Reviewerly/exHarmony huggingface dataset:
DWT-DQFT-Based Color Image Blind Watermark with QR Decomposition

In order to improve the performance of color image digital watermarking, a watermarking algorithm based on integration of Discrete Wavelet Transform (DWT) and Discrete Quaternion Fourier Transform (DQFT) combined with QR matrix decomposition is proposed. The three channels of a color image are processed as a whole to embed watermark information. Experimental results show that the watermarking algorithm has good robustness against attacks such as JPEG compression, cropping, and median filtering.
Strong Authentication Protocol for Identity Verification in Internet of Things (IoT)

The use of Internet of things (IoT) devices in smart cities has been increasing in recent years. The use of IoT devices, which facilitate the daily life of people living in the smart city, on wearable and mobile devices causes the vulnerability. Some countermeasures should be taken to prevent unauthorized access to IoT devices that contain personal data and to protect the data. In this study, the protocol created to ensure the security of the data communication of IoT devices in smart cities is explained. In the proposed design, IoT device-based secure data communication protocol with limited resources is presented. Data privacy methods that will work on IoT devices are designed to achieve high performance by consuming as few resources as possible. The proposed protocol provides secure data communication against 4 different attacks: Man-in-the-middle attack, malicious code injection attack, denial of service (DoS) attack, and replay attack. As a result of the formal analysis made with the Scyther tool, it is shown that data security is ensured.
Using In-Browser Augmented Reality to Promote Knowledge-Based Engineering throughout the Product Life Cycle

While industry vastly undergoes digitalization, knowledge-based engineering becomes a powerful tool, helping enterprises to operate in context of shorter product life cycles and complex value chains. However, there are several challenges to be addressed in order to make knowledge-based engineering a common industry practice. There is a need for affordable tools, trained professionals, and extended use of outcomes of knowledge-based engineering processes beyond design phase of product life cycle. This article describes how web-based system delivering mobile augmented reality experience in browser may leverage results of product design process implemented with knowledge-based engineering tools in order to integrate information, and support its integrity and consistency for different stakeholders along the product life cycle. The approach relies on use of open standards and libraries in order to insure affordability and ease of integration, which is necessary for wider adoption of knowledge-based engineering among small and medium enterprises.
Efficient Learning Approach for Pronominal Anaphora and Ellipsis Identification and Resolution in Arabic Texts

The anaphors and ellipses resolution is a challenging task for most of NLP applications. However, few works have addressed this problem in Arabic. In fact the richness of the Arabic language and the scarcity of labeled data make the task more difficult. This paper offers a generic approach that deals with the identification and the resolution of both pronominal anaphors and ellipses in Arabic texts. To identify referential anaphors and ellipses positions, we propose a self-training SVM method based on a set of pattern-based and linguistic-based criteria. For the resolution step, we propose a novel hybrid method combining a reinforcement learning method with Word Embedding models. The reinforcement learning method uses an adapted version of Q-learning algorithm to finds the optimal combination of features. It exploits a set of morphological and syntactical features. The Word Embedding based method uses word representation models to check the semantic validity of the candidates. The evaluation of the identification approach gives a precision of 99.23% for pronominal anaphors and 94.33% for ellipses. The resolution approach reaches a precision of 80% for pronominal anaphors and 97.5% for ellipses. Our proposed approaches can be easily adapted to English and French to provide multilingual approaches.
PPHR: Blockchain-based Privacy Protection House Rental System

In recent years, the sharing economy is developing rapidly and has become a popular economic model. For example, sharing platforms such as Airbnb have gradually entered people ’s field of vision and obtained high economic benefits. However, the information managed by such a centralized platform may be used by criminals and people’s privacy may be violated. In order to achieve the goal of decentralization, we propose a blockchain-based house sharing platform. Users can directly conduct transactions on the blockchain by running smart contracts. Our scheme guarantees the anonymity of users through commitment scheme and zero-knowledge technology. In order to deal with the problems that may be encountered in reality, we propose a trusted authority to trace malicious users. In addition, our platform has reputation mechanism to restrict the behavior of users. We analyze its security and performance, and the result shows that it is feasible in reality.
Novel Stream Ciphering Algorithm for Big Data Images Using Zeckendorf Representation

Big data is a term used for very large data sets. Digital equipment produces vast amounts of images every day; the need for image encryption is increasingly pronounced, for example, to safeguard the privacy of the patients’ medical imaging data in cloud disk. There is an obvious contradiction between the security and privacy and the widespread use of big data. Nowadays, the most important engine to provide confidentiality is encryption. However, block ciphering is not suitable for the huge data in a real-time environment because of the strong correlation among pixels and high redundancy; stream ciphering is considered a lightweight solution for ciphering high-definition images (i.e., high data volume). For a stream cipher, since the encryption algorithm is deterministic, the only thing you can do is to make the key “look random.” This article proves that the probability that the digit 1 appears in the midsection of a Zeckendorf representation is constant, which can be utilized to generate the pseudorandom numbers. Then, a novel stream cipher key generator (ZPKG) is proposed to encrypt high-definition images that need transferring. The experimental results show that the proposed stream ciphering method, with the keystream of which satisfies Golomb’s randomness postulates, is faster than RC4 and LSFR with indistinguishable performance on hardware depletion, and the method is highly key sensitive and shows good resistance against noise attacks and statistical attacks.
Modulating Bottom-Up and Top-Down Visual Processing via Language-Conditional Filters

How to best integrate linguistic and perceptual processing in multi-modal tasks that involve language and vision is an important open problem. In this work, we argue that the common practice of using language in a top-down manner, to direct visual attention over high-level visual features, may not be optimal. We hypothesize that the use of language to also condition the bottom-up processing from pixels to high-level features can provide benefits to the overall performance. To support our claim, we propose a U-Net-based model and perform experiments on two language-vision dense-prediction tasks: referring expression segmentation and language-guided image colorization. We compare results where either one or both of the top-down and bottom-up visual branches are conditioned on language. Our experiments reveal that using language to control the filters for bottom-up visual processing in addition to top-down attention leads to better results on both tasks and achieves competitive performance. Our linguistic analysis suggests that bottom-up conditioning improves segmentation of objects especially when input text refers to low-level visual concepts. Code is available at https://github.com/ilkerkesen/bvpr.
Perspectives on interaction with dead users' profiles on Facebook

With the Covid-19 pandemic, death has become an increasingly present and visible phenomenon on social networks. On Facebook, there is not a homogeneous treatment of death among deceased users' profiles: some remain active, whereas others are turned into memorials, but the criteria for this transformation are not clear. In both cases, privacy settings limit the interaction. These different possibilities give rise to different interactions and reactions among users. This paper addresses how dead users' profiles, multiplied due to the current pandemic, have been treated by Facebook's social-technical system, in comparison to possible posthumous interactions in the physical world. Our main objective is to understand to what extent Facebook's technical system supports or restricts social interactions concerning users' deaths. We carried out a qualitative exploratory analysis of data from 54 Facebook profiles belonging to people who passed away between June 2020 and March 2021. Among other results, we noticed: that Facebook fails to publicize the criteria for transforming active profiles into memorials and who are their heir contacts; that there is a difference in the amount and frequency of interactions between profiles transformed into memorials and those that remain active; and that profiles' privacy settings interfere with social interaction. This situation directly influences the ways other users relate or not with such profiles, which is an object of paramount importance in Human-Computer Interaction studies, especially when considering interaction as existence.
Neural Network Compatible Off-Policy Natural Actor-Critic Algorithm

Learning optimal behavior from existing data is one of the most important problems in Reinforcement Learning (RL). This is known as “off-policy control” in RL where an agent's objective is to compute an optimal policy based on the data obtained from the given policy (known as the behavior policy). As the optimal policy can be very different from the behavior policy, learning optimal behavior is very hard in the “off-policy” setting compared to the “on-policy” setting where new data from the policy updates is typically utilized in learning. This work proposes an off-policy natural actor-critic algorithm that utilizes state-action distribution correction for handling the off-policy behavior and the natural policy gradient for sample efficiency. The existing natural gradient-based actor-critic algorithms with convergence guarantees require fixed features for approximating both policy and value functions. This often leads to sub-optimal learning in many RL applications. On the other hand, our proposed algorithm utilizes compatible features that enable one to use arbitrary neural networks to approximate the policy and the value function and yet guarantee convergence to a locally optimal policy. We illustrate the benefit of the proposed off-policy natural gradient algorithm by comparing it with the vanilla gradient actor-critic algorithm on benchmark RL tasks.
Higher-Order Unscented Estimator

This paper introduces a new extension of the unscented Kalman filter with asymmetric sample points and weights chosen to match third- and fourth-order moments in addition to the mean and covariance. Explicit solutions are obtained for sample points and weights, making their evaluation efficient and robust, and rigorous constraints are derived for their applicability. The use of the new filter is demonstrated with three dynamic systems (an aircraft coordinated turn model, a rotating rigid body, and a projectile with drag), and filter performance is compared with that of the conventional unscented Kalman filter and conjugate unscented transform filters. The new filter is found to be more robust in most cases where the initial distribution, process noise, and measurement noise have a high kurtosis, in that it does not generate extreme outliers in the estimation error. Also, execution times for the new filter are found to be only slightly longer than for the conventional unscented Kalman filter and significantly shorter than for the conjugate unscented transform filters.
Omotenashi

The development and use of intelligent agents are currently growing vertiginously. This growth has raised awareness of big technology companies that launched platforms that ease chatbot development. However, chatbots still fail to meet users' social expectations associated with cultural values imbued during the chatbot development (concerned or not with anthropomorphic topics). In literature, nowadays, there is a lack of research on culture in chatbot building platforms, primarily focusing on non-programmers. This research investigates interactive resources for cultural adaptation on the Blip Builder platform based on Semiotic Engineering. We evaluate the Blip communicability using the Semiotic Inspection Method combined with the Cultural Viewpoint Metaphors, followed by an autoethnography step in the design process of a conversational agent based on Omotenashi, a Japanese cultural characteristic. The main results indicate that the platform offers opportunities that enable cultural adaptation, mainly improving the adaptation of the chatbot behavior to the Omotenashi properties. But also convey some barriers for cultural expression.
Psychometric Evaluation of the Cybersecurity Concept Inventory

We present a psychometric evaluation of a revised version of the Cybersecurity Concept Inventory (CCI) , completed by 354 students from 29 colleges and universities. The CCI is a conceptual test of understanding created to enable research on instruction quality in cybersecurity education. This work extends previous expert review and small-scale pilot testing of the CCI. Results show that the CCI aligns with a curriculum many instructors expect from an introductory cybersecurity course, and that it is a valid and reliable tool for assessing what conceptual cybersecurity knowledge students learned.
Comparison of the Parametrically Optimized Implementation of Viola–Jones Object Detection Method and MTCNN

Face detection on images is a classical computer vision task. Solution to this problem is used in a wide range of apps, mainly in those connected with providing for the elements of administration (access to the data based on a person's face) and elements of control (perimeter safety control systems). The most spread object's face detecting methods may be divided in two groups: classical one (common example is Viola–Jones method) and one based on a neural network (common example is a cascaded convolutional neural network). There are plenty comparisons of face detection methods that, however, do not usually consider the potential for optimization within the parameters of realization methods in question. This paper focuses on the comparison of the parametrically optimized ways of implementation of methods which are typical for each class. Optimization is being carried out in accordance with the algorithm suggested in this paper. It reveals that the optimized Viola–Jones method is inferior to MTCNN by 20–50 % in view of the quality metrics but outruns it 7–14 times when it comes to the processing speed per image. It shows that without using algorithm optimization by parameters, it is barely impossible to get a fair quality and capacity just as the average value of the quality and capacity metrics are visibly lower than the optimal ones.
Aspect-level sentiment analysis for based on joint aspect and position hierarchy attention mechanism network

The purpose of aspect-level sentiment analysis is to identify the contextual sentence expressions given by sentiment for some aspects. For previous works, many scholars have proved the importance of the interaction between aspects and contexts. However, most existing methods ignore or do not specifically capture the position information of the aspect targets in the sentence. Thus, we propose an aspect-level sentiment analysis based on joint aspect and position hierarchy attention mechanism network. At the same time, the model adopts a joint approach to make the model of the aspect features and the position features. On the one hand, this method clearly captures the interaction between aspect words and context when inputting word vector information. On the other hand, this method can enhance the importance of position information in the sentence and boost the information retrieval ability of the model. Additionally, the model utilizes a hierarchical attention mechanism to extract feature information and to differentiate sentiment towards, which is similar to filtering useless information again. Experiment on the SemEval 2014 dataset represent that our model achieves better performance on aspect-level sentiment classification.
Multi-Agent Ergodic Coverage in Urban Environments

An important aspect of dynamic urban coverage is how building collision avoidance is incorporated into the overall coverage mission. We consider a multi-agent urban dynamic coverage problem in which a team of flying agents uses downward facing cameras to observe the street-level environment outside of buildings. Cameras are assumed to be ineffective above a maximum altitude (lower than building height), such that agents must move around or over buildings to complete their mission. The main objective of this paper is to compare three different building avoidance strategies that are compatible with dynamic ergodic methods. To provide context for these results, we also compare our results to three other common coverage methods including: boustrophedon coverage (lawn-mower sweep), Voronoi region based coverage, and a naive grid method. All algorithms are evaluated in simulation with respect to four performance metrics (percent coverage, revisit count, revisit time, and the integral of area viewed over time), across team sizes ranging from 1 to 25 agents, and in five types of urban environments of varying density and height. We find that the relative performance of algorithms changes based on the ratio of team size to search area, as well the height and density characteristics of the urban environment.
The Singing Gallery

This paper presents, The Singing Gallery, an artistic exploration developed to discover the extent to which music can affect one's viewing experience of static visual artworks in a gallery setting. This project was conceived to explore integration between vision and sound in an art gallery environment. Specifically, we aimed to assess the extent to which musical cues can enhance or modify the viewer's experience of abstract art. The Singing Gallery takes shape in the form of a virtual reality (VR) art gallery, featuring a selection of visual artworks. Each artwork is associated with a unique piece of acousmatic music, which has been specifically composed for by the first author, which increases in volume as the audience member moves towards the painting. In the paper, we discuss the epistemological grounding and detail the development of the interactive VR system. We then offer concluding remarks based on auto-ethnographic reflections.
Reversible data hiding based on histogram shifting with sorted pairs of points

In recent years, Reversible Data Hidden (RDH) has been paid more and more attention by researchers. Existing RDH algorithms based on histogram shifting require that there should be no overlap between the multiple pairs of maximum and minimum points, so that the embedding capacity is relatively small. In order to improve the embedding capacity, we propose a histogram shifting technique which allows the region between the multiple pairs of maximum and minimum points to be overlapped. This scheme simplifies the process of selecting frequency points. It only needs to find some maximum points and minimum points, and one maximum point matches another minimum point according to their distance. For each pair of maximum and minimum point, histogram shifting is applied to embed the secret information. The experimental results show that the embedding capacity is greatly improved while the image quality reduces slightly.
Enhancing semantics with multi‐objective reinforcement learning for video description

Video description is challenging due to the high complexity of translating visual content into language. In most popular attention-based pipelines for this task, visual features and previously generated words are usually concatenated as a vector to predict the current word. However, the errors caused by the inaccuracy of the predicted words may be accumulated, and the gap between visual features and language features may bring noises into the description model. Facing these problems, a variant of recurrent neural network is designed in this work, and a novel framework is developed to enhance the visual clues for video description. Moreover, a multi-objective reinforcement learning strategy is implemented to build a more comprehensive reward with multiple metrics to improve the consistency and semantics of the generated description sentence. The experiments on the benchmark MSR-VTT2016 and MSVD datasets demonstrate the effectiveness of the proposed approach.
Dynamic Knowledge Distillation with Cross-Modality Knowledge Transfer

Supervised learning for vision tasks has achieved great success be-cause of the advances of deep learning research in many areas, such as high quality datasets, network architectures and regularization methods. In the vanilla deep learning paradigm, training a model for visual tasks is mainly based on the provided training images and annotations. Inspired by human learning with knowledge transfer where information from multiples modalities are considered, we pro-pose to improve visual tasks' performance by introducing explicit knowledge extracted from other modalities. As the first step, we propose to improve image classification performance by introducing linguistic knowledge as additional constraints in model learning. This knowledge is represented as a set of constraints to be jointly utilized with visual knowledge. To coordinate the training dynamic, we propose to imbue our model the ability of dynamic distilling from multiple knowledge sources. This is done via a model agnostic knowledge weighting module which guides the learning process and updates via meta-steps during training. Preliminary experiments on various benchmark datasets validate the efficacy of our method. Our code will be made publicly available to ensure reproducibility.
Moods in Book Reviews: Text Mining Approach

Abstract Spiteri and Pecoskie (2018) proposed a taxonomy of terms to describe emotion and tone in novels. We tested those terms against 5,144 full‐text book reviews from the New York Times Book Review to discover whether the proposed terms were used in published reviews to describe books, and of those terms used, which were most used. The objective of this study is to explore whether emotional contents can be identified by a text mining approach without investing too much time and efforts of information professionals. Findings demonstrate that the terms chosen by Spiteri and Pecoskie are used in professional book reviews, though some may be used in multiple ways, rather than only related to emotional content. Results of this work contribute to a larger scale project of testing machine models of identifying emotional content in books and ultimately being able to create automated media recommendation systems that include emotion as an identifier.