visual color similarity between the stereo images but also improves the accuracy of their stereo matching.
Sentiment Classification of User Reviews Using Supervised Learning Techniques with Comparative Opinion Mining Perspective

Comparative opinion mining has received considerable attention from both individuals and business companies for analyzing public feedback about the competing products. The user reviews about the different products posted on social media sites, provide an opportunity to opinion mining researchers to develop applications capable of performing comparative opinion mining on different products. Therefore, it is an important task of investigating the applicability of different supervised machine learning algorithms with respect to classification of comparative reviews. In this work different machine learning algorithms are applied for performing multi-class classification of comparative user reviews into different classes. The results show that Random Forest outperforms amongst all other classifiers used in the research.
Objective QoE Assessment of DASH Adaptation Algorithms over Multipath TCP

With the widespread use of dynamic adaptive streaming over HTTP (DASH) for delivering videos in the Internet, assuring an acceptable quality of experience (QoE) to the end-user is vital. Due to the use of conventional TCP for DASH, the QoE gets affected by well-known characteristics of mobile networks. MultiPath TCP (MPTCP), an extension of TCP, addresses the several limitations of TCP by utilizing the bandwidth on two or more network paths simultaneously. In this work, we investigate the influence of MPTCP on the QoE in mobile networks. We implement three common DASH adaptation algorithms on an emulated DASH over TCP testbed and assess the QoE using objective metrics. The experimental results demonstrate a significant improvement in the QoE by using MPTCP for DASH.
Using collocation clusters to detect and correct English L2 learners’ collocation errors

In this article, we describe an online English collocation explorer developed to help English L2 learners produce correct and appropriate collocations. Our tool, which is able to visually represent relevant correct/incorrect collocations on a single webpage, was designed based on the notions of collocation clusters and intercollocability proposed by Cowie and Howarth. As they pointed out, in a collocation cluster L2 learners generally cannot distinguish true collocations (e.g., tell truth, state truth, and state fact) from impossible combinations (e.g., *say fact and *say truth). Accordingly, our tool applies natural language processing techniques to construct collocation clusters to enable learners to easily differentiate between correct and incorrect pairs. Relying on data from a reference corpus, our system instantaneously processes the collocability of users’ target combination (verb–noun or adj–noun) and all other relevant words and presents true/false collocations that L2 learners should master/avoid. To assess our tool, we investigated its performance in detecting and correcting learners’ V–N and A–N errors, with results comparable to those of most previous studies. Piloted using a sample of 13 intermediate- or upper-intermediate level English as a foreign language learners, our tool was found to help them self-correct their collocation errors effectively. Compared with similar tools or approaches, our tool requires much less data resources, but still demonstrates a remarkable capability to detect/correct errors and generate useful collocational knowledge in English.
Effective and Efficient Content Redundancy Detection of Web Videos

Currently, an unprecedentedly vast amount of videos are hosted on the Internet and shared by users across the world. Within these videos, a considerable portion is duplicate or near-duplicate. Consequently, building an effective yet efficient content-based redundancy detection system is of importance, as this research would be beneficial to a variety of applications. Despite the progress in this field, designing a practical detection system for web videos continues to be difficult, because of the contradictions between the accuracy and speed requirements. In this paper, we propose a novel near-duplicate video detection system, CompoundEyes, whose design philosophy deviates from the conventional feature-centered paradigm. Instead, the focus of our system has been shifted from the design of an advanced feature representation to the design of system architecture. This design methodology not only ensures a decent detection accuracy by the collaboration of the classifiers but also substantially accelerates the detection speed due to the low dimensionality of the feature representations and the exploitation of the parallelism among the components. Experiments have been conducted to demonstrate that the CompoundEyes is both accurate and fast.
Augmented Reality Model to Aid Al-Quran Memorization for Hearing Impaired Students

Industrial Revolution 4.0 becomes the driver of innovation like Augmented Reality. Nowadays, Augmented Reality is applied in the education area to prompt and enhance students’ learning ability. Islamic education is not spared from this; innovations in Islamic educational materials should be enhanced so that students are able to learn better, especially among the special needs students. Hence, this research work is aimed at developing an integrated software model, which can assist the Quran memorization among the hearing-impaired students. This proposed prototyped model is based on Augmented Reality Based Content (ARBC). It is called mAR-Quran and it meets the Augmented Reality environmental development requirement. It enables the students to arrange the sequence verses of surahs in the correct order so that eventually, they memorize verses of Quran better and easier.
A multi-label classification on topics of Indonesian news using K-Nearest Neighbor

News has become a basic human need along with technological and internet developments. This causes the process of disseminating information on the news that switched from print media to the digital era. Another problem that appears when classifying news is multi-label. Multi-label classification is different from single label classification. A single label classification will classify documents into one label only. While multi-label classification can group documents into more than one label. For example, news articles that discuss in detail the early detection of ovarian cancer with a bioinformatics approach may have more than one label such as health, bioinformatics, and women. In this paper, a classification model is developed that can identify classes in each multi-label news article using K-Nearest Neighbor. The advantages of K-Nearest Neighbor are algorithms that are very suitable for multi-label cases; even KNN can be superior to other classifiers. From the system created, the results of the value of system performance as measured by the size of the closeness are the comparison between Manhattan Distance, Euclidean Distance and Supremum Distance using the K = 11 parameters, resulting in a Hamming Loss value of 11.16.%.
Digital Information in Criminal Proceedings: The Concept and Evidential Significance

The purpose of the study is to clarify the concept of “digital information” in the criminal process and to determine its evidentiary significance. The paper analyzes the concept of “digital information” and clarifies its difference from the concept of “electronic information”. We have drawn a conclusion that the concerned information is digital in its form, and the concept of “electronic information”, which is used by a number of scientists, is incorrect. Methods for the preservation, transfer and use of digital information for evidence are considered. The study revealed the main problems of using digital information in criminal proceedings. One of them is the confusion between the concepts of “electronic”/“digital” information and “electronic evidence”, due to the lack of understanding of the specifics of this information, the characteristics of its storage and movement, which can be explained by the rapid transition from industrial to information society. The adoption of information technology and digital information is so specific that users only see the tools with which this information is transmitted, it means, its electronic media (previously there were only electronic computers), hence the substitution of concepts. The inference is drawn that digital information with the relevance property contained in computers and other hardware (electronic storage media) may be present in the materials of the criminal case in the form of a printout of a text file, a screenshot of the video, decryption of a video or phonogram, i.e., in documents (paragraph 6, part 2, Art. 74 of the Criminal Procedure Code). In cases where digital information is attached to the materials of the criminal case on electronic media (flashcard, CD-ROM, memory cards, etc.), we can speak of material evidence (paragraph 4, part 2, Art. 74 of the Criminal Procedure Code). Thus, the addition of the criminal procedure law to a new type of evidence—digital/electronic evidence is not required.
Multiview Active Learning Optimization Based on Genetic Algorithm and Gaussian Mixture Models for Hyperspectral Data

In this letter, we proposed a novel optimal view generation framework based on the genetic algorithm (GA) and Gaussian mixture models (GMMs) to improve multiview active learning (MV-AL). AL methods enlarge training data sets, by iteratively selecting the most informative samples, in order to improve the classification performance. By using multiple views to build multiple classifiers, the information content of each unlabeled samples can be more accurately estimated. The MV-AL methods are more inherently suitable for high-dimensional data such as hyperspectral images. This hybrid framework simultaneously constructs the optimal number of diverse and sufficient views. The proposed algorithm has two main steps. In the first step, by applying a cluster distortion function-based GMMs, the actual number of available independent views is determined. In the next step, a hybrid GA approach selects the optimal combination of views using two different criteria. The experiments were conducted on two benchmark hyperspectral data sets, namely, Kennedy Space Center (KSC) and Indian Pines AVIRIS. The results demonstrated an increase in diversity and sufficiency of the views compared to the traditional view generation methods. Furthermore, the performance of MV-AL has also been significantly improved.
Multi-Attribute Recognition of Facial Images Considering Exclusive and Correlated Relationship Among Attributes

Multi-attribute recognition is one of the main topics attaining much attention in the pattern recognition field these days. The conventional approaches to multi-attribute recognition has mainly focused on developing an individual classifier for each attribute. However, due to rapid growth of deep learning techniques, multi-attribute recognition using multi-task learning enables the simultaneous recognition of more than two relevant recognition tasks through a single network. A number of studies on multi-task learning have shown that it is effective in improving recognition performance for all tasks when related tasks are learned together. However, since there are no specific criteria for determining the relationship among attributes, it is difficult and confusing to choose a good combination of tasks that have a positive impact on recognition performance. As one way to solve this problem, we propose a multi-attribute recognition method based on the novel output representations of a deep learning network which automatically learns the exclusive and joint relationship among attribute recognition tasks. We apply our proposed method to multi-attribute recognition of facial images, and confirm the effectiveness through experiments on a benchmark database.
Travelling Salesperson in an Immersive Virtual Environment: Experimental Evaluation of Tracking System Device

Nowadays Virtual Reality (VR) is an extremely versatile technology capable to cope with many areas of human life, and its fast development requires constant evaluation and validation. Cognitive models of human behavior play a central role in this evaluation, aiming at obtaining high quality, safe and usable products. A problem currently faced by VR users inside immersive Virtual Environments (iVEs) consists in the Simulator-induced Sickness (SS), a particular kind of motion sickness evoked by the simulated visual motion. SS can reduce subjects’ performances, and bias data collected with VR. Although Tracking Systems (TS) were thought to reduce SS symptoms, their effective contribution is not clear. A task based on the Traveling Salaperson was implemented in an iVE to investigate whether TS (a) evoked less SS symptoms and (b) facilitated performance in participants with respect to a control condition without TS. Results showed that TS allowed reduction of many SS symptoms, but this did not produced clear benefits on the cognitive performance, mainly true for female subjects. While TSs may facilitate enjoyment of iVE reducing SS, the higher susceptibility of females suggested that VR designers and producers should consider valuable a certain training before using the iVEs.
New Approach of Big Data and Education

The purpose of this paper is to introduce a new approach that must cover all the terms. Therefore, people's educational process is like making a variety of choices in a super-chessboard of language or a matrix composed of words formally. The method of redemption is: First, construct the chessboard, and then, through human-computer interaction and collaboration, generate massive amounts of big data, including various terms representing knowledge, and finally, through machine learning and man-machine interactive to analyze, compare, and query or reuse any of these terms. The result: an accurate query of terms, which can be automatically queried in multiple ways through bilingual or multi-lingual converters. The significance is that the method and its results can be used not only for machine-assisted instruction in the network environment, but also for machine-assisted intelligent text analysis and knowledge module finishing in the network environment, thus opening up view of big data and education. The new approach, because any term must be in the word matrix, each user and its agents query them very accurately and efficiently.