Reviewerly/exHarmony huggingface dataset:
Multiple cross-attention for video-subtitle moment retrieval

Given a natural language query, video-subtitle moment retrieval (VSMR) aims at localizing a short video moment from a video with subtitles. Different from the extensively studied video moment retrieval task locating vision moments to match the text query, VSMR is a more challenging task because the retrieval results have to contain both vision and subtitle contents, which needs a deep understanding of one more subtitle modality in addition to the query text and the video itself. Towards this end, we design a mutually guided cross-attention block by uniting multiple self-attention units and guided-attention units with successively mutual connections, and therefrom propose a novel Multiple Cross-Attention (MCA) network for multi-modal interaction and matching. Through such an attention interaction among multiple modalities, the proposed MCA can favorably model both the query-video relations and query-subtitle relations in word-by-clip level for VSMR. We quantitatively and qualitatively evaluate our proposed MCA on TVR, which is the most challenging VSMR dataset available. Empirical evidences demonstrate that our method outperforms the state-of-the-art ones.
Staging Constructionist Learning about Energy for Children with Electrochromic Displays and Low-Cost Materials

Well-funded classrooms often provide a variety of learning materials such as computers, robotics and other expensive equipment to facilitate STEM Learning activities for children. However, exploring natural phenomena such as electrical energy is possible through very simple activities. In this paper we explore how cheap low-cost materials and simple electrochromic displays can be designed to support experiential learning about energy and power generation. For this we employed a mix of open-ended play and exploration as well as staged and goal-oriented activities. We developed two learning activities that involve children constructing working models that generate power including constructing a wind turbine and assembling a solar power harvesting house. We studied how children engaged in the activities and how the materials helped them understand the topic. All children could construct and complete the building tasks and were generally positive about the experience. We identified challenges encountered by children including interactions with the construction materials and electrochromic screens as well as insights about the mental models children have. We discuss challenges for staging learning through play with found low cost and local materials and provide implications for the design of constructionist oriented STEM learning.
Fusion Moves for Graph Matching

We contribute to approximate algorithms for the quadratic assignment problem also known as graph matching. Inspired by the success of the fusion moves technique developed for multilabel discrete Markov random fields, we investigate its applicability to graph matching. In particular, we show how fusion moves can be efficiently combined with the dedicated state-of-the-art dual methods that have recently shown superior results in computer vision and bioimaging applications. As our empirical evaluation on a wide variety of graph matching datasets suggests, fusion moves significantly improve performance of these methods in terms of speed and quality of the obtained solutions. Our method sets a new state-of-the-art with a notable margin with respect to its competitors.
A sophisticated offline network developed for recognizing handwritten Chinese character efficiently

In this paper, we designed an efficient network structure by combining multiple scale convolution shuffle (MSCS) module and attention features spatial aggregation (ASA) module. MSCS module can obtain local and global multiple receptive fields feature information, and it contains the structure idea of the lightweight network convolution block. Moreover, we have proposed ASA module to reducing the information loss of different features in spatial dimension reduction. Meanwhile, we introduce the joint loss function to strengthen the ability of the model to distinguish the difference of attention features between inter-class and intra-class. The experimental results tested on offline HCCR competition dataset ICDAR-2013 show that our network model trained only on handwritten dataset only takes 3.97 ms to recognize a character image and achieves 97.63% accuracy just requires 22.9 MB for storage. Therefore, our method reaches the state-of-the-art in efficient single networks particularly in view of the storage space and inference time.
CL-BC: A Secure Data Storage Model for Social Networks

While bringing convenience to people, social networks cause various security problems, such as information leakage, which belongs to the security category of data storage. The related research focuses more on the confidentiality design and lacks the discussion of data integrity protection technology. Thus, the electronic data editability invites the risk of data tampering and destruction, making other operations meaningless once the data are inaccurate. A social network-oriented data security storage model, CL-BC, included conceptual design, preliminary framework design, and detailed flow design, to improve the security threats such as overauthorization and illegal execution in data storage. The blockchain technology is combined with the improved Clark–Wilson model to achieve data integrity access control protection through execution rules and authentication rules. The system uses the subalgorithm of DR-BFT for reference when deploying the algorithm program and adopts the intelligent contract to complete the design rules. The experimental analysis shows the CL-BC model adapts to the actual security environment, ensures the data storage integrity in the social network stored process, and has application value.
IDARTS: Interactive Differentiable Architecture Search

Differentiable Architecture Search (DARTS) improves the efficiency of architecture search by learning the architecture and network parameters end-to-end. However, the intrinsic relationship between the architecture’s parameters is neglected, leading to a sub-optimal optimization process. The reason lies in the fact that the gradient descent method used in DARTS ignores the coupling relationship of the parameters and therefore degrades the optimization. In this paper, we address this issue by formulating DARTS as a bi-linear optimization problem and introducing an Interactive Differentiable Architecture Search (IDARTS). We first develop a backtracking backpropagation process, which can decouple the relationships of different kinds of parameters and train them in the same framework. The backtracking method coordinates the training of different parameters that fully explore their interaction and optimize training. We present experiments on the CIFAR10 and ImageNet datasets that demonstrate the efficacy of the IDARTS approach by achieving a top-1 accuracy of 76.52% on ImageNet without additional search cost vs. 75.8% with the state-of-the-art PC-DARTS.
Interactive data visualizations of Earth’s atmosphere: Effects on student engagement and perceived learning

The Fluid Earth (FE) is an interactive data visualization initially developed for learning about Earth’s atmosphere in informal educational settings. In this study, we tested FE in middle school classes to assess student engagement in a formal educational setting. Using a quasi-experimental design, students were assigned to interact with the data visualization website (high interactivity) or prerecorded videos of the visualization (low interactivity). Students who used the website demonstrated greater engagement and perceived learning than students who used videos, but no differences were observed for behavioral intentions to use FE in the future. Results provide preliminary evidence that the interactive interface provided a more engaging educational experience than videos. Findings suggest that interactivity is an important component of science education visualizations, and there is value in creating interactive tools for students to explore geosciences processes and concepts. Additional work is needed to assess the visualization’s impact on student learning of content knowledge and its applicability to more diverse student groups.
Sentiment-Based Abstractive Text Summarization Using Attention Oriented LSTM Model

Product reviews are essential as they help customers to make purchase decisions. Often these product reviews are used to be too abundant, lengthy and descriptive. However, the abstractive text summarization (ATS) system can help to build an internal semantic representation of the text with the use of natural language processing to create sentiment-based summaries of the entire reviews. An ATS system is proposed in this work, which is called a many-to-many sequence problem where attention-based long short-term memory (LSTM) is incorporated to generate the summary of the product reviews. The proposed ATS system works in two stages. In the first stage, a data processing module is designed to create the structured representation of the text, where noise and other irrelevant data are also removed. In the second stage, the attention-based long short-term memory (LSTM) model is designed to train, validate and test the system. It is found that the proposed approach can better deal with the rare words, also can generate readable, short and informative summaries. An experimental analysis has been carried out over the Amazon product review dataset, and to validate the result, the ROUGE measure is used. The obtained result reflects the efficacy of the proposed method over other state-of-the-art methods.
Software Architecture Based on Web Standards for Gesture Input with Smartwatches and Smartglasses

We employ the ISO/IEC 25010 standard for systems and software quality requirements and evaluation to present a proposal of a software architecture design for gesture input with two types of wearables: smartwatches, which have already entered mainstream, and smartglasses, forecast to become a growing market due to increased adoption of augmented reality applications. We present the technical details of our software architecture and describe practical implementation aspects that employ web standards.
Deep Learning Approach for Malaria Parasite Detection in Thick Blood Smear Images

Malaria is caused by a bite of female anopheles mosquitos transmitting the parasite Plasmodium into human bodies. Malaria is a common disease in tropical and subtropical regions and is also a severe public health problem due to its risk. Early diagnosis is required to avoid the hazard of death from malaria. Microscopic analysis of blood smears remains a standard method for malaria analysis. However, manual microscopic observation is laborious, and the results have a heavy dependence on the examiner’s skill. To alleviate this problem, this study proposed a deep learning method for detecting malaria automatically malaria parasite on thick blood smear microscopic images. The proposed approach achieved the fastest examination at 0.25 sec/image or more than 20 times faster compared to that of previous with mAP, sensitivity, and a precision score of 72, 78.4, and 83.2 %, respectively. These performances indicated that the proposed approach can be a promising alternative to CAD systems for fast parasite detection.
Hyperledger Fabric based Medical Record Security

Hyperledger Fabric is a permissioned distributed ledger framework for developing solutions and applications that works at the enterprise grade. It is one of the latest blockchain framework that is built as foundation for developing products, solutions, and applications based on blockchain. Lately, the pandemic has changed a lot in the medical system throughout the world. Patients required immediate attention where doctors were scarce. This has led to the implementation of blockchain technologies for designing online applications to provide patients with better and secure services. The private blockchain is implemented using Hyperledger Fabric that records the patients' data. This provides better security and prevents loss of data through redundancy. Blockchain has gained popularity lately due to the rise of the cryptocurrencies like Bitcoin, Ethereum etc. Their value has sky-rocketed with the amount of attention they gained during the pandemic. An application of blockchain technology i.e. Distributed Ledger Protocol (DLT) called Hyperledger Fabric developed by Linux Foundation is used to solve many business use cases like storing health records, managing financial transactions etc.
Neural Photofit: Gaze-based Mental Image Reconstruction

We propose a novel method that leverages human fixations to visually decode the image a person has in mind into a photofit (facial composite). Our method combines three neural networks: An encoder, a scoring network, and a decoder. The encoder extracts image features and predicts a neural activation map for each face looked at by a human observer. A neural scoring network compares the human and neural attention and predicts a relevance score for each extracted image feature. Finally, image features are aggregated into a single feature vector as a linear combination of all features weighted by relevance which a decoder de-codes into the final photofit. We train the neural scoring network on a novel dataset containing gaze data of 19 participants looking at collages of synthetic faces. We show that our method significantly outperforms a mean baseline predictor and report on a human study that shows that we can decode photofits that are visually plausible and close to the observer’s mental image.
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In conventional multi-image secret sharing schemes (MISSS) images are shared by the trusted dealer and the shares are sent to the set of participants through a secure channel. During reconstruction, the participants submit shares to a trusted combiner. But the method will collapse if any of the actors perform cheating. This brings verifiable image secret sharing in the research arena. Verifying the trustworthiness of the dealer by the shareholders before accepting shares (dealer verification), examining the genuineness of the shares submission request received from a combiner (combiner verification), checking the authenticity of the shares received from participants by the combiner (cheating detection/ cheater identification) are techniques related to verifiable secret sharing. Medical images, images used at the military or diplomatic level; contain sensible information. Thus the authenticity of the reconstructed images should be checked beforehand. In the case of multi-image secret sharing, the researchers use bit padding if the plaintext images are of different sizes. This adds an extra level of burden during sharing and retrieval. A verifiable varying size (m,n,n) multi-image secret sharing is addressed in this article. Here m (wherem≤n) varying sized images are shared among n participants and during reconstruction all the shares are required. The major contribution of the addressed technique is that it has the capability of dealer authentication, combiner verification, and cheater identification. Another advancement is that most of the communication can be made through a public channel. The test results generate noise like images and statistical analysis, security analysis say in favor of the claims. Comparison with some state-of-the-art techniques gives it a stable platform in verifiable multi-image secret sharing.
Generalized Concatenated Codes over Gaussian Integers for the McEliece Cryptosystem

Code-based cryptography is a promising candidate for post-quantum public-key encryption. The classic McEliece system uses binary Goppa codes, which are known for their good error correction capability. However, the key generation and decoding procedures of the classic McEliece system have a high computation complexity. Recently, q-ary concatenated codes over Gaussian integers were proposed for the McEliece cryptosystem together with the one-Mannheim error channel, where the error values are limited to Mannheim weight one. For this channel, concatenated codes over Gaussian integers achieve a higher error correction capability than maximum distance separable (MDS) codes with bounded minimum distance decoding. This improves the work factor regarding decoding attacks based on information-set decoding. This work proposes an improved construction for codes over Gaussian integers. These generalized concatenated codes extent the rate region where the work factor is beneficial compared to MDS codes. They allow for shorter public keys for the same level of security as the classic Goppa codes. Such codes are beneficial for lightweight code-based cryptosystems
Artificial intelligence in peripheral blood films: an evolving landscape

We read with interest the Editorial entitled "Artificial intelligence—refinement and possibilities".1 Two key domains of artificial intelligence (AI)—namely, computer vision and machine learning—enable preclassification of blood cells or parasites, thereby assisting microscopy slide review and facilitating the reporting of peripheral blood films and core laboratory processes, such as quality assurance, competency training, education, and research.2
CLEAR: Clean-up Sample-Targeted Backdoor in Neural Networks

The data poisoning attack has raised serious security concerns on the safety of deep neural networks, since it can lead to neural backdoor that misclassifies certain inputs crafted by an attacker. In particular, the sample-targeted backdoor attack is a new challenge. It targets at one or a few specific samples, called target samples, to misclassify them to a target class. Without a trigger planted in the backdoor model, the existing backdoor detection schemes fail to detect the sample-targeted backdoor as they depend on reverse-engineering the trigger or strong features of the trigger. In this paper, we propose a novel scheme to detect and mitigate sample-targeted backdoor attacks. We discover and demonstrate a unique property of the sample-targeted backdoor, which forces a boundary change such that small "pockets" are formed around the target sample. Based on this observation, we propose a novel defense mechanism to pinpoint a malicious pocket by "wrapping" them into a tight convex hull in the feature space. We design an effective algorithm to search for such a convex hull and remove the backdoor by fine-tuning the model using the identified malicious samples with the corrected label according to the convex hull. The experiments show that the proposed approach is highly efficient for detecting and mitigating a wide range of sample-targeted backdoor attacks.
Neural Network Based Approach on Sentimental Analysis Using Herb

Mindsets of people change time to time, thus generating the need to identify the sentiments of the people regarding a product to improvise the market. This proposed system is the Homogenous Ensemble – Radial Basis Function Neural Network (HERB) approach that use varying levels of categorization for classifying sentiments. They are validated using a dataset consisting of news headlines. These datasets are preprocessed using a data mining tool which is then classified using a radial basis function i.e., a neural network-based approach. This classified output is used to identify the emotions of readers under each section and evaluate the shares on social media. Thus, the most popular column of the newspaper is deduced. The association of the paper is depicted in the following way: Segment 2 depicts the writing survey and highlights, and difficulties of various feeling evaluation. The stages of feeling evaluation of paper articles are displayed in Section 3. Area 4 talks about the proposed design of HERB and Area 5 about the RBFN arrangement calculation. Section 6 depicts the outcomes and conversations of the trial. At last, the closure of the paper is given in Section 7.
A privacy-preserving multisubset data aggregation scheme with fault resilience for intelligent transportation system

In Intelligent transportation system (ITS) applications, such as road safety and traffic monitoring, useful information is frequently gathered and transmitted to the management center for analysis, to provide a wide range of control, communication, and management measures. However, privacy of users must be preserved, since data transmitted by users may reveal sensitive private information such as location, travel route, etc. In this paper, we propose a privacy-preserving data aggregation scheme for ITS applications. The proposed scheme employs the combination of modified Paillier cryptosystem, Chinese Remainder theorem, Boneh short signature, and hash function operations not only to aggregate data of heterogeneous vehicles into one but also to provide fault resilience in a scenario when some devices are malfunctioning and not reporting to the management center. In the proposed scheme, each vehicle collects its data and computes a one-time secret key shared with the gateway to mask its data. We develop a hash function-based technique to ensure the authenticity and integrity of the aggregated encrypted data without revealing the individual data. Security analysis shows that the proposed scheme is secure, privacy-preserving, and supports conditional traceability. Extensive performance analysis and comparison demonstrate that the scheme is significantly more efficient than the related schemes.
Impact of Technology in Sustainable Tourism Development

Tourism has been one of the engines powering the thriving economies, leveraging the quality of lives and transforming the people's sociocultural status worldwide. There is no doubt that innovative technologies are critical and significantly efficient for the economies during a hard time and in blossoming times. Moreover, it is also vital to understand and present some essential issues that harm tourism development. Thus, this chapter strives to offer innovative technology-oriented alternative strategies and practical implications to benefit all the stakeholders at the destination and the consumers by focusing on the long-term benefits. This chapter also aims to understand how technology can take a role to stimulate visitors' motivation and behaviour towards a touristic product or a destination.
Distributed joint target detection, tracking and classification via Bernoulli filter

This paper aims to solve the problem of distributed joint detection, tracking and classification (D-JDTC) of a target on a peer-to-peer sensor network. The target can be present or not, can belong to different classes, and depending on its class can behave according to different kinematic modes. Accordingly, it is modelled as a suitably extended Bernoulli random finite set (RFS) uniquely characterized by existence, classification, class-conditioned mode and class & mode-conditioned state probability distributions. Existing algorithms have been devised to perform target JDTC based on a single sensor and can only be easily extended to multiple sensors in a centralized configuration, wherein a fusion centre gathers measurements from all sensors. In this paper, by designing a suitable rule for fusing local posteriors that convey information on target existence, class, mode and state from different sensor nodes, a novel scalable and fault-tolerant D-JDTC Bernoulli filter is proposed, and its performance is evaluated by means of simulation experiments.