Reviewerly/exHarmony huggingface dataset:
An FPP-resistant SVD-based image watermarking scheme based on chaotic control

Image watermarking commonly involves singular value decomposition (SVD) because of its simplicity and minimal effect on image quality. However, SVD-based image watermarking schemes suffer from some drawbacks such as the false positive problem (FPP), and an undesirable trade-off between vital properties such as imperceptibility, embedding capacity, and robustness. To address these drawbacks, we improve upon the SVD-based color image watermarking by incorporating integer wavelet transform (IWT) and chaotic maps. A grayscale image watermark is decomposed into eight gray levels (bit-planes) before being encrypted and re-ordered by a chaotic sequence. Each encrypted bit-plane is inserted into singular values of the sub-bands of a host image. The embedding process is controlled by chaos-based multiple scaling factors (MSF) which are updated in each embedded bit-plane. The resulting values are involved in generating a hash value at the end of the embedding processes. The hash value is used to overcome FPP issues and improves security. Our findings illustrate the proposed scheme’s robustness, security, imperceptibility, and capacity. It also exhibits excellent robustness against attacks and its performance surpasses a variety of existing schemes. In addition, the proposed scheme is hypersensitive to even the slightest secret key variations.
Revisiting Reflection in HCI

Reflection is a commonly addressed design goal in commercial systems and in Human-Computer Interaction (HCI) research. Yet, it is still unclear what tools are at the disposal of designers who want to build systems that support reflection. Understanding the design space of reflection support systems and the interaction techniques that can foster reflection is necessary to enable building technologies that contribute to the users' well-being. In order to gain additional insight into how interactive artefacts foster reflection, we investigated past research prototypes and reflection-supporting smartphone applications (apps). Through a structured literature review and an analysis of app reviews, we constructed four design resources for reflection: temporal perspective, conversation, comparison and discovery. We also identified design patterns in past digital artefacts that implement the resources. Our work constitutes intermediate-level knowledge that is intended to inspire future technologies that better support reflection.
The Handbook on Socially Interactive Agents

The Handbook on Socially Interactive Agents provides a comprehensive overview of the research fields of Embodied Conversational Agents, Intelligent Virtual Agents, and Social Robotics. Socially Interactive Agents (SIAs), whether virtually or physically embodied, are autonomous agents that are able to perceive an environment including people or other agents, reason, decide how to interact, and express attitudes such as emotions, engagement, or empathy. They are capable of interacting with people and one another in a socially intelligent manner using multimodal communicative behaviors, with the goal to support humans in various domains. Written by international experts in their respective fields, the book summarizes research in the many important research communities pertinent for SIAs, while discussing current challenges and future directions. The handbook provides easy access to modeling and studying SIAs for researchers and students, and aims at further bridging the gap between the research communities involved. In two volumes, the book clearly structures the vast body of research. The first volume starts by introducing what is involved in SIAs research, in particular research methodologies and ethical implications of developing SIAs. It further examines research on appearance and behavior, focusing on multimodality. Finally, social cognition for SIAs is investigated using different theoretical models and phenomena such as theory of mind or pro-sociality. The second volume starts with perspectives on interaction, examined from different angles such as interaction in social space, group interaction, or long-term interaction. It also includes an extensive overview summarizing research and systems of human–agent platforms and of some of the major application areas of SIAs such as education, aging support, autism, and games.
Adoption Factors and Moderating Effects of Age and Gender That Influence the Intention to Use a Non-Directive Reflective Coaching Chatbot

Chatbots are increasingly applied in various contexts including helping professions, such as organizational and life coaching. Coaching facilitates individual wellness, behavioral change, and goal attainment in a reflective, non-directive manner, and is considered one of the fastest-growing professions. The use of knowledge imparting service chatbots have been studied; however, the application of chatbots in coaching has received scant research attention, raising the question about which factors and moderating effects play a role in the adoption of reflective, non-directive coaching chatbots. In this study, we applied a modified Unified Theory of the Acceptance and Use of Technology (UTAUT) model to determine factors and moderating effects of age and gender that influence the adoption of a goal-attainment coaching chatbot. Partial least squares structural equation modeling (PLS-SEM) was used for the analysis of a cross-sectional UTAUT survey ( n = 226). Performance expectancy, social influence, and facilitating conditions had significant roles as direct determinants of intent to use the coaching chatbot. Gender moderated performance expectancy and age showed a moderation tendency on effort expectancy. This study on non-directive, reflective chatbots in the organizational, and life coaching domains contributes to our understanding of how to design chatbots aimed at helping people find their own answers.
Experience Replay-based Deep Reinforcement Learning for Dialogue Management Optimisation

Dialogue policy is a crucial component in task-oriented Spoken Dialogue Systems (SDSs). As a decision function, it takes the current dialogue state as input and generates appropriate system’s response. In this paper, we explore the reinforcement learning approaches to solve this problem in an Indic language scenario. Recently, Deep Reinforcement Learning (DRL) has been used to optimise the dialogue policy. However, many DRL approaches are not sample-efficient. Hence, particular attention is given to actor-critic methods based on off-policy reinforcement learning that utilise the Experience Replay (ER) technique for reducing the bias and variance to achieve high sample efficiency. ER based actor-critic methods, such as Advantage Actor-Critic Experience Replay (A2CER) are proven to deliver competitive results in gaming environments that are fully observable and have a very small action-set. While, in SDSs, the states are not fully observable and often have to deal with the large action space. Describing the limitations of traditional methods, i.e., value-based and policy-based methods, such as high variance, low sample-efficiency, and often converging to local optima, we firstly explore the use of A2CER in dialogue policy learning. It is shown to beat the current state-of-the-art deep learning methods for SDS. Secondly, to handle the issues of early-stage performance, we utilise a demonstration corpus to pre-train the models prior to on-line policy learning. We thus experiment with the A2CER on a larger action space and find it significantly faster than the current state-of-the-art. Combining both approaches, we present a novel DRL based dialogue policy optimisation method, A2CER and its effectiveness for a task-oriented SDS in the Indic language.
Energy-aware and carbon-efficient VM placement optimization in cloud datacenters using evolutionary computing methods

One of the most critical concerns of cloud service providers is balancing renewable and fossil energy consumption. On the other hand, the policy of organizations and governments is to reduce energy consumption and greenhouse gas emissions in cloud data centers. Recently, a lot of research has been conducted to optimize the virtual machine placement on physical machines to minimize energy consumption. Many previous studies have not considered the deadline and scheduling of Internet of Things (IoT) tasks. Therefore, the previous modelings are mainly not well-suited to the IoT environments where requests are time-constraint. Unfortunately, both the sub-problems of energy consumption minimization and scheduling fall into NP-hard issues. This study proposes a multi-objective virtual machine placement to jointly minimize energy costs and scheduling. After presenting a modified Memetic algorithm, we compare its performance with baseline methods and state-of-the-art ones. The simulation results on the CloudSim platform show that the proposed method can reduce energy costs, carbon footprints, service-level agreement violations, and the total response time of IoT requests.
Quantum Computing: Fundamentals, Implementations and Applications

Quantum Computing is a technology, which promises to overcome the drawbacks of conventional CMOS technology for high density and high performance applications. Its potential to revolutionize today&#x0027;s computing world is attracting more and more researchers towards this field. However, due to the involvement of quantum properties, many beginners find it difficult to follow the field. Therefore, in this research note an effort has been made to introduce the various aspects of quantum computing to researchers, quantum engineers and scientists. The historical background and basic concepts necessary to understand quantum computation and information processing have been introduced in a lucid manner. Various physical implementations and potential application areas of quantum computation have also been discussed in this paper. Recent developments in each realization, in the context of the DiVincenzo criteria, including ion traps based quantum computing, superconducting quantum computing, nuclear magnetic resonance (NMR) quantum computing, spintronics and semiconductor based quantum computing have been discussed.
The ACM Multimedia 2022 Computational Paralinguistics Challenge

The ACM Multimedia 2022 Computational Paralinguistics Challenge addresses four different problems for the first time in a research competition under well-defined conditions: In the Vocalisations and Stuttering Sub-Challenges, a classification on human non-verbal vocalisations and speech has to be made; the Activity Sub-Challenge aims at beyond-audio human activity recognition from smartwatch sensor data; and in the Mosquitoes Sub-Challenge, mosquitoes need to be detected. We describe the Sub-Challenges, baseline feature extraction, and classifiers based on the 'usual' ComParE and BoAW features, the auDeep toolkit, and deep feature extraction from pre-trained CNNs using the DeepSpectrum toolkit; in addition, we add end-to-end sequential modelling, and a log-mel-128-BNN.
Centralized Feature Pyramid for Object Detection

The visual feature pyramid has shown its superiority in both effectiveness and efficiency in a variety of applications. However, current methods overly focus on inter-layer feature interactions while disregarding the importance of intra-layer feature regulation. Despite some attempts to learn a compact intra-layer feature representation with the use of attention mechanisms or vision transformers, they overlook the crucial corner regions that are essential for dense prediction tasks. To address this problem, we propose a Centralized Feature Pyramid (CFP) network for object detection, which is based on a globally explicit centralized feature regulation. Specifically, we first propose a spatial explicit visual center scheme, where a lightweight MLP is used to capture the globally long-range dependencies, and a parallel learnable visual center mechanism is used to capture the local corner regions of the input images. Based on this, we then propose a globally centralized regulation for the commonly-used feature pyramid in a top-down fashion, where the explicit visual center information obtained from the deepest intra-layer feature is used to regulate frontal shallow features. Compared to the existing feature pyramids, CFP not only has the ability to capture the global long-range dependencies but also efficiently obtain an all-round yet discriminative feature representation. Experimental results on the challenging MS-COCO validate that our proposed CFP can achieve consistent performance gains on the state-of-the-art YOLOv5 and YOLOX object detection baselines.
A Hybrid Visualization Model for Knowledge Mapping: Scientometrics, SAOM, and SAO

Predicting the crowd flow in various areas of the city is of strategic importance for traffic control and public safety. In recent years, crowd flow prediction based on spatio-temporal data are gaining more and more attention. In order to better understand the current status of spatio-temporal crowd flow prediction research and global cooperation, we use scientometric methods, social network analysis, and Stochastic Actor-oriented Model (SAOM) to visualize and analyze the source journals, hotspot co-occurrence networks, and national cooperation networks based on the relevant literature included in the Web of Science database, so as to explore the current status and characteristics of related academic research. In addition, this paper constructs a technical framework based on Subject–Action–Object (SAO) structural information, and draws a technical roadmap containing five levels: data, technology, influence factors, objectives, and applications. The visual mapping of the evolutionary path of technology topics based on SAO structure information can assist in analyzing the evolutionary path of technology topics and their development trends. This study contributes to the existing knowledge system of spatio-temporal crowd flow prediction by proposing a new, integrated, and holistic knowledge map.
Evaluation of children cursive handwritten words for e-education

As part of an innovative e-education project, a digital workbook is being developed to help teach handwriting at school for children aged three to seven. The main objective of this project is to offer an advanced digital writing experience at school by using pen-based tablets. In this paper, an automatic qualitative analysis process of cursive handwriting words is presented. This approach is original because the goal is not to recognise the word that was handwritten by children (it is an explicit instruction) but to design a precise evaluation of the quality of his handwriting production to give them a real-time feedback. The presented method is based on a specific explicit elastic letter spotting segmentation able to deal with the imprecision of the handwriting of young children. This approach is suited to automatically and precisely highlight the difficulties encountered by children (adding or missing letters, incorrect shapes...). The validation of the proposed approach has been done on a dataset collected in French preschools and primary schools from 231 children. Beyond quantitative results, this paper reports the very positive impact of using this digital workbook that allows children to work independently with online and real-time feedbacks.
Online multi-object tracking combining optical flow and compressive tracking in Markov decision process

Effective features are important for visual tracking, and efficiency also needs to be considered especially for multi-object tracking. Thanks to the simplicity, we think compressive sensing features are suitable for this task. In this paper, we use compressive sensing features to improve the Markov decision process (MDP) multi-object tracking framework. First, we design a single object tracker which uses the compressive tracking to correct the optical flow tracking and apply this tracker into the MDP tracking framework. The appearance model constructed during compressive tracking also helps for data association. In order to validate our method, we firstly test the designed single object tracker with a common dataset. Then, we test our multi-object tracking method for vehicle tracking. Finally, we analyze and test our approach in the multi-object tracking (MOT) benchmark for pedestrian tracking. The results show our approach performs superiorly against several state-of-the-art online multi-object trackers.
Modeling Interaction Structure for Robot Imitation Learning of Human Social Behavior

This study presents a learning-by-imitation technique that learns social robot interaction behaviors from natural human- human interaction data and requires minimum input from a designer. To solve the problem of responding to ambiguous human actions, a novel topic clustering algorithm based on action cooccurrence frequencies is introduced. The system learns human-readable rules that dictate which action the robot should take, based on the most recent human action and the current estimated topic of conversation. The technique is demonstrated in a scenario where the robot learns to play the role of a travel agent. The proposed technique outperformed several baseline techniques in qualitative and quantitative evaluations. It responded more accurately to ambiguous questions and participants found it was easier to understand, provided more information, and required less effort to interact with.
Signal correction by detection of scanning position in a white-light interferometer for exact surface profile measurement

In order to perform an exact surface profile measurement with a white-light scanning interferometer (WLSI), an actual optical path difference (OPD) changing with time is detected with an additional interferometer in which the light source of the WLSI and an optical band-pass filter are used. This interferometer is simply equipped in the WLSI and does not negatively influence the WLSI. The real OPD is easily calculated from an interference signal with the same signal processing as that in the WLSI. The interference signal of the WLSI is corrected with the real OPD values or the real scanning position values. The corrected interference signal with a constant sampling interval is obtained with an interpolation method. With this correction method, a surface profile with a step shape of 3-μm height is measured accurately with an error less than 2 nm.
Finding a Nash equilibrium is no easier than breaking Fiat-Shamir

The Fiat-Shamir heuristic transforms a public-coin interactive proof into a non-interactive argument, by replacing the verifier with a cryptographic hash function that is applied to the protocol's transcript. Constructing hash functions for which this transformation is sound is a central and long-standing open question in cryptography.
Your 2 is My 1, Your 3 is My 9: Handling Arbitrary Miscalibrations in Ratings

A key step in building multi-agent systems is to gather data reported by the agents (people), in either cardinal (numeric ratings) or ordinal (rankings) form. Cardinal scores collected from people are well known to suffer from miscalibrations. A popular approach to address this issue is to assume simplistic models of miscalibration (such as linear biases) to de-bias the scores. This approach, however, often fares poorly because people's miscalibrations are typically far more complex and not well understood. It is widely believed that in the absence of simplifying assumptions on the miscalibration, the only useful information in practice from the cardinal scores is the induced ranking. In this paper we address the fundamental question of whether this widespread folklore belief is actually true. We consider cardinal scores with arbitrary (or even adversarially chosen) miscalibrations that is only required to be consistent with the induced ranking. We design rating-based estimators and prove that despite making no assumptions on the ratings, they strictly and uniformly outperform all possible estimators that rely on only the ranking. These estimators can be used as a plug-in to show the superiority of cardinal scores over ordinal rankings for a variety of applications, and we provide examples for A/B testing and ranking as a proof of concept. Our results thus provide novel fundamental insights in the eternal debate between cardinal and ordinal data.
Positional Normalization

A widely deployed method for reducing the training time of deep neural networks is to normalize activations at each layer. Although various normalization schemes have been proposed, they all follow a common theme: normalize across spatial dimensions and discard the extracted statistics. In this paper, we propose a novel normalization method that deviates from this theme. Our approach, which we refer to as Positional Normalization (PONO), normalizes exclusively across channels, which allows us to capture structural information of the input image in the first and second moments. Instead of disregarding this information, we inject it into later layers to preserve or transfer structural information in generative networks. We show that PONO significantly improves the performance of deep networks across a wide range of model architectures and image generation tasks.
Recognizing Conflict Opinions in Aspect-level Sentiment Classification with Dual Attention Networks

Aspect-level sentiment classification, which is a fine-grained sentiment analysis task, has received lots of attention these years.There is a phenomenon that people express both positive and negative sentiments towards an aspect at the same time.Such opinions with conflicting sentiments, however, are ignored by existing studies, which design models based on the absence of them.We argue that the exclusion of conflict opinions is problematic, for the reason that it represents an important style of human thinking -dialectic thinking.If a realworld sentiment classification system ignores the existence of conflict opinions when it is designed, it will incorrectly mixed conflict opinions into other sentiment polarity categories in action.Existing models have problems when recognizing conflicting opinions, such as data sparsity.In this paper, we propose a multilabel classification model with dual attention mechanism to address these problems.
Realtime Indoor Workout Analysis Using Machine Learning &amp; Computer Vision

These days there are thousands of workout videos available on the internet. Samsung Health <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> [1] provides a dedicated section called programs containing short workout videos for various exercises. The goal is to assist people perform these workouts independently on their own. A common observation is that even people who visit gym regularly find it difficult to perform all steps (body pose alignments) in a workout accurately. Continuously doing an exercise incorrectly may eventually cause severe long term injuries. To help solve this problem and provide assistance in form of a visual feedback while performing a workout, we propose a system to analyze user's body posture during a workout and compare it to a professional's reference workout. We represent human body as a collection of limbs and analyze angle between limb pairs to detect errors and provide corrective action to the user. Our system builds on the latest advancements using deep learning for human body pose estimation. We use techniques for time series data alignment like DTW [2] (Dynamic Time Warping) along with optical flow tracking to synchronize user/reference videos. We are able to detect and locate errors in user's activity (pose) very effectively based on some threshold deviation between the limb angles. The system in future can be extended to be used by physicians to monitor patient's recovery following an injury.