Reviewerly/exHarmony huggingface dataset:
Synthesizing Barrier Certificates of Neural Network Controlled Continuous Systems via Approximations

The paper presents a barrier certificate based approach to verifying safety properties of closed-loop systems using neural networks as controllers. It deals with the verification problem in the infinite time horizon and exploits the approximated system of the original one to synthesize the candidate barrier certificates, where the behavior of a neural network controller is approximated by a polynomial with a bounded error. Satisfiability Modulo Theories solvers are then utilized to identify real barrier certificates from those candidates. As a barrier certificate can separate the over-approximation of the reachable set from the unsafe region, once it is constructed, the safety property gets proved. We show the advantage of our approach in barrier certificates synthesis by comparing it with the state-of-the-art work on a set of benchmarks.
Ways to Success in Education: Students’ Behavioral Strategies in Regional Universities of Russia

The article discusses the behavioral strategies of students of regional Russian universities, focused on achieving educational success. The situation of uncertainty in modern higher education is shown as a social context that determines the variability of students’ behavioral strategies. A factor that has a significant impact on the phenomenon under study is the change in the views of the main educational communities (teachers and students) about educational success: the expansion of the range of its components, differentiation among different groups of students, orientation to non-educational benefits and values that can be obtained by university graduates in the future at the expense of educational capital. Based on the data of an empirical study conducted in the universities of the Sverdlovsk region in 2019-2021, the characteristic of behavioral strategies for achieving educational success implemented by students of Ural universities is given. It is noted that strategies for achieving success in education begin to be formed in the pre-university period, as part of the transition from school to university. It is concluded that during the university period, in response to new goals and learning difficulties, different groups of students develop four types of behavioral strategies – traditional, innovative, imitation, and «withdrawal» strategies.
Device-Agnostic Augmented Reality Rendering Pipeline for AR in Medicine

Visual augmented reality (AR) headsets have the potential to enhance surgical navigation by providing physicians with an egocentric visualization interface capable of seamlessly blending the virtual navigation aid with the real surgical scenario. However, technological and human-factor limitations still hinder the routine use of commercial AR headsets in clinical practice. The aim of this work is to unveil the AR rendering pipeline of a device-agnostic software framework conceived to fulfill strict requirements towards the realization of a functional and reliable AR-based surgical navigator and capable of supporting the deployment of AR applications for image-guided surgery on different AR headsets. The AR rendering pipeline provides highly accurate AR overlay under both video and optical see-through modalities with almost no perceivable difference in terms of perception of relative distances and depths when used in the peripersonal space. The rendering pipeline allows the setting of the intrinsic and extrinsic projection parameters of the virtual rendering cameras offline and at runtime: under video see-through modality, the rendering pipeline can be modified to adapt the warping of the camera frames and pursue an orthostereoscopic and almost natural perception of the real scene in the peripersonal space. Similarly, under optical see-through modality, the calibrated intrinsic and extrinsic parameters of the eye-display model can be updated by the user to account for the actual user’s eye position. The results of the performance tests with an eye-replacement camera show an average motion-to-photon latency of around 110 ms for both AR rendering modalities. The AR platform for surgical navigation has already proven its efficacy and reliability under VST modality during real surgical operations in craniomaxillofacial surgery.
Fail-Safe Human Detection for Drones Using a Multi-Modal Curriculum Learning Approach

Drones are currently being explored for safetycritical applications where human agents are expected to evolve in their vicinity.In such applications, robust people avoidance must be provided by fusing a number of sensing modalities in order to avoid collisions.Currently however, people detection systems used on drones are solely based on standard cameras besides an emerging number of works discussing the fusion of imaging and event-based cameras.On the other hand, radar-based systems provide up-most robustness towards environmental conditions but do not provide complete information on their own and have mainly been investigated in automotive contexts, not for drones.In order to enable the fusion of radars with both event-based and standard cameras, we present KUL-UAVSAFE, a first-of-its-kind dataset for the study of safety-critical people detection by drones.In addition, we propose a baseline CNN architecture with crossfusion highways and introduce a curriculum learning strategy for multi-modal data termed SAUL, which greatly enhances the robustness of the system towards hard RGB failures and provides a significant gain of 15% in peak F1 score compared to the use of BlackIn, previously proposed for cross-fusion networks.We demonstrate the real-time performance and feasibility of the approach by implementing the system in an edge-computing unit.We release our dataset and additional material in the project home page.
Diversity-Based Trajectory and Goal Selection with Hindsight Experience Replay

Hindsight experience replay (HER) is a goal relabelling technique typically used with off-policy deep reinforcement learning algorithms to solve goal-oriented tasks; it is well suited to robotic manipulation tasks that deliver only sparse rewards. In HER, both trajectories and transitions are sampled uniformly for training. However, not all of the agent's experiences contribute equally to training, and so naive uniform sampling may lead to inefficient learning. In this paper, we propose diversity-based trajectory and goal selection with HER (DTGSH). Firstly, trajectories are sampled according to the diversity of the goal states as modelled by determinantal point processes (DPPs). Secondly, transitions with diverse goal states are selected from the trajectories by using k-DPPs. We evaluate DTGSH on five challenging robotic manipulation tasks in simulated robot environments, where we show that our method can learn more quickly and reach higher performance than other state-of-the-art approaches on all tasks.
RODA: Reverse Operation Based Data Augmentation for Solving Math Word Problems

Automatically solving math word problems is a critical task in the field of natural language processing. Recent models have reached their performance bottleneck and require more high-quality data for training. We propose a novel data augmentation method that reverses the mathematical logic of math word problems to produce new high-quality math problems and introduce new knowledge points that can benefit learning the mathematical reasoning logic. We apply the augmented data on two SOTA math word problem solving models and compare our results with a strong data augmentation baseline. Experimental results show the effectiveness of our approach (we release our code and data at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/yiyunya/RODA</uri> ).
Using Simple Design Features to Recapture the Essence of Real-Time Strategy Games

Real-time strategy (RTS) games simulate battlefield leadership and tactical and strategic operations. Most overemphasize the number of actions per minute (APM), which encourages players to click rapidly and constantly rather than apply deliberate and finely tuned strategies or tactics. New RTS games featuring resource dispersion game mechanics aimed at reducing APM demand and promoting strategic planning are being released. We created three versions of a single RTS game, recruited players, recorded their game control data, and asked them to complete a simple after-game questionnaire. Data were used to analyze tactical and strategic applications. Player actions were observed and player opinions analyzed in an attempt to identify an optimal game structure as measured by strategic and tactical play.
MapChain: A DHT-Based Dual-Blockchain Data Structure for Large-Scale IoT Systems

The rapid growth of Internet of Things (IoT) devices is creating enormous amounts of data in large-scale IoT systems. In such systems, the efficiency, trustworthiness, confidentiality, and integrity of data storage and retrieval are significant challenges. In this paper, we propose a dual-blockchain distributed data structure as a highly scalable solution for resource-constrained IoT systems. Our proposed model is based on a distributed hash table (DHT) which is implemented and benchmarked in an experimental IoT network environment. Performance analysis and experimental results highlight that our dual-chain model provides better scalability in terms of space consumption, latency and computational complexity compared to baseline single-chain models. Security analyses are provided to discuss the feasibility of distributed trust management, fault tolerance, as well as confidentiality of data stored in our model and conflict avoidance mechanisms.
BBS: A Blockchain Big-Data Sharing System

Chain of custody is needed to document the sequence of custody of sensitive big data. In this paper, we design a blockchain big-data sharing system (BBS) based on Hyperledger Fabric. We denote the data stored outside of a ledger for sharing as "off-state" and "big data" (referring to extremely large data) is in this category. In our off-state sharing protocol, a sender registers a file with BBS for sharing. To acquire the file, an authenticated and authorized receiver has to use transactions and interacts with BBS in four phases, including the file transfer request, encrypted file transfer, key retrieval, and file decryption. The corresponding transactions are recorded in the ledger and serve as chain of custody to document the trail of the data. Compared with related work, BBS can perform the four phases autonomously. It utilizes the permissioned blockchain, i.e. Hyperledger Fabric, for access control and can defeat dishonest receivers. We design and implement a prototype of BBS for big file sharing. Extensive experiments were performed to validate its feasibility and performance.
Trust Management Issues in Cloud Computing Ecosystems

Over the past years due the advent of Cloud computing, because of its unprecedented advantages, has given rise to influential information technologies such as Big data and IoT. In spite of all the social and economic benefits of its on demand nature, the web of the connected data ecosystems has yet to combat many underlying challenges. The primary concerns include privacy, security and trust which are the major barriers to the adoption of cloud by individuals and organizations as a whole. Trust has been the least looked into since it includes both subjective and objective characteristics. In this paper we highlight some of the significant challenges faced by cloud computing environments towards addressing the problem of trust management. These potential challenges once efficiently addressed will provide a roadmap for trustworthy ecosystems.
A Formal Approach to Confidentiality Verification in SoCs at the Register Transfer Level

We propose a formal verification methodology to detect security-critical bugs in the hardware (HW) and in the hardware/firmware interface of SoCs. Our approach extends Unique Program Execution Checking (UPEC), originally proposed for detecting transient execution side channels, to also detect all functional design bugs that cause confidentiality violations, and to cover not only the processor but also its peripherals. The proposed methodology is particularly effective in capturing security vulnerabilities that are introduced based on cross-modular effects (integration and communication issues) or poorly understood hardware/firmware interaction. Such bugs are known to be hard to detect by previous methods.We demonstrate a compositional approach where vulnerabilities discovered by our method can be used to create restrictions for the software (SW). This supports design fixes not only at the HW but also at the SW level. We present experiments for the Pulpissimo platform (v4.0) where several security-critical bugs were identified (and confirmed), as well as for RocketChip.
Image Disguising for Protecting Data and Model Confidentiality in Outsourced Deep Learning

Large training data and expensive model tweaking are common features of deep learning development for images. As a result, data owners often utilize cloud resources or machine learning service providers for developing large-scale complex models. This practice, however, raises serious privacy concerns. Existing solutions are either too expensive to be practical, or do not sufficiently protect the confidentiality of data and model. In this paper, we aim to achieve a better trade-off among the level of protection for outsourced DNN model training, the expenses, and the utility of data, using novel image disguising mechanisms. We design a suite of image disguising methods that are efficient to implement and then analyze them to understand multiple levels of tradeoffs between data utility and protection of confidentiality. The experimental evaluation shows the surprising ability of DNN modeling methods in discovering patterns in disguised images and the flexibility of these image disguising mechanisms in achieving different levels of resilience to attacks.
Two-way Integration of Service-Oriented Systems-of-Systems with the Web of Things

The Internet of Things (IoT) is nowadays affected by significant interoperability issues.One of the most popular countermeasures is the Web of Things (WoT), proposed recently in a consistent standardization effort.On the other hand, several IoT-oriented frameworks are already established in industrial scenarios and provide SOA-like features such as discovery and orchestration.In this paper, we study how to bridge these two worlds by proposing a tool that enables a two-way translation between a WoT ecosystem and a System-of-Systems composed of well-described Web services.We evaluate the efficiency and scalability of our solution over the Eclipse Arrowhead framework through a series of experiments that assess the scalability of our solution under realistic workloads.
GraN-GAN: Piecewise Gradient Normalization for Generative Adversarial Networks

Modern generative adversarial networks (GANs) predominantly use piecewise linear activation functions in discriminators (or critics), including ReLU and LeakyReLU. Such models learn piecewise linear mappings, where each piece handles a subset of the input space, and the gradients per subset are piecewise constant. Under such a class of discriminator (or critic) functions, we present Gradient Normalization (GraN), a novel input-dependent normalization method, which guarantees a piecewise K-Lipschitz constraint in the input space. In contrast to spectral normalization, GraN does not constrain processing at the individual network layers, and, unlike gradient penalties, strictly enforces a piecewise Lipschitz constraint almost everywhere. Empirically, we demonstrate improved image generation performance across multiple datasets (incl. CIFAR-10/100, STL-10, LSUN bedrooms, and CelebA), GAN loss functions, and metrics. Further, we analyze altering the often untuned Lipschitz constant K in several standard GANs, not only attaining significant performance gains, but also finding connections between K and training dynamics, particularly in low-gradient loss plateaus, with the common Adam optimizer.
Defending Against Data Poisoning Attacks: From Distributed Learning to Federated Learning

Abstract Federated learning (FL), a variant of distributed learning (DL), supports the training of a shared model without accessing private data from different sources. Despite its benefits with regard to privacy preservation, FL’s distributed nature and privacy constraints make it vulnerable to data poisoning attacks. Existing defenses, primarily designed for DL, are typically not well adapted to FL. In this paper, we study such attacks and defenses. In doing so, we start from the perspective of DL and then give consideration to a real-world FL scenario, with the aim being to explore the requisites of a desirable defense in FL. Our study shows that (i) the batch size used in each training round affects the effectiveness of defenses in DL, (ii) the defenses investigated are somewhat effective and moderately influenced by batch size in FL settings and (iii) the non-IID data makes it more difficult to defend against data poisoning attacks in FL. Based on the findings, we discuss the key challenges and possible directions in defending against such attacks in FL. In addition, we propose detect and suppress the potential outliers(DSPO), a defense against data poisoning attacks in FL scenarios. Our results show that DSPO outperforms other defenses in several cases.
Comparing Voice Assistant Risks and Potential with Technology-Based Users: A Study from Germany and Spain

Currently, voice assistants (VAs) are trendy and highly available. The VA adoption rate of internet users differs among European countries and also in the global view. Due to speech intelligibility and privacy concerns, using VAs is challenging. Additionally, user experience (UX) assessment methods and VA improvement possibilities are still missing, but are urgently needed to overcome users&amp;#x2019; concerns and increase the adoption rate. Therefore, we conducted an intercultural study of technology-based users from Germany and Spain, expecting that higher improvement potential would outweigh concerns about VAs. We investigated VA use in terms of availability versus actual use, usage patterns, concerns, and improvement proposals. Comparing Germany and Spain, our findings show that nearly the same amount of intensive VA use is found in both technology-based user groups. Despite cultural differences, further results show very similar tendencies, e.g., frequency of use, privacy concerns, and demand for VA improvements.
Generative Adversarial Networks for Facial Image Inpainting and Super-Resolution

Abstract Inpainting helps to fill in the lost data in visual images. Inpainting techniques also refer to unusual image editing in distorted regions. These include areas that are noisy, blurred and watery areas. The most appropriate pixel values must be replaced in these regions to achieve good performance. Artists used to play it, and still now, pieces that are not in the picture can be inpainted in the same manner, though it takes more time. In the present age of automation, inpainting can be automated to obtain quicker and better outcomes by deep learning technologies. In this area, many of the latest techniques have been created, however, many methods produce blurred findings and data loss. Two adversarial networks are used to achieve this task, where first network aims at inpainting and the second network aims at super-resolution. The input generated as a part of first stage network is passed on to the second stage super-resolution network to overcome blurriness that is caused in the initial inpainting network. The network efficiency is determined in terms of increased PSNR obtained which is 28.19 dB with less training period of approximately 14 hours in comparison with other network models which performs similar task.
Exploring LSB Steganography Possibilities in RGB Images

Steganography is the method by which we hide information in ordinary files like text, audio, video, images, etc. In this paper, we have explored image steganography using the least significant bit (LSB) approach combined with AES-128 encryption. We proposed to change the order of traversing the RGB planes while embedding a secret message in the image. The order of traversal was determined by image attributes like file type and resolution so that the steganography capacity of the image remains unchanged. RMSE and PSNR were used as error and performance metrics respectively. The experimental results confirmed that the method proposed was successfully implemented without increasing the number of bits embedded in the image.
Robust Deep Reinforcement Learning for Quadcopter Control

Deep reinforcement learning (RL) has made it possible to solve complex robotics problems using neural networks as function approximators. However, the policies trained on stationary environments suffer in terms of generalization when transferred from one environment to another. In this work, we use Robust Markov Decision Processes (RMDP) to train the drone control policy, which combines ideas from Robust Control and RL. It opts for pessimistic optimization to handle potential gaps between policy transfer from one environment to another. The trained control policy is tested on the task of quadcopter positional control. RL agents were trained in a MuJoCo simulator. During testing, different environment parameters (unseen during the training) were used to validate the robustness of the trained policy for transfer from one environment to another. The robust policy outperformed the standard agents in these environments, suggesting that the added robustness increases generality and can adapt to nonstationary environments.
Combination of Isolation Forest and LSTM Autoencoder for Anomaly Detection

Land monitoring is important in agriculture. Early warning information regarding the land condition enable farmers to respond quickly when anomaly condition occures. However, identifying anomaly of land condition is not a simple task. In this research, a model of anomaly detection for land monitoring system is proposed. Raw data collected from land monitoring sensors is used as the dataset. Isolation Forest is used to transform the unlabeled data into labeled data. The labeled dataset is then used to create anomaly detection model using Long Short-Term Memory (LSTM) autoencoder. The experiments results show that the Isolation Forest has the potential to label data. The LSTM autoencoder has the accuracy 0.95 precision 0.96, recall 0.99 and flscore 0.97.
Towards Contactless Learning Activities during Pandemics Using Autonomous Service Robots

The COVID-19 pandemic has had a significant impact worldwide, impacting schools, undergraduate, and graduate university education. More than half a million lives have been lost due to COVID-19. Moving towards contactless learning activities has become a research area due to the rapid advancement of technology, particularly in artificial intelligence and robotics. This paper proposes an autonomous service robot for handling multiple teaching assistant duties in the educational field to move towards contactless learning activities during pandemics. We use SLAM to map and navigate the environment to proctor an exam. We also propose a human–robot voice interaction and an academic content personalization algorithm. Our results show that our robot can navigate the environment to proctor students avoiding any static or dynamic obstacles. Our cheating detection system obtained a testing accuracy of 86.85%. Our image-based exam paper scanning system can scan, extract, and process exams with high accuracy.