Reviewerly/exHarmony huggingface dataset:
Deep Network-based Hand Gesture Recognition using Optical Flow guided Trajectory Images

The use of body gestures and specially hand ges-tures can be a convenient and useful alternative tool for many utilizations in the human-computer interaction community. A typical hand gesture recognition system comprises different stages like detection, representation and recognition. In this process of hand gesture recognition, proper detection and tracking of the moving hand in a cluttered background play an important role due to the varied shape and size of the hand. In this work, we propose a framework for the recognition of isolated gestures where the moving hand with different shapes, size and colours is detected through optical flow, and the proper hand gesture is recognized using a VGG16 architecture. This paper utilizes the optical flow to track points of interest in video and store the tracked motion as images that we call trajectory-based images. These images are then fed to a VGG16 network for classification. For feature learning and recognition, a deep learning based method is used due to its inherent ability to extract robust and effective features for classification purposes. The main benefits of the proposed method is its simplicity and ease of implementation. This method has offered higher multi-class classification accuracy with a limited amount of continuous isolated hand gesture video dataset.
Low-Light Image Enhancement for Autonomous Driving Systems using DriveRetinex-Net

Most autonomous driving algorithms are designed for normal-light images. Hence, insufficient lighting during image capture significantly degrades the visibility of images and hurts the performance of many computer vision systems. Retinex theory is an effective tool for enhancing the illumination and detail of images. In this paper, we collected a Low-Light Drive (LOL-Drive) dataset and applied a deep retinex neural network, named DriveRetinex, which was taught using this dataset. The deep Retinex-Net consists of two subnetworks: Decom-Net (decomposes a color image into a reflectance map and an illumination map) and Enhance-Net (enhances the light level in the illumination map). The whole architecture can be trained in an end-to-end fashion. Extensive experiments demonstrate that the proposed method not only achieves visually appealing low-light enhancement, but it also increases the accuracy of object detection in autonomous driving systems.
Collaborative qualitative research at scale: Reflections on 20 years of acquiring global data and making data global

Abstract A 5‐year project to study scientific data uses in geography, starting in 1999, evolved into 20 years of research on data practices in sensor networks, environmental sciences, biology, seismology, undersea science, biomedicine, astronomy, and other fields. By emulating the “team science” approaches of the scientists studied, the UCLA Center for Knowledge Infrastructures accumulated a comprehensive collection of qualitative data about how scientists generate, manage, use, and reuse data across domains. Building upon Paul N. Edwards's model of “making global data”—collecting signals via consistent methods, technologies, and policies—to “make data global”—comparing and integrating those data, the research team has managed and exploited these data as a collaborative resource. This article reflects on the social, technical, organizational, economic, and policy challenges the team has encountered in creating new knowledge from data old and new. We reflect on continuity over generations of students and staff, transitions between grants, transfer of legacy data between software tools, research methods, and the role of professional data managers in the social sciences.
Reconfigurable Behavior Trees: Towards an Executive Framework Meeting High-level Decision Making and Control Layer Features

Behavior Trees (BTs) constitute a widespread artificial intelligence tool that has been successfully adopted in robotics. Their advantages include simplicity, modularity, and reusability of code. However, Behavior Trees remain a high-level decision making engine; control features cannot easily be integrated. This paper proposes Reconfigurable Behavior Trees (RBTs), an extension of the traditional BTs that incorporates sensed information coming from the robotic environment in the decision making process. We endow RBTs with continuous sensory data that permits the online monitoring of the task execution. The resulting stimulus-driven architecture is capable of dynamically handling changes in the executive context while keeping the execution time low. The proposed framework is evaluated on a set of robotic experiments. The results show that RBTs are a promising approach for robotic task representation, monitoring, and execution.
Bidirectional recurrent neural networks for seismic event detection

Real-time accurate passive seismic event detection is a critical safety measure across a range of monitoring applications, from reservoir stability to carbon storage to volcanic tremor detection. The most common detection procedure remains the short-term average to long-term average (STA/LTA) trigger developed in the 1970s, in part due to its easy implementation and real-time processing capability. However, it has several well-documented limitations, such as requiring a signal-to-noise ratio greater than one and being highly sensitive to trigger parameters. Although numerous alternatives have been proposed, they often are tailored to a specific monitoring setting and therefore cannot be widely applied, or they are too computationally expensive and therefore cannot be run in real time. This work introduces a deep learning approach to event detection that is an alternative to the STA/LTA trigger. A bidirectional, long short-term memory, neural network (NN) is trained solely on synthetic traces. Evaluated on synthetic and field data, the NN approach significantly outperforms the STA/LTA trigger on the number of correctly detected arrivals as well as on reducing the number of falsely detected events. Its applicability is proven with 600 traces processed in real time on a single processing unit.
Locating Waterfowl Farms from Satellite Images with Parallel Residual U-Net Architecture

For the epidemic prevention of avian influenza, there exist lots of differences between ideality and reality. This is why the epidemic is usually out of control. One of the reasons is that many illegal waterfowl farms are built without government registration. In this work, we proposed a new method trying to directly locate waterfowl farms, including both registered and unregistered ones without the need of human labeling. This will not only save human labors, but also update the location and size information of waterfowl farms regularly due to the computing speed of computers. In this work, we proposed a new method for satellite image augmentation. The layers of the model we proposed are not deeper than the other deep neural network models. However, we show that using the existing simple U-Net combined with residual blocks has better performance than the other deep models in this task.
Domain-Driven Service Design

Service-oriented architectures and microservices have gained much attention in recent years; companies adopt these concepts and supporting technologies in order to increase agility, scalability, and maintainability of their systems. Decomposing an application into multiple independently deployable, appropriately sized services and then integrating them is challenging. Domain-driven Design (DDD) is a popular approach to identify (micro-)services by modeling so-called Bounded Contexts and Context Maps. In our previous work, we proposed a Domain-specific Language (DSL) that leverages the DDD patterns to support service modeling and decomposition. The DSL is implemented in Context Mapper, a tool that allows software architects and system integrators to create domain-driven designs that are both human- and machine-readable. However, we have not covered the tool architecture, the iterative and incremental refinement of such maps, and the transition from DDD pattern-based models to (micro-)service-oriented architectures yet. In this paper, we introduce the architectural concepts of Context Mapper and seven model refactorings supporting decomposition criteria that we distilled from the literature and own industry experience; they are grouped and serve as part of a service design elaboration method. We also introduce a novel service contract generation approach that leverages a new, technology-independent Microservice Domain-Specific Language (MDSL). These research contributions are implemented in Context Mapper and being validated using empirical methods.
Joint Contrast Enhancement and Noise Reduction of Low Light Images Via JND Transform

Low light images suffer from a low dynamic range and severe noise due to low signal-to-noise ratio (SNR). In this paper, we propose joint contrast enhancement and noise reduction of low light images via just-noticeable-difference (JND) transform. We adopt the JND transform to achieve both contrast enhancement and noise reduction based on human visual perception. First, we generate a JND map based on an the human visual system (HVS) response model from foreground and background luminance, called JND transform. Second, for base image, we perform perceptual contrast enhancement based on luminance adaptation to effectively allocate a dynamic range to each gray level while preventing under enhancement (tone distortion) and over-enhancement. Third, we refine the JND map using Weber's law, luminance adaptation and visual masking. Weber's law enhances the JND map based on the luminance variation after contrast enhancement. Luminance adaptation suppresses noise for smooth regions, while visual masking enforces detail enhancement for textural regions. Fourth, we perform inverse JND transform to generate the enhanced luma channel from the JND map and base image. Finally, we conduct chroma denoising by transferring texture information of the enhanced luma channel to the chroma channels with guided filtering. Experimental results show that the proposed method achieves both contrast enhancement and noise reduction for low light images as well as outperforms state-of-the-art methods in terms of quantitative measurements.
Learning from Vulnerabilities - Categorising, Understanding and Detecting Weaknesses in Industrial Control Systems

Compared to many other areas of cyber security, vulnerabilities in industrial control systems (ICS) can be poorly understood. These systems form part of critical national infrastructure, where asset owners may not understand the security landscape and have potentially incorrect security assumptions for these closed source, operational technology (OT) systems. ICS vulnerability reports give useful information about single vulnerabilities, but there is a lack of guidance telling ICS owners what to look for next, or how to find these. In this paper, we analyse 9 years of ICS Advisory vulnerability announcements and we recategorise the vulnerabilities based on the detection methods and tools that could be used to find these weaknesses. We find that 8 categories are enough to cover 95% of the vulnerabilities in the dataset. This provides a guide for ICS owners to the most likely new vulnerabilities they may find in their systems and the best ways to detect them. We validate our proposed vulnerability categories by analysing a further 6 months of ICS Advisory reports, which shows that our categories continue to dominate the reported weaknesses. We further validate our proposed detection methods by applying them to a range of ICS equipment and finding four new critical security vulnerabilities.
IoT sensors integrated with the distributed protocol IOTA/Tangle: Bosch XDK110 use case

Decentralized systems can provide privacy, security, and immutability without relying on a central authority. In the context of the Internet of Things, Distributed Ledger Technologies (DLT) can be important facilitators for implementing IoT economies. IOTA is a public DLT, which was created to provide support for the Internet of Things. The IOTA protocol enables high transaction throughput at zero cost, while also being highly scalable. These characteristics make it possible to collect sensor data with transparency and security, using a distributed public network. Despite the great potential of IOTA as a distributed protocol for sensor data, there are still no studies that demonstrate how the connection between sensors and IOTA can occur, and what the characteristics and limitations of the system are. We began exploratory technological research which includes the modeling of a system that allows the integration of IOTA with sensors, using IOTA as the data layer. An implementation of the model has been developed, based on data collection from the Bosch XDK110 multisensor and a storage/visualization application. Because the IOTA protocol requires Proof of Work to send and broadcast data over the network, which takes a few seconds, it is not possible to have granular live data from sensors directly interacting with the DLT. However, we will study the case of an application that allows for this by buffering data and inserting it to the IOTA network at a rate that nodes can handle.
An Analysis of Routing Attacks Against IOTA Cryptocurrency

IOTA is a new type of distributed ledger designed for allowing fee-less and rate-scalable micropayments in Internet of Things applications. Security research on IOTA has focused mainly on attacks involving its cryptographic operations or its consensus algorithm. In this paper, we present a preliminary analysis of the IOTA security with respect to malicious Autonomous Systems (ASes), which can intercept IOTA connections by manipulating routing advertisements (BGP hijacking) or by naturally intercepting traffic. We make the simplifying assumption that the malicious AS can intercept routes between hosts without causing side effects, or without these side effects being noticed by the intercepted hosts. We identify three notable attacks that can lead to permanent money freeze, and to local or global interruptions of the consensus mechanisms. We then analyze the vulnerability of IOTA against malicious ASes on the real Internet topology, and we show that IOTA cryptocurrency is, at the time of writing, pretty susceptible of these attacks because quite centralized from the point of view of BGP routing. We then study the routing-level security of the next version of IOTA (post-coordicide), which has been proposed by the IOTA Foundations to make the cryptocurrency fully distributed.
Atmospheric water vapor radiative effects on shortwave radiation under clear skies: A global spatiotemporal analysis

This global study analyzes how the total column water vapor (in terms of precipitable water, PW) is related to the attenuation of shortwave solar radiation (SWR) using data from the MERRA-2 reanalysis during 2000–2014 and SWR simulations obtained with the REST2 clear-sky radiation model. The water vapor radiative effect (WVRE) on SWR attenuation is investigated by comparing the clear-sky SWR predictions under humid and ideally dry atmospheres, whereas the quantitative effect of PW on WVRE is determined using the concept of precipitable water efficiency (PWE). At global scale, WVRE exhibits a distinct seasonal pattern ranging from –80.2 to 0 W m‐2, with lower values in humid regions where PW is typically high (>4 cm), making the water vapor contribution relatively small. This is attributed to the saturation effect in water vapor absorption. Over arid regions, the solar irradiance attenuation is mainly controlled by the high aerosol loads, and less negative WVRE values are found. Over high-altitude regions, the low PW reduces the atmospheric absorption, resulting in low absolute WVRE magnitudes. PWE is found to extend globally between –133.7 and 0 W m‐2 cm‐1 on a monthly scale. This range is smaller on a long-term mean monthly or annual basis. On a long-term basis, the monthly PWE results are mainly concentrated between −20 and 0 W m−2 cm−1, generating leptokurtic and left-skewed distributions peaking between −10 and −8 W m−2 cm−1, depending on month. PWE is inversely related to PW on a long-term annual scale, indicating that PWE tends to zero under very humid conditions. A general nonlinear model is proposed to evaluate the hourly PWE at global scale on a monthly basis, separately for the two hemispheres. A PW trend analysis reveals that the Middle East, equatorial regions, central Europe, and Australia exhibit significant temporal trends, further inducing significant trends in WVRE. For those areas, WVRE trends are calculated between −3.3 and 1.5 W m−2 over the entire 15-year period. In addition, the global WVRE over landmasses is classified in terms of the Köppen-Geiger (KG) climate classification system. The most pronounced radiative effects are found over the equatorial regions, with an average WVRE of −63 W m−2. The Arid KG class provides a complex WVRE pattern ranging from −66 to −24 W m−2. Further segregation of this class shows strongly variable WVRE, induced by local climate differentiation. Over Warm Temperate climates, WVRE is found to vary widely according to the various KG subclasses. For the Cold climate class, PW is mainly concentrated around 1 cm and WVRE is low. The Polar regions include high mountains where WVRE is low, with a median close to −28 W m−2.
Phishing Website Detection Based on Machine Learning Algorithm

Phishing websites are a means to deceive users' personal information by using various means to impersonate the URL address and page content of a real website. This paper analyzes the structural features of the URL of the phishing website, extracts 12 kinds of features, and uses four machine learning algorithms for training. Then, use the best performing algorithm as our model to identify unknown URLs. After the recognition is completed, a snapshot of the web page is extracted and compared with the regular web page snapshot to implement the recommendation of the original regular web page of the phishing web page.
Pedestrian Detection for Autonomous Cars: Occlusion Handling by Classifying Body Parts

In this work, we address the problem of detecting body parts of pedestrians using deep neural networks. In particular, we consider the occluded pedestrian detection problem in autonomous driving settings. While state-of-the-art deep neural models perform reasonably well for detecting full-body pedestrians, their performances are not satisfactory for occluded pedestrians. Introducing a new training strategy along with a fusion mechanism, we enhance the performance of the SSD-Mobilenet and the Faster R-CNN by utilizing body parts information to handle occluded pedestrians. We evaluate our method by training these two deep neural networks using a public dataset as well as our dataset. The performance of the two developed models is compared both in terms of detection accuracy and runtime efficiency.
Heuristic Phishing Detection and URL Checking Methodology Based on Scraping and Web Crawling

With the improvement of techniques used by the attackers, the detection and prevention of threats such as phishing and malware can represent a problem and computational challenge. Recent research has found that phishing and malicious code infection are the main threats triggered by social engineering. In this work, the attack vectors that cause these threats are analyzed, proposing a method of checking specific strings in URLs and e-mail messages, which can be used in conjunction with proxies and Anti Spam filters. The method was implemented in an experimental scenario and is capable of detecting the presence of the main elements that have direct contact with the user, such as: form fields, redirection of links and downloadable files. Furthermore, the proposed method was able to detect phishing URLs with accuracy values between 73.3% and 97.66% with an average time of 30 seconds.
Unifying Framework for Accelerated Randomized Methods in Convex Optimization

In this paper, we consider smooth convex optimization problems with simple constraints and inexactness in the oracle information such as value, partial or directional derivatives of the objective function. We introduce a unifying framework, which allows to construct different types of accelerated randomized methods for such problems and to prove convergence rate theorems for them. We focus on accelerated random block-coordinate descent, accelerated random directional search, accelerated random derivative-free method and, using our framework, provide their versions for problems with inexact oracle information. Our contribution also includes accelerated random block-coordinate descent with inexact oracle and entropy proximal setup as well as derivative-free version of this method. Moreover, we present an extension of our framework for strongly convex optimization problems. We also discuss an extension for the case of inexact model of the objective function.
Privacy-Preserving Similarity Search With Efficient Updates in Distributed Key-Value Stores

Privacy-preserving similarity search plays an essential role in data analytics, especially when very large encrypted datasets are stored in the cloud. Existing mechanisms on privacy-preserving similarity search were not able to support secure updates (addition and deletion) efficiently when frequent updates are needed. In this article, we propose a new mechanism to support parallel privacypreserving similarity search in a distributed key-value store in the cloud, with a focus on efficient addition and deletion operations, both executed with sublinear time complexity. If search accuracy is the top priority, we further leverage Yao's garbled circuits and the homomorphic property of Hash-ElGamal encryption to build a secure evaluation protocol, which can obtain the top-R most accurate results without extensive client-side post-processing. We have formally analyzed the security strength of our proposed approach, and performed an extensive array of experiments to show its superior performance as compared to existing mechanisms in the literature. In particular, we evaluate the performance of our proposed protocol with respect to the time it takes to build the index and perform similarity queries. Extensive experimental results demonstrated that our protocol can speedup the index building process by up to 800x with 2 threads and the similarity queries by up to -7x with comparable accuracy, as compared to the state-of-the-art in the literature.
Automata design for honeybee search algorithm and its applications to 3D scene reconstruction and video tracking

Honeybees, as social insects, follow a modular strategy applied to dynamic environments to provide reasonable opportunities for partial solutions to evolve in the form of interacting coadapted subcomponents. The honeybee search algorithm combines concepts from the areas of evolutionary algorithms and swarm intelligence to solve optimization problems. This algorithm is mainly based on the foraging behavior of honeybees and the search power of evolution strategies, a type of evolutionary algorithm used for real-valued problems. This paper shows the integration between an automaton and the honeybee search algorithm to formalize the algorithm mathematically. The combination mentioned above is tested here with the innovative applications of three-dimensional scene reconstruction and video tracking. The experimental results for both applications show evidence that the honeybee search algorithm can be used to improve time costs in challenging computer vision tasks through controlled experiments and objective comparisons. Also, the validation of results demonstrates that the measured accuracy ranks top-tier among other algorithms in the ALOV++ benchmark.