Reviewerly/exHarmony huggingface dataset:
A novel feature extractor for human action recognition in visual question answering

Recognizing and classifying human actions in video clips is a powerful technology for surveillance applications. However, most of the state-of-the-art approaches for this task lack the possibility of being implemented in real-time applications without causing a critical delay. Thus, we propose a fast method to human action recognition for visual question answering, based on a novel feature extractor developed by us, 2D pose estimation, and machine learning techniques. Our extractor obtains features based on distances, angles, and positions of detected anatomical keypoints. We used the UCF101 dataset that corresponds to 13,320 videos with realistic human actions, collected from YouTube, for our work evaluation. The proposed feature extractor, combined with the Complement Naive Bayes classifier, reached a mean Average Precision (mAP) of 62.03% and processed 5.26 frames per second, proving to be faster than most methods while achieving a decent mAP.
Depth Image Completion Using Anisotropic Operators

Our work tackles the problem of anisotropic data interpolation in the context of depth computation in large regions of an image. Usually, depth data present areas without depth data due to low confidence in the data. We consider the image endowed with an anisotropic metric \(g_{ij}\) taking spatial and the geometry information. Our proposal is a numerical implementation based on “eikonal” operators to compute the solution of the biased Infinity Laplacian or biased Absolutely Minimizing Lipschitz Extension (bAMLE). The infinity Laplacian operator interpolates creating cones. The biased infinity Laplacian interpolates creating exponential cones, making it more adapted to real scenes that, in general, present smooth surfaces. Thus, the biased Infinity Laplacian properties make this interpolator able of completing depth data to large image areas. We performed an experimental evaluation comparing our numerical model comparing Infinity Laplacian, biased Infinity Laplacian, and bilateral filter in two databases: Middlebury2014 and KITTI (for depth completion). In Middlebury2014 biased infinity Laplacian outperforms Infinity Laplacian in the up-sampling depth data task, and in KITTI dataset, completing depth images, biased Infinity Laplacian exceeds both bilateral and Infinity Laplacian. The simplicity of our practical implementation makes biased Infinity Laplacian a low-cost-implementation alternative for many computer vision community interpolation problems.
Verifiable Identity-Based Encryption with Keyword Search for IoT from Lattice

Internet of Things (IoT), which provides the solution of connecting things and devices, has increasingly developed as vital tools to realize intelligent life. Generally, source-limited IoT sensors outsource their data to the cloud, which arises the concerns that the transmission of IoT data is happening without appropriate consideration of the profound security challenges involved. Though encryption technology can guarantee the confidentiality of private data, it hinders the usability of data. Searchable encryption (SE) has been proposed to achieve secure data sharing and searching. However, most of existing SE schemes are designed under conventional hardness assumptions and may be vulnerable to the adversary with quantum computers. Moreover, the untrusted cloud server may perform an unfaithful search execution. To address these problems, in this paper, we propose the first verifiable identity-based keyword search (VIBKS) scheme from lattice. In particular, a lattice-based delegation algorithm is adopted to help the data user to verify both the correctness and the integrity of the search results. Besides, in order to reduce the communication overhead, we refer to the identity-based mechanism. We conduct rigorous proof to demonstrate that the proposed VIBKS scheme is ciphertext indistinguishable secure against the semi-honest-but-curious adversary. In addition, we give the detailed computation and communication complexity of our VIBKS and conduct a series of experiments to validate its efficiency performance.
MEC-Assisted FoV-Aware and QoE-Driven Adaptive 360° Video Streaming for Virtual Reality

Virtual reality (VR) has been envisioned as the killer-application in the 5G mobile networks. Among numerous VR services, 360° video streaming is the most promising one. Nevertheless, its wide adoption is hindered by large latency incurred in cloud-based video delivery and insufficient bandwidth resource in Radio Access Network (RAN). Fortunately, the emergence of Multi-access Edge Computing (MEC) become an enabler to fulfill the potential of VR by providing caching and computing resources at network edges. Also, since a user can view only a part of the entire 360° video frame due to the limitation of eye vision, users' Quality of Experience (QoE) can be further enhanced if we can predict his Field of View (FoV). In this paper, we propose a novel MEC-assisted FoV-aware and QoE-driven Adaptive Streaming (MFQAS) scheme for 360° videos. Specifically, we first provide a comprehensive QoE model for 360° video streaming. Second, we adopt AutoRegression Moving Average (ARMA) model in FoV prediction. Finally, we propose a heuristic algorithm to optimize the caching and computing decision at MEC server based on predicted FoV so that users' QoE can be enhanced. The simulation results show that our proposed method can provide much better prediction accuracy and QoE compared with baseline algorithms.
Face Recognition of Children Using AI Classification Approaches

According to the Census of India, children below 6 years of age have 13.12 % of the total population [21]. Security and healthcare of children is an important aspect of all countries. However, the span from newborn to toddler, recognition of them is still challenging using biometrics [1]. Recognition of children based on biometric can be used to minimize problems related to their security and health care. We require a well suited biometric based system for recognition of children. As per our knowledge, the accuracy of children identification in multiple sessions is not yet studied. Jain et. al studied Infants identification, achieved the accuracy of 38.44 % below 4 weeks and 73.98 % for above 4 weeks. In this paper we are presenting various face recognition techniques accuracy using different classification techniques for children. For this study the database of 90 subjects for the same session and 50 subjects of cross session is collected. Span between the data acquisition sessions is 2 months to 1 year. Detailed study of Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) feature extraction using different machine learning classifiers on children database of the same session and cross session is carried out. Also, the Convolution Neural Network (CNN) optimized model with data augmentation is used to compare the machine learning and Deep learning classifier on our database. Same session accuracy of 98 % is achieved with LDA feature extraction and 5 -fold cross validation with LDA classifier. Cross session accuracy of 94 % is achieved with LDA feature extraction and Logistic regression classifier with 5- fold cross validation.
Review on Medical Image Denoising Techniques

In recent times, with rapid growth in technology, medical imaging has become popular in healthcare. The impression of these improvements in the medical field has become evident that technology can able to diagnose disease in a much better way than before. Medical images play a vital role in the detection and prediction of disease but these images may tend to corrupt by some kind of noise or artifacts during the image acquisition process. The presence of noise makes images unclear, fine details and features of the original image are lost which leads to inaccurate detection of disease. Hence, different denoising methods are required to eliminate noise without losing image features (edges, corners, and other sharp structures). Researchers have already proposed different tools and techniques to reduce noise. Each technique has its merits and demerits. Hence preprocessing of medical images is a mandatory and essential process to get accurate results. This review article provides a comprehensive survey of different noises, denoising models, contrast enhancement methods, quality matrices. In addition, the main aim of this paper is to conduct a detailed analysis of various preprocessing techniques used on medical images which include Computed Tomography (CT), Magnetic Resonance images (MRI), Positron Emission Tomography (PET), 2D/3DULTRASOUND images.
YOLO Nano Underwater: A Fast and Compact Object Detector for Embedded Device

Recent researches on Underwater object detection have progressed with the development of deep learning methods. A large portion of ROVs and AUVs are working on constrained environments with limited power supply and computing capability. In this paper, we propose a fast and compact object detector for several marine products, such as scallop, starfish, echinus and holothurian. The novel proposed model named YOLO Nano Underwater is modified based on YOLO Nano architecture to reduce the inference time. And instance normalization is introduced to replace the batch normalization in some early layers for a precision boost. The model achieves a competitive performance on edge computing devices, like NVIDIA Jetson Nano. Comparing to YOLO v3, our model can get competitive performance in precision, but with only 5% computation. Meanwhile, we re-annotated the training part of the URPC 2019 dataset and the refined annotations can be public available at https://github.com/wangsssky/Refined-training-set-of-URPC2019/.
Bitcoin Price Alert and Prediction System using various Models

Abstract From last many years it has been a trend to invest in cryptocurrency especially (Bitcoin) because it is one of the most popular and decentralized digital currency. However, its prices keep on fluctuating very much that makes it difficult to predict. So, our research aim is to find the less time consuming and accurate model for the prediction of Bitcoin price from different machine learning models like (Multivariate Linear Regression, Theil-Sen Regression, Huber Regression) and deep learning algorithms like (LSTM, GRU). The dataset that we will use for our prediction purpose will be stored in MongoDB (Big-Data Tool) because it consists of huge data points. We have also implemented IOT in our system to create an alert system, which alerts user when the value of bitcoin price reaches a threshold value.
Blockchain cross-chain protocol and platform research and development

With the vigorous development of blockchain technology, the application of blockchain networks in various fields is becoming more and more complex. In order to enable information interaction between homogeneous and heterogeneous blockchains to meet business needs in different scenarios, this article propose an improved cross-chain protocol and develop a high-performance cross-chain platform. This article first elaborates and summarizes the current blockchain cross-chain technology, then improves the existing cross-chain protocol based on atomic swaps, develops a set of smart contract templates for the improved protocol, and combines the improved cross-chain protocol. The chain protocol and smart contract are applied to the cross-chain platform, and finally the cross-chain platform is tested. The test results show that the cross-chain platform has high throughput and low latency.
BlockChain Based Inventory Management by QR Code Using Open CV

Inventory management is a part of the supply chain where inventory and quantities of stock are tracked in and out of the stockroom. Proper handling of inventory will leads to successful supply chain management in any organization. QR codes make this inventory management speedy. This fast information transfer will also reduce the number of errors in inventory records and also gives accurate results to make informed decisions during frequent reviews. But based on Quick Response (QR) code for inventory management will become a centralized database. Blockchain facilitates manufacturers to connect each party from distribution centers and retail partners, to suppliers and production sites - with an abiding record of each, single exchange that occurred. These put away records are available to everybody inside the P2P organize and gives decentralization. The degree of straightforwardness and permanency gave in blockchain are frequently useful for manufacturers to oversee item roots and traceability. Smart contracts, one of the features of blockchain, have built-in automation, which makes a lot of sense for transaction management. In this paper, we are using both the features of QR code and blockchain for transparent, distributed, and reliable inventory management.
Building partially understandable convolutional neural networks by differentiating class-related neural nodes

In recent years, convolutional neural networks (CNNs) have been successfully applied in the field of image processing, and have been deployed to a variety of artificial intelligence systems. However, such neural models are still considered to be "black box" for most tasks. Two of fundamental issues underlying this problem are as follows: 1. What type of knowledge learned by a neural network in a task was not understandable and predictable? 2. The decision made by a neural model was generally not evaluable. Like neural coding in the brain, some neurons only participated in encoding a particular task. Inspired by this, in this paper, we propose a method to modify traditional CNN models into understandable CNNs, to clarify the information coding in high conv-layers of CNNs and further evaluate the decisions made by a neural model. In our understandable CNN models, each neural node (feature map) in a selected conv-layer was assigned to participate in encoding only one class in a classification task. Our models use the same training data as ordinary models without the need for additional annotations for supervision. We applied our method to the ResNet and DenseNet models. The experiments showed that new models can learn the information coding mode that we expected in an image-recognition task, and, using the pre-assigned coding mode, we can interpret why a neural model makes a right or wrong decision, which decisions are credible, and which are not.
A novel embedding approach to learn word vectors by weighting semantic relations: SemSpace

In this study, we propose a novel embedding approach, called as SemSpace, to determine word vectors of synsets and to find the best weights for semantic relations. First, SemSpace finds the optimum weights to the semantic relations in WordNet by aligning them to values produced by human intelligence, and then, determines word vectors of synsets by adjusting euclidean distances among them. Proposed approach requires two inputs; first, a lexical-semantic network such as WordNet, second, a word-level similarity dataset generated by people. In the experiments, we used WordNet 3.0 data for the lexical-semantic network, and three (RG65, WS353, and MEN3K) benchmark testsets to align semantic weights. Using the aligned semantic weights and the determined word vectors, the obtained resultsresults on the benchmark testsets are compared with literature studies. According to the obtained results, it might be concluded that SemSpace is not only successful to find word level semantic similarity values and semantic weights, but also to discover new semantic relations with their semantic levels.
Active Learning for Arabic Text Classification

Active Learning explores the use of minimal human intervention to improve the efficiency of supervised machine learning algorithms during the learning/training phase. Active learning improves machine learning algorithms performance, especially for ambiguous or unknown cases that are not clearly defined in the classification criteria applied to data. In machine learning, the quality of used data greatly determines the quality of the classification task outcomes. Especially with the current abundance of data resources, the data labeling process represents a major hurdle to data classification. In this paper, we share our results of using active learning approach for Arabic text classification. We demonstrate in this work how active learning approach greatly improves the efficiency of machine learning systems when compared to traditional passive learning approaches. This work introduces our preliminary results of using active learning approach to help annotate the ever-growing Arabic data corpora using state-of-the-art learning techniques.
Becoming Interdisciplinary

ICTs such as mapping platforms, algorithms, and databases are a central component of how society responds to the threats posed by disasters. However, these systems have come under increasing criticism in recent years for prioritizing technical disciplines over insights from the humanities and social science and failing to adequately incorporate the perspectives of at-risk or affected communities. This paper describes a unique month-long workshop that convened interdisciplinary experts to collaborate on projects related to flood data. In addition to findings about the practical accomplishment of interdisciplinary collaboration, we offer three interrelated contributions. First, we position interdisciplinarity as a critical practice and offer a detailed example of how we staged this process. We then discuss the benefits to interdisciplinarity of expanding the range of temporal logics normally deployed in design workshops. Finally, we reflect on approaches to evaluating the event's contributions toward sustained critique and reform of expert practice.
Feature-oriented Design of Visual Analytics System for Interpretable Deep Learning based Intrusion Detection

Deep Learning models have demonstrated significant performance on different tasks such as computer vision, natural language processing, etc. In recent years, these models have also achieved remarkable progress in Intrusion Detection Systems. However, the mechanism of these models is often hard to understand, especially for researchers in the domain of network security. In this paper, we propose a visual analytics system for interpretable deep learning based intrusion detection. During the design of this visual analytics system, we follow the requirements and features of explainable artificial intelligence for users in the domain of network security. The system allows users to select the best parameters to construct the model, to better understand the role of neurons in a deep learning model, to select instances and explore the detection mechanism of the model on these instances. We present multiple use cases to demonstrate the effectiveness of our system.
Networked Authoritarianism at the Edge

This paper describes how village-level officials, relatively new to the Internet, use popular digital platforms on smartphones to supplement and extend long-standing patterns of information control and authoritarian power in rural Cambodia. They use these tools to monitor local affairs, report to the central government, and promote local government activities, practices which intimidate villagers and encourage their political withdrawal and self-censorship. This paper makes three contributions to the literature on networked authoritarianism and rural governance. First, technological changes currently underway in the Cambodian rural bureaucracy reflect a generational transition, as long-standing officials struggle to use new media easily or effectively, leading to new anxieties and breakdowns for these traditional holders of power. Second, bureaucratic information practices in these villages rely on material practices ranging from paper, face to face meetings, and loudspeakers, to new tools such as Facebook and smartphones - underlining significant continuities in mechanisms of bureaucratic power and control. Third, networked authoritarian practices conjure for villagers the historical links between information control and violence, and the effectiveness of these tactics on chilling speech is often rooted in villagers' memories of fear.
Modeling Multiple Coexisting Category-Level Intentions for Next Item Recommendation

Purchase intentions have a great impact on future purchases and thus can be exploited for making recommendations. However, purchase intentions are typically complex and may change from time to time. Through empirical study with two e-commerce datasets, we observe that behaviors of multiple types can indicate user intentions and a user may have multiple coexisting category-level intentions that evolve over time. In this article, we propose a novel Intention-Aware Recommender System (IARS) which consists of four components for mining such complex intentions from user behaviors of multiple types. In the first component, we utilize several Recurrent Neural Networks (RNNs) and an attention layer to model diverse user intentions simultaneously and design two kinds of Multi-behavior GRU (MGRU) cells to deal with heterogeneous behaviors. To reveal user intentions, we carefully design three tasks that share representations from MGRUs. The next-item recommendation is the main task and leverages attention to select user intentions according to candidate items. The remaining two (item prediction and sequence comparison) are auxiliary tasks and can reveal user intentions. Extensive experiments on the two real-world datasets demonstrate the effectiveness of our models compared with several state-of-the-art recommendation methods in terms of hit ratio and NDCG.
Combining Readability Formulas and Machine Learning for Reader-oriented Evaluation of Online Health Resources

Websites are rich resources for the public to access health information, and readability ensures whether the information can be comprehended. Apart from the linguistic features originated in traditional readability formulas, the reading ability of an individual is also influenced by other factors such as age, morbidities, cultural and linguistic background. This paper presents a reader-oriented readability assessment by combining readability formula scores with machine learning techniques, while considering reader background. Machine learning algorithms are trained by a dataset of 7 readability formula scores for 160 health articles in official health websites. Results show that the proposed assessment tool can provide a reader-oriented assessment to be more effective in proxy the health information readability. The key significance of the study includes its reader centeredness, which incorporates the diverse backgrounds of readers, and its clarification of the relative effectiveness and compatibility of different medical readability tools via machine learning.