Reviewerly/exHarmony huggingface dataset:
Implementation of Internet of Things-Based Sentiment Analysis for Farming System

The Internet is slowly shaping to be the primary information source that fulfils all the needs of a person. Whenever someone plans to buy a product, they tend to consult with the reviews online to get a clear idea of the product in terms of its various aspects. The problem is that the information available about a single product is so much in volume that the users not be able to extract the information they require from this massive amount of data. The paper proposes a system that generates a temporal aspect based text summary of user opinions that are collected from different sources across the Internet with their time-stamp. These comments are broken into sentences and sub-sentences after predefined based classification. Then, Sentiment analysis is performed. The time relationship is taken into account, and the causal relationship is identified at the deflection points or the time frames during which there is a significant opinion change. The major advantage of this system is that the changes in user opinions with time can be traced and the cause of this sentiment change can be found out in addition to offering customers a quick, convenient and easy way to consume information about a product to help them decide whether or not to purchase it. It also helps enterprises to get relevant insights related to their products based on the customer reviews online.
iMER: Iterative process of entity relationship and business process model extraction from the requirements

Extracting conceptual models, e.g., entity relationship model or Business Process model, from software requirement document is an essential task in the software development life cycle. Business process model presents a clear picture of required system's functionality. Operations in business process model together with the data entity consumed, help the software developers to understand the database design and operations to be implemented. Researchers have been aiming at automatic extraction of these artefacts from the requirement document. In this paper, we present an automated approach to extract the entity relationship and business process models from requirements, which are possibly in different formats such as general requirements, use case specification and user stories. Our approach is based on the efficient natural language processing techniques. It is an iterative approach of Models Extraction from the Requirements (iMER). iMER has multiple iterations where each iteration is to address a sub-problem. In the first iteration, iMER extracts the data entities and attributes. Second iteration is to find the relationships between data entities, while extracting cardinalities is in the third step. Business process model is generated in the fourth iteration, containing the external (actors') and internal (system's) operations. To evaluate the performance and accuracy of iMER, experiments are conducted on various formats of the requirement documents. Additionally, we have also evaluated our approaches using the requirement documents which been modified by shuffling the sentences and by merging with other requirements. Comparative study is also performed. The preliminary results show a noticeable improvement. The iMER is an efficient automated iterative approach that is able to extract the conceptual models from the various formats of requirements.
Towards Increasing Naturalness and Flexibility in Human-Robot Dialogue Systems

The chapter discusses some approaches to increasing the naturalness and flexibility of human-robot interaction, with examples from the WikiTalk dialogue system. WikiTalk enables robots to talk fluently about thousands of topics using Wikipedia-based talking. However, there are three challenging areas that need to be addressed to make the system more natural: speech interaction, face recognition, interaction history. We address these challenges and describe more context-aware approaches taking the individual partner into account when generating responses. Finally, we discuss the need for a Wikipedia-based listening capability to enable robots to follow the changing topics in human conversation. This would allow robots to join in the conversation using Wikipedia-based talking to make new topically relevant dialogue contributions.
Reliable JPEG Forensics via Model Uncertainty

Many methods in image forensics are sensitive to varying amounts of JPEG compression. To mitigate this issue, it is either possible to a) build detectors that better generalize to unknown JPEG settings, or to b) train multiple detectors, where each is specialized to a narrow range of JPEG qualities. While the first approach is currently an open challenge, the second approach may silently fail, even for only slight mismatches in training and testing distributions. To alleviate this challenge, we propose a forensic detector that is able to express uncertainty in its predictions. This allows detecting test samples for which the training distribution is not representative. More specifically, we propose Bayesian logistic regression as an instance of an infinite ensemble of classifiers. The ensemble agrees in its predictions from test samples similar to the training data but its predictions diverge for unknown test samples. The applicability of the proposed method is evaluated on the task of detecting JPEG double compression. The detector achieves high performance on two goals simultaneously: It accurately detects double-JPEG compression, and it accurately indicates when the test data is not covered by the training data. We assert that the proposed method can assist a forensic analyst in assessing detector reliability and in anticipating failure cases for specific inputs.
<i>ProSAS</i>: Proactive Security Auditing System for Clouds

The multi-tenancy in a cloud along with its dynamic and self-service nature could cause severe security concerns, such as isolation breaches among cloud tenants. To mitigate such concerns and ensure the accountability and transparency of the cloud providers towards their tenants, verifying cloud states against a list of security policies, a.k.a. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">security auditing</i> , is a promising solution. However, the existing security auditing solutions for clouds suffer from several limitations. First, the traditional auditing approach, which is retroactive in nature, can only detect violations after the fact and hence, often becomes ineffective while dealing with the dynamic nature of a cloud. Second, the existing runtime approaches can cause significant delay in the response time while dealing with the sheer size of a cloud. Finally, the current proactive approaches typically rely on prior knowledge about future changes in a cloud and also require significant manual efforts, and thus become less practical for a dynamic environment like cloud. To address those limitations, we present a novel proactive security auditing system, namely, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ProSAS</i> , which can prevent violations to security policies at runtime with a practical response time, and yet does not require prior knowledge about future changes. More specifically, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ProSAS</i> first establishes its models (e.g., dependency relationships between cloud events, and critical events) through learning from historical data (e.g., logs); it then predicts future critical events which would likely follow a received event by leveraging the dependency relationships; afterwards, it proactively verifies the impacts of those future events, and prevents those events which can cause violations of security policies. ProSAS is integrated into OpenStack, a popular cloud management platform, and we provide a concrete guideline to port ProSAS to other popular cloud platforms, such as Google Cloud Platform, and Amazon EC2. Our experiment results using both real and synthetic data demonstrate the improvement of efficiency (i.e., reducing response time to 1,450 nanoseconds at best and 8.5 milliseconds on average for a large-scale cloud with 10,000 tenants) and level of automation (i.e., learning more than 20 new critical events spanning 100 days) in proactive security auditing by ProSAS.
A Blockchain-Based Authentication and Service Provision Scheme for Intemet of Things

The 6G network will provide integrated ubiquitous coverage of air, space, ground and sea, and support the full connection of a large number of heterogeneous terminals. This kind of open network and access brings great security risks to the communication network. Therefore, security issues have become an important factor in the widespread promotion of Internet of Things (IoT) applications. This paper proposes a secure endogenous wireless access network architecture based on blockchain, including communication plane and blockchain plane, the later plane includes blockchain networks and their applications. Based on this architecture, a unified identity authentication framework is proposed, which is based on blockchain technology to manage the identity certificates of entities in the network and supervise the authority. Based on this framework, a distributed identity authentication scheme, a service discovery and provision scheme in the IoT combined with smart contracts are given. Numerical simulation results demonstrate that the proposed schemes outperform existing algorithm in terms of communication overhead and time consumption.
AutoBayes: Automated Bayesian Graph Exploration for Nuisance- Robust Inference

Learning data representations that capture task-related features, but are invariant to nuisance variations remains a key challenge in machine learning. We introduce an automated Bayesian inference framework, called AutoBayes, that explores different graphical models linking classifier, encoder, decoder, estimator and adversarial network blocks to optimize nuisance-invariant machine learning pipelines. Auto Bayes also enables learning disentangled representations, where the latent variable is split into multiple pieces to impose various relationships with the nuisance variation and task labels. We benchmark the framework on several public datasets, and provide analysis of its capability for subject-transfer learning with/without variational modeling and adversarial training. We demonstrate a significant performance improvement with ensemble learning across explored graphical models.
Deep Learning-Based Incorporation of Planar Constraints for Robust Stereo Depth Estimation in Autonomous Vehicle Applications

In autonomous vehicles, depth information for the environment surrounding the vehicle is commonly extracted using time-of-flight (ToF) sensors such as LiDARs and RADARs. Those sensors have some limitations that may potentially degrade the quality and utility of the depth information to a substantial extent. An alternative solution is depth estimation from stereo pairs. However, stereo matching and depth estimation often fails at ill-posed regions including areas with repetitive patterns or textureless surfaces which are commonly found on planar surfaces. This paper focuses on designing an efficient framework for stereo depth estimation, using deep learning technique, that is robust against the mentioned ill-posed regions. With the observation that disparities of all pixels belonging to planar areas (scene plane) viewed by two rectified stereo images can be described using affine transformations, our proposed method predicts pixel-wise affine transformation parameters based on the depth information encoded in the aggregated cost volume. We also introduce a propagation term which enforces all pixels belonging to the same scene plane to be transformed using the same parameters. Disparity can then be computed by multiplying the predicted affine parameters with the corresponding pixel locations. The proposed method was evaluated on several benchmark datasets. We are able to obtain competitive results and at the same time reducing the processing time of common convolution neural network (CNN) in stereo matching by 50%. Analysis of the findings shows that our method can produce reliable results at the ill-posed regions which are challenging to the current state-of-the-arts methods.
Smart Rings vs. Smartwatches: Utilizing Motion Sensors for Gesture Recognition

Since electronic components are constantly getting smaller and smaller, sensors and logic boards can be fitted into smaller enclosures. This miniaturization lead to the development of smart rings containing motion sensors. These sensors of smart rings can be used to recognize hand/finger gestures enabling natural interaction. Unlike vision-based systems, wearable systems do not require a special infrastructure to operate in. Smart rings are highly mobile and are able to communicate wirelessly with various devices. They could potentially be used as a touchless user interface for countless applications, possibly leading to new developments in many areas of computer science and human–computer interaction. Specifically, the accelerometer and gyroscope sensors of a custom-built smart ring and of a smartwatch are used to train multiple machine learning models. The accuracy of the models is compared to evaluate whether smart rings or smartwatches are better suited for gesture recognition tasks. All the real-time data processing to predict 12 different gesture classes is done on a smartphone, which communicates wirelessly with the smart ring and the smartwatch. The system achieves accuracy scores of up to 98.8%, utilizing different machine learning models. Each machine learning model is trained with multiple different feature vectors in order to find optimal features for the gesture recognition task. A minimum accuracy threshold of 92% was derived from related research, to prove that the proposed system is able to compete with state-of-the-art solutions.
Mesh Manifold Based Riemannian Motion Planning for Omnidirectional Micro Aerial Vehicles

This letter presents a novel on-line path planning method that enables aerial robots to interact with surfaces. We present a solution to the problem of finding trajectories that drive a robot towards a surface and move along it. Triangular meshes are used as a surface map representation that is free of fixed discretization and allows for very large workspaces. We propose to leverage planar parametrization methods to obtain a lower-dimensional topologically equivalent representation of the original surface. Furthermore, we interpret the original surface and its lower-dimensional representation as manifold approximations that allow the use of Riemannian Motion Policies (RMPs), resulting in an efficient, versatile, and elegant motion generation framework. We compare against several Rapidly-exploring Random Tree (RRT) planners, a customized CHOMP variant, and the discrete geodesic algorithm. Using extensive simulations on real-world data we show that the proposed planner can reliably plan high-quality near-optimal trajectories at minimal computational cost. The accompanying multimedia attachment demonstrates feasibility on a real OMAV. The obtained paths show less than 10% deviation from the theoretical optimum while facilitating reactive re-planning at kHz refresh rates, enabling flying robots to perform motion planning for interaction with complex surfaces.
Unsupervised Domain Adaptation Network With Category-Centric Prototype Aligner for Biomedical Image Segmentation

With the widespread success of deep learning in biomedical image segmentation, domain shift becomes a critical and challenging problem, as the gap between two domains can severely affect model performance when deployed to unseen data with heterogeneous features.To alleviate this problem, we present a novel unsupervised domain adaptation network, for generalizing models learned from the labeled source domain to the unlabeled target domain for cross-modality biomedical image segmentation.Specifically, our approach consists of two key modules, a conditional domain discriminator (CDD) and a category-centric prototype aligner (CCPA).The CDD, extended from conditional domain adversarial networks in classifier tasks, is effective and robust in handling complex cross-modality biomedical images.The CCPA, improved from the graph-induced prototype alignment mechanism in cross-domain object detection, can exploit precise instance-level features through an elaborate prototype representation.In addition, it can address the negative effect of class imbalance via entropy-based loss.Extensive experiments on a public benchmark for the cardiac substructure segmentation task demonstrate that our method significantly improves performance on the target domain.
Contrastive learning meets transfer learning: a case study in medical image analysis

Annotated medical images are typically more rare than labeled natural images, since they are limited by domain knowledge and privacy constraints. Recent advances in transfer and contrastive learning have provided effective solutions to tackle such issues from different perspectives. The state-of-the-art transfer learning (e.g., Big Transfer (BiT)) and contrastive learning (e.g., Simple Siamese Contrastive Learning (SimSiam)) approaches have been investigated independently, without considering the complementary nature of such techniques. It would be appealing to accelerate contrastive learning with transfer learning, given that slow convergence speed is a critical limitation of modern contrastive learning approaches. In this paper, we investigate the feasibility of aligning BiT with SimSiam. From empirical analyses, different normalization techniques (Group Norm in BiT vs. Batch Norm in SimSiam) is a key hurdle of adapting BiT to SimSiam. When combining BiT with SimSiam, we evaluated the performance of using BiT, SimSiam, and BiT+SimSiam on CIFAR-10 and HAM10000 datasets. The results suggest that the BiT models accelerate the convergence speed of SimSiam. When used together, the model gives superior performance over both of its counterparts. We hope this study will motivate researchers to revisit the task of aggregating big pretrained models with contrastive learning models for image analysis.
Automatic Virtual 3D City Generation for Synthetic Data Collection

Computer vision has achieved superior results with the rapid development of new techniques in deep neural networks. Object detection in the wild is a core task in computer vision, and already has many successful applications in the real world. However, deep neural networks for object detection usually consist of hundreds, and sometimes even thousands, of layers. Training such networks is challenging, and training data has a fundamental impact on model performance. Because data collection and annotation are expensive and labor-intensive, lots of data augmentation methods have been proposed to generate synthetic data for neural network training. Most of those methods focus on manipulating 2D images. In contrast to that, in this paper, we leverage the realistic visual effects of 3D environments and propose a new way of generating synthetic data for computer vision tasks related to city scenes. Specifically, we describe a pipeline that can generate a 3D city model from an input of a 2D image that portrays the layout design of a city. This pipeline also takes optional parameters to further customize the output 3D city model. Using our pipeline, a virtual 3D city model with high-quality textures can be generated within seconds, and the output is an object ready to render. The model generated will assist people with limited 3D development knowledge to create high quality city scenes for different needs. As examples, we show the use of generated 3D city models as the synthetic data source for a scene text detection task and a traffic sign detection task. Both qualitative and quantitative results show that the generated virtual city is a good match to real-world data and potentially can benefit other computer vision tasks with similar contexts.
Co-creation as a Facilitator for Co-regulation in Child-Robot Interaction

While interacting with a social robot, children have a need to express themselves and have their expressions acknowledged by the robot. A need that is often unaddressed by the robot, due to its limitations in understanding the expressions of children. To keep the child-robot interaction manageable the robot takes control, undermining children's ability to co-regulate the interaction. Co-regulation is important for having a fulfilling social interaction. We developed a co-creation activity that aims to facilitate more co-regulation. Children are enabled to create sound effects, gestures, and light shows for the robot to use during their conversation. Results from a user study (N = 59 school children, 7-11 y.o.) showed that the co-creation activity successfully facilitated co-regulation by improving children's agency. Co-creation furthermore increases children's acceptance of the robot.
NRA-Net—Neg-Region Attention Network for Salient Object Detection with Gaze Tracking

In this paper, we propose a detection method for salient objects whose eyes are focused on gaze tracking; this method does not require a device in a single image. A network was constructed using Neg-Region Attention (NRA), which predicts objects with a concentrated line of sight using deep learning techniques. The existing deep learning-based method has an autoencoder structure, which causes feature loss during the encoding process of compressing and extracting features from the image and the decoding process of expanding and restoring. As a result, a feature loss occurs in the area of the object from the detection results, or another area is detected as an object. The proposed method, that is, NRA, can be used for reducing feature loss and emphasizing object areas with encoders. After separating positive and negative regions using the exponential linear unit activation function, converted attention was performed for each region. The attention method provided without using the backbone network emphasized the object area and suppressed the background area. In the experimental results, the proposed method showed higher detection results than the conventional methods.
The role of Hofstede's cultural dimensions in the design of user interface: the case of Arabic

Abstract A user interface (UI) design that meets the preferences, differences, and needs of the group of users can potentially increase the usability of a system. Users, in general, feel more familiar with the context that reflects their cultural values and practices. The Arabic culture plays a significant role in how Arab users interact and communicate with technologies. The customs, artifacts, and traditions of the Arab world are different in nature from the Western cultures. Thus, it is essential to consider these differences when designing the UI prototype. This study investigated the role of certain cultural preferences in the design of UI for Arab users. A think-aloud approach and Hofstede's cultural dimensions were used on 23 Arab users to generate the necessary design guidelines for the UI of mobile health application. Then, 78 participants were recruited to evaluate the proposed UI design. The usability results showed high satisfaction among Arab users about the role of culture in the design of the UI. Findings from this study can be used by designers and developers to aid their design of UI for group-specific cultural preferences and values.