Reviewerly/exHarmony huggingface dataset:
BYOD Supported Multimodal Classroom Interaction

Classroom is an exclusive place of face to face interaction, where learning takes place primarily in a broadcast mode and knowledge is transferred from teacher to students in a one to many face to face interaction manner. Significantly during this interaction, different cognitive level of students are assembled together. Because of variation in intellectual levels of students, some students can grab the lecture very quickly while others may lag. Therefore, systematic interaction in classroom is very essential at present context where there are large number of learning alternatives available and importance of classroom may be misunderstood by students. The development and usage of different technologies for teaching and learning in last few years have grown exponentially whereas no such advancements in case of classroom interactions. This work is an emphasis on exploring BYOD systems which include mobile phones, tablets, laptops etc as a cost effective interaction tool in face to face learning. A Multilayer interaction was used where a teacher can get a feedback from the students as well as students can express their doubts in non-verbal manner. It was tried to put emphasis on human natural interaction ability and expressions, not revealing ones personal doubts in public as well as removing the stigma of public hand raising. Non-verbal interaction in term of screen tapping and feedback are combined together to find the comfortable interaction between the teachers and students. It is found use of mobile devices is a cost effective system that students are more active and engaged on in interacting in teaching learning activity in traditional classroom pedagogy. Overall this work is an effort to increase the pleasure level of students, by allowing them to participate in an active formative class room interaction and the classroom can be extended as an augmented layer for teacher to have an insight in to an individual students.
Data Sources for Information Extraction in Automotive Forensics

In digital forensic analysis, it is not possible to pre-determine the exact nature of information relevant for a case. Subsequently, complete physical images of digital memory chips or devices are extracted. In the case of car forensics, it is thus crucial to have knowledge about the format, type and the logical and physical location of the information in a vehicle. Next to increasing utilization of electronics in vehicle control, the situation is further compounded by the introduction of so-called connected car or smart car solutions. In contrast to other areas like smartphone forensics, digital forensics in the automotive field is more complex as it encompasses a multitude of hardware- and software systems both in place and in remote locations, including smartphones themselves, in order to reach a complete view of relevant information. The article gives an overview of the different types of digital information stored in the respective areas. In the following, a separation of data sources is considered with data preserved inside the vehicle (front end and vehicle electronics) as well as outside of the vehicle (connected back end). For handling the increasing amount of data present in modern vehicles, a corresponding solution for data preservation and exchange is necessary. Finally to solve the problem of attributing collected data to individual drivers, pattern-recognition-based methods are presented.
TATIS: Trustworthy APIs for Threat Intelligence Sharing with UMA and CP-ABE

Threat intelligence platforms offer cyber emergency teams and security stakeholders access to sightings of cyberthreats and indicators of compromise. Given the sensitivity of the information, access may be restricted to certain members within an organization, offered to the general public, or anything in between. Service providers that host such platforms typically expose APIs for threat event producers and consumers, and to enable interoperability with other threat intelligence platforms. Not only is API security a growing concern, the implied trust by threat event producers and consumers in the platform provider remains a non-trivial challenge. This paper addresses these challenges by offering protection against honest but curious platform providers, and putting the access control back into the hands of the owner or producer of the threat events. We present TATIS, a solution for fine-grained access control to protect threat intelligence APIs using User Managed Access (UMA) and Ciphertext-Policy Attribute-Based Encryption (CP-ABE). We test the feasibility of our solution using the Malware Information Sharing Platform (MISP). We validate our contribution from a security and privacy point of view. Experimental evaluation on a real-world OSINT threat intelligence dataset illustrates our solution imposes an acceptable performance overhead on the latency of API requests.
Comparison of two vehicle detection methods based on the oriented gradients histogram and the deep neural network

In this article, we present two approaches to mobile object recognition in a real-time video scene. The first method is based on deep learning to detect moving objects. The second method is based on extracting the HOG characteristics associated with each object. The results obtained by these two methods are interesting in the detection of moving objects. For both approaches, the classification phase is done by the Alexnet neuron network.
On the stationary frequency of programmed ribosomal −1 frameshift

We present a stochastic model for programmed ribosomal −1 frameshift, triggered by a slippery sequence and a following pseudoknot on the mRNA template, that allows for the exact derivation of the stationary distribution of ribosome positions and for exact analytical calculations of the stationary rate of frameshift, its efficiency and other quantities of interest. We also present the stationary phase diagram as a function of the initiation rate and the density ribosomes that the pseudoknot can support. These observations provide mathematically rigorous evidence for the notion that the density of molecular motors is an important control parameter for the elongation rate in the presence of slippery sequences both in transcription of RNA and translation of proteins.
VLAD-SSTA: VLAD with Soft Spatio-Temporal Assignment for Action Recognition

It is important to simultaneously characterize videos with spatial and temporal information, especially for human action recognition, as spatial cue can model the human appearance while the dynamic motion need to be represented by temporal cue. The vector of locally aggregated descriptor (VLAD) whose assignment with the shortage of temporal information, can be regarded as a suboptimal solution for action recognition. In this paper, VLAD with a soft spatio-temporal assignment, named VLAD-SSTA, is proposed to further boost the performance of action recognition by employing the soft assignment with spatio-temporal characteristic. Specifically, the Spatio- Temporal Aware module is creatively devised with a series of 3D convolutions to capture the spatio-temporal characteristic. Experimental results show that the proposed approach yields state-of-the-art performance on challenging datasets.
An Efficient Dense Network for Semantic Segmentation of Eyes Images Captured with Virtual Reality Lens

Eye-tracking and Gaze estimation are difficult tasks that may be used for several applications including human-computer interfaces, salience detection and Virtual reality amongst others. This paper presents a segmentation algorithm based on deep learning that efficiently discriminates pupils, iris, and sclera from the background in images captured using a Virtual Reality lens. A light network called DensetNet10 trained from scratch is proposed. It contains fewer parameters than traditional architectures based on DenseNet which allows it to be used in mobile device applications. Experiments show that this network achieved higher IOU rates when comparing with DensetNet56-67-103 and DeeplabV3+ models in the Facebook database. Furthermore, this method reached 8th place in The Facebook semantic segmentation challenge with 0.94293 mean IOU and 202.084 parameters with a final score of 0.97147.
On quasi-inconsistency and its complexity

We address the issue of analyzing potential inconsistencies in knowledge bases. This refers to knowledge bases that contain rules which will always be activated together, and the knowledge base will become inconsistent, should these rules be activated. We investigate this problem in the context of the industrial use-case of business rule management, where it is often required that sets of (only) rules are analyzed for potential inconsistencies, e.g., during business rule modelling. To this aim, we introduce the notion of quasi-inconsistency, which is a formalization of the above-mentioned problem of potential inconsistencies. We put a specific focus on the analysis of computational complexity of some involved problems and show that many of them are intractable.
Unsupervised Learning for Stereo Matching Using Single-View Videos

This paper proposes an unsupervised approach to construct a deep learning based stereo matching method using single-view videos (SMV). From videos, a set of corresponding points are computed between images, and image patches that center at the computed points are extracted. Negative and positive samples constitute a dataset to train a similarity network that is then used as a matching cost function. In addition, we propose a local-global matching cost network that exploits the first feature maps (local features) accompanying with last feature maps (global features) as output feature of the proposed network. The concatenated features are connected to full-connected layers and the network outputs a similarity measure of an image patch pair as a matching cost. Computed matching costs are aggregated using semi-global matching and cross-based cost aggregation, followed by sub-pixel interpolation, left-right consistency check, median and bilateral filtering. We evaluate the proposed stereo matching methods using popular stereo matching datasets, including KITTI 2012 and 2015, and Middlebury. We submit the disparity maps to their benchmark servers to evaluate the performance of SMV. We also compared the generalization of SMV and baseline methods using the training sets of the three datasets. The benchmark results show that SMV is the most accurate method among unsupervised approach, and it even outperforms several deep learning based stereo matching using supervised manner. The evaluation results of generalization show that SMV is comparative with the baseline method, MC-CNN, which is trained with supervision.
PowerCA: A Fast Iterative Implementation of Correspondence Analysis

The visual exploration of big data requires interactivity as well as the possibility to update an existing solution as new data becomes available in real time. Enhanced exploratory data visualization is provided by dimension reduction methods. Eigenvalue and singular value decompositions are the core of most of the dimension reduction techniques, such as principal component analysis (PCA) and multiple correspondence analysis (MCA). An efficient implementation of MCA, called PowerCA, is proposed that exploits enhanced computations of the sparse matrix transformations and fast iterative methods provided by intelligent initializations in case of repeated analyses. The aim is to extend the applicability of MCA to computational demanding application such as streaming text and web-log data visualization as well as bootstrap-based sensitivity analysis.
The Speech Synthesis of Yi Language Based on DNN

This paper is mainly about a speech synthesis system based on Deep Neural Network (DNN) model of Yi languages, a kind of minority language in China. The system is composed of relatively complete text analysis of Yi, model training and speech synthesis module. Especially in front-end, the word segmentation, pause handling, word-to-phoneme conversion and label processing are used to analysis text of Yi language. We designed the question set for decision tree of DNN model training and used vocoder: WORLD for synthesis. The system achieves a relatively good Mean Opinion Score (MOS) of 3.93 by Yi undergraduates as evaluators compared with a MOS of 4.58 of original speech. To investigate the factors affecting the quality of synthesized Yi speech, this paper also objectively evaluates the performance of different training set and DNN model. The system successfully synthesized Yi speech for the first time and synthesized speech is relatively good as the result of an only complete minority language speech synthesis system.
A Meta-Learning-Based Solution to Address the Sparsity Problem of Recommender Systems

Sparsity is an important challenge for recommender systems. Most of the existing solutions to sparsity problem are achieved by adding auxiliary data. However, in practical recommender systems, auxiliary data is often difficult to obtain and limited in number. Considering the characteristics of data sparsity, this paper transforms it into a meta-learning problem. We propose a new meta-learning solution based on multi-layer perception (MLP) network to solve the sparsity problem of recommender systems from the perspective of optimizing parameters with a network using meta-learning. We split the behavior data into small sets by users and consider the training process of a small set as a task, and train an MLP network through these tasks to learn a parameter-optimizing strategy of the recommendation model. The network can quickly fit the extremely sparse data and optimize recommendation model parameters. We then predict the recommendation results on the test set with the optimized model parameters. Experiments on movielens and NetFlixPrize datasets show that the Root Mean Square Error (RMSE) value of the reference recommendation model with meta-MLP increases by 1%-10% when the data sparsity is less than 0.001%.
Emotion recognition based on deep learning with auto-encoder

Facial expression is one way of expressing emotions. Face emotion recognition is one of the important and major fields of research in the field of computer vision. Face emotion recognition is still one of the unique and challenging areas of research because it can be combined with various methods, one of which is deep learning. Deep learning is popular in the research area because it has the advantage of processing large amounts of data and automatically learning features on raw data, such as face emotion. Deep learning consists of several methods, one of which is the convolutional neural network method that will be used in this study. This study also uses the convolutional auto-encoder (CAE) method to explore the advantages that can arise compared to previous studies. CAE has advantages for image reconstruction and image de-noising, but we will explore CAE to do classification with CNN. Input data will be processed using CAE, then proceed with the classification process using CNN. Face emotion recognition model will use the Karolinska Directed Emotional Faces (KDEF) dataset of 4900 images divided into 2 groups, 80% for training and 20% for testing. The KDEF data consists of 7 emotional models with 5 angles from 70 different people. The test results showed an accuracy of 81.77%.
Switching Decision of Air-Ground Amphibious Robot using Neural Network-based Reinforcement Learning

This paper studies the problem of autonomous decision-making motion mode switching based on external environmental information when the Air-Ground Amphibious Robot is in complex environment. In the process of robot autonomous decision-making, the limitation of motion time and energy consumption should be considered, and the motion state with short motion time and low energy consumption should be selected. In this paper, the methods of reinforcement learning based on neural network are put forward to solve the problem of intelligent switching decision of the Air-Ground Amphibious Robot, aiming at making the robot choose the appropriate mode to move in a certain state of environment. Reinforcement learning based on neural network can not only generate decision function in the process of learning, but also solve the reinforcement problem of continuous environment state space. When a robot performs a task, it is important to reduce the energy consumption of motion. Therefore, this paper takes the energy consumption and motion time as the basis of robot decision-making and the standard of motion evaluation. In this paper, we provide the simulation results and demonstrate the feasibility of our method, which can effectively realize the high-efficiency motion ability of the Air-Ground Amphibious Robot in the complex environment.
Notice of Violation of IEEE Publication Principles: A Robust Database Watermarking using Local Differential Privacy

Nowadays, privacy protection is built on the huge sharing of big data development and rapid development of information, and copyright protection is needed for immediate resolution. However, any kind of technology is not existing which can conclude the problems of copyright and the protection of privacy continuously. Existing database watermarking systems have less privacy protection though the database watermarking is a research topic that has been widely studied for its feature of copyright tracing. In this paper, we develop an experimental technique, call local differential privacy-based database watermarking to protect the privacy as well as the ownership of the database. In the technique, watermarking embeds the database to determine the location of the database and local differential privacy protect the privacy of users. Especially our two proposed methods are the Laplace Mechanism-based Database Watermarking (LMDW) and Randomized Response-based Database Watermarking (RRDW) for two classical local differential privacy mechanisms the Laplace Mechanism (LM) and the Randomized Response (RR) respectively. Watermarking is designed with the distortion created by local differential privacy so that algorithms can reduce the data distortion caused by local differential privacy and maintain the local differential privacy nature which is proven by formal theoretical analysis and experimental evaluation. A sensitive database test suggests that the proposed algorithms have higher utility and strong consistency against various watermarking attacks.
Weighted product Taxonomy for Mobile-Commerce site in Recommendation of Product based on Heuristic Approach

As we know number of smart phone users grows rapidly and Indian government survey said that there are 10+ billion smart phone users by 2020. We know that in metro cities people does not have time to purchase products of their daily needs. For this reason Online shopping using mobile phone is now in trend and survey also says that number of online shopping users grows exponentially. There are various types of factor plays important role in recommendation of product to customer. Product taxonomy, Customer behavioral and navigational factor are among them which are analyzed in this paper. Importance of weighted product taxonomy in fast recommendation of product is highlighted in this paper by authors. Authors proposed a greedy heuristic algorithm to search product in weighted product taxonomy. To proof the efficiency of proposed algorithm they use one tail independent sample`t' test with 5% level of significance and found that results are significant.
Visualising Railway Safety Verification

The application of formal methods to the railway domain has a long-standing history within the academic community. Many approaches can provide both successful proofs of safety and, in the case of failure, traces explaining the failure. However, if a given model does produce a failure, it is difficult to understand the conditions that led to the issue. We present a method to visualise railway safety issues to help engineers and researchers explore the problem so that they can adjust their designs accordingly. We evaluate our approach through qualitative real-world case studies with researchers and railway engineers.
Augmented Reality in Cultural Heritage: Designing for Mobile AR User Experiences

Cultural institutions are increasingly manifesting their need to become more relevant in a society which is more technology driven than ever before. Today’s generations seem to be present more in the online sphere, choosing Google for virtual research over physical spaces. This is making more institutions feel at greater risk of becoming more irrelevant as human presence has been markedly declining. In this chapter, we describe Mobile Augmented Reality (mAR), as one of the ways which cultural institutions are choosing to adopt to attract a bigger and more diverse audience in an attempt to bring people closer to cultural heritageCultural heritage and art.
Compact aggregate short-lived signatures for consortium consensus protocols

Cryptography and cryptographic primitives are widely considered to be the most important fundamental of blockchain that provide secure and anonymity guaranteed decentralized solutions. In the history of papers study cryptographic primitives used in blockchain, the focus has always been in the use cases of blockchain in different aspects of industry, example health care, IoT, information security, consensus building systems and some other fields. To the best of our knowledge, current cryptography techniques used in blockchain, or still theoretical but the security proof can be proven under certain security assumption, so can be used in blockchain has gathered numerous awareness in the last five years. In this paper, we fully review and analysis some cryptographic techniques used in designing a distributed consensus protocol that is efficient, decentralized, and flexible as a framework which are already used in blockchain. Take in consideration deploying a permissioned consensus like delegated proof-of-stack (DPoS) in the decentralized IOT applications is hard. The IOT systems needs to consider a larger data size and a larger span deployment. With this in mind we point to the special challenge for the IOT applications related to blockchain. Several modern cryptography techniques have been adopted to enhance the consensus process with respect to the computational overhead vs communication overhead and storage cost. Additionally, we re-examine non-interactive signature and public key aggregation in digital signature on different messages types and present a change in the aggregation scheme, as far as we know this is the first time that pairing become more faster in verify signatures, and to enable more transaction in the block.
Intelligent Text Scoring System Based on Deep Learning

Abstract In view of the lack of unified standards for emerging local service industries and the impact on users’ reasonable choices, this thesis takes the field of B&amp;B as an example to design an analysis and scoring system based on massive online data. The system first obtains massive online data, including text-type data, through web crawlers. Then analyze and pre-process the captured information to build a corpus; use Chinese word segmentation and label clustering to mine the keywords in the listing introduction and comments. The sentiment polarity of the tenant reviews was analyzed based on the support vector machine (SVM) and sentiment lexicon. Finally, an optimized scoring system was obtained.
A generalization of Dickson’s commutative division algebras

Dickson’s commutative semifields are an important class of finite division algebras. We generalize Dickson’s construction over any base field by doubling not just finite field extensions (which would correspond to the classical setup), but also central simple algebras. The latter case yields division algebras which are no longer commutative nor associative. We compute their automorphisms.