Reviewerly/exHarmony huggingface dataset:
Evaluation of Deep Learning Models for Person Authentication Based on Touch Gesture

Touch gesture biometrics authentication system is the study of user's touching behavior on his touch device to identify him.The features traditionally used in touch gesture authentication systems are extracted using hand-crafted feature extraction approach.In this work, we investigate the ability of Deep Learning (DL) to automatically discover useful features of touch gesture and use them to authenticate the user.Four different models are investigated Long-Short Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN) combined with LSTM (CNN-LSTM), and CNN combined with GRU (CNN-GRU).In addition, different regularization techniques are investigated such as Activity Regularizer, Batch Normalization (BN), Dropout, and LeakyReLU.These deep networks were trained from scratch and tested using TouchAlytics and BioIdent datasets for dynamic touch authentication.The result reported in terms of authentication accuracy, False Acceptance Rate (FAR), False Rejection Rate (FRR).The best result we have been obtained was 96.73%, 96.07%and 96.08% for training, validation and testing accuracy respectively with dynamic touch authentication system on TouchAlytics dataset with CNN-GRU DL model, while the best result of FAR and FRR obtained on TouchAlytics dataset was with CNN-LSTM were FAR was 0.0009 and FRR was 0.0530.For BioIdent dataset the best results have been obtained was 84.87%, 78.28% and 78.35% for Training, validation and testing accuracy respectively with CNN-LSTM model.The use of a learning based approach in touch authentication system has shown good results comparing with other state-of-the-art using TouchAlytics dataset.
Intelligent Computer Assisted Language Learning

Intelligent Computer Assisted Language Learning (ICALL) is a field within Computer Assisted Language Learning (CALL) that applies concepts, techniques, algorithms, and technologies from artificial intelligence to CALL
Blending Output from Generative Adversarial Networks to Texture High-Resolution 2D Town Maps for Roleplaying Games

The recent success of Generative Adversarial Networks (GAN s) in image and video applications led to the development of numerous variants specialised for particular tasks, such as conditional GANs for image-to-image translation. In spite of the research done in fine-tuning architectures and applying them to different subjects, techniques still deal with stand-alone images, such as nature scenes, city landmarks, faces and others. The task of producing contiguous colour data - namely adjacent parts of the same image, not textures - has not been attempted before in literature related to generative machine learning techniques. Achieving this feat would allow large images to be processed in smaller parts, hence removing the architectural maximum to the output resolution that can be achieved by the network. Concurrent state-of-the-art architectures for conditional image-to-image translation are in the range of 2k x 1k pixels and typically take several days to train on powerful hardware. The proposed contiguous technique, in this case applied on fantasy maps for roleplaying games, can achieve higher resolutions with smaller networks that can be trained faster, within a single day. The technique is capable of maintaining as much quality as allowed by the detail of the semantic layouts provided, even at 4k and higher, but it suffers when detail in these is too sparse. A sample of images produced by the system were shown to survey participants who judged their appeal as 3.49 on a Likert scale of 5, and segmentation analysis reported an average weighted inter-class accuracy score of 0.689 (0.448 unweighted).
HAR: Hardness Aware Reweighting for Imbalanced Datasets

Class imbalance is a significant i ssue t hat causes neural networks to underfit t o t he r are c lasses. Traditional mitigation strategies include loss reshaping and data resampling which amount to increasing the loss contribution of minority classes and decreasing the loss contributed by the majority ones. However, by treating each example within a class equally, these methods lead to undesirable scenarios where hard-to-classify examples from the majority classes are down-weighted and easy-to-classify examples from the minority classes are up-weighted. We propose the Hardness Aware Reweighting (HAR) framework, which circumvents this issue by increasing the loss contribution of hard examples from both the majority and minority classes. This is achieved by augmenting a neural network with intermediate classifier b ranches t o e nable e arly-exiting d uring t raining. Experimental results on large-scale datasets demonstrate that HAR consistently improves state-of-the-art accuracy while saving up to 20% of inference FLOPS.
Ensemble Machine Learning Algorithms for the Diagnosis of Cervical Cancer

Cervical cancer is a deadly form of cancer and early screening is important for preventing it. In many countries there are limited facilities for the screening of cervical cancer. The paper is centered on the application of machine learning algorithms for the diagnosis of cervical cancer. For this, a dataset is accumulated from the repository of University of California Irvine (UCI).The UCI dataset has 36 attributes or features of 858 samples. The dataset has imbalance in the number of patients and normal samples and hence Borderline Synthetic Minority Over-sampling Technique (Borderline-SMOTE) is used. A single target variable representing whether the individual has cancer or not is formed by using the logical OR operation among the four target attributes namely Hinselmann, Schiller, Cytology, and Biopsy. Next, extra tree classifier method is applied to the dataset to find the most important attributing to cervical cancer. Using the feature importance values, the 10 most important features are determined for classification. A number of base classifiers and several ensemble methods including bagging, boosting and stacking are applied. Results show that stacking with a mixture of random forest, support vector machine, ExtraTreeClassifier, XGBoost, and Bagging has the highest classification accuracy of 94.4%.
Dataset-Driven Unsupervised Object Discovery for Region-Based Instance Image Retrieval

Instance image retrieval could greatly benefit from discovering objects in the image dataset. This not only helps produce more reliable feature representation but also better informs users by delineating query-matched object regions. However, object classes are usually not predefined in a retrieval dataset and class label information is generally unavailable in image retrieval. This situation makes object discovery a challenging task. To address this, we propose a novel dataset-driven unsupervised object discovery framework. By utilizing deep feature representation and weakly-supervised object detection, we explore supervisory information from within an image dataset, construct class-wise object detectors, and assign multiple detectors to each image for detection. To efficiently construct object detectors for large image datasets, we propose a novel "base-detector repository" and derive a fast way to generate the base detectors. In addition, the whole framework is designed to work in a self-boosting manner to iteratively refine object discovery. Compared with existing unsupervised object detection methods, our framework produces more accurate object discovery results. Different from supervised detection, we need neither manual annotation nor auxiliary datasets to train object detectors. Experimental study demonstrates the effectiveness of the proposed framework and the improved performance for region-based instance image retrieval.
An FPGA Based Adaptive Real-Time Enhancement System for Dental X-rays

Dental X-ray imaging which shows the clear internal structure of teeth is essential for diagnosis. However, the image details in both dark and bright regions are often hard to distinguish due to the misoperation and the limitation of dental Xray imaging machines. In addition, the emergence of some realtime X-ray imaging machines has put forward higher requirements for the processing speed of enhancement systems. In this paper, an adaptive enhancement system consisting of a top control unit (TCU), an image quality feature extraction unit (FEU), a multilayer perception (MLP) unit and an adaptive image processing unit (IPU) is presented. Four image quality features as well as MLPs are proposed to detect image quality problems and control the image processing performance. For IPU, a novel fast contrast limited adaptive histogram equalization (FCLAHE) is proposed to accelerate the interpolation process. Modified guided filter, laplacian filter, gamma correction, and FCLAHE are integrated into the IPU. The proposed system is implemented in register transfer level (RTL) and demonstrated on field programmable gate array (FPGA). It runs at a clock frequency of 133 MHz and is capable of processing $1980\times 1080$ images or videos with a high throughput of 127.35 Mpixels/s. Moreover, the proposed system offers the top image enhancement performance among the state-of-the-art implementations and its peak throughput is $37\times$ higher than a personal computer (PC) with Core i7 8750H.
Differential Fault Attack on Espresso

In this paper we analyze the 5G standard cipher Espresso against differential fault attack. The attack outcome results in a complete internal state recovery by injecting only 4 random faults into the state of the keystream generation phase of Espresso. Since the round update function of Espresso is reversible it allows recovery of the secret key just by doing the inverse key-IV initialization process. To the best of our knowledge this is the first differential fault attack analysis of Espresso which finds the secret key of the cipher with a very minimal number of fault injection. We also provide a hardware implementation of the cipher to support our software implemented results.
A Novel Change Detection Technique for Occluded Objects using Ultra-Wideband Radar

The burgeoning e-commerce business model has witnessed several cases where the packages with faulty goods, returned by the buyers, did not contain the procured object, rather being replaced by a different device. There is a need to unobtrusively detect any such changes in the content of the sealed package. In this paper, the penetrability of Ultra-Wideband (UWB) radar is utilized to build a change detection technique for making a quantitative assessment of the presence of an 8″ Tab inside its sealed box, as opposed to other devices inside the same box. For this purpose, an electronic device is placed on an oscillating platform. A sliding Least-Of Constant False Alarm Rate (LO-CFAR) algorithm is applied on the radar scan data and the concept of detection sensitivity is introduced. Multiple experiments at different measurement instances are conducted to evaluate 1-class classification using detection sensitivity levels. These experiments show that the detection sensitivity levels provide a statistical basis for differentiating between objects. Also measurements were done at different distances, thus giving the result that the detection sensitivity level alone can be a determinant of the object type.
Intention-based Long-Term Human Motion Anticipation

Recently, a few works have been proposed to model the uncertainty of the future human motion. These works do not forecast a single sequence but multiple sequences for the same observation. While these works focused on increasing the diversity, this work focuses on keeping a high quality of the forecast sequences even for very long time horizons of up to 30 seconds. In order to achieve this goal, we propose to forecast the intention of the person ahead of time. This has the advantage that the generated human motion remains goal oriented and that the motion transitions between two actions are smooth and highly realistic. We furthermore propose a new quality score for evaluation that correlates better with human perception than other metrics. The results and a user study show that our approach forecasts multiple sequences that are more plausible compared to the state-of-the-art.
Action recognition by key trajectories

Abstract Human action recognition is an active field of research that intends to explain what a subject is doing in an input video. Deep learning architectures serve as the foundation for cutting-edge approaches. Recent research, on the other hand, indicates that hand-crafted characteristics are complementary and, when combined, can enhance classification accuracy. Cutting-edge approaches are based on deep learning architectures. Recent research, however, indicates that hand-crafted features complement each other and can help boost classification accuracy when combined. We introduce the key trajectories approach that is based on the popular, hand-crafted method, improved dense trajectories. Our work explores how pose estimation can be used to find meaningful key points to reduce computational time, undesired noise, and to guarantee a stable frame processing rate. Furthermore, we tested how feature-tracking behaves with dense inverse search and with a frame to frame subject key point estimation. Our proposal was tested on the KTH and UCF11 datasets employing Bag-of-words and on the UCF50 and HMDB datasets using Fisher Vector, where we got an accuracy performance of 95.71, 84.88, 92.9, and 81.3%, respectively. Also, our proposal can recognize subject actions in video eight times faster compared to its dense counterpart. To maximize the bag-of-words classification performance, we illustrate how the hyperparameters affect both accuracy and computation time. Precisely, we present an exploration of the vocabulary size, the SVM hyperparameter, the descriptor’s distinctiveness, and the subject body key points.
Preventing Fake News Propagation in Social Networks Using a Context Trust-Based Security Model

Online Social Networks (OSN) security issues have been extensively researched in the past decade. Information is posted and shared by individuals and organizations in social networks in huge quantities. One of the most important non-resolved topics is the Fake News propagation problem. Fake news propagates because of several reasons, one of which is non-trustworthy users. These users, some with malicious intentions, and some with low social media awareness, are the ones actually spreading misleading information. As this occurs, other users, that are valid reliable users, are exposed to false information. In our previous research we have devised a comprehensive Trust-based model that can handle this problem from the user Trust aspect. The model involves Access Control for the direct circle of friends and Flow Control for the friends' networks. In this paper we use this model as a basis for the purpose of prevention of Fake News. We add context awareness and user profiling by analyzing the user's activity in the network (posts, shares, etc.), and then use Machine Learning to detect these problematic users by analyzing data items that are fake or misleading. This addition creates a much more accurate picture of OSN users and their data and helps revealing the sources of the Fake News propagation and can prevent it. These aspects of the model create a strong reliable OSN data infrastructure.
A Systematic Mapping Literature of Immersive Learning from SVR Publications

Immersive Learning (iL) is known as a recent area of research that uses three-dimensional virtual environments and multi-sensory devices, also known as immersive technologies, to support the improvement of learning outcomes. This work aims to obtain evidence of theoretical and technological aspects of iL from the Symposium on Virtual and Augmented Reality (SVR) publications. A Systematic Literature Mapping protocol was developed and executed in order to select the primary studies to perform the analysis and data extraction. 76 primary studies helped to answer the research questions. A large part of the contributions by the SVR community are virtual environments that support education in the health area. In addition, some gaps and research opportunities were identified: virtual environments that serve audiences with special needs; development frameworks that consider pedagogical aspects and the use of biometric measures to support the validation of improved learning outcomes.
Faster RCNN for Face Detection on a FACENET Model

In this work, an individual face detection process is involved and outcomes which are more exact are gained within a short amount of spell through less amount of intricacy on a face detection yardstick that is well-thought-out as a podium by using FACENET. Enhancement is added to the performance by using a Faster Regional Convolutional Neural Networks (RCNN) process entailing diverse number of stratagems named as hard negative mining that separates positive and negative examples, multi-scale training to train different scales images, model pre-training and concatenation of characteristics on one shot learned images with loss functions with the support of region of interest (ROI) and regional proposed networks (RPN). As a consequence, this scheme comprises of one of the best face detection performance, as the images in dataset are one shot learned. It has the triplet loss which helps to avoid more unrelated example of pictures while passing through the convolutional sheets.
Detecting socially occupied spaces with depth cameras: evaluating location and body orientation as relevant social features

Studying people's movement and social interaction in indoor spaces requires detecting where and when interactions happen. So far, sensor-based systems for detecting interactions focused on identifying physically occupied spaces, i.e., spaces determined by the presence of physically measurable objects. In contrast, our goal is to detect socially occupied spaces, i.e., spaces occupied not by people but by social interactions that they support. We focus on detecting F-Formations, a representation of the space structure between two or more potentially interacting building occupants. We propose a system that uses infrared depth cameras to anonymously derivate body orientation from skeleton joints. The orientation of the shoulders and spine, together with the face orientation and the temporal information on occupants' walking trajectories, is used to calculate the body orientation from which socially occupied space is identified. In a user study evaluating the system, we collected data in 32 patterns within two distinct cases: individuals and dyads and evaluated the system's accuracy. The evaluation included the intended orientation and the socially accepted orientation. Experimental results show accuracy well above 90% for assessing the socially-relevant body orientation. Our algorithm to detect body orientation contributes to the automated detection of socially occupied spaces. The system shows potential to detect more complex social structures. This would have an impact on research areas that study group interactions within complex indoor settings.
GPS Signal Authentication Using a Chameleon Hash Keychain

Global navigational satellite systems provide accurate time synchronization and location services. Satellites transmit navigation messages that can be used by a receiver to compute its location. However, most navigation messages are not protected and are easily spoofed. Several attacks have been reported that transmit spoofed or replayed GPS signals to divert or hijack autonomous vehicles, ships and drones. Unfortunately, non-cryptographic protection methods that use antenna arrays, pseudorange differences and multi-receivers to detect GPS spoofing tend to be inaccurate due to environmental conditions. This chapter proposes an efficient GPS signal authentication protocol that engages a dedicated server to continuously compute hash-based message authentication codes of GPS navigation messages received from satellites using the chameleon hash keychain. The keychain is practically unbounded, which enables GPS receivers to easily authenticate the server and verify GPS signals concurrently by checking the hash-based message authentication codes. A proof-of-concept prototype has been developed to demonstrate the feasibility of the authentication scheme. Experimental results demonstrate that the hash key in the keychain can be updated every 30 seconds, enabling every five GPS message subframes to be secured with a different hash key. This makes it difficult for attackers to compromise GPS navigation
Blockchain Matters—Lex Cryptographia and the Displacement of Legal Symbolics and Imaginaries

This article focusses on the social and legal implications that blockchain technology brings about, not only due to its ideological framework, but also, and especially, due to the concept of law it inaugurates. Thus, this article claims, that, by interlocking technological and legal structures, blockchain technology initiates a profound displacement of legal symbolics and imaginaries. It shows how blockchain law, by emancipating itself from three essential dimensions of law-language, territory, and the body-implies a profound disruption of how we perceive law and its legitimacy. Starting with an overview of the technological details of blockchain, the paper then addresses its ideological context and traces the underlying ideas, values and functions and their relation with-and impact on-the general perception of law and legal issues. By critically assessing the claim that blockchain will liberate the subject from any heteronymic constraints, this paper analyses to what extent this technology has social and legal implications that reach far beyond its virtual, purely blockchain-related scope of applications-and why this technology should matter to us all.
Pedagogical Considerations for Technology-Enhanced Learning

Abstract Technology-enhanced learning is a term often used in discussions about the place and role of technology in education. Yet many gaps still need to be addressed from a pedagogical perspective to remove the veil of fascination from technology-enhanced learning and to ensure that its use is planned purposefully. Planning should take account of the intended uses of the technology, which may be multifold. This paper summarizes the pedagogical considerations for a technology-enhanced learning process. In fact it is often thought that introducing technology to the educational environment is sufficient, without specifically restructuring the pedagogical process. The paper analyzes the potential goals of using technology in the educational process. It also looks at the pedagogical factors that make technology-enhanced learning successful: learning motivation, cognitive development, cognitive load, and knowledge development in the discourse of technology-enhanced learning. The chapter is organized as follows: an introduction to the terms followed by two sections—“Technology-enhanced learning” and “Pedagogical considerations”—and a discussion.