Reviewerly/exHarmony huggingface dataset:
A user independent hand gesture recognition system using deep CNN feature fusion and machine learning technique

Hand gesture recognition is one of the active research areas in the field of human-computer interface due to its flexibility and user friendliness. The gesture recognition technique is used to develop a system that can be used to convey information among disabled people or for controlling a device. Major challenges for the development of an efficient hand gesture recognition technique are illumination variation, nonuniform backgrounds, diversities in the size and shape of a user's hand, and high interclass similarities between hand gesture poses. In this chapter, a user independent static hand gesture recognition technique is analyzed using handcrafted features such as a histogram of oriented gradients (HOG) and a deep convolutional neural network (CNN). The deep features are extracted from fully connected layers of two different well-known pretrained CNNs such as AlexNet and VGG-16. The fusion of feature vectors extracted from different fully connected layers of both the CNNs is proposed for the enhancement of gesture recognition accuracy. The proposed CNN-based feature does not require any hand segmentation or background subtraction technique to segment the hand region from the input image. A support vector machine as a machine learning algorithm is used to classify the gesture poses. A comparison of the HOG feature and the deep CNN feature is presented for the recognition of static hand gesture poses. The proposed deep CNN feature fusion-based hand gesture recognition is robust to illumination variation, nonuniform backgrounds, and interclass similarities. The performance of the proposed technique is evaluated using leave-one-subject-out cross-validation (LOO CV) and holdout CV tests. The extensive analysis is performed on three benchmark static hand gesture datasets with uniform and nonuniform backgrounds on both the CV tests. A significant improvement of user independent static hand gesture recognition performance using the LOO CV test is found using the proposed technique. The experimental results show that the proposed technique is superior as compared to individual deep CNN features and state-of-the-art techniques. A real-time application of the gesture recognition system is developed and tested using the proposed technique.
Scene-Context-Aware Indoor Object Selection and Movement in VR

Virtual reality (VR) applications such as interior design typically require accurate and efficient selection and movement of indoor objects. In this paper, we present an indoor object selection and movement approach by taking into account scene contexts such as object semantics and interrelations. This provides more intelligence and guidance to the interaction, and greatly enhances user experience. We evaluate our proposals by comparing them with traditional approaches in different interaction modes based on controller, head pose, and eye gaze. Extensive user studies on a variety of selection and movement tasks are conducted to validate the advantages of our approach. We demonstrate our findings via a furniture arrangement application.
Triples of rational points on the Hermitian curve and their Weierstrass semigroups

In this paper, we study configurations of three rational points on the Hermitian curve over Fq2 and classify them according to their Weierstrass semigroups. For q>3, we show that the number of distinct semigroups of this form is equal to the number of positive divisors of q+1 and give an explicit description of the Weierstrass semigroup for each triple of points studied. To do so, we make use of two-point discrepancies and derive a criterion which applies to arbitrary curves over a finite field.
Design and fabrication of customary class unmanned air vehicle

The objective of the project is to fabricate a regular class UAV with a Raked wing-let along with wing fence. The analysis and testing of the fabricated model should give a brief idea of the impacts of Raked wing-let on generally speaking execution of UAV, providing the practicality of the proposed design.
Non-Overlapping LZ77 Factorization and LZ78 Substring Compression Queries with Suffix Trees

We present algorithms computing the non-overlapping Lempel–Ziv-77 factorization and the longest previous non-overlapping factor table within small space in linear or near-linear time with the help of modern suffix tree representations fitting into limited space. With similar techniques, we show how to answer substring compression queries for the Lempel–Ziv-78 factorization with a possible logarithmic multiplicative slowdown depending on the used suffix tree representation.
Automotive Interior Sensing - Towards a Synergetic Approach between Anomaly Detection and Action Recognition Strategies

With the appearance of Shared Autonomous Vehicles there will no longer be a driver responsible for maintaining the car interior and well-being of passengers. To counter this, it is imperative to have a system that is able to detect any abnormal behaviors, more specifically, violence between passengers. Traditional action recognition algorithms build models around known interactions but activities can be so diverse, that having a dataset that incorporates most use cases is unattainable. While action recognition models are normally trained on all the defined activities and directly output a score that classifies the likelihood of violence, video anomaly detection algorithms present themselves as an alternative approach to build a good discriminative model since usually only non-violent examples are needed. This work focuses on anomaly detection and action recognition algorithms trained, validated and tested on a subset of human behavior video sequences from Bosch's internal datasets. The anomaly detection network architecture defines how to properly reconstruct normal frame sequences so that during testing, each sequence can be classified as normal or abnormal based on its reconstruction error. With these errors, regularity scores are inferred showing the predicted regularity of each frame. The resulting framework is a viable addition to traditional action recognition algorithms since it can work as a tool for detecting unknown actions, strange/violent behaviors and aid in understanding the meaning of such human interactions.
Robustly Safe Compilation, an Efficient Form of Secure Compilation

Security-preserving compilers generate compiled code that withstands target-level attacks such as alteration of control flow, data leaks, or memory corruption. Many existing security-preserving compilers are proven to be fully abstract, meaning that they reflect and preserve observational equivalence. Fully abstract compilation is strong and useful but, in certain cases, comes at the cost of requiring expensive runtime constructs in compiled code. These constructs may have no relevance for security, but are needed to accommodate differences between the source and target languages that fully abstract compilation necessarily needs. As an alternative to fully abstract compilation, this article explores a different criterion for secure compilation called robustly safe compilation or RSC . Briefly, this criterion means that the compiled code preserves relevant safety properties of the source program against all adversarial contexts interacting with the compiled program. We show that RSC can be proved more easily than fully abstract compilation and also often results in more efficient code. We also present two different proof techniques for establishing that a compiler attains RSC and, to illustrate them, develop three illustrative robustly safe compilers that rely on different target-level protection mechanisms. We then proceed to turn one of our compilers into a fully abstract one and through this example argue that proving RSC can be simpler than proving full abstraction. To better explain and clarify notions, this article uses syntax highlighting in a way that colourblind and black-8-white readers can benefit from Reference [58]. For a better experience, please print or view this article in colour . 1
On Procrustes Analysis in Hyperbolic Space

Congruent Procrustes analysis aims to find the best matching between two point sets through rotation, reflection and translation. We formulate the Procrustes problem for hyperbolic spaces, review the canonical definition of the center of point sets, and give a closed form solution for the optimal isometry for noise-free measurements. We also analyze the performance of the proposed method under measurement noise.
Towards verifiable evidence generation in forensic-ready systems

With the increasing threat of cybercrime, there is also an increasing need for the forensic investigation of those crimes. However, the topic of systematic preparation on the possible forensic investigation during the software development, called forensic readiness, has only been explored since recently. Thus, there are still many challenges and open issues. One of the obstacles is ensuring the correct implementation. Moreover, the growing volume and variety of digital evidence produced by the systems have to be put into consideration. It is especially important in the critical information infrastructure domain where potential cyberattacks could impact the safety of people. In this paper, we present research towards verification of forensic readiness in software development, with a focus on digital evidence they produce, to assist the advancement of this research domain. Furthermore, we formulate a process that serves a template for designing, developing, and refining a verification method for forensic-ready software systems.
An Anomaly Detection Algorithm Selection Service for IoT Stream Data Based on Tsfresh Tool and Genetic Algorithm

Anomaly detection algorithms (ADA) have been widely used as services in many maintenance monitoring platforms. However, there are numerous algorithms that could be applied to these fast changing stream data. Furthermore, in IoT stream data due to its dynamic nature, the phenomena of conception drift happened. Therefore, it is a challenging task to choose a suitable anomaly detection service (ADS) in real time. For accurate online anomalous data detection, this paper developed a service selection method to select and configure ADS at run-time. Initially, a time-series feature extractor (Tsfresh) and a genetic algorithm-based feature selection method are applied to swiftly extract dominant features which act as representation for the stream data patterns. Additionally, stream data and various efficient algorithms are collected as our historical data. A fast classification model based on XGBoost is trained to record stream data features to detect appropriate ADS dynamically at run-time. These methods help to choose suitable service and their respective configuration based on the patterns of stream data. The features used to describe and reflect time-series data’s intrinsic characteristics are the main success factor in our framework. Consequently, experiments are conducted to evaluate the effectiveness of features closed by genetic algorithm. Experimentations on both artificial and real datasets demonstrate that the accuracy of our proposed method outperforms various advanced approaches and can choose appropriate service in different scenarios efficiently.
Do End-to-end Stereo Algorithms Under-utilize Information?

Deep networks for stereo matching typically leverage 2D or 3D convolutional encoder-decoder architectures to aggregate cost and regularize the cost volume for accurate disparity estimation. Due to content-insensitive convolutions and down-sampling and up-sampling operations, these cost aggregation mechanisms do not take full advantage of the information available in the images. Disparity maps suffer from over-smoothing near occlusion boundaries, and erroneous predictions in thin structures. In this paper, we show how deep adaptive filtering and differentiable semi-global aggregation can be integrated in existing 2D and 3D convolutional networks for end-to-end stereo matching, leading to improved accuracy. The improvements are due to utilizing RGB information from the images as a signal to dynamically guide the matching process, in addition to being the signal we attempt to match across the images. We show extensive experimental results on the KITTI 2015 and Virtual KITTI 2 datasets comparing four stereo networks (DispNetC, GCNet, PSMNet and GANet) after integrating four adaptive filters (segmentation-aware bilateral filtering, dynamic filtering networks, pixel adaptive convolution and semi-global aggregation) into their architectures. Our code is available at https://github.com/ccj5351/DAFStereoNets.
Visual World to an Audible Experience: Visual Assistance for the Blind And Visually Impaired

This paper aims at assisting visually impaired people through Deep Learning (DL) by providing a system that can describe the surroundings as well as answer questions about the surroundings of the user. The system majorly consists of two models, an Image Captioning (IC) model, and a Visual Question Answering (VQA) model. The IC model is a Convolutional Neural Network and Recurrent Neural Network based architecture that incorporates a form of attention while captioning. This paper proposes two models, Multi-Layer Perceptron based and Long Short Term Memory (LSTM) based, for the VQA task that answer questions related to the input image. The IC model has achieved an average BLUE 1 score of 0.46. The LSTM based VQA model has given an overall accuracy of 47 percent. These two models are integrated along with Speech to Text and Text to Speech components to form a single system that works in real time.
Precursory Analysis of Attack-Log Time Series by Machine Learning for Detecting Bots in CAPTCHA

CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) is commonly utilized as a technology for avoiding attacks to Web sites by bots. State-of-the-art CAPTCHAs vary in difficulty based on the client's behavior, allowing for efficient bot detection without sacrificing simplicity. In this research, we focus on detecting bots by supervised machine learning from access-log time series in the past. We have analysed access logs to several Web services which are using a commercial cloud-based CAPTCHA service, Capy Puzzle CAPTCHA. Experiments show that bot detection in attacks over a month can be performed with high accuracy by precursory analysis of the access log in only the first day as training data. In addition, we have manually analyzed the data that are found to be False Positive in the discrimination results, and it is found that the proposed model actually detects access by bots, which had been overlooked in the first-stage manual discrimination of flags in preparation of training data.
An intent recognition model supporting the spoken expression mixed with Chinese and English

Intent recognition is one of the most essential foundations as well as a very challenging task for language understanding, especially for spoken language. As spoken text is short, and lack of full context. Moreover, it may mix multi-language forms. These non-standard spoken expressions further lead to the shortage of text information. In consideration that sparse text information seriously affects the effect of intention understanding, a multi-feature fusion-based intent recognition model for the bilingual phenomenon mixed with Chinese and English is proposed. Combining word2vec and multilingual wordNets with the same synset_id (synonym set id), the model can mask the differences between different languages. Meanwhile, it can enrich the information representation of the spoken text by fusing the word intention features with the context-dependent features represented by transformer as well as the word frequency features. To verify the correctness and effectiveness of the model, extensive experiments were conducted on a real online logistics customer service platform and SMP2018-ECDT dataset. The results show that our model is superior to other models. And it improves the accuracy of intent recognition in logistics data by 20% compared with that of transformer.
Automated Waste Sorting Using Convolutional Neural Network

The waste classification has become a crucial mission for sustaining worldwide economic growth and preserving the environment. Using deep learning to sort solid waste automatically is necessary since it could minimize the time taken to categorize a large amount of rubbish manually and health risks created by working with polluted waste. In this study, we take advantage of several Convolutional Neural Networks such as VGG, Resnet, Efficientnet, etc. to solve this problem. The test accuracies achieved by training Resnet101, EfficientNet-B0, and EfficientNet-B1 on the dataset of 6640 images are 92.43%, 90.02%, and 91.53% respectively. We also build an ensemble model on the base of these three models, which attains an accuracy of 94.11%. The dataset is from the Trashnet dataset of Stanford and images collected on the Internet. This approach can be potentially applied to real-life environmental problems.
On the uniform concentration bounds and large sample properties of clustering with Bregman divergences

Clustering with Bregman divergence has been used in literature to unify centroid‐based parametric clustering approaches and to allow the detection of nonspherical clusters within the data. Although empirically useful, the large sample theoretical aspects of Bregman clustering techniques remain largely unexplored. In this paper, we attempt to bridge the gap between the theory and practice of centroid‐based Bregman hard clustering by providing uniform deviation bounds on the clustering objective. Our theoretical analysis relies on the famous Vapnik–Chervonenkis (VC) theory, which, although has been extensively used in a supervised learning context, remains largely unexplored in finding empirical risks for unsupervised learning scenarios. As opposed to most of the theoretical works on clustering, our framework allows the number of features ( p ) to vary with the number of observations ( n ). The strong consistency of the sample cluster centroids, under standard assumptions in the literature, also follows as a corollary under this general framework. Furthermore, we show that the rate of convergence is at most of the order , under the standard regularity conditions.
Secure and Efficient Delegation of Pairings with Online Inputs

Delegation of pairings from a computationally weaker client to a computationally stronger server has been advocated to expand the applicability of pairing-based cryptographic protocols to computation paradigms with resource-constrained devices. Important requirements for such delegation protocols include privacy of the client's inputs and security of the client's output, in the sense of detecting, with high probability, any malicious server's attempt to convince the client of an incorrect pairing result. In this paper we show that pairings with inputs only available in the online phase can be efficiently, privately and securely delegated to a single, possibly malicious, server. We present new protocols in 2 different scenarios: (1) the two pairing inputs are publicly known; (2) privacy of both pairing inputs needs to be maintained (left open in previous papers; e.g., [27]). In both cases, we improve the online-phase client's runtime with respect to previous work. In the latter case, we show the first protocol where the client's online-phase runtime is faster than non-delegated computation for all of the most practical known curves. In previous work, the client's runtime was worse, especially for one of the most practical elliptic curves underlying the pairing function (i.e., BN-12).
Improving Word Recognition in Speech Transcriptions by Decision-Level Fusion of Stemming and Two-Way Phoneme Pruning

We introduce an unsupervised approach for correcting highly imperfect speech transcriptions based on a decision-level fusion of stemming and two-way phoneme pruning. Transcripts are acquired from videos by extracting audio using Ffmpeg framework and further converting audio to text transcript using Google API. In the benchmark LRW dataset, there are 500 word categories, and 50 videos per class in mp4 format. All videos consist of 29 frames (each 1.16 s long) and the word appears in the middle of the video. In our approach we tried to improve the baseline accuracy from 9.34% by using stemming, phoneme extraction, filtering and pruning. After applying the stemming algorithm to the text transcript and evaluating the results, we achieved 23.34% accuracy in word recognition. To convert words to phonemes we used the Carnegie Mellon University (CMU) pronouncing dictionary that provides a phonetic mapping of English words to their pronunciations. A two-way phoneme pruning is proposed that comprises of the two non-sequential steps: 1) filtering and pruning the phonemes containing vowels and plosives 2) filtering and pruning the phonemes containing vowels and fricatives. After obtaining results of stemming and two-way phoneme pruning, we applied decision-level fusion and that led to an improvement of word recognition rate upto 32.96%.
Interactive learning in the classroom: A mobile augmented reality assistance application for learning

Abstract The rapid evolution of augmented reality (AR) technology has presented new opportunities in the domain of education. Acting as a bridge between the virtual and real worlds, AR technology overcomes the physical limitations of our classrooms at a low cost and provides an interactive learning experience. In this study, we present a smartphone AR application, named the AR‐E‐Helper, which assists the learning of students in higher education lectures. Our goal is to provide an AR‐enhanced learning experience for students. To validate the effectiveness of the AR‐E‐Helper, we conducted an experiment that compares three classes: AR‐enhanced, smartphone‐enhanced, and nontechnology‐enhanced classes. Through the experiment, we observed that our application was helpful in maintaining student's focus in class, promoting their interest, and increasing their satisfaction. Furthermore, we also found how to improve our application based on the observations that the application brought some downsides to the learning activities. We expect that this study will be helpful to design AR learning tools in the future.
Contra-∗: Mechanisms for countering spam attacks on blockchain's memory pools

Blockchain-based cryptocurrencies, such as Bitcoin, have seen on the rise in their popularity and value, making them a target to several forms of Denial-of-Service (DoS) attacks, and calling for a better understanding of their attack surface from both security and distributed systems standpoints. In this paper, and in the pursuit of understanding the attack surface of blockchains, we explore a new form of attack that can be carried out on the memory pools (mempools), and mainly targets blockchain-based cryptocurrencies. We study this attack on Bitcoin's mempool and explore the attack's effects on transactions fee paid by benign users. To counter this attack, this paper further proposes Contra-∗, a set of countermeasures utilizing fee, age, and size (thus, Contra-F, Contra-A, and Contra-S) as prioritization mechanisms. Contra-∗ optimize the mempool size and help in countering the effects of DoS attacks due to spam transactions. We evaluate Contra-∗ by simulations and analyze their effectiveness under various attack conditions.