Reviewerly/exHarmony huggingface dataset:
Beyond visual semantics: Exploring the role of scene text in image understanding

Images with visual and scene text content are ubiquitous in everyday life. However, current image interpretation systems are mostly limited to using only the visual features, neglecting to leverage the scene text content. In this paper, we propose to jointly use scene text and visual channels for robust semantic interpretation of images. We do not only extract and encode visual and scene text cues, but also model their interplay to generate a contextual joint embedding with richer semantics. The contextual embedding thus generated is applied to retrieval and classification tasks on multimedia images, with scene text content, to demonstrate its effectiveness. In the retrieval framework, we augment our learned text-visual semantic representation with scene text cues, to mitigate vocabulary misses that may have occurred during the semantic embedding. To deal with irrelevant or erroneous recognition of scene text, we also apply query-based attention to our text channel. We show how the multi-channel approach, involving visual semantics and scene text, improves upon state of the art.
Uncoordinated Pseudonym Changes for Privacy Preserving in Distributed Networks

Pseudonyms have been adopted to preserve identity privacy of nodes in distributed networks. Frequent and unlinkable changes of pseudonyms need to be enabled by having at least k nodes change together to confuse potential eavesdroppers. Existing approaches either depend on the coordination from central controllers, or involve interactive signaling between the nodes. This can potentially compromise privacy. This paper proposes a fully uncoordinated approach to change pseudonyms in distributed networks, where each node uses a pseudonym until its expiration and then changes after a random delay. We develop a new model to analyse the time-varying population of changing pseudonyms. Critical conditions are analytically established, under which individual nodes can independently change their pseudonyms while their identity privacy is preserved. The conditions are validated by illustrative examples. Corroborated by simulations, the accuracy of the analytical model improves, as the number of nodes increases. The analysis confirms that, the k-anonymity can be achieved at a negligible throughput loss in the case of large networks.
High-Capacity Reversible Data Hiding in Encrypted Images by Bit Plane Partition and MSB Prediction

This paper proposes a high-capacity scheme for reversible data hiding (RDH) in encrypted images.The proposed scheme is based on a new preprocessing method by bit plane partition.Specifically, the values in the less significant bit planes are reversibly hidden into the other bit planes.Consequently, the room in the less significant bit planes can be vacated to generate a preprocessed image, from which the original image can be recovered by extracting the hidden bit values and writing them back.After encrypting the preprocessed image with a stream cipher, the vacated room can be used to accommodate extra data, which can be retrieved without image decryption.In addition, the embedding capacity can be further increased by adopting an efficient most significant bit (MSB) prediction method before image encryption.Compared with the state-of-the-art RDH schemes for encrypted images, the experimental results show that higher embedding capacity can be achieved with our proposed one.The numerical results are provided to show the performances of the proposed scheme in different cases of bit-plane partition and MSB prediction.
Human tracking from single RGB-D camera using online learning

Human detection and tracking is an essential component in several robotics applications, especially in indoor built environments where humans and robots are expected to coexist or collaborate. Due to their low cost and capability to capture both color and depth data, RGB-D cameras have shown significant promise in human detection and tracking for robotic applications. In this paper, a new human tracking method is proposed to detect and track a specific individual from a single RGB-D sensor using online learning classifiers with no ground plane assumption. Given a previous target human position, a candidate sampling method is designed to find potential positive samples while negative samples are obtained by a random sampling process. The kernelized Support Vector Machine (SVM) is employed as the online classifier to recognize the target human and updated using both the positive and negative examples. The experimental results on six RGB-D videos of a public dataset demonstrate that the proposed method achieves higher success rates compared to a 2D tracker and a 3D human detection method at a frame rate of 3.8 fps, and is capable of efficiently retrieving the target human following intermittent occlusion.
Face detection authentication on Smartphones: End Users Usability Assessment Experiences

New smartphones by Apple and Samsung have used facial features to recognize their users. These smartphone manufacturers claim that this technology is the most secure and reliable biometrics methods. This paper investigates the usability aspects of face identification technique embedded in these smartphones. The results of this survey have shown that more than half of smartphone users are satisfied with the face detection technique while unlocking their phone. However, 59 percent of smartphones don't use face detection technique while doing purchasing in the app store, showing less trust on this feature where financial transactions are involved. Additionally, the result showed wearing head accessories have reduced the efficiency of face detection technique such as glasses and niqab for Muslim women. Despite some usability issues in using face detection technique, majority still believe it is a powerful tool and they are willing to continue using it.
Generating Titles for Web Tables

Descriptive titles provide crucial context for interpreting tables that are extracted from web pages and are a key component of search features such as tabular featured snippets from Google and Bing. Prior approaches have attempted to produce titles by selecting existing text snippets associated with the table. These approaches, however, are limited by their dependence on suitable titles existing a priori. In our user study, we observe that the relevant information for the title tends to be scattered across the page, and often-more than 80% of the time-does not appear verbatim anywhere in the page. We propose instead the application of a sequence-to-sequence neural network model as a more generalizable approach for generating high-quality table titles. This is accomplished by extracting many text snippets that have potentially relevant information to the table, encoding them into an input sequence, and using both copy and generation mechanisms in the decoder to balance relevance and readability of the generated title. We validate this approach with human evaluation on sample web tables and report that while sequence models with only a copy mechanism or only a generation mechanism are easily outperformed by simple selection-based baselines, the model with both capabilities performs the best, approaching the quality of crowdsourced titles while training on fewer than ten thousand examples. To the best of our knowledge, the proposed technique is the first to consider text-generation methods for table titles, and establishes a new state of the art.
Delay independent robust stability analysis of delayed fractional quaternion-valued leaky integrator echo state neural networks with QUAD condition

This paper studies the stability of fractional order (FO) continuous-time quaternion-valued Leaky Integrator Echo State Neural Networks (NN). The delay independent robust stability of NN with QUAD vector field activation functions under time varying delays is derived. The analysis follows the FO Lyapunov theorem while decomposing the NN into four real-valued systems. The existence and uniqueness of the equilibrium point are also verified by means of a contraction map on the decomposed NN. The new approach is tested in the stability analysis of the FO quaternion-valued Echo State NN whitout time delays, complex-valued Echo State and quaternion-/complex-valued Hopfield NN with/without time delays. Two numerical examples demonstrate the feasibility of the proposed method.
A New Approach for Calculating Semantic Similarity between Words Using WordNet and Set Theory

Calculating semantic similarity between words is a challenging task of a lot of domains such as Natural language processing (NLP), information retrieval and plagiarism detection. WordNet is a lexical dictionary conceptually organized, where each concept has several characteristics: Synsets and Glosses. Synset represent sets of synonyms of a given word and Glosses are a short description. In this paper, we propose a new approach for calculating semantic similarity between two concepts. The proposed method is based on set theory's concepts and WordNet properties, by calculating the relatedness between the synsets' and glosses's of the two concepts.
Augmented reality action assistance and learning for cognitively impaired people

Augmented reality (AR) is a promising tool for many situations in which assistance is needed, as it allows for instructions and feedback to be contextualized. While research and development in this area have been primarily driven by industry, AR could also have a huge impact on those who need assistance the most: cognitively impaired people of all ages. In recent years some primary research on applying AR for action assistance and learning in the context of this target group has been conducted. However, the research field is sparsely covered and contributions are hard to categorize. An overview of the current state of research is missing. We contribute to filling this gap by providing a systematic literature review covering 52 publications. We describe the often rather technical publications on an abstract level and quantitatively assess their usage purpose, the targeted age group and the type of AR device used. Additionally, we provide insights on the current challenges and chances of AR learning and action assistance for people with cognitive impairments. We discuss trends in the research field, including potential future work for researchers to focus on.
Cyberbiosecurity Challenges of Pathogen Genome Databases

Pathogen detection, identification, and tracking is shifting from non-molecular methods, DNA fingerprinting methods, and single gene methods to methods relying on whole genomes. Viral Ebola and influenza genome data are being used for real-time tracking, while food-borne bacterial pathogen outbreaks and hospital outbreaks are investigated using whole genomes in the UK, Canada, the USA and the other countries. Also, plant pathogen genomes are starting to be used to investigate plant disease epidemics such as the wheat blast outbreak in Bangladesh. While these genome-based approaches provide never-seen advantages over all previous approaches with regard to public health and biosecurity, they also come with new vulnerabilities and risks with regard to cybersecurity. The more we rely on genome databases, the more likely these databases will become targets for cyber-attacks to interfere with public health and biosecurity systems by compromising their integrity, taking them hostage, or manipulating the data they contain. Also, while there is the potential to collect pathogen genomic data from infected individuals or agricultural and food products during disease outbreaks to improve disease modeling and forecast, how to protect the privacy of individuals, growers, and retailers is another major cyberbiosecurity challenge. As data become linkable to other data sources, individuals and groups become identifiable and potential malicious activities targeting those identified become feasible. Here, we define a number of potential cybersecurity weaknesses in today's pathogen genome databases to raise awareness, and we provide potential solutions to strengthen cyberbiosecurity during the development of the next generation of pathogen genome databases.
Learning and recommending treatments using electronic medical records

Treatment directly affects patient health status. In recent years, the fast development electronic medical records (EMRs) has provided valuable resources for solving healthcare issues, especially learning and recommending treatments. However, most of the related studies are limited in exploiting various patient information, handling varying-length treatment records, capturing different kinds of treatment patterns and interpreting the recommendation mechanism. This research proposes three methods, one for learning and two for recommending treatments, to overcome the above drawbacks. All methods adopt a mixed-variate restrict Boltzmann machine to represent different kinds of patient records. The treatment learning method captures significant changes in prescription indication to split varying-length records flexibly and organizes sequences of prescription drugs into regimen trees to reveal many more different kinds of treatment patterns. The two treatment recommendation methods illustrate different ideas that can improve the treatment recommendation mechanism by combining the treatments derived from patient groups and neighbor patients. Our experimental evaluation was conducted on three acute disease cohorts extracted from the MIMIC III database. The obtained results show that the proposed methods are able to provide different kinds of treatment patterns and yield competitive efficacy with better interpretability as compared to relevant studies.
Policy-Based Sanitizable Signatures

Sanitizable signatures are a variant of signatures which allow a single, and signer-defined, sanitizer to modify signed messages in a controlled way without invalidating the respective signature. They turned out to be a versatile primitive, proven by different variants and extensions, e.g., allowing multiple sanitizers or adding new sanitizers one- by-one. However, existing constructions are very restricted regarding their flexibility in specifying potential sanitizers. We propose a different and more powerful approach: Instead of using sanitizers' public keys directly, we assign attributes to them. Sanitizing is then based on policies, i.e., access structures defined over attributes. A sanitizer can sanitize, if, and only if, it holds a secret key to attributes satisfying the policy associated to a signature, while offering full-scale accountability.
The Effects of Multi-Sensory Augmented Reality on Students’ Motivation in English Language Learning

English as Second Language (ESL) learning widely implemented in the global education system as English is essential for workplace, diplomate communication, and contractual matters. The effective teaching and learning of ESL since early age provide the students competitive advantages. Deployment of multimedia such as Augmented Reality (AR) in ESL had been implemented for this purpose. This study used multimedia learning environment AR English Vocabulary Acquisition (ARenVA) to test its effectiveness of learning among 37 Malaysia primary school students from 4 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> , 5 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> , and 6 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> grade. Explanatory research was conducted with instruments namely visual text and AR 2D (ARvt), spoken text and AR 2D (ARst), visual text, spoken text and AR 2D (ARvtst). Interview, observation, pre-test/post-test questionnaire and English test were used to identify students' perceived motivation and English performance. The perceived motivation is in relation with Keller's ARCS model of Attention, Relevance, Confidence and Satisfaction. SPSS was used to analyze the quantitative data; while NVivo and content analysis were utilized to analyze the qualitative datasets of semi-structured interview. Findings indicated that the ARenVA's ARstvt treatment mode significantly improves the vocabulary acquisition of ESL learning by 30.76% and perceived motivation by 11.50%; more effective in motivating students by 32 times as compared to ARvt treatment mode. The novelty of this study situated on the effects of AR English learning towards improving motivation and English vocabulary acquisition of ESL learning among 4 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> , 5 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> and 6 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> grade Malaysia student, significantly identified its benefit through the numerical percentages. Nevertheless, the instructional design of AR English learning controls the success of learning results. The paper reviews on the success systematic implementation of different ARenVA treatment modes.
Character-based handwritten text transcription with attention networks

The paper approaches the task of handwritten text recognition (HTR) with attentional encoder-decoder networks trained on sequences of characters, rather than words. We experiment on lines of text from popular handwriting datasets and compare different activation functions for the attention mechanism used for aligning image pixels and target characters. We find that softmax attention focuses heavily on individual characters, while sigmoid attention focuses on multiple characters at each step of the decoding. When the sequence alignment is one-to-one, softmax attention is able to learn a more precise alignment at each step of the decoding, whereas the alignment generated by sigmoid attention is much less precise. When a linear function is used to obtain attention weights, the model predicts a character by looking at the entire sequence of characters and performs poorly because it lacks a precise alignment between the source and target. Future research may explore HTR in natural scene images, since the model is capable of transcribing handwritten text without the need for producing segmentations or bounding boxes of text in images.
GazeButton

The button is an element of a user interface to trigger an action, traditionally using click or touch. We introduce GazeButton, a novel concept extending the default button mode with advanced gaze-based interactions. During normal interaction, users can utilise this button as a universal hub for gaze-based UI shortcuts. The advantages are: 1) easy to integrate in existing UIs, 2) complementary, as users choose either gaze or manual interaction, 3) straightforward, as all features are located in one button, and 4) one button to interact with the whole screen. We explore GazeButtons for a custom-made text reading, writing, and editing tool on a multitouch tablet device. For example, this allows the text cursor position to be set as users look at the position and tap on the GazeButton, avoiding costly physical movement. Or, users can simply gaze over a part of the text that should be selected, while holding the GazeButton. We present a design space, specific application examples, and point to future button designs that become highly expressive by unifying the user's visual and manual input.
Ferns for area of interest free scanpath classification

Scanpath classification can offer insight into the visual strategies of groups such as experts and novices. We propose to use random ferns in combination with saccade angle successions to compare scanpaths. One advantage of our method is that it does not require areas of interest to be computed or annotated. The conditional distribution in random ferns additionally allows for learning angle successions, which do not have to be entirely present in a scanpath. We evaluated our approach on two publicly available datasets and improved the classification accuracy by ≈ 10 and ≈ 20 percent.
Programmer eXperience: A Systematic Literature Review

Programmers use various software development artifacts in their work, such as programming environments, design documents, and programming codes.These software artifacts can be studied and improved based on usability and User eXperience (UX) factors.In this paper, we consider programmers to be a specific case of users and analyze different elements that influence their experience in this specific context.We conducted a systematic literature review of papers published over the last ten years related to 1) the definition of the Programmer eXperience (PX); 2) the PX, UX, and usability factors regarding the programming environments, design documents, and programming codes; and 3) sets of heuristics to evaluate the software development artifacts mentioned before.We analyzed 73 articles, and the results obtained show that: 1) the important elements that influence the PX are the motivation of programmers and the choice of tools they use in their work, such as programming environments; 2) most of the identified studies (59%) aimed to evaluate the influence of the PX, UX, and usability on programming environments; 3) the majority of the studies (70%) used methods such as usability tests and/or heuristic evaluation methods; and 4) four sets of heuristics are used to evaluate software development artifacts in relation to programming environments, programming languages, and application programming interfaces.The results suggest that further research in this area is necessary to better understand and evaluate the concept of the PX.
Implementation of FPGA-based Accelerator for Deep Neural Networks

At present, there are many researches on deep neural network (DNN) applied in life. In the task of object recognition, deep convolutional neural network (CNN) has a good performance, but it relies on GPU to solve a large number of complex operations. Thus the hardware accelerator of DNN is concerned by many people. In order to implement the DNN model on hardware, complex connection relationship and memory usage scheduling are needed. This paper presnets the design of FPGA-based accelerator for DNN. The proposed architecture is implemented on Xilinx Zynq-7020 FPGA. It takes the advantages of low latency and low usage in the task of MNIST digital identification, and keeps the 96 % recognition rate.
Multi-platform Chatbot Modeling and Deployment with the Jarvis Framework

Chatbot applications are increasingly adopted in various domains such as e-commerce or customer services as a direct communication channel between companies and end-users. Multiple frameworks have been developed to ease their definition and deployment. They typically rely on existing cloud infrastructures and artificial intelligence techniques to efficiently process user inputs and extract conversation information. While these frameworks are efficient to design simple chatbot applications, they still require advanced technical knowledge to define complex conversations and interactions. In addition, the deployment of a chatbot application usually requires a deep understanding of the targeted platforms, increasing the development and maintenance costs. In this paper we introduce the Jarvis framework, that tackles these issues by providing a Domain Specific Language (DSL) to define chatbots in a platform-independent way, and a runtime engine that automatically deploys the chatbot application and manages the defined conversation logic. Jarvis is open source and fully available online.