Reviewerly/exHarmony huggingface dataset:
A Comprehensive Assessment Framework for Evaluating Adaptive Security and Privacy Solutions for IoT e-Health Applications

There exist numerous adaptive security and privacy (S&P) solutions to manage potential threats at runtime.However, there is a lack of a comprehensive assessment framework that can holistically validate their effectiveness.Existing Adaptive S&P assessment efforts either focus on privacy or security in general, or are focused on specific adaptive S&P attributes, e.g.authentication, and, at certain times, disregards the architecture in which they should be comprehended.In this paper, we propose a holistic assessment framework for evaluating adaptive S&P solutions for IoT e-health.The framework utilizes a proposed classification of essential attributes necessary to be recognized, evaluated, and incorporated for the effectiveness of adaptive S&P solutions for the most common IoT architectures, fog-based and cloud/server-based architectures.As opposed to the existing related work, the classification comprehensively covers all the major classes of essential attributes, such as S&P objectives, contextual factors, adaptation action aptitude, and the system's self-* properties.Using this classification, the framework assists to evaluate the existence of a given attribute with respect to the adaptation process and in the context of the architectural layers.Therefore, it stresses the importance of where an essential attribute should be realized in the adaptation phases and in the architecture for an adaptive S&P solution to be effective.We have also presented a comparison of the proposed assessment framework with existing related frameworks and have shown that it exhibits substantial completeness over the existing works to assess the feasibility of a given adaptive S&P solution.
Design of Intelligent Classification Garbage Can Based on STM32 Control

Intelligent classification garbage can is widely used, and society and enterprises have a demand for intelligent garbage can. In view of this demand, the system adopts modular design, mainly including the mechanical design and electrical system design of the training module. The mechanical design is mainly the design of intelligent garbage can three wheel chassis, barrel body and cover. The electrical system design includes steering gear, speech recognition module, human body infrared sensor module and sensor. Through the combination of each module, the intelligent sorting garbage can is designed. Through the experiment, the module has intelligent, tracking, classification assembly and automatic classification. It can bring convenience to people in the room through the platform adjustment.
VPN: Verification of Poisoning in Neural Networks

Neural networks are successfully used in a variety of applications, many of them having safety and security concerns. As a result researchers have proposed formal verification techniques for verifying neural network properties. While previous efforts have mainly focused on checking local robustness in neural networks, we instead study another neural network security issue, namely model poisoning. In this case an attacker inserts a trigger into a subset of the training data, in such a way that at test time, this trigger in an input causes the trained model to misclassify to some target class. We show how to formulate the check for model poisoning as a property that can be checked with off-the-shelf verification tools, such as Marabou and nneum, where counterexamples of failed checks constitute the triggers. We further show that the discovered triggers are ‘transferable’ from a small model to a larger, better-trained model, allowing us to analyze state-of-the art performant models trained for image classification tasks.
Traditional Face-to-Face Educational Modality vs. Remote Face-to-Face: Its Impact on Academic Performance in the Context of the Covid 19 Pandemic

The purpose of this research proposal was aimed at analyzing the existence of a significantly differential academic performance depending on the class modality: traditional face-to-face vs. remote face-to-face, in students of the psychology program, who were studying the subject of fundamentals of measurement and psychological evaluation in a higher education institution in the city of Barranquilla/Colombia. To achieve this objective, a study framed in the postpositivist paradigm was developed, based on quantitative, non-experimental, correlational, cross-sectional and field research. The sample consisted of 37 students, divided into two groups: Group 1 traditional face-to-face modality: 20 students and Group 2 remote face-to-face modality with an N of 17. For the analysis of the data, the t-test was applied, for independent samples, using the SPSS version 18 statistical package. The results allowed to establish the existence of a significantly higher academic performance that favors the group that studied the subject under the traditional face-to-face modality. Elements that could explain this superior performance under the traditional education format, have to do with low level of adaptability of those involved to this new way of developing the teaching-learning process, alteration and emotional management in the face of this new learning atmosphere, low experience and little preparation of students and the teacher to digital management and connection platforms for the development of classes from face-to-face remote, not having the right tools to be able to work effectively lowers the remote connection.
Fire ant optimisation feature selection method for breast cancer prediction

Breast cancer is common disease in the today's world and many techniques are used to extract the cancer cell from the breast image. Most of the systems use features extracted from the images and these images are selected using feature selection techniques. The feature selection techniques help to a greater level in removal of irrelevant data from huge amount of data and fine tune the identification process and accuracy of relevant data. But still the prediction accuracy and number of the features selection factors are not yielding 100% prediction result. In this work, Nearest Density Fire Ant (NDFA) is proposed for breast cancer prediction and used for feature selection techniques. This technique is used for diagnosing and producing results compared to the previous methods such as Random Forest, Ant Colony and Genetic Algorithm. The proposed techniques are inspected on Wisconsin Breast Cancer Database (WBCD) and Breast Cancer Wisconsin WDBC data sets. The experimental result shows that proposed NDFA using Fire Ant Optimisation (FAO) technique produces better results in prediction and diagnosing of breast cancer.
Flexibility in augmented reality storytelling apps

&lt;p&gt;This research inquiries about the flexibility of two augmented reality storytelling apps, as well as eight specific variables of this cognitive characteristic of creativity from the Creapp 6–12 questionnaire. The study concludes that they meet all the specific variables of flexibility, used in convergent and divergent mode activities: they stimulate critical and divergent thinking, accessibility and adaptation to different levels of difficulty, including variety of codes, allow the interrelation of disparate elements, build different stories, manipulate and exchange elements, change and reformulate the story.&lt;/p&gt;
WearROBOT: An Energy Conservative Wearable Obstacle Detection Robot With LP Multi-Commodity Graph

Human activity wearable obstacle detection for the visually impaired (VI) is developed for routine monitoring and observation of surrounding events. Environmental observation, home surveillance, and assistive supports are now built on wearable devices using Inertia-based sensors, such as accelerometers, linear acceleration, and gyroscopes. Previous assisted living system (ALS) still faces challenges in energy management and resource allocation when performing daily activities, particularly with ambulation. Legacy systems cannot fully improve self-esteem, hence, WearROBOT. It detects rearview obstacles and has an audio feedback system incorporated for voicing out once an obstacle is detected. Linear programing (LP) multi-commodity graph (LMCG) learning model is proposed while coupling the shortest path resource allocation for space diversity linearization. An Infrared sensor problem function that minimizes link utilization is derived. Angle-Intensity analysis (AIA) is carried out on various use case scenarios to enable the user to know the best angle to consider depending on its usage and battery conservation. The work showed how intensity differs at various angles at 5°,15°,20° 35°, and 45° respectively. Also, the reflectivity of different materials and how it affects the battery life are studied. As the wearable robot moves away from the node-obstacle, the LMCG narrow-band sensor node (LMCG-NB-IoT) drops energy significantly. The Low Power WAN (LP-WAN), Bluetooth Low-Energy (BLE) and proposed LMCG-NB-IoT offered 51.28%, 33.33%, and 15.39% respectively. In terms of energy latency, the schemes gave 65.63%, 31.25%, and 3.12% respectively. Similarly, the proposed LMCG-NB-IoT had a 50% battery life profile. Finally, WearROBOT mobility aid minimizes injuries experienced by the visually impaired.
Dynamic Path Planning of a mobile robot adopting a costmap layer approach in ROS2

Mobile robots can highly contribute to achieve the production flexibility envisaged by the Industry 4.0 paradigm, provided that they show an adequate level of autonomy to operate in a typical industrial environment, in which the presence of both static and dynamic obstacles must be managed. Robot Operating System (ROS) is a well known open-source platform for the development of robotic applications, recently updated to the enhanced ROS2 version, including a navigation stack (Nav2) providing most, but not all the capabilities required to a mobile robot operating in an industrial environment. In particular, it does not embed a strategy for dynamic obstacle handling. Aim of this paper is to enhance Nav2 through the development of a Dynamic Obstacle Layer, as a plug and play solution suitable for the integration of the dynamic obstacle information acquired by a generic 2D LiDAR sensor. The effectiveness of the proposed solution is validated through a campaign of simulation tests, carried out in Webots for a TurtleBot3 burger robot, equipped with a RPLIDAR A3 LiDAR sensor.
Permissioned Blockchain Approach using Open Data in Healthcare

Digital health records play a key role in the area. However, it is difficult to obtain a unified view of your data, as it is distributed among different providers, spread over several places, and is not integrated. To address these problems, blockchain technology and the openEHR interoperability standard have emerged. Blockchain is a new wave of disruption that has come to redesign interactions that involve any form of exchange of values, with the potential to improve healthcare, bringing a new perspective on security, resilience, and effectiveness of systems. In turn, with the use of openEHR, the standardization of electronic records is guaranteed, offering fine-grained access permissions for stakeholders. In addition to the use of archetypes as a reference to make the templates, where they are integrated to build a module with compatible standards. Based on an open data framework, OpenEHR, and blockchain technology, this paper has conceptualised a proposed two architectures that will be implemented within a Portuguese hospital, at the ICU, to increase and provide support for clinical decision-making, ensuring interoperability between systems, as well as the veracity, privacy and security of the data being used.
Feedback-Driven Incremental Imitation Learning Using Sequential VAE

Variational Autoencoders (VAEs) have attracted a lot of attention from the machine learning community in recent years. The usage of VAEs in learning by demonstration and robotics is still very restricted due to the need for effective learning from only a few examples and due to the difficult evaluation of the reconstruction quality. In this paper, we utilize the current models of conditional variational autoencoders for the purpose of teaching a robot simple actions from demonstration in an incremental fashion. We in detail evaluate various training approaches and define parameters that are important for enabling high-quality samples and reconstructions. The quality of the generated samples in different stages of learning is evaluated both quantitatively and qualitatively on the humanoid robot Pepper. We show that the robot can reach a reasonable quality of generated actions already after 20 observed samples.
Uncovering Discussion Groups on Claims of Election Fraud from Twitter

Twitter was widely used during the 2020 U.S. election to disseminate claims of election fraud. As a result, a number of works have examined this phenomenon from a variety of perspectives. However, none of them focus on analyzing topics behind the general fraud claims and associating them with user communities. To fill this gap, we propose to uncover and characterize groups of Twitter users engaging in discussions about election fraud claims during the 2020 U.S. election using a large dataset that spans seven weeks during this period. To accomplish this, we model a sequence of co-retweet networks and employ a backbone extraction method that controls for inherent traits of social media applications, particularly, user activity levels and the popularity of tweets (which together generate many spurious edges in the network), thus allowing us to reveal topics of tweets that lead users to retweet them. After extracting the backbones, we identify user groups representative of the communities present in the network backbones and finally analyze the topics behind the retweeted tweets to understand how they contributed to the spread of fraud claims at that time. Our main results show that (i) our approach uncovers better-structured communities than the original network in terms of users spreading discussions about fraud; and (ii) these users discuss 25 topics with specific psycholinguistic and temporal characteristics.
Using Tree-Based Algorithm to Predict Informal Workers' Willingness to Pay National Health Insurance after Tele-Collection

This study aims to determine the main factors influencing the informal worker sector's willingness to pay (WTP) the National Health Insurance System (NHIS) payments following tele-collection. The difficulty for the NHIS program's viability in terms of contributions is the low WTP for payments from informal participants. Social Security Agency for Health (SSAH/BPJS Kesehatan) has made numerous initiatives to collect payments from informal participants, but their effectiveness remains low. Therefore, the author analyzed the tele-collection data set from the SSAH using socio-demographic data, product or service factors, and billing time as independent factors to predict WTP. In addition, this study used tree-based algorithms for modelling and using accuracy values and area under the ROC curve (AUC) to evaluate predictive results. Findings/Results: (a) The determinant factors of WTP for informal participants after tele-collection in this study are: access to facility health services, billing time, number of months in arrears, and the age of the family head, which collectively yielded a predicted probability of 82.4 per cent; (b) the Light Gradient Boosting Machine (LGBM) algorithm performed more accurately on the collected data compared to other tree-based algorithms. The findings of this study can be used practically by SSAH to improve the probability of success in their tele-collection program, which was previously less effective.
Development of a Negotiation-based Serious Game in Virtual Reality to Help Teach Responsible Consumption and Production

Environmental negotiations and decision-making take place at the science-policy interface. While this is well known within academic literature, it is often difficult to convey to students how science and policy interact in environmental studies and social sciences. Negotiation simulations are an effective experimental learning tool to teach this to students. As the world is transitioning into a digital age, these negotiation situations can be simulated online in virtual environments using gamification techniques. This paper presents work on the virtual "Phosphorus Negotiation Game" (P-Game) that is currently under development at the University of Pannonia in Hungary and the University for Continuing Education Krems in Austria. The game is based on a live simulation and is now being digitalized with a frontend that is a virtual environment and a backend that is a smartphone application. In the game participants debate among themselves with prerecorded audio using rules of University Debating as a blueprint. The creation of this virtual game is presented in this paper, while also detailing the research question and measurement plan of the authors.
Posture Estimation of Articulated Robot Based on Multi-cylinder Segmentation

The purpose of this paper is to address the problem of robot posture estimation during augmented virtual scene construction. In this paper, a multi-cylinder segmentation algorithm based on point cloud curvature features is proposed for cylindrical articulated robots. First, 2D images and Point Cloud Data(PCD) from multiple viewpoints are captured by an RGB-D camera, and each viewpoint contains the PCD of robot links and ChAcUco marker in the environment. Secondly, the multi-view PCD are fused, and processing operations such as Gaussian noise filtering, pass-through filtering, and point cloud surface smoothing are performed. Third, the PCD of each robot link is separated from the fused PCD based on the point cloud curvature feature. For the segmented link's PCD, the point cloud is cylindrically fitted based on the point cloud normal and the distance of the point to obtain the link direction. Finally, the robot joint angles are solved based on the link orientation. The experimental results show that the algorithm is robust to the robot posture, and has high computational efficiency and accurate computational results.
How kidneys work? Developing of Android-based Adobe animate media for senior high school students

Media is one of the determining factors for learning success which can accommodate student critical thinking. Moreover, during the COVID-19 pandemic, learning media became crucial in online learning. This study aims to produce and test the feasibility of android-based media on kidney material developed through Adobe Animate CC software. In addition, it also to find the effectiveness of the media in improving the critical thinking skills of eleventh graders students when used in online learning. Borg &amp; Gall's model was used in this research and development but was limited to the seventh stage. Data collection techniques used interviews, validation (media and material experts), and test. The instruments used are interview sheets, validation sheets, response questionnaires, and essays (five critical thinking questions). The test subjects were 15 senior high school students in Bantur, East Java, Indonesia. The validation results from media and material experts show that the media is "very feasible" to use. Teachers and students stated that the media was "very practical" in online learning. There is a significant difference between students' critical thinking skills before and after using the media, t(14) = 5.65, p &lt; 0.001. The study finding becomes the basis for continuing the process of media dissemination.
Introducing the Challenges in Cybersecurity and Privacy: The European Research Landscape

The continuous, rapid and widespread usage of ICT systems, the constrained and large-scale nature of certain related networks such as IoT (Internet of Things), the autonomous nature of upcoming systems, as well as the new cyber-threats appearing from new disruptive technologies, are given rise to new kind of cyberattacks and security issues.In this sense, this book chapter categorises and presents 10 current main cybersecurity and privacy research challenges, as well as 14 European research projects in the scope of cybersecurity and privacy, analysed further throughout this book, that are addressing these challenges.
Cloth-Aware Center Cluster Loss for Cloth-Changing Person Re-identification

Person re-identification (Re-ID) has achieved great progress recently. However, it was limited by the inconsistency of clothes in the case of long-term person Re-ID. Cloth-changing person Re-ID focus on solving this problem, which is challenging since large intra-class variation and small inter-class variation. Existing methods mainly focus on introducing cloth-irrelevant cues such as joint points, contours, and 3D shapes through additional modules. Few studies investigate directly improving cross-clothing similarity via metric loss. In this paper, we propose a novel loss function, called Cloth-Aware Center Cluster loss (CACC loss) to reduce the intra-class cross-clothing variations. Specifically, the CACC loss supervises the network to learn cross-clothing invariant information by constraining the distance among samples and cloth-aware sample centers. Our method is simple and efficient, using only RGB modality without any additional modules, which can be a strong baseline to boost future research. Extensive experiments on cloth-changing person Re-ID datasets demonstrate the effectiveness of the proposed methods, which achieved the start-of-the-art performance.
SBCMA: Semi-Blind Combined Middle-Round Attack on Bit-Permutation Ciphers With Application to AEAD Schemes

Side-channel and fault injection attacks are well-researched topics within physical security of cryptographic implementations. They can reduce the complexity of the key retrieval to trivial numbers. It was shown that their combination is even more effective, for example in breaking redundancy countermeasures. In this paper, we present the first semi-blind combined attack that allows secret key retrieval in unknown plaintext and ciphertext scenario. SBCMA – Semi-Blind Combined Middle-round Attack is aimed at bit-permutation ciphers, such as GIFT-128 which serves as a case study for this work. On average, it can recover GIFT-128 master key with 92.17 encryptions and 91.17 faults. For GIFT-128 based NIST LWC Round 2 candidates, SBCMA requires 13.79 sessions and 94.32 faults on average.
Plot Composition by Mapping Situation Calculus Schemas into Petri Net Representation

In this paper we propose a new plot composition method based on situation calculus and Petri net models, which are applied, in a complementary fashion, to a narrative open to user co-authorship. The method starts with the specification of situation calculus schemas, which allow a planning algorithm to check if the specification covers the desired cases. A Petri net is then automatically derived from the schemas in a second phase, guiding interactive plot generation and dramatization. The applicability of the proposed method is validated through the implementation of an interactive storytelling system capable of representing the generated Petri net models using 2D graphics and animations.