Reviewerly/exHarmony huggingface dataset:
An Unsupervised Learning Approach for Road Anomaly Segmentation Using RGB-D Sensor for Advanced Driver Assistance System

Condition monitoring of road surfaces has acquired a lot of attention in the field of computer vision throughout the years. It is due to two main reasons; firstly, it produces safety and comfort to the community, and secondly, it causes less damage to the vehicles for an advanced driver assistance system (ADAS). To this extent, this article aims to present a real-time vision-based approach that automatically segments the road anomalies from the drivable area. An Intel RealSense D435 depth camera has been employed to capture RGB and depth (RGB-D) images of the road surface. An unsupervised learning method based on diffusion process has been employed to learn the affinity matrix of the RGB-D data and spectral clustering has been applied on the updated affinity matrix to cluster the road images. Image multiplex visibility graphs of the input sensor data are diffused by regularized diffusion process (RDP) to update the affinity matrix followed by generation of saliency map of the road surfaces. The prime motive to employ RDP is to use the graph Laplacian as a tool for similarity measurement for preserving the manifold structure. Qualitative and quantitative results reveal the efficacy of the proposed system with state-of-the-art methods on our RGB-D dataset. Benchmark datasets (KITTI and Cityscapes) are also used to validate the proposed method for segmentation of the drivable area for an intelligent transportation system.
Developing With Formal Methods at BedRock Systems, Inc.

The BedRock HyperVisor (trademarked) is a commercial, highly concurrent, verified virtualization platform that employs formal methods to enable proofs of complex, lock-free concurrent code; support automating proofs of large programs; and integrate with “informal” parts of the software lifecycle.
CERCoin: Carbon tracking enabling Blockchain system for Electric Vehicles

The world is at a critical juncture in CO2 emissions. A core creator of these emissions are the activities associated with the use of combustible engine vehicles. Moving from petrol and diesel vehicles to electric vehicles (EV s) will help in the reduction of these greenhouse emissions. This research proposes a novel Blockchain-based approach to complement existing incentives to further the adoption of EV s. This will be done through the integration of smart car technology with Blockchain to allow EV owners mine a tradeable token called CERCoin through their 'green’ mileage. This approach addresses a gap in the current Blockchain research to leverage private citizens and transport fleets to reduce carbon intensive activities. The evaluation of the proposed approach that is based also on interviews demonstrates the potential and the benefits of the solution.
Improving Robustness and Visibility of Adversarial CAPTCHA Using Low-Frequency Perturbation

CAPTCHA is a type of Turing test used to distinguish between humans and computing machine. However, image-based CAPTCHAs are losing their function as Turing tests owing to the improvement of image recognition using machine learning. This paper proposes an Adversarial CAPTCHA that provides attacking resistance to CAPTCHAs by using Adversarial Example (AE) as well as maintaining visibility by reducing image degradation. The proposed CAPTCHA maintains the difficulty of solving CAPTCHAs using computing machine by adding resistance against the attack using a machine learning classifiers. The proposed CAPTCHA is evaluated using three evaluation experiments, i.e., the attack using a machine learning classifier, the image quality, and the solving workload. The three evaluation experiments show that an Adversarial CAPTCHA is resistant to the attack by machine learning and is as convenient as the existing CAPTCHA.
The Future of Interaction: Augmented Reality, Holography and Artificial Intelligence in Early Childhood Science Education

There is little doubt that the development of technology has changed the landscape of science learning in both formal and informal settings. However, often existing research studies lack a strong conceptual underpinning in terms of pedagogic theory. Regardless of the fair body of studies relating to early childhood education and science education, early childhood science learning remains a relatively under-researched area. As representatives of advanced technologies which have been widely adopted in many fields, augmented reality (AR), holography and artificial intelligence (AI) have rarely been applied and studied in early childhood science education despite the enormous potential they offer. Drawing upon Vygotsky’s notions of the zone of proximal development (ZPD), tools and mediation, this chapter provides a new perspective by exploring the potential use of AR applications (apps), holography and AI-based tools in early childhood science education. The key argument is that these tools can potentially change the nature of the interaction between learners and learning materials, and they offer significant affordances in early childhood science education. The mission of the present chapter is to inform the design and development of educational technology based on psychological and pedagogical perspectives, and help parents and early childhood teachers understand the potential use of AR, holography and AI in science education.
Unpaired referring expression grounding via bidirectional cross-modal matching

Referring expression grounding is an important and challenging task in computer vision. To avoid the laborious annotation in conventional referring grounding, unpaired referring grounding is introduced, where the training data only contains a number of images and queries without correspondences. The few existing solutions to unpaired referring grounding are still preliminary, due to the challenges of learning vision-language correlation and lack of the top-down guidance with unpaired data. Existing works are only able to learn vision-language correlation by modality conversion, where critical information are lost. They also heavily rely on pre-extracted object proposals and thus cannot generate correct predictions with defective proposals. In this paper, we propose a novel bidirectional cross-modal matching (BiCM) framework to address these challenges. Particularly, we design a query-aware attention map (QAM) module that introduces top-down perspective via generating query-specific visual attention maps to avoid the over-reliance on pre-extracted object proposals. A cross-modal object matching (COM) module is further introduced to predict the target objects from a bottom-up perspective. This module exploits the recently emerged image-text matching pretrained model, CLIP, to learn cross-modal correlation without modality conversion. The top-down and bottom-up predictions are then integrated via a similarity fusion (SF) module. We also propose a knowledge adaptation matching (KAM) module that leverages unpaired training data to adapt pretrained knowledge to the target dataset and task. Experiments show that our framework significantly outperforms previous works on five grounding datasets.
A CPU-FPGA Heterogeneous Acceleration System for Scene Text Detection Network

Scene text detection network, such as Connectionist Text Proposal Network (CTPN), takes CNN-RNN hybrid neural network as the main body, which can effectively recognize the text arranged horizontally in the image. However, complex structures and intensive calculations lead to longer network inference time. FPGAs have been widely used in data centers to accelerate neural network inference due to their flexibility and low power consumption. In this brief, a CPU-FPGA based heterogeneous acceleration system and a subgraph segmentation scheme for CNN-RNN hybrid neural networks are proposed. Winograd algorithm is applied to accelerate CNN. In RNN accelerating, fixed-point quantization, loop tiling, and piecewise linear approximation of activation function are used to reduce hardware resource usage and achieve a high degree of parallelization. CTPN is tested in our system with Intel Xeon 4116 CPU and Arria10 GX1150 FPGA, and the throughput reaches 1223.53GOP/s.
Analysis of the NTRU Post-Quantum Cryptographic Scheme in Constrained IoT Edge Devices

Research on post-quantum cryptography aims to solve the problematic of modern public-key cryptography being broken by attacks coming from quantum computers in the future and, moreover, by using classical electronics. This task is so critical that the National Institute of Standards and Technology (NIST) is in the final process of standardizing post-quantum schemes for the future protection of embedded applications. Though there are some research work done on embedded systems, it is important to study the impact of these proposals in realistic environments for the Internet of Things (IoT), where the limited computational resources and the strict requirements for power consumption can become incompatible with the usage of cryptographic schemes. In this work, the performance of one of the finalists of the standardization process called NTRU is studied and implemented in a custom wireless sensor node designed for applications in the extreme edge of the IoT. The cryptosystem is implemented and evaluated within the processes of the Contiki-NG operating system. Furthermore, additional experiments are performed to check if commonly integrated hardware peripherals for cryptography inside modern microcontrollers can be used to achieve better performance with NTRU, not only at the single node level but also at the network level, where the NTRU key encapsulation mechanism is tested in a real communication process. The results derived from these experiments show that NTRU is suitable for modern microcontrollers targeting wireless sensor networks design, while old devices present in popular platforms might not afford the cost of its implementation.
Can't Understand SLAs? Use the Smart Contract

A Service Level Agreement (SLA) is a special kind of legal contract that binds a vendor to its customers where the vendor commits to provide certain services in exchange for certain payment from the customers. On the other hand, a Smart Contract is a contract that is a computer program that also binds multiple parties into given agreements but is a set of a precise rules and is self-enforceable and self-executable. Since almost all legal contracts are ambiguous by nature and are complex to read and understand, we perform a novel study on how we can replace the traditional vague legal contract with the smart contact and the effect of the ambiguity on the smart contract by performing a thorough analysis on SLAs by measuring their ambiguities in various aspects. We take several samples of real SLAs from six different popular broadband vendors. We use four random SLAs to train the machine learning model to classify and then detect ambiguous words in two unseen SLAs which were the SLAs of Ziply Fiber and Century Link. As different people form different interpretations while reading the ambiguous legal contracts, we generate various human interpretations from the machine detected ambiguous words and convert all those generated interpretations into Smart Contracts to perform testing in Ethereum-based Blockchain to identify the most ambiguous as well as accurate interpretation of the SLA. From our analysis and observation, we were able to find out the most ambiguous interpretation of SLAs and we concluded that the SLA of Ziply Fiber was more ambiguous in general compared to the SLA of Century Link. Moreover, our proposed approach to detect ambiguous terms and to translate an ambiguous legal contract to a smart legal contract using a formal language to measure the degree of ambiguity can be extrapolated and replicated to legal contracts from other types of industries as well.
Deep Neural Network Based TensorFlow Model for IoT Lightweight Cipher Attack

The internet of Things (IoT) technology is present in all aspects of our modern lives, and its standard usage is increasing remarkably. But their inherent limitations in size, storage memory, and power consumption limit its specific functionality in the secure transmission of sensitive information, where the development of lightweight ciphers responds adequately to these limitations. However, the conventional cryptanalysis of these modern ciphers can be impractical or demonstrate apparent limitations to be generalized. Because they frequently require a large amount of considerable time, known plain texts, and big storage memory, they are typically performed without the restriction of keyspace, or only the reduced round variants are attacked. This work proposes a deep learning (DL) model-based approach for a successful attack that discovers the plain text from cipher text one, it’s demonstrated that the proposed DL-based cryptanalysis represents a promising step towards a more efficient and automated test to verify the security of emerging lightweight ciphers. The results are communicated to demonstrate precisely the effective performance of the attack, and numerous experiments were performed to confirm the study.
Utilization of E-Learning System for Innovative Methods Implementation in Humanities Pedagogy

The article presents the results of the research on e-learning system utilization at Borys Grinchenko Kyiv University by humanities students and teachers. Digitalization of humanities pedagogy is an integral part of the educational process today and e-learning systems belong to the most powerful digital instruments used at higher education institutions. Therefore, the topic of digital humanities implementation in existing at universities e-learning systems is high on the agenda. In particular, attention should be paid to the possibility of innovative teaching methods utilization with the help of the e-learning system resources. The authors focus on the capabilities of LMS Moodle for implementation of collaboration, flipped classroom technology, peer assessment and project-based learning. Utilization of such activities as Workshop, Wiki, Google Meet for Moodle as well as instruments and settings (embedded video, group submission) for implementation of innovative teaching methods are considered. The correlation between the request of humanities teachers and students and Moodle LMS options for its implementation is shown.Increased use of the e-learning system for innovative teaching methods provision is highlighted as a development area for humanities teachers.
A Comprehensive Study of Different Types of Deduplication Technique in Various Dimensions

In the current digital era, the growth of digital data is highly exceptional.There are various sources available for these digital data.The quantity of digital data being produced rose exponentially with time because of organizations and even by individuals, finally end up in the need of huge storage space.Cloud storage provides the storage space for such requirement.Since the storage space is utilized by many different users, having the duplicate data cannot be avoided.So it is necessary to make use of some storage optimization technique to handle such duplicate contents.Deduplication is a technique which is used to evade redundant data get stored.Among the various digital data, the possibility of having duplicate copies is high for data.In this research work, we review the benefits of having deduplication in optimizing the usage of storage space and study about the various types of deduplication techniques in different dimensions which can be used for data.It helps to select the appropriate data deduplication technique to increase their effective storage utilization and reduce the wastage of memory space because of duplicate data.
Real-time Wireless Transmitter Authorization: Adapting to Dynamic Authorized Sets with Information Retrieval

As the Internet of Things (IoT) continues to grow, ensuring the security of systems that rely on wireless IoT devices has become critically important. Deep learning-based passive physical layer transmitter authorization systems have been introduced recently for this purpose, as they accommodate the limited computational and power budget of such devices. These systems have been shown to offer excellent outlier detection accuracies when trained and tested on a fixed authorized transmitter set. However in a real-life deployment, a need may arise for transmitters to be added and removed as the authorized set of transmitters changes. In such cases, the system could experience long down-times, as retraining the underlying deep learning model is often a time-consuming process. In this paper, we draw inspiration from information retrieval to address this problem: by utilizing feature vectors as RF fingerprints, we first demonstrate that training could be simplified to indexing those feature vectors into a database using locality sensitive hashing (LSH). Then we show that approximate nearest neighbor search could be performed on the database to perform transmitter authorization that matches the accuracy of deep learning models, while allowing for more than 100× faster retraining. Furthermore, dimensionality reduction techniques are used on the feature vectors to show that the authorization latency of our technique could be reduced to approach that of traditional deep learning-based systems.
LatentHuman: Shape-and-Pose Disentangled Latent Representation for Human Bodies

3D representation and reconstruction of human bodies have been studied for a long time in computer vision. Traditional methods rely mostly on parametric statistical linear models, limiting the space of possible bodies to linear combinations. It is only recently that some approaches try to leverage neural implicit representations for human body modeling, and while demonstrating impressive results, they are either limited by representation capability or not physically meaningful and controllable. In this work, we propose a novel neural implicit representation for the human body, which is fully differentiable and optimizable with disentangled shape and pose latent spaces. Contrary to prior work, our representation is designed based on the kinematic model, which makes the representation controllable for tasks like pose animation, while simultaneously allowing the optimization of shape and pose for tasks like 3D fitting and pose tracking. Our model can be trained and fine-tuned directly on non-watertight raw data with well-designed losses. Experiments demonstrate the improved 3D reconstruction performance over SoTA approaches and show the applicability of our method to shape interpolation, model fitting, pose tracking, and motion retargeting.
Fashion Style Generation: Evolutionary Search with Gaussian Mixture Models in the Latent Space

This paper presents a novel approach for guiding a Generative Adversarial Network trained on the FashionGen dataset to generate designs corresponding to target fashion styles. Finding the latent vectors in the generator’s latent space that correspond to a style is approached as an evolutionary search problem. A Gaussian mixture model is applied to identify fashion styles based on the higher-layer representations of outfits in a clothing-specific attribute prediction model. Over generations, a genetic algorithm optimizes a population of designs to increase their probability of belonging to one of the Gaussian mixture components or styles. Showing that the developed system can generate images of maximum fitness visually resembling certain styles, our approach provides a promising direction to guide the search for style-coherent designs.
Core Interests Focused Self-attention for Sequential Recommendation

Recent studies identify that sequential recommender systems (SRSs) are improved by self-attention mechanism due to its ability to capture the correlation between interactions. However, two major limitations remain unaddressed by existing works. First, user behaviors in long sequences contain many implicit and noisy preference signals that cannot sufficiently reflect users' actual preferences. Therefore, modeling all the interactive behaviors will worsen the representation of their actual interests. Second, most models only consider the interaction histories as ordered sequences, while ignoring the time interval between interactions, which leads to a loss of effective information. To tackle these issues, we herein propose CIFARec (Core Interests Focused self-Attention based sequential recommendation), which can explicitly extract those interest-relevant interactions from users' implicit feedback information and focus on users' core interests adaptively. Meanwhile, our model takes into account time intervals to retain valid information. Extensive experiments on five benchmark datasets show that our model outperforms various state-of-the-art sequential models consistently.
Experimental Evaluation of Rule-Based Autonomic Computing Management Framework for Cloud-Native Applications

The policy-based management paradigm in a flexible manner governs the system behavior. For Cloud-native applications, additionally, it simplifies the compliance with CI/CD objectives. Hence, the velocity of changes in requirements made at runtime does not influence the system implementation. Continuously the adjustments are integrated into the system on the fly. This paper evaluates the rule-based approach to representing policies in the context of Cloud-native applications. Deploying applications in orchestrated environments is one of the main principles of Cloud-native. Our approach represents the extension of the management characteristics that are available in current implementations of the orchestrators. The presented study also shows a general methodology for experimental evaluation of complex Cloud-native environments. We propose two categories of experiments. Both evaluate the rule-based approach. The first category evaluates the impacts of dynamic adjustment of resources in the context of the Cloud-native execution environment. The second category assesses the influence of the rule engine approach on the autonomic management process. Given the wide range of available experiments, we additionally assume that evaluation is performed from the point of view of the execution environment's resources. This approach tightly embraces the capabilities of the proposed solution realized by the AMoCNA system and demonstrates its usability.