Reviewerly/exHarmony huggingface dataset:
Anti-Drone and Anti-Autonomy: Achieving Drone Control via System Logic Analysis

Drones (more formally known as unmanned aerial vehicles or unmanned aerial systems) are poised to provide benefits in numerous areas of society. Their application areas range from package delivery to military surveillance to scientific data collection. However, drone presence and activities are, in some cases, undesirable. Whether launched by a prankster, criminal, terrorist organization or state actor - or perhaps just an average citizen who has lost control of the craft - there is a need to prevent drone operations. This paper proposes a method that does not require physical interaction with the drone or the ability to compromise its ground control station or its onboard security or other software. Instead, the proposed method focuses on the identification of the logical processes that are used for decision making, their decomposition into rules (including rules that must be represented using partial membership and fuzzy logic) and the mapping of the rules into an expert system-style network. From this network, a solver algorithm can be utilized to identify solutions that modify external inputs (electronic data and sensed information) to produce a desired response from the UAV's autonomous command system. These responses could include departure from a restricted area, positioning for in-air capture or targeting, landing or a targeted crash. This paper presents the proposed system, the logic-to-rule decomposition process and a solver mechanism for the rule-based system. In also discusses the implementation of the system and its testing, before concluding with a discussion of its efficacy for various applications and pathways for future work.
Litedge

Wireless surveillance systems are rapidly gaining popularity due to their easier deployability and improved performance. However, cameras inside are generating a large amount of data, which brings challenges to the transmission through resource-constrained wireless networks. Observing that most collected consecutive frames are redundant with few objects of interest (OoIs), the filtering of these frames can dramatically relieve the transmission pressure. Additionally, real-world environment may bring shielding or blind areas in videos, which notoriously affects the accuracy of frame analysis. The collaboration between cameras facing at different angles can compensate for such accuracy loss.
Spatial correlation and breast cancer risk

We present a novel method for evaluating local spatial correlation structure in two-dimensional (2D) mammograms and evaluate its capability for risk prediction as one possible application. Two matched case-control studies were analyzed. Study 1 included women (N = 588 pairs) with mammograms acquired with either Hologic Selenia full field digital mammography (FFDM) units or Hologic Dimensions digital breast tomosynthesis units. Study 2 included women (N =180 pairs) with mammograms acquired with a General Electric Senographe 2000D FFDM unit. Matching variables included age, HRT usage/duration, screening history, and mammography unit. Local autocorrelation functions were determined with Fourier analysis and compared with a template defined as a 2D double-sided exponential function with one spatial extent parameter: n = 4, 12, 24, 50, 74, 100, and 124, where (n+1)×(n+1) is the area of the local spatial extent measured in pixels. The difference between the local correlation and template was gauged within an adjustable parameter kernel and summarized, producing two measures: the mean (mn+1), and standard deviation (sn+1). Both adjustable parameters were varied in Study 1. Select measures that produced significant associations with breast cancer were translated to Study 2. Breast cancer associations were evaluated with conditional logistic regression, adjusted for body mass index and ethnicity. Odds ratios (ORs) were estimated as per standard deviation increment with 95% confidence intervals (CIs). Two measures were selected for breast cancer association analysis in Study 1: m75 and s25. Both measures revealed significant associations with breast cancer: OR = 1.45 (1.23, 1.66) for m75 and OR = 1.30 (1.14, 1.49) for s25. When translating to Study 2, these measures also revealed significant associations: OR = 1.49 (1.12, 1.96) for m75 and OR = 1.34 (1.06, 1.69) for s25. Novel correlation metrics presented in this work produced significant associations with breast cancer risk. This approach is general and may have applications beyond mammography.
NONREPRESENTABLE RELATION ALGEBRAS FROM GROUPS

Abstract A series of nonrepresentable relation algebras is constructed from groups. We use them to prove that there are continuum many subvarieties between the variety of representable relation algebras and the variety of coset relation algebras. We present our main construction in terms of polygroupoids.
Bidirectional Transformer Based Multi-Task Learning for Natural Language Understanding

We propose a multi-task learning based framework for natural language understanding tasks like sentiment and topic classification. We make use of bidirectional transformer based architecture to generate encoded representations from given input followed by task-specific layers for classification. Multi-Task learning (MTL) based framework make use of a different set of tasks in parallel, as a kind of additional regularization, to improve the generalizability of the trained model over individual tasks. We introduced a task-specific auxiliary problem using the k-means clustering algorithm to be trained in parallel with main tasks to reduce the model's generalization error on the main task. POS-tagging was also used as one of the auxiliary tasks. We also trained multiple benchmark classification datasets in parallel to improve the effectiveness of our bidirectional transformer based network across all the datasets. Our proposed MTL based transformer network improved state-of-the-art overall accuracy of Movie Review (MR), AG News, and Stanford Sentiment Treebank (SST-2) corpus by 6%, 1.4%, and 3.3% respectively.
A WS-Agreement Based SLA Ontology for IoT Services

In the Internet of Things (IoT), billions of physical devices, distributed over a large geographic area, provide a near real-time state of the world. These devices' capabilities can be abstracted as IoT services and delivered to users in a demand-driven way. In such a dynamic large-scale environment, a service provider who supports a service level agreement (SLA) can have a comprehensive competitive edge in terms of service quality management, service customization, optimized resource allocation, and trustworthiness. However, there is no consistent way of drafting an SLA with respect to describing heterogeneous IoT services, which obstructs automatic service selection, SLA negotiation, and SLA monitoring. In this paper, we propose an ontology, WIoT-SLA, to achieve semantic interoperability. We combine IoT service properties with two prominent web service SLA specifications: WS-Agreement and WSLA, to take advantage of their complementary features. This ontology is used to formalize the SLAs and SLA negotiation offers, which further facilitates the service selection and automatic SLA negotiation. It can also be used by a monitoring engine to detect SLA violations by providing the semantics of service level objectives (SLOs) and quality metrics. To evaluate our work, a prototype is implemented to demonstrate its feasibility and efficiency.
An Embodied Approach to AI Art Collaboration

AI art tools based on interactive evolutionary computation provide a form of creative collaboration between the user and machine. While some see the involvement of the user in the process as indication of the failure to truly develop computational creativity, another perspective would view it as an instance of a distributed system leveraging the strength of both the human and the computer. ImageSpace was developed to explore this dynamic further, providing an embodied interface based on spatial layout to make the communication more fluid. This paper discusses the way information is exchanged through interaction with the space, and how it can improve the experience of creating artwork with the system.
<i>WhereWear</i>

With the growth of wearable devices, numerous health and smart building applications are enabled. As a result, many people wear multiple devices for different applications, such as fitness tracking. Being able to match devices' physical identity (e.g., smartwatch on Bob's left forearm) to their virtual identity (e.g., IP 192.168.0.22) is often a requirement for these applications, especially when different people could use the same device --e.g., home, gym equipment-- or be worn in different places. Context-aware sensing, utilizing the insight that co-located sensors detect the same events, has been used to establish such association ubiquitously. However, challenges arise with the growing number of on-body devices, and existing approaches fail to distinguish where the devices are on the body.
A <sup>2</sup> CMHNE

Network representation learning is playing an important role in network analysis due to its effectiveness in a variety of applications. However, most existing network embedding models focus on homogeneous networks and neglect the diverse properties such as different types of network structures and associated multimedia content information. In this article, we learn node representations for multimodal heterogeneous networks, which contain multiple types of nodes and/or links as well as multimodal content such as texts and images. We propose a novel attention-aware collaborative multimodal heterogeneous network embedding method (A 2 CMHNE), where an attention-based collaborative representation learning approach is proposed to promote the collaboration of structure-based embedding and content-based embedding, and generate the robust node representation by introducing an attention mechanism that enables informative embedding integration. In experiments, we compare our model with existing network embedding models on two real-world datasets. Our method leads to dramatic improvements in performance by 5%, and 9% compared with five state-of-the-art embedding methods on one benchmark (M10 Dataset), and on a multi-modal heterogeneous network dataset (WeChat dataset) for node classification, respectively. Experimental results demonstrate the effectiveness of our proposed method on both node classification and link prediction tasks.
Boosting sparsity-induced autoencoder: A novel sparse feature ensemble learning for image classification

As a kind of unsupervised learning model, the autoencoder is usually adopted to perform the pretraining to obtain the optimal initial value of parameter space, so as to avoid the local minimality that the nonconvex problem may fall into and gradient vanishment of the process of back propagation. However, the autoencoder and its variants have not taken the statistical characteristics and domain knowledge of the train set and also lost plenty of essential representaions learned from different levels when it comes to image processing and computer vision. In this article, we firstly add a sparsity-induced layer into the autoencoder to exploit and extract more representative and essential features exist in the input and then combining the ensemble learning mechanism, we propose a novel sparse feature ensemble learning method, named Boosting sparsity-induced autoencoder, which could make full use of hierarchical and diverse features, increase the accuracy and the stability of a single model. The classification results on different data sets illustrated the effectiveness of our proposed method.
Design and Implementation of a Research and Education Cybersecurity Operations Center

The growing number and severity of cybersecurity threats, combined with a shortage of skilled security analysts, has led to an increased focus on cybersecurity research and education. In this article, we describe the design and implementation of an education and research Security Operations Center (SOC) to address these issues. The design of a SOC to meet educational goals as well as perform cloud security research is presented, including a discussion of SOC components created by our lab, including honeypots, visualization tools, and a lightweight cloud security dashboard with autonomic orchestration. Experimental results of the honeypot project are provided, including analysis of SSH brute force attacks (aggregate data over time, attack duration, and identification of well-known botnets), geolocation and attack pattern visualization, and autonomic frameworks based on the observe, orient, decide, act methodology. Directions for future work are also be discussed.
Perception-Constrained Robot Manipulator Planning for Satellite Servicing

Satellite servicing is a rapidly developing industry which requires a number advances in semi- and fully-automated space robotics to unlock many key servicing capabilities. One upcoming mission example is the NASA Restore-L Robotic Servicing spacecraft, which is equipped with two 7-joint robotic manipulators used to capture a satellite and perform a complex series of refueling tasks, including swapping between various end-effector tools stored on board. In this scenario, planning of the manipulator motions must account for a number of constraints, such as collision avoidance and the potential need for uninterrupted visual tracking of objects or of the end-effector. Such complex constraints in a cluttered environment, such as the interface between two spacecraft, are time-consuming to incorporate into hand-designed trajectories. Thus, in this work we present a software tool which uses robot motion planning and path refinement algorithms for automated, real-time computation of near-optimal, collision-free trajectories which satisfy the aforementioned perception constraints. The tool is built on the ROS MoveIt! framework, which can simulate and visualize trajectories as well as seamlessly switch between motion planning and refinement algorithms depending on task requirements. Additionally, we performed experimental campaigns to benchmark a number of available algorithms for performance in handling such perception constraints. Although the framework is applied to a mock-up of Restore-L satellite servicer in this paper, the tool can be applied to any fixed-base manipulator planning scenario with a similar class of constraints.
The McKinsey-Tarski Theorem for Topological Evidence Logics

We prove an analogue of the McKinsey and Tarski theorem for the recently introduced dense-interior semantics of topological evidence logics. In particular, we show that in this semantics the modal logic $$\mathsf {S4.2}$$ is sound and complete for any dense-in-itself metrizable space. As a result $$\mathsf {S4.2}$$ is complete with respect to the real line $$\mathbb {R}$$, the rational line $$\mathbb {Q}$$, the Baire space $$\mathfrak {B}$$, the Cantor space $$\mathfrak {C}$$, etc. We also show that an extension of this logic with the universal modality is sound and complete for any idempotent dense-in-itself metrizable space, obtaining as a result that this logic is sound and complete with respect to $$\mathbb {Q}$$, $$\mathfrak {B}$$, $$\mathfrak {C}$$, etc.
Real-time dynamic gesture recognition and hand servo tracking using PTZ camera

A technology of real-time dynamic gesture recognition and hand tracking using a Pan-Tilt-Zoom (PTZ) camera was presented in this study. It was aimed to achieve robust scheme that stably recognized simple hand gestures and tracked the hand by means of a PTZ camera to keep the fingertip remaining in the center of the camera. For this purpose, the hand region was initially segmented in a cluttered environment using skin color segmentation in YCbCr color space to get the silhouette of the hand. Furthermore, the Monte Carlo Sampling method was used to estimate the Cubic Bezier curves best fitted to the sub contour points centralized in each contour point, and the fingertips were detected by combining the local maximums of a cumulative curvature with detection of convex defects. After that, feature triangle analysis was utilized to achieve dynamic recognition of simple gestures including “right click down” and “right click up”. Finally, the PTZ camera was driven by the algorithm to achieve servo tracking with the target fingertip when the gesture “right click down” was detected. As is shown by the experimental results, the proposed approach recognized the dynamic gestures and located the fingertips’ positions precisely, and realized the follow-up servo tracking using PTZ camera in real-time.
Polaron-transformed dissipative Lipkin-Meshkov-Glick model

We investigate the Lipkin-Meshkov-Glick model coupled to a thermal bath. Since the isolated model itself exhibits a quantum phase transition, we explore the critical signatures of the open system. Starting from a system-reservoir interaction written in positive definite form, we find that the position of the critical point remains unchanged, in contrast to the popular mean-field prediction. Technically, we employ the polaron transform to be able to study the full crossover regime from the normal to the symmetry-broken phase, which allows us to investigate the fate of quantum-critical points subject to dissipative environments. The signatures of the phase transition are reflected in observables like stationary mode occupation or waiting-time distributions.
Auto-Generation System Based on Fractal Geometry for Batik Pattern Design

In order to obtain the automatic simulation generation of traditional handmade batik patterns in a computer, this paper proposes the automatic generation method of batik flower patterns based on fractal geometry. Firstly, we analyze the fractal characteristics of batik flowers and design an automatic flower generation algorithm based on a two-dimensional iterated function system (IFS) and a curve function. The algorithm forms a complete flower pattern. Secondly, a nonlinear function is defined and the flower pattern is introduced into the nonlinear function to iterate and change. On this basis, we present an automatic generation method of different distribution patterns for flower patterns which obtains the most effective range of each parameter value for each function. Finally, in order to verify the feasibility of the automatic generation method of batik flower patterns, we develop an automatic generation experiment system for batik patterns via an interactive way of working. The results show that the user or designer can quickly and automatically simulate a series of flower patterns by changing the relevant parameter values, realizing the digitization and innovative design of the pattern and enriching the batik pattern base.
HeritageGO (HeGO)

In this digital era, one of the main challenge faced by cultural heritage is digitization. This challenge is particularly hard in countries like Italy, characterized by an extremely high number of Cultural goods. Data acquisition for many of these Cultural Heritage is extremely difficult, because of the complexity of surveys through traditional methodologies. In this paper, we propose a novel approach to the knowledge and data acquisition Cultural Heritage based on social media. The proposed approach, named "HeritageGo" (HeGo), transforms the user as an actor of the procedures for the acquisition of raw data. The paper also describes the first experiments focusing on the metric quality of the models obtained with SfM methodologies from raw data acquired by users.
MinScIE: Citation-Centered Open Information Extraction

Acknowledging the importance of citations in scientific literature, in this work we present MinScIE, an Open Information Extraction system which provides structured knowledge enriched with semantic information about citations. By comparing our system to it's original core, MinIE, we show that our approach improves extraction precision by 3 percentage points.
Salp swarm algorithm based on particle-best

Aiming at the problem of salp swarm algorithm(SSA) that the global exploration and local exploitation ability is difficult to coordinate and it is easy to fall into local optimum, a new algorithm of salp swarm based on particle-best is proposed. According to the leadership function of the leader, the algorithm divides the iteration into two stages: global exploration and local exploitation. In the global exploration stage, the leader conducts wide-area search centering on the particle-best position. In the local exploitation stage, the leader performs a fine search centering on the global optimal position. The follower is between the particle-best position and the individual current position. The experimental results of 23 general benchmark functions show that the algorithm based on particle-best has a great improvement in convergence speed, accuracy and robustness compared with other algorithms.
Big-Data Clustering with Genetic Algorithm

The data emerging from Internet of Things (IoT) usually exhibits a wide variety and is big in terms of quantity, i.e., Big-Data. As a preprocessing step, clustering is a regular task in such systems and normally is done using evolutionary algorithms. In this paper, we propose a new automatic clustering algorithm based on Genetic Algorithm (GA), in which, it is NOT mandatory to know the number of clusters. The proposed algorithm uses a very short chromosome encoding and proposes relevant crossover and mutation operators that lead to a very good clustering performance. Our algorithm, uses an unsupervised learning paradigm to classify the data points into clusters. To demonstrate the performance of the proposed algorithm, it is evaluated with balanced/unbalanced real-world data containing 13-tuple data vectors, and also with a 1.000.000-sample artificially generated random data set. At either cases, our algorithm outperforms the other algorithms.
Fuzzy FMECA risk evaluation and its applications in Chinese train control systems based on cloud model

In view of defects that traditional Fuzzy Analytical Hierarchy Process (FAHP) cannot accurately describe the ambiguity and randomness of the assessment, and as well as inconsistency existed in judgment process, in this paper a novel risk evaluation method is proposed using Fuzzy Failure Modes Effec ts and Criticality Analysis (FMECA) based on could model. The method firstly applies FMECA to identify the risk, and then uses FAHP to determine the subjection degree function with cloud model based. In the end, the group decision can be conducted with the synthetically aggregated cloud model, which can be directly observed through the distribution of the cloud pictures. Compared with traditional FAHP, the relevant practical examples in Chinese train control (CTC) systems show that the results of the two possess difference due to their original data coming from different 20-expert questionnaires, the reason is found that there exists inconsistence in 20-expert questionnaires on FAHP via t-examination method. Hence, though another 20-expert questionnaires and after inconsistence test, we obtain consistent result in both methods, but the Fuzzy-FMECA with cloud model based could implement the transformation between exact value and quantized one by incorporating the ambiguity and randomness, and provide more abundant information than subjection degree function of the conventional FAHP method, and possesses better consistency, and is a feasible and more effective decision method. In addition, the correlation coefficient method and center-of-gravity method are also applied to verify the correctness and effectiveness of the proposed method, and such that it can be widely applied to solve real-world practical issues.