Reviewerly/exHarmony huggingface dataset:
A Hierarchical Deliberative-Reactive System Architecture for Task and Motion Planning in Partially Known Environments

We describe a task and motion planning architecture for highly dynamic systems that combines a domain-independent sampling-based deliberative planning algorithm with a global reactive planner. We leverage the recent development of a reactive, vector field planner that provides guarantees of reachability to large regions of the environment even in the face of unknown or unforeseen obstacles. The reachability guarantees can be formalized using contracts that allow a deliberative planner to reason purely in terms of those contracts and synthesize a plan by choosing a sequence of reactive behaviors and their target configurations, without evaluating specific motion plans between targets. This reduces both the search depth at which plans will be found, and the number of samples required to ensure a plan exists, while crucially preserving correctness guarantees. The result is reduced computational cost of synthesizing plans, and increased robustness of generated plans to actuator noise, model misspecification, or unknown obstacles. Simulation studies show that our hierarchical planning and execution architecture can solve complex navigation and rearrangement tasks, even when faced with narrow passageways or incomplete world information.
Enthusiastic Cyber Surveillance for Intimidation Comprehension on the Dark Web and the Deep Web

The authors offer an operational method for obtaining cyber intimidation intelligence from diverse social platforms on the internet, notably dark-web and deep-web sites with Tor, in this study. They concentrate their efforts on gathering information from hacker forums and marketplaces that sell harmful hacking-related items and services. They've established an operational mechanism for gathering information from these sites. This system now collects 400 high-quality cyber-intimidation notifications every week on average. These danger alerts provide details on newly generated malware and exploits that have yet to be used in a cyber-attack. This is a valuable service for cyber-surveillance. Various machine learning approaches are used to dramatically improve the system. They can recall 93% of items in marketplaces and 85% of comments on forums about harmful hacking with great precision using machine learning models. They do preliminary analysis on the data gathered, illustrating how it might be used to assist a security professional in improved intimidation analysis.
Crowdsourcing forensics: Creating a curated catalog of digital forensic artifacts

The increasing volume, variety, velocity, distribution, structural intricacy, and complexity of use of digital evidence can make it difficult for practitioners to find and understand the most forensically useful information (Casey E. Digital evidence and computer crime: Forensic science, computers, and the Internet. Academic Press; 2011. p. 31; Pollitt M. The hermeneutics of the hard drive: Using narratology, natural language processing, and knowledge management to improve the effectiveness of the digital forensic process [PhD dissertation]. University of Central Florida; 2011). Digital forensic practitioners currently search for information and solutions in an ad hoc manner, leading to results that are unstructured, unverified, and sometimes incomplete. As a result, certain digital evidence is being missed or misinterpreted. To mitigate risks of knowledge gaps, there is a pressing need for a systematic mechanism that practitioners can use to codify and combine their collective knowledge. This work presents the design and development of a solution that catalogs crowdsourced knowledge of digital forensic artifacts in a well-structured, easily searchable form to support efficient and automated extraction of pertinent information, improving availability and reliability of interpretation of artifacts (general acceptance). Technical implementation and artifact curation are discussed with illustrative examples and recommendations for future work.
Towards FAIR Explainable AI: a standardized ontology for mapping XAI solutions to use cases, explanations, and AI systems

Several useful taxonomies have been published that survey the eXplainable AI (XAI) research field. However, these taxonomies typically do not show the relation between XAI solutions and several use case aspects, such as the explanation goal or the task context. In order to better connect the field of XAI research with concrete use cases and user needs, we designed the ASCENT (Ai System use Case Explanation oNTology) framework, which is a new ontology and corresponding metadata standard with three complementary modules for different aspects of an XAI solution: one for aspects of AI systems, another for use case aspects, and yet another for explanation properties. The descriptions of XAI solutions in this framework include whether the XAI solution has a positive, negative, inconclusive or unresearched relation with use case elements. Descriptions in ASCENT thus emphasize the (user) evaluation of XAI solutions in order to support finding validated practices for application in industry, as well as being helpful for identifying research gaps. Describing XAI solutions according to the proposed common metadata standard is an important step towards the FAIR (Findable, Accessible, Interoperable, Reusable) usage of XAI solutions.
Design and Development of Granular Fuzzy Rule-Based Models for Knowledge Transfer

As an effective way for knowledge representation and processing, fuzzy rule-based models have been extensively studied and widely used in practice. In many circumstances, a very limited amount of data or insufficient computational resources make the construction of accurate models a genuine challenge. In this study, a granular augmentation of fuzzy rule-based models is proposed with intent to realize knowledge transfer in system modeling. This research mainly focuses on how to effectively exploit the existing fuzzy model, which has been constructed on extensive previously acquired experimental evidence and could be regarded as source of knowledge, in a new environment where only very limited experimental evidence is available. Rather than constructing a new model from scratch, knowledge conveyed by the existing model could be retained and reused in the target domain. The originality and innovation of this study lies in the adaption of the existing model to the new environment through optimal allocation of information granularity to produce granular fuzzy models, which are more abstract and general than the original numeric constructs. The granular fuzzy models yield results in a granular form whose quality is evaluated using the coverage and specificity criteria.
DeResolver: A Decentralized Conflict Resolution Framework with Autonomous Negotiation for Smart City Services

As various smart services are increasingly deployed in modern cities, many unexpected conflicts arise due to various physical world couplings. Existing solutions for conflict resolution often rely on centralized control to enforce predetermined and fixed priorities of different services, which is challenging due to the inconsistent and private objectives of the services. Also, the centralized solutions miss opportunities to more effectively resolve conflicts according to their spatiotemporal locality of the conflicts. To address this issue, we design a decentralized negotiation and conflict resolution framework named DeResolver, which allows services to resolve conflicts by communicating and negotiating with each other to reach a Pareto-optimal agreement autonomously and efficiently. Our design features a two-step self-supervised learning-based algorithm to predict acceptable proposals and their rankings of each opponent through the negotiation. Our design is evaluated with a smart city case study of three services: intelligent traffic light control, pedestrian service, and environmental control. In this case study, a data-driven evaluation is conducted using a large dataset consisting of the GPS locations of 246 surveillance cameras and an automatic traffic monitoring system with more than 3 million records per day to extract real-world vehicle routes. The evaluation results show that our solution achieves much more balanced results, i.e., only increasing the average waiting time of vehicles, the measurement metric of intelligent traffic light control service, by 6.8% while reducing the weighted sum of air pollutant emission, measured for environment control service, by 12.1%, and the pedestrian waiting time, the measurement metric of pedestrian service, by 33.1%, compared to priority-based solution.
Adaptive measurement-assignment marginal multi-target Bayes filter with logic-based track initiation

The measurement-assignment marginal multi-target Bayes (AMMTB) filter assigns individual observations to either clutter or an object by maximizing the joint likelihood function of the observation partition. A label is also introduced in this filter to provide the target trajectory. However, the AMMTB filter requires that the initial state and error covariance of a newborn target are known in advance. Therefore it fails to track the target appearing from an unknown location. To address this problem, an optimized AMMTB filter is given and an adaptive establishing method of the newborn trajectory is proposed. By applying the adaptive establishing method of the newborn trajectory to the optimized AMMTB filter, an adaptive AMMTB filter with logic-based track initiation is developed. This filter uses the logic-based track initiation technique to establish the newborn track and employs the least squares technique to form the initial mean vector and error covariance of the newborn track. The assignment-based technique is also used in this filter to associate the observations and targets. Simulation results show that the proposed adaptive AMMTB filter can efficiently detect and track the target with an unknown appearing location. It has a lower computational requirement and less cardinality error, OSPA error and OSPA(2) error than the existing adaptive filters.
4K Real Time Image to Image Translation Network With Transformers

CNNs have traditionally been applied in computer vision. Recently, applying Transformer networks, originally a technique in natural language processing, to computer vision has received much attention and produced superior results. However, Transformers and their derivation have drawbacks that the computational cost and memory usage increase rapidly with the image resolution. In this paper, we propose the Laplacian Pyramid Translation Transformer (LPTT) for image to image translation. The Laplacian Pyramid Translation Network, a previous study of this work, creates Laplacian pyramid of the input images and processes each component with CNNs. However, LPTT transforms the high-frequency components with CNNs and the low-frequency components with Axial Transformer blocks. LPTT can have Transformer&#x2019;s expressive power while reducing the computational cost and memory usage. LPTT significantly improves the quality of generated images and inference speed for high-resolution images over conventional methods. LPTT is the first network with a Transformer that can perform practical inference in real time on 4K resolution images. LPTT can also process 8K images in real time depending on the model conditions and the performance of the GPU. The ablation study in this paper suggests that even when processing high-resolution images, the performance is improved while maintaining the inference speed by computing the low-resolution component with a Transformer. LPTT improves PSNR value by 0.41 dB in MIT-Adobe FiveK dataset. The greater the number of layers in the Laplacian pyramid, the greater the improvement of LPTT over the Laplacian Pyramid Translation Network.
Fast Collision Checking for Dual-Arm Collaborative Robots Working in Close Proximity

We present a fast collision checking/avoidance algorithm for collaborative robot arms that work in close proximity. We formulate forward kinematics and separating distance function using DH convention and Taylor models (the tight enclosure of a function), and then compute their tight bounds for determining interference between robot arms. Our algorithm allows the collaborative robot arms to perform fine and dexterous tasks in close spatial proximity.
Combining Defences Against Data-Poisoning Based Backdoor Attacks on Neural Networks

Machine learning-based systems are increasingly used in critical applications such as medical diagnosis, automotive vehicles, or biometric authentication. Because of their importance, they can become the target of various attacks. In a data poisoning attack, the attacker carefully manipulates some input data, e.g. by superimposing a pattern, e.g. to insert a backdoor (a wrong association of the specific pattern to a desired target) into the model during the training phase. This can later be exploited to control the model behaviour during prediction, and attack its integrity, e.g. by identifying someone as the wrong user or not correctly identifying a traffic sign, thus causing road incidents. Poisoning of the training data is difficult to detect, as often, only small amounts of the data need to be manipulated to achieve a successful attack. The backdoors inserted into the model are hard to detect as well, as its unexpected behaviour manifests only when the specific backdoor trigger, which is only known to the attacker, is presented. Nonetheless, several defence mechanisms were proposed, and in the right setting, they can yield usable results; however, they still show shortcomings and insufficient effectiveness in several cases. In this work, we thus try to answer the extent to which combinations of these defences can improve their individual effectiveness. To this end, we first build successful attacks for two datasets and investigate factors influencing the attack success. Our evaluation shows a substantial impact of the type of neural network models and datasets on the effectiveness of the defence. We also show that the choice of the backdoor trigger has a big impact on the attack and its success. Finally, our evaluation shows that a combination of defences can improve existing defences in several cases.
A Brief History of Liberal Education in Ancient and Medieval Europe－Focusing on the Formation and Evolution of Liberal Arts

This paper examined the history of liberal education in ancient and medieval Europe, focusing on the formation and evolution of liberal arts using materials concerning the history of education, history of university, history of philosophy, and history of science. The elementary form of liberal education emerged at Greece in the fourth century BC. The philosophers’ tradition and the orators’ tradition made two approaches to liberal education. Greek scholarship was accepted in the form of encyclopedic publications in the Roman era, and &lt;i&gt;Disciplinarium libri novem&lt;/i&gt;, the first work which contained the idea of liberal arts, was written in the first century BC. In the early medieval ages, so called dark ages, secular scholarship maintained its existence through the medium of liberal arts. In the fifth century, liberal arts became actualized into the seven liberal arts through &lt;i&gt;De nuptiis Philologiae et Mercurii&lt;/i&gt;. Since the sixth century, the seven liberal arts were divided into trivium and quadrivium. In the Renaissance of the twelfth century, ancient writings were extensively translated including Aristotelian works, and the attempt to relate Greek philosophies and the seven liberal arts was tried. Circa 1250, some universities were set up in the major cities of Europe, and medieval universities were mostly organized with the faculties of arts, theology, law, and medicine. Since the late thirteenth century, the arts faculty extended the scope of liberal arts by adding three philosophies to the existing seven liberal arts: natural philosophy, metaphysics and ethics. Based on the above examination, this paper showed that the scope of liberal arts was not fixed but continuously changed, and that humanities and science were not separated in ancient and medieval Europe.
Monocular Weakly-Supervised Camera-Relative 3D Human Pose Estimation

This paper presents a 3D human pose estimation framework based on Deep Neural Networks (DNNs). It builds upon existing weakly-supervised methods that predict 2D-3D correspondences and improves them by introducing a geometrical-alignment pre-processing step and a 3D skeleton-refinement post-processing step. The geometrical-alignment pre-processing step is applied on the ground-truth 3D human poses and transforms them, in order to enable the utilized 2D-to-3D skeleton mapping DNN to be efficiently trained in a weakly-supervised manner. The 3D skeleton-refinement post-processing step acts on the DNN outputs and enables the proposed 3D human pose estimation framework to predict the camera-relative 3D human poses. Experiments on the widely used public showed that the proposed framework managed to predict camera-relative 3D human poses with increased accuracy.
Evaluation of Teachers' Digital Competencies in the Digitalization Process in Educational Organizations

The education process has started to go beyond the traditional patterns with topics such as new teaching environment designs blended with technology, digital native learners, and the prominent competencies of the 21st century. At the center of these concepts, which have begun to transform education, teachers are in an important position that ensures the continuation of education system. In education systems changed by technology, it is known that teachers' digital competencies will affect the success of learners. Teachers who can be digitally competent will be more active in managing the process more effectively in the education of digital native generations. As the difficulties faced by teachers who are not digitally competent in the changing teaching process with the effect of technology will bring new problems for the education process, it is important to determine the digital competence levels of the teachers and to create action plans and policies. In this chapter, it is emphasized what the digital competencies of teachers are and why teachers should have digital competencies. The education process has started to go beyond the traditional patterns with topics such as new
Brave and Kind Superheroes – Children's Reflections on the Design Protagonist Role

A design protagonist role for children has recently gained attention in political participatory design, with the aim of reframing the position of a child in relation to technology. However, the concept and the characteristics of the design protagonist remain quite vague still. We conducted a “My Superhero” design project with 13-14-year-old schoolchildren, giving them a voice, nurturing their agency, and inviting them to reflect on their design experiences and the notion of the design protagonist. The findings show the children spontaneously adopted different roles, also the design protagonist role, in the design process without us intentionally guiding them towards those. The findings reveal children's views on the characteristics of a design protagonist and their perceptions of their own characteristics and learning within the design process. Several factors mediating children's adoption of the protagonist role, including context, personal interest, empathy, prior joint experience, adults’ role, self-learning, and soft skills, were discovered.
EfficientNeXt: EfficientNet for Embedded Systems

Convolutional neural networks have come a long way since AlexNet. Each year the limits of the state-of-the-art are being pushed to new levels. EfficientNet pushed the performance metrics to a new high and EfficientNetV2 even more so. Even so, architectures for mobile applications can benefit from improved accuracy and reduced model footprint. The classic Inverted Residual block has been the foundation upon which most mobile networks seek to improve. EfficientNet architecture is built using the same Inverted Residual block. In this paper we experiment with Harmonious Bottlenecks in place of the Inverted Residuals to observe a reduction in the number of parameters and improvement in accuracy.
Graph Convolutional Networks and Attention-Based Outlier Detection

Outlier detection is a significant research direction in machine learning and has many applications in finance, network security, and other areas. Outlier detection of Euclidean datasets is a mainstream problem in outlier detection. Most detection methods often ignore the connection of its nodes. To collect the representation information of feature sets and node connections to improve the detection of outliers in Euclidean datasets Accuracy rate, we propose a novel Graph Convolutional and Attention-Based Outlier Detection (GCA).The GCA first converts the Euclidean structure data into directed graphs using locally sensitive hashing; then, by applying a Graph Convolutional Network, the data features and their connectivity graph are fed into the neural network; secondly, it fuses the extracted features and the features reconstructed by the attention mechanism; finally, calculating the outlier factors of the objects. Comparing eight state-of-art algorithms on ten real-world datasets shows that GCA achieves the highest Area Under ROC Curve (AUC) on datasets and also achieves equally good results in Accuracy (ACC) and False Alarm Rate (FAR). This study fills the gap of upgraded GCNs in detecting outliers to the best of our knowledge and provides a new way to convert Euclidean data to graphs.
An Upgraded Object Detection Model for Enhanced Perception and Decision Making in Autonomous Vehicles

In Internet of Vehicles (IoV), accurate object detection is one of the basic requirements for Autonomous Vehicles (AV s) and Vision-based Self Driver Assistance System (VSDAS). With restricted processing power of sensor nodes and network bandwidth, enhanced object detection with low energy consumption and acceptable rate of accuracy is still a major issue that makes VSDAS untrust-worthy and unsustainable. Considering this situation, in this paper, the authors propose an upgraded Object Detection model for simulating the enhanced perception and decision making in autonomous vehicles. In this paper, we evaluate the performance of three methods namely Histogram of Ori-ented Gradients (HOG), Local Binary Pattern (LBP), HAAR utilizing KITTI dataset. The results reveal that Haar exhibits higher detection rate than the other two methods. For the enhanced object detection, we utilize a frame similarity difference technique for filtering out the duplicate frames and generating key frames. Finally, an upgraded Haar-cascade classification algorithm is proposed for accurate and fast object detection. Our comprehensive experimental outcomes on the eminent publicly available dataset (KITTI) showed that our model not only significantly improves the performance of object detection however, also saves the energy consumption of edee devices.