Reviewerly/exHarmony huggingface dataset:
Social barriers to open (water) data

Abstract Although the proliferation of open water data platforms and initiatives in recent years is a laudable phenomenon, there is little empirical evidence indicating whether and to what extent these efforts are generating anticipated benefits of improved transparency, citizen participation, innovation, and water resource decision making. Relatedly, water resource researchers have devoted little attention to identifying and accounting for barriers that may be limiting open water data efforts from realizing their potential. The premise of this overview is that (a) open water data efforts could be improved with a better understanding of the non‐technical challenges and that (b) water researchers interested in open water data would benefit from delving into the emerging body of research in public policy, information science, and other disciplines on open data barriers more broadly. However, the research on open data barriers has neglected water sustainability issues. In light of this asymmetry, the aim of this overview is to foster interdisciplinary engagement on this topic by introducing the water resource community to this literature via a discussion of key social barriers to open data. Additionally, I hope to motivate water resource researchers to develop a two‐way interdisciplinary engagement by making original research contributions to this larger literature. This article is categorized under: Engineering Water Science of Water &gt; Methods Human Water &gt; Water Governance Human Water &gt; Water as Imagined and Represented
Zenithal isotropic object counting by localization using adversarial training

Counting objects in images is a very time-consuming task for humans that yields to errors caused by repetitiveness and boredom. In this paper, we present a novel object counting method that, unlike most of the recent works that focus on the regression of a density map, performs the counting procedure by localizing each single object. This key difference allows us to provide not only an accurate count but the position of every counted object, information that can be critical in some areas such as precision agriculture. The method is designed in two steps: first, a CNN is in charge of mapping arbitrary objects to blob-like structures. Then, using a Laplacian of Gaussian (LoG) filter, we are able to gather the position of all detected objects. We also propose a semi-adversarial training procedure that, combined with the former design, improves the result by a large margin. After evaluating the method on two public benchmarks of isometric objects, we stay on par with the state of the art while being able to provide extra position information.
Implementation of an Organic Database Structure for Population-Based Structural Health Monitoring

Population-based Structural Health Monitoring (PBSHM) considers grouping multiple structures into a single population, thus allowing additional insight to be gained from SHM feature data as a whole, compared to insights gained for any individual structure. Current data storage methodologies for SHM often operate from a single structure point of view. Different types of data can often be stored in different systems, files, structures and languages. This approach means that bespoke methodologies must be developed for any new SHM application, making analysing and processing of SHM data costly. The advantage of the proposed unified approach is the facilitation of information sharing across populations of structures, a key principle in PBSHM. A generic pool of methodologies can then be developed, allowing any set of PBSHM data to be quickly and efficiently analysed and processed, bringing down the total cost of running a system. In this paper a PBSHM schema is defined to store multiple populations of PBSHM data inside a NoSQL database via a Time First approach. The schema allows a single database that organically grows as more data/knowledge about the population becomes available. As everything is grouped by Time, the approach allows previously unknown data relationships to be easily viewed. In this paper the PBSHM schema is implemented in a MongoDB database and sensor data are added into the system to show the ease of use of a standardised data schema.
Inter‐cloud secure data sharing and its formal verification

Abstract With the development of cloud computing, data sharing and collaboration have become increasing among cloud‐oriented organizations. In addition to trivial management work, data providers face many difficulties in managing access control policies in cloud data sharing, to prevent permission abuse and information leakage. This article mainly answers the following question: is it possible to achieve verifiably secure inter‐cloud data sharing? This article proposes an inter‐cloud data sharing (ICDS) approach based on ciphertext‐policy attribute‐based encryption (CP‐ABE) to ensure the security of ICDS. The security properties that ICDS holds are formally verified, where the information flow rules are defined and used in the verification. The information flow of the proposed ICDS is firstly modeled by use of high‐level Petri nets (HLPN) formally, then being represented in Z language, and at last the security properties of ICDS are verified by the Z3 solver. The formal analysis proves that the ICDS holds the security properties of confidentiality, authenticity, and collusion‐resistance.
A Novel Video Transmission Optimization Mechanism Based on Reinforcement Learning and Edge Computing

As we know, the video transmission traffic already constitutes 60% of Internet downlink traffic. The optimization of video transmission efficiency has become an important challenge in the network. This paper designs a video transmission optimization strategy that takes reinforcement learning and edge computing (TORE) to improve the video transmission efficiency and quality of experience. Specifically, first, we design the popularity prediction model for video requests based on the RL (reinforcement learning) and introduce the adaptive video encoding method for optimizing the efficiency of computing resource distribution. Second, we design a video caching strategy, which adopts EC (edge computing) to reduce the redundant video transmission. Last, simulations are conducted, and the experimental results fully demonstrate the improvement of video quality and response time.
Neural PathSim for Inductive Similarity Search in Heterogeneous Information Networks

PathSim is a widely used meta-path-based similarity in heterogeneous information networks. Numerous applications rely on the computation of PathSim, including similarity search and clustering. Computing PathSim scores on large graphs is computationally challenging due to its high time and storage complexity. In this paper, we propose to transform the problem of approximating the ground truth PathSim scores into a learning problem. We design an encoder-decoder based framework, NeuPath, where the algorithmic structure of PathSim is considered. Specifically, the encoder module identifies Top T optimized path instances, which can approximate the ground truth PathSim, and maps each path instance to an embedding vector. The decoder transforms each embedding vector into a scalar respectively, which identifies the similarity score. We perform extensive experiments on two real-world datasets in different domains, ACM and IMDB. Our results demonstrate that NeuPath performs better than state-of-the-art baselines in the PathSim approximation task and similarity search task.
Active Sampling for the Quickest Detection of Markov Networks

Consider <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> random variables forming a Markov random field (MRF). The true model of the MRF is unknown, and it is assumed to belong to a binary set. The objective is to sequentially sample the random variables (one-at-a-time) such that the true MRF model can be detected with the fewest number of samples, while in parallel, the decision reliability is controlled. The core element of an optimal decision process is a rule for selecting and sampling the random variables over time. Such a process, at every time instant and adaptively to the collected data, selects the random variable that is expected to be most informative about the model, rendering an overall minimized number of samples required for reaching a reliable decision. The existing studies on detecting MRF structures generally sample the entire network at the same time and focus on designing optimal detection rules without regard to the data-acquisition process. This paper characterizes the sampling process for general MRFs, which is shown to be optimal in the asymptote of large <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> . The critical insight in designing the sampling process is devising an information measure that captures the decisions’ inherent statistical dependence over time. Furthermore, when the MRFs can be modeled by acyclic probabilistic graphical models, the sampling rule is shown to take a computationally simple form. Performance analysis for the general case is provided, and the results are interpreted in several special cases: Gaussian MRFs, non-asymptotic regimes, Chernoff’s rule for controlled (active) sensing, and the problem of cluster detection.
On Multimodal Microblog Summarization

Microblog summarization systems are gaining importance during natural disasters. A lot of tweets are posted along with multimedia content during the occurrence of any natural disaster event. Extracting relevant information/summary from these tweets is important for the smooth functioning of the rescue operation. Moreover, because of the limited size of the tweets, in many cases, tweets are associated with images. The current work is the first of its kind where both the image and the tweet text are utilized simultaneously to generate a summary from microblog data generated during a disaster event. Different aspects, such as syntactic similarity, the maximum length of the tweets, retweet score, and antiredundancy, are considered as objective functions and those are simultaneously optimized using a metaheuristic population-based evolutionary strategy to select a good set of tweets to form a good quality summary. In order to extract information from images, a dense captioning model is utilized and the dense captions are further utilized for calculating the antiredundancy measure. We employed word mover distance to capture the semantic similarity between two tweets. Due to the unavailability of the dataset for multimodal microblog summarization tasks in a disaster-event scenario, datasets are created and made openly available to the community. The obtained summarization results are evaluated using the well-known ROUGE measure.
Deep Convolutional Neural Network with KNN Regression for Automatic Image Annotation

Automatic image annotation is an active field of research in which a set of annotations are automatically assigned to images based on their content. In literature, some works opted for handcrafted features and manual approaches of linking concepts to images, whereas some others involved convolutional neural networks (CNNs) as black boxes to solve the problem without external interference. In this work, we introduce a hybrid approach that combines the advantages of both CNN and the conventional concept-to-image assignment approaches. J-image segmentation (JSEG) is firstly used to segment the image into a set of homogeneous regions, then a CNN is employed to produce a rich feature descriptor per area, and then, vector of locally aggregated descriptors (VLAD) is applied to the extracted features to generate compact and unified descriptors. Thereafter, the not too deep clustering (N2D clustering) algorithm is performed to define local manifolds constituting the feature space, and finally, the semantic relatedness is calculated for both image–concept and concept–concept using KNN regression to better grasp the meaning of concepts and how they relate. Through a comprehensive experimental evaluation, our method has indicated a superiority over a wide range of recent related works by yielding F1 scores of 58.89% and 80.24% with the datasets Corel 5k and MSRC v2, respectively. Additionally, it demonstrated a relatively high capacity of learning more concepts with higher accuracy, which results in N+ of 212 and 22 with the datasets Corel 5k and MSRC v2, respectively.
Improved strategies of relation extraction based on graph convolutional model on tree structure for web information processing

In the Industry 4.0/5.0 era, information integration is employed to fuse information from different companies to facilitate interoperation. However, information extraction is an important preprocessing phase that must be performed prior to integrating data from different contexts. Relation extraction, which is an element of information extraction, is typically the basis of many upper-level applications, e.g., information visualization and inference. Some current models may not fully consider the complementary effect of information at different levels of granularity featured by different neural networks. In this paper, two improvement relation extraction strategies based on the graph convolutional model on tree structure (GCNTree) are proposed. The first strategy integrates a hierarchical attention mechanism and correlation analysis between subjects and objects to generate sentence and entity vectors, respectively. The second strategy merges a named-entity recognition subnetwork with GCNTree to realize joint learning of relation and entity extraction. Experimental results demonstrate that the proposed strategies are comparable to state-of-the-art methods.1
Cyber Security and APT Groups

This research aims to describe and analyse APT (Advanced Persistent Thread) groups and their activities. Information was taken from articles, reports, and studies; published by organizations dealing with cybersecurity. APT groups often operate in state sponsorship, but could also be sponsored by a company to obtain sensitive information about their competitors. The term APT is explained and then described in detail. The well-known APT groups from China, Russia, North Korea and Iran are included. The main goal of APT activities is espionage, financial gain, intelligent property theft, and sabotage. The life cycle of the APT attack is analysed and discussed. In the paper are compared four life cycle examples; they are divided into phases, from five to ten. The initial phase of all life cycles is „reconnaissance”. It determines the success of the attack and, of course, the reliability of the defence. The future research of APT groups is mentioned.
HowToo: A Platform for Sharing, Finding, and Using Programming Strategies

Developers rely heavily on resources to find technical insights on how to use languages, APIs, and platforms, seeking help from Stack Overflow, GitHub, meetups, blogs, live streams, forums, documentation, and more. However, there is one kind of knowledge for which resources are hard to find: strategic knowledge. In contrast to technical knowledge, strategic knowledge provides insight into how to approach problem-solving. Prior work has demonstrated that developers can make use of written strategies to improve their problem-solving. However, there is currently no way for developers to share, curate, and search for this knowledge at scale. To address this gap, we contribute HowToo, a platform for sharing, finding, and using programming strategies. Its key insight is that there are many different approaches to the same problem, and developers may need different strategies depending on their situation. In a longitudinal evaluation with more than 30 students in a project-based software engineering course, we found that: 1) students viewed HowToo as complementary to technical resources; 2) students viewed strategies as helping them be more systematic and complete in their work; 3) HowToo helped students be more confident in their problem solving; 4) when students were under time pressure, they were less inclined to use HowToo to structure their work, as being mindful required them to slow down.
Constraint Answer Set Programming: Integrational and Translational (or SMT-based) Approaches

Abstract Constraint answer set programming or CASP, for short, is a hybrid approach in automated reasoning putting together the advances of distinct research areas such as answer set programming, constraint processing, and satisfiability modulo theories. CASP demonstrates promising results, including the development of a multitude of solvers: acsolver, clingcon, ezcsp, idp, inca, dingo, mingo, aspmt2smt, clingo[l,dl], and ezsmt . It opens new horizons for declarative programming applications such as solving complex train scheduling problems. Systems designed to find solutions to constraint answer set programs can be grouped according to their construction into, what we call, integrational or translational approaches. The focus of this paper is an overview of the key ingredients of the design of constraint answer set solvers drawing distinctions and parallels between integrational and translational approaches. The paper also provides a glimpse at the kind of programs its users develop by utilizing a CASP encoding of Traveling Salesman problem for illustration. In addition, we place the CASP technology on the map among its automated reasoning peers as well as discuss future possibilities for the development of CASP.
Cosmetic Skin Type Classification Using CNN With Product Recommendation

The skin spectrum is used in a wide range of studies like dermatology, biometric recognition, cosmetic research and disease detection. In cosmetic research, skin can be classified into four main categories as, normal, dry, oily and combination. The current methods to identify the cosmetic skin type are time consuming and error prone. Recently, Deep Learning algorithms have been in the limelight for various classification problems like text, audio, image and video classifications. In this paper, the applications of Convolutional Neural Network for skin type classification have been studied. To train the model, we have created a dataset of over 80 skin images,collected by web scraping and classified into oily and dry categories. To evaluate the performance of our model, we have used the trained model on a small sample of easily distinguishable images. The results of our CNN classification model show an accuracy of about 85% with a slight bias towards oily images. The results show that Deep Learning has great potential in the field of skin type classification from facial images, and with a dataset of greater size, could give more optimal and even lesser error prone results.
KA-NER: Knowledge Augmented Named Entity Recognition

Most named entity recognition models comprehend words based solely on their contexts and man-made features, but neglect relevant knowledge. Incorporating prior knowledge from a knowledge base is straightforward but non-trivial due to two challenges: knowledge noise from unrelated span-entity mappings and knowledge gap between a text and a knowledge base. To tackle these challenges, we propose KA-NER, a novel knowledge-augmented named entity recognition model, in which sanitized entities from a knowledge base are injected to sentences as prior knowledge. Specifically, our model consists of two components: a knowledge filtering module to filter domain-relevant entities and a knowledge fushion module to bridge the knowledge gap when incorporating knowledge into a NER model. Experimental results show that our model achieves significant improvements against baseline models on different domain datasets.
DISCO – U-Net based Autoencoder Architecture with Dual Input Streams for Skeleton Image Drawing

In this paper, we propose a DISCO, which is a manner of designing autoencoder architecture to process dual input streams for skeletal image generation. The DISCO was designed to be dealing with binary masks and skeletonized images concurrently at the input side. We expected the skeletonized images using traditional thinning algorithms could help to boost skeleton prediction performances. Inside the DISCO architecture, there exist two encoders and a single decoder. Each functional block is stacked with multiple logical layers. We designed that logical layer outputs of encoders transferred corresponding counterpart layers in a decoder referring to U-Net architecture. In addition, we proposed hybrid-type encoder models based on the DISCO architecture to capitalize on the effect of the model ensemble. We demonstrated performances of the DISCO-A and DISCO-B models derived from the proposed architecture in terms of f1-score and loss convergence per each epoch. We confirmed the DISCO-B had produced the best performance under symbolic label usage. In the development phase, our best score reached 0.7386 with 500 epochs.
Understanding Safety for Unmanned Aerial Vehicles in Urban Environments

When Unmanned Aerial Vehicles (UAVs) autonomously operate in urban environments, it is especially important for these systems to behave safely and not harm anybody or anything. However, it is challenging to ensure that these systems behave safely in all possible situations and to clearly define this “safe” behavior for each situation. In this work, we provide a methodology for testing the safe behavior of UAV s while considering their environment with the help of scenario-based testing and search-based techniques. Additionally, we explore two cases throughout the paper: (i) A safety distance is specified, and we can use it for testing. (ii) No safety distance is defined, but we still aim to test the safe behavior of UAV s. In our experiments, we show the effectiveness and applicability of the proposed methods by discovering several safety distance violations and questionable behaviors of the tested UAV for both cases and four scenarios that represent all alternatives to avoid an obstacle.
Where Did I See It? Object Instance Re-Identification with Attention

Existing methods dealing with object instance re-identification (OIRe-ID) look for the best visual features match of a target object within a set of frames. Due to the nature of the problem, relying only on the visual appearance of object instances is likely to provide many false matches when there are multiple objects with similar appearance or multiple instances of same object class present in the scene. We focus on a rigid scene setup and to limit the negative effects of the aforementioned cases, we propose to exploit the background information. We believe that this would be particularly helpful in a rigid environment with a lot of reoccurring identical models of objects since it would provide rich context information. We introduce an attention-based mechanism to the existing Mask R-CNN architecture such that we learn to encode the important and distinct information in the background jointly with the foreground features relevant to rigid real-world scenarios. To evaluate the proposed approach, we run compelling experiments on the ScanNet dataset. Results demonstrate that we outperform significantly compared to different baselines and SOTA methods.