Reviewerly/exHarmony huggingface dataset:
Multi-Robot Unknown Area Exploration Using Frontier Trees

This paper presents a novel approach for multi-robot unknown area exploration. Recently, the frontier tree data structure was used in single robot exploration to memorize frontiers, their positions, exploration state, and the map. This tree could be queried to decide on further exploration steps. In this paper, we take the concept further for multi-robot exploration by proposing a new abstraction called the ‘group,’ meant to share information through a common frontier tree, requisite operations at the group level, and a method to assign goals to multiple robots. A group is a set of robots, the union of whose explored regions forms a contiguous region (a single connected region in a topological sense). As a group has precisely one tree, the robots share a common state of the exploration task. We propose techniques to merge groups and their frontier trees once their maps overlap. Finally, we suggest a method to designate and assign exploration goals to the individual robots by choosing nodes from the frontier tree. The proposed approach outperforms seven state-of-the-art research works in simulation.
Advancing Data Fusion in Earth Sciences

Artificial intelligence (AI) algorithms have proven to be quite effective in Earth observation applications, often, when extensive amounts of representative training data are available. At large, processing large volumes of observation data can be challenging due to a myriad of reasons that include the cost of acquiring labeled samples, computing resources, identifying critical data features for model prototyping, standardization of model building, and deployment. Practical novel tools and approaches are emerging across different communities. In this paper, we discuss several such recent methods from machine learning and share lessons from advanced, scalable workflows that could impact the advancement of multimodal data fusion for Earth Science applications.
A Deterministic Least Squares Approach for Simultaneous Input and State Estimation

In this article, we consider a deterministic estimation problem to find the input and state of a linear dynamical system, which minimize a weighted integral squared error between the resulting output and the measured output. A completion of squares approach is used to find the unique optimum in terms of the solution of a Riccati differential equation. The optimal estimate is obtained from a two-stage procedure that is reminiscent of the Kalman filter. The first stage is an end-of-interval estimator for the finite horizon, which may be solved in real time as the horizon length increases. The second stage computes the unique optimum over a fixed horizon by a backward integration over the horizon. A related tracking problem is solved in an analogous manner. Making use of the solution to both the estimation and tracking problems, a constrained estimation problem is solved which shows that the Riccati equation solution has a least squares interpretation that is analogous to the meaning of the covariance matrix in stochastic filtering. This article shows that the estimation and tracking problems considered here include the Kalman filter and the linear quadratic regulator as special cases. The infinite horizon case is also considered for both the estimation and tracking problems. Stability and convergence conditions are provided and the optimal solutions are shown to take the form of left inverses of the original system.
Federated Unlearning: Guarantee the Right of Clients to Forget

The Right to be Forgotten gives a data owner the right to revoke their data from an entity storing it. In the context of federated learning, the Right to be Forgotten requires that, in addition to the data itself, any influence of the data on the FL model must disappear, a process we call “federated unlearning.” The most straightforward and legitimate way to implement federated unlearning is to remove the revoked data and retrain the FL model from scratch. Yet the computational and time overhead associated with fully retraining FL models can be prohibitively expensive. In this article, we take the first step to comprehensively investigate the way to settle the unlearning paradigm in the context of federated learning. First, we define the problem of efficient federated unlearning, including its challenges and goals, and we identify three common types of federated unlearning requests: class unlearning, client unlearning, and sample unlearning. Based on those challenges and goals, a general pipeline is proposed for federated unlearning for the above three types of requests. We revisit how the training data affects the final FL model performance and thereby empowers the proposed framework with the reverse stochastic gradient ascent (SGA) and elastic weight consolidation (EWC). Various experiments are conducted to verify effectiveness of the proposed method in both aspects of unlearning efficacy and efficiency. We believe the proposed method will perform as an essential component for future machine unlearning systems.
Semantic Enrichment of Taxonomy for BI Applications using Multifaceted data sources through NLP techniques

Taxonomies are crucial for executing Business Intelligence (BI) applications by preventing users from being overwhelmed with information. The business applications require knowledge of the key concepts and their organization to perform the classification of news articles. To ensure and maintain reliable information classification quality, it is crucial to keep the same definitions and organization of these concepts. This indicates the major significance of BI taxonomies in organizations. However, their development in business information systems follows an ad hoc process in most cases. Compared to many other domains, e.g. environmental and life sciences research, no mature and updated BI taxonomies are available in the literature. Existing studies cover BI taxonomies, but these are excessively generic and domain-specific. As a result, the BI domain suffers from many immature, incorrect, and incomplete notions of concepts. New BI-related concepts emerge rapidly, making it essential to include them in existing taxonomies during the enrichment process. The contribution of our research is the exploration of the possibilities of taxonomy enrichment using existing datasets. The expansion of the existing business taxonomy using multifaceted data sources to capture new concepts comprising 1) lexical datasets, 2) pre-trained word embeddings, 3) linked open data vocabulary, and 4) corpus-based relevant thematic extraction of features from news articles using Natural Language Processing (NLP) techniques. The highest semantic enrichment rate of a taxonomy got on a combination of these 4 methods. Eventually, enriched business taxonomy will contribute to the improved classification of news articles.
DICCh-D: Detecting IPv6-Based Covert Channels Using DNN

Stegomalware uses Information hiding techniques within Network protocols to leak out sensitive data and/or to exchange hidden commands between secretly communicating parties. Internet Protocol version 6 (IPv6) is a network layer protocol that is rapidly replacing Internet Protocol version 4 (IPv4). This has resulted in an increase in the availability of IPv6 packets over the internet, thereby making IPv6 a good candidate for the establishment of Network covert channels (NCCs). NCCs use either network flows or network packets to communicate in a hidden way such that no one notices the presence of secret information inside them. This secret information can be injected into network flows or network packets in the inter-packet delays and/or in any of the redundant, unused, reserved storage areas of the packets. Substantial research work has already been done in the area of covert channel detection in IPv4. Now, with IPv6 taking over the internet, the focus has shifted towards the detection of possible covert channels in IPv6. Thus this paper proposes DICCh-D, a model for the Detection of IPv6-based Covert Channels using DNN. For experimentation, the dataset was constructed using normal IPv6 packets obtained from CAIDA’s dataset and covert IPv6 packets created by running an IPv6-based covert packets generation tool. The proposed method outperforms CNN, LSTM and SVM in terms of accuracy with acceptable training and testing time. DICCh-D attained an accuracy of 99.59% and a recall value of 0.99, which is better than the existing technique used in literature.
Usability Evaluation Methods of Mobile Applications: A Systematic Literature Review

At this time, mobile devices have an important role because it provides changes for users. Not only making calls, but many features and applications are used to make work easier. Mobile application development brings new challenges to mobile devices, such as usability context when users use mobile applications. Usability is an important factor in mobile applications' role in making them easy to use and efficient. User Interface (UI) and User Experience (UX) has a critical role in software development. It has a function that is one of the keys to the effectiveness of using the software. This study aims to find popular usability evaluation on mobile applications with the help of a Systematic Literature Review (SLR). The usability evaluation process of mobile applications is carried out with a systematic literature review of 22 papers. The results show that 73% of the methods used are usability testing, 23% heuristic evaluations, and 4% are user satisfaction usability evaluations. To conduct usability evaluation on mobile applications, usability testing is carried out in five stages, involves participants, and has the characteristics of Effectiveness, Efficiency, and User Satisfaction. Meanwhile, in heuristic evaluation, experts must be involved in evaluating the user interfaces. This study proposes a mixed method of usability testing and heuristic evaluation in conducting usability evaluation on mobile applications. This method is expected to provide effective and objective usability issues from the perspective of users and experts in improving User Interface (UI) and User Experience (UX).
Forward Propagation, Backward Regression, and Pose Association for Hand Tracking in the Wild

We propose HandLer, a novel convolutional architecture that can jointly detect and track hands online in unconstrained videos. HandLer is based on Cascade-RCNN with additional three novel stages. The first stage is Forward Propagation, where the features from frame t −1 are propagated to frame t based on previously detected hands and their estimated motion. The second stage is the Detection and Backward Regression, which uses outputs from the forward propagation to detect hands for frame t and their relative offset in frame t −1. The third stage uses an off-the-shelf human pose method to link any fragmented hand tracklets. We train the forward propagation and backward regression and detection stages end-to-end together with the other Cascade-RCNN components. To train and evaluate HandLer, we also contribute YouTube-Hand, the first challenging large-scale dataset of unconstrained videos annotated with hand locations and their trajectories. Experiments on this dataset and other benchmarks show that HandLer outperforms the existing state-of-the-art tracking algorithms by a large margin. Code and data are available at https://vision.cs.stonybrook.edu/~mingzhen/handler/.
3D Gaze in Virtual Reality: Vergence, Calibration, Event Detection

Eye movement analysis in modern 3D rendering systems is reviewed and three new techniques are derived inspired by work developed in early Virtual Reality so-called 2.5D implementations, namely (a) gaze depth (i.e., vergence) estimation, (b) vergence calibration, and (c) real-time 3D event detection that considers eye- and head-coupling. The new 3D calibration shows excellent error reduction in terms of Mean Squared Error (MSE).
Deep Learning for Phishing Website Detection

Data security has become a major issue as digitalization across the world is rapidly increasing. Personal information of the user is extracted and exploited using one of the cyber-attack known as phishing. To detect phishing, and to avoid the user from visiting a non-legitimate website, a method is proposed in this paper. This method uses unique features of Uniform Resource Locator which can differentiate between legitimate and non-legitimate websites. To classify websites, Random forest and Support Vector Machines are two machine learning methods that are employed in this work. The internet offers a wealth of information that may be accessed. Because of the rapid development of technology, an unavoidable dependency on the Internet has emerged in all aspects of life. As the number of apps that run on the Internet continues to rise, there is also a rising worry of threats and a need to address problems that are connected to security. There are numerous web application threats in the Internet domain. These threats aim to either steal sensitive information from users, change the database of web servers, or undermine the credibility of a particular web application. There are many web application threats in the Internet domain. These threats include: One of the most significant dangers to information security is the character. Attacks that Deny Service to Others Session Hijacking attempts The CrossSite Scripting Language XSS Phishing and Buffer Overflow are Two Common Scams Phishing is one of these methods, and it involves tricking people into giving sensitive information such as usernames and passwords, credit card details, and sensitive bank information by way of email spoofing, instant messaging, or fake web sites whose look and feel give the appearance of a legitimate one. Examples of such information include usernames and passwords for online accounts, credit card details, and sensitive bank information. Important forms of phishing include deceptive phishing, malware-based phishing, host file poisoned content, and others. injection phishing through man-in-the-middle attacks, phishing via search engines, and phishing via social engineering Due to the fact that phishing may cause significant losses, it is necessary to implement new line processes in order to identify and stop phishing attacks. At this time, there are a number of techniques for detecting phishing. Some of these methods include blacklisting and whitelisting, visual resemblance, content-based approaches, and detecting phishing.
Developing an Application to Recommend Major Based on Personality Test using Waterfall Method

Education is one of the most fundamental aspects for humans. From human birth to growing up, we never stop learning. Especially now that we are in an era where technology develops rapidly. One of the most confusing decisions for someone to make is choosing a major when they enter college. That's why we created this app to help them determine what major suits them best based on their personality. We chose the personality factor since a person's personality has been shown to have a significant impact on their decision to pursue the appropriate major. we use Holland Code (RIASEC) to test the personality of students. This app was constructed using the waterfall method, which works best for us, helping us to get through the process of developing each part more efficiently and organized. We hope that by creating this application, more people will be able to select the correct major according to their abilities and obtain a more suited employment.
Graph Convolutional Networks for Semi-Supervised Image Segmentation

The problem of image segmentation is one of the most significant ones in computer vision. Recently, deep-learning methods have dominated state-of-the-art solutions that automatically or interactively divide an image into subregions. However, the limitation of deep-learning approaches is that they require a substantial amount of training data, which is costly to prepare. An alternative solution is semi-supervised image segmentation. It requires rough denotations to define constraints that are next generalized to precisely delimit relevant image regions without using train examples. Among semi-supervised strategies for image segmentation, the leading are graph-based techniques that define image segmentation as a result of pixel or region affinity graph partitioning. This paper revisits the problem of graph-based image segmentation. It approaches the problem as semi-supervised node classification in the SLIC superpixels region adjacency graph using a graph convolutional network (GCN). The performance of both spectral and spatial graph convolution operators is considered, represented by Chebyshev convolution operator and GraphSAGE respectively. The results of the proposed method applied to binary and multi-label segmentation are presented, numerically assessed, and analyzed. In its best variant, the proposed method scored the average DICE of 0.86 in the binary segmentation task and 0.79 in the multi-label segmentation task. Comparison with state-of-the-art graph-based methods, including Random Walker and GrabCut, shows that graph convolutional networks can represent an attractive alternative to the existing solutions to graph-based semi-supervised image segmentation.
Understanding Catastrophic Forgetting of Gated Linear Networks in Continual Learning

In this paper, we consider the recently proposed family of continual learning models, called Gated Linear Networks (GLNs), and study two crucial aspects impacting on the amount of catastrophic forgetting affecting gated linear networks, namely, data standardization and gating mechanism. Data standardization is particularly challenging in the online/continual learning setting because data from future tasks is not available beforehand. The results obtained using an online standardization method show a considerably higher amount of forgetting compared to an offline -static- standardization. Interestingly, with the latter standardization, we observe that GLNs show almost no forgetting on the considered benchmark datasets. Secondly, for an effective GLNs, it is essential to tailor the hyperparameters of the gating mechanism to the data distribution. In this paper, we propose a gating strategy based on a set of prototypes and the resulting Voronoi tessellation. The experimental assessment shows that the proposed approach is more robust to different data standardizations compared to the original one, based on a halfspace gating mechanism, and shows improved predictive performance.
Characterizing the memory capacity of transmon qubit reservoirs

Quantum Reservoir Computing (QRC) exploits the dynamics of quantum ensemble systems for machine learning. Numerical experiments show that quantum systems consisting of 5–7 qubits possess computational capabilities comparable to conventional recurrent neural networks of 100 to 500 nodes. Unlike traditional neural networks, we do not understand the guiding principles of reservoir design for high-performance information processing. Understanding the memory capacity of quantum reservoirs continues to be an open question. In this study, we focus on the task of characterizing the memory capacity of quantum reservoirs built using transmon devices provided by IBM. Our hybrid reservoir achieved a Normalized Mean Square Error (NMSE) of 6 × 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">–4</sup> for the NARMA (Non-linear Auto-regressive Moving Average) task which is comparable to recent benchmarks. The Memory Capacity characterization of a n-qubit reservoir showed a systematic variation with the complexity of the topology and exhibited a peak for the configuration with n – 1 self-loops. Such a peak provides a basis for selecting the optimal design for forecasting tasks.
Motion Planning by Search in Derivative Space and Convex Optimization with Enlarged Solution Space

To efficiently generate safe trajectories for an autonomous vehicle in dynamic environments, a layered motion planning method with decoupled path and speed planning is widely used. This paper studies speed planning, which mainly deals with dynamic obstacle avoidance given a planned path. The main challenges lie in the optimization in a non-convex space and the trade-off between safety, comfort, and efficiency. First, this work proposes to conduct a search in second-order derivative space for generating a comfort-optimal reference trajectory. Second, by combining abstraction and refinement, an algorithm is proposed to construct a convex feasible space for optimization. Finally, a piecewise Bézier polynomial optimization approach with trapezoidal corridors is presented, which theoretically guarantees safety and significantly enlarges the solution space compared with the existing rectangular corridors-based approach. We validate the efficiency and effectiveness of the proposed approach in simulations.
Text-Based Temporal Localization of Novel Events

Recent works on text-based localization of moments have shown high accuracy on several benchmark datasets. However, these approaches are trained and evaluated relying on the assumption that the localization system, during testing, will only encounter events that are available in the training set (i.e., seen events). As a result, these models are optimized for a fixed set of seen events and they are unlikely to generalize to the practical requirement of localizing a wider range of events, some of which may be unseen. Moreover, acquiring videos and text comprising all possible scenarios for training is not practical. In this regard, this paper introduces and tackles the problem of text-based temporal localization of novel/unseen events. Our goal is to temporally localize video moments based on text queries, where both the video moments and text queries are not observed/available during training. Towards solving this problem, we formulate the inference task of text-based localization of moments as a relational prediction problem, hypothesizing a conceptual relation between semantically relevant moments, e.g., a temporally relevant moment corresponding to an unseen text query and a moment corresponding to a seen text query may contain shared concepts. The likelihood of a candidate moment to be the correct one based on an unseen text query will depend on its relevance to the moment corresponding to the semantically most relevant seen query. Empirical results on two text-based moment localization datasets show that our proposed approach can reach up to 15% absolute improvement in performance compared to existing localization approaches.
Attentive Task Interaction Network for Multi-Task Learning

Multitask learning (MTL) has recently gained a lot of popularity as a learning paradigm that can lead to improved per-task performance while also using fewer per-task model parameters compared to single task learning. One of the biggest challenges regarding MTL networks involves how to share features across tasks. To address this challenge, we propose the Attentive Task Interaction Network (ATI-Net). ATI-Net employs knowledge distillation of the latent features for each task, then combines the feature maps to provide improved contextualized information to the decoder. This novel approach to introducing knowledge distillation into an attention based multitask network outperforms state of the art MTL baselines such as the standalone MTAN and PAD-Net, with roughly the same number of model parameters.