Reviewerly/exHarmony huggingface dataset:
Integrated data space randomization and control reconfiguration for securing cyber-physical systems

Non-control data attacks have become widely popular for circumventing authentication mechanisms in websites, servers, and personal computers. Moreover, in the context of Cyber-Physical Systems (CPS) attacks can be executed against not only authentication but also safety. With the tightly coupled nature between the cyber components and physical dynamics, any unauthorized change to safety-critical variables may cause damage or even catastrophic consequences. Moving target defense (MTD) techniques such as data space randomization (DSR) can be effective for protecting against various types of memory corruption attacks including non-control data attacks. However, in terms of CPS it is also critical to ensure the timely Cyber-Physical interactions after attacks thwarted by MTD. This paper addresses the problem of maintaining system stability and security properties of a CPS in the face of non-control data attacks by developing a DSR approach for randomizing binaries at runtime, creating a variable redundancy based detection algorithm for identifying variable integrity violations, and integrating a control reconfiguration architecture for maintaining safe and reliable operation. Our security framework is demonstrated utilizing an autonomous vehicle case study.
Detecting Buffer-Overflow Vulnerabilities in Smart Grid Devices via Automatic Static Analysis

As a modern power transmission network, smart grid connects plenty of terminal devices. However, along with the growth of devices are the security threats. Different from the previous separated environment, an adversary nowadays can destroy the power system by attacking these devices. Therefore, it's critical to ensure the security and safety of terminal devices. To achieve this goal, detecting the pre-existing vulnerabilities of the device program and enhance the terminal security, are of great importance and necessity. In this paper, we propose a novel approach that detects existing buffer-overflow vulnerabilities of terminal devices via automatic static analysis (ASA). We utilize the static analysis to extract the device program information and build corresponding program models. By further matching the generated program model with pre-defined vulnerability patterns, we achieve vulnerability detection and error reporting. The evaluation results demonstrate that our method can effectively detect buffer-overflow vulnerabilities of smart terminals with a high accuracy and a low false positive rate.
Automatic camera calibration and sensor registration of a multi-sensor fringe measurement system using hexapod positioning

For the requirements of newly developed production processes like sheet bulk metal forming a previously developed measuring method exists for dimensional measurement of these manufactured parts. The presented multi-sensor approach allows the combination of several fringe projection sensors with different measurement resolutions and ranges to meet the requirements of the new production technology. The measurement setup includes a high precision hexapod to position the investigated workpiece inside the measuring volume. The measurement devices utilized are high precision fringe projection systems with 17 µm lateral and 1 µm vertical resolution. An additional overview sensor captures the whole measurement range of the hexapod. This paper presents two approaches that can be used for camera calibration and sensor registration of the fringe projection systems in a global coordinate system. A speckle pattern and a random dot pattern are placed on the hexapod to calculate the intrinsic parameters of the sensor camera and extrinsic parameters of the fringe sensor in one single step. To obtain metric lengths with the calibration process, a defined scale is included additionally on the pattern. The calibration process is executed by an automatic and random movement of the hexapod in the recording area of the sensors. Measurements show a direct transferability from the camera to the sensor coordinate system. This allows the measurement datasets to be directly merged together without the use of a separate registration routine.
A Vision-Based Technique for in-Flight Measurement of Helicopter Blade Motion

The measurement of helicopter main rotor blade angles during flight is a key capability to implement advanced applications, such as strategies for the reduction of emitted noise and to develop innovative flight control laws. The approach proposed in this work for the real-time estimation of blade angles is based on a stereoscopic system mounted on the top of the main rotor and pointing to an optical target placed on the blade root. An advanced image-processing algorithm was developed to match the target features in the left and right camera images, which was required for the 3D reconstruction of the target based on a triangulation method. This algorithm was customized for the target used in this specific application, in order to implement a procedure that is both reliable in blob matching and characterized by a very low computation effort. This allowed the system to speed up the triangulation procedure aimed at obtaining the 3-D coordinates of the features, in view of real-time applications, even with very compact processing units that can be accommodated on the main rotor head. An inverse problem for the 3-D rotation of the target was solved using the Singular Value Decomposition technique, thus improving the robustness of the measurement. The stereoscopic system was developed in order to be integrated on board an AW139 helicopter main rotor hub, equipped with a synchronous lighting device and a pre-processing unit. The latter enabled the system to automatically extract the minimum set of information to be transferred, by means of a slipring, to the processing unit hosted in the fuselage. For the full assessment of the reliability and accuracy of the integrated system, harsh dynamic and accuracy trials were conducted on laboratory test benches. The harsh dynamic tests demonstrated that the system can work continuously in realistic conditions without any structural or data acquisition problems. The accuracy tests, based on a robot test rig simulating the motion of the blade, demonstrated the capability of the system and the accuracy of the measurement technique developed. The discrepancy between the reference blade angles and the estimated ones was found to be less than 0.360 for all the realistic blade angle combinations tested.
Capturing Deep Dynamic Information for Mapping Users across Social Networks

Nowadays, it is common that a netizen creates multiple accounts across social platforms. Mapping accounts across platforms could facilitate various applications in security. Existing methods usually focus on profile and network based features. In this paper, we concentrate on capturing dynamic information of social users and present a deep dynamic user mapping model to identify the accounts across platforms. The proposed model captures dynamic latent features from three aspects including posting pattern, writing pattern, and emotional fluctuation. We also develop a matching network that fuses dynamic and traditional features to identify accounts. To the best knowledge of ourselves, this is the first trial that applies deep neural network in mapping users with dynamic information. Experiments on real world dataset demonstrated the effectiveness of the proposed method.
Evaluating a Test Automation Decision Support Tool

Goldrich and Flamm developed the MITRE Automated Test Decision Framework (ATDF) to help MITRE government sponsors (and, via sharing on GitHub, development organizations in general) move from manually tested legacy software towards automated test, continuous integration, continuous deployment, and, ultimately, DevOps. Often such legacy systems comprise multiple components with manual test procedures. The objective of the empirical study described in this paper is to determine whether ATDF usefully ranks components with respect to Return on Investment (ROI) when introducing automated tests. ROI is simply the ratio of profit to cost. When adding automated tests, what will be the profit that these tests will carry? What is the cost or level of effort to engineer a sufficient set of automated tests? Our evaluation approach models ROI using static defect counts identified by SonarLint and estimated cost to complete testing. We found positive Pearson correlations between normalized ATDF rankings versus the normalized rankings of our evaluation approach. We reject the null hypothesis that there is no correlation between the two rankings.
Adaptive cognitive robot using dynamic perception with fast deep-learning and adaptive on-line predictive control

This paper presents a novel adaptive cognitive robot control architecture able to adapt the robot actions and motions to the dynamics of both environment and human involving an “expressive states” in a cognitive model that adapts directly the robot optimal control. We developed an integrated system that performs dynamic perception with fast deep-learning algorithms, cognition models based on affects, and adaptive generalized predictive controllers (AGPC). The adaptation works with the perceptive states, which is transformed in cognitive data to use as the main requirement in the control design. The perception level detects and tracks to react to the environment in order to create personalized actions. The cognition is created using PAD model which defines different robot states related with the robot actions/tasks, it is created by KNN algorithm. The adaptation is commanded by an AGPC that is changed according to the cognitive states. The AGPC cost functions are calculated with the PAD values. Results showed the ability to perform robot tasks with expressive and personalized behaviours continuously.
A Wearable System for Path Finding to Assist Elderly People in an Indoor Environment

Elderly people tend to forget their path within their living areas for example, within their own residence or within the area of an old home due to the aging process. Moreover, in a developing country, the cost of caregivers for elderly adults is an extra burden to the family. Thus giving quality-care to elderly people has become a great concern to every nation specially to the developing countries. The objective of this paper is to develop a wearable system to assist elderly people for path finding in indoor environment. In this paper, firstly a requirements elicitation study was carried out to understand the needs and problems of the aging population for their normal movement in an indoor environment like old home. Secondly, an wearable system was developed using arduino, infrared module, speaker and GSM. The system includes two key features: a) The system can identify the current location of the elderly people within the specified area, takes destination as input and it gives audio guidance about the path direction to the user; and b) The elderly people can call the specific person giving him or her the current location from where the help is requested. Finally, a light-weighted experiment was conducted with twelve participants(elderly people) in an indoor environment. The evaluation result showed that the system works effectively and efficiently; and the participants showed satisfactions of using such kind of system for their well-being.
Teach Once and Use Everywhere -- Building AI Assistant Eco-Skills via User Instruction and Demonstration (poster)

Voice-enabled AI assistants rely on developers to build every single skill, although many skills share similar functions. We propose a concept and prototype system, \ksystem, to automatically build a set of similar skills in the ecosystem (eco-skills) with one-time teaching from end users. During teaching, a user only needs to demonstrate on the screen in one (native) mobile app and provides natural language (NL) instructions.
Entropy As an Assessment Factor of the Current State of Vessel When Approaching an Object of Maneuver

Abstract The paper describes a method for assessing the current state of a vessel as an element of a complex dynamic system when approaching a mobile object. As an evaluation criterion, it is proposed to use the maximum value of the entropy of the “vessel – the object of maneuver” system with the equality of probabilities of possible states of this system. The relevance of the study is due to the need to improve the classical methods of analysis and solving the problems of the vessel’s approach to the object of maneuver. Classical deterministic methods often prove ineffective if the object of maneuver performs unpredictable random actions in relation to the maneuvering vessel. Such objects include, for example, mobile shoals of fish or other vessels operated by inexperienced navigators. The purpose of the study is to justify the method of assessing the current state of the vessel when approaching the object of maneuver on the calculated deterministic trajectory. The solution of the control example confirmed the possibility of using the entropy of the “vessel – the object of maneuver” system as a universal estimation parameter of vessel’s current state when approaching an object of maneuver. The materials of the paper can be of practical and theoretical interest for specialists in the field of automation of ship movement control, navigators of merchant and fishing ships.
Automatic Evaluation of Local Topic Quality

Topic models are typically evaluated with respect to the global topic distributions that they generate, using metrics such as coherence, but without regard to local (token-level) topic assignments.Token-level assignments are important for downstream tasks such as classification.Recent models, which claim to improve token-level topic assignments, are only validated on global metrics.We elicit human judgments of token-level topic assignments: over a variety of topic model types and parameters, global metrics agree poorly with human assignments.Since human evaluation is expensive we propose automated metrics to evaluate topic models at a local level.Finally, we correlate our proposed metrics with human judgments: an evaluation based on the percent of topic switches correlates most strongly with human judgment of local topic quality.This new metric, which we call consistency, should be adopted alongside global metrics such as topic coherence.
Fuzzy Local Information C-Means Algorithm for Histopathological Image Segmentation

Accurate analysis of cellular structures has great importance for cancer diagnosis in histopathological images. Manual analysis of sections carried out by pathologists is time-consuming and costly. Analysis of cell structures with computer aid supports pathologists to diagnose cancer easily. In this paper, automated cell nuclei segmentation from histopathological images is investigated by using Fuzzy Local Information C-means Clustering (FLICM) Method. The Cancer Genome Atlas data set annotated by expert pathologists is used to evaluate the method. Compared with the other related studies, the highest f-measure and overlap values are obtained with this method.
Towards a Socio-Technical Framework for Bridging the Digital Divide in Rural Emergency Preparedness and Response

In rural areas, emergency preparedness and response (ERP) could be greatly affected by the lack of access to technology and the Internet and, therefore, to timely and accurate information about a natural or human-made disaster. This paper proposes a socio-technical framework that integrates a potential technology solution, with optimization methods, and strategies for user adoption. We argue that in order to bridge the digital divide in rural ERP, all these factors need to be systematically considered and integrated into a comprehensive framework, which could guide potential implementation.
Unconventional Geometric Phase Gate of Transmon Qubits With Inverse Hamiltonian Engineering

In this paper, we propose a scheme for realizing controlled phase gate of transmon qubits by combining the unconventional geometric phase and the inverse Hamiltonian engineering. The scheme possesses built-in fault tolerance and can be implemented in only one step without additional single qubit operations on each transmon qubit. In addition, the Rabi frequencies of the designed driving fields are smooth and vanish at boundary, so that the driving fields can be easily turned on and off. Numerical simulations show that the controlled phase gate is robust against several types of noise and the decoherence factors. Thus, the scheme may provide an alternative approach in constructing controlled phase gate with superconducting qubits.
Chinese flower-bird character generation based on pencil drawings or brush drawings

Chinese flower-bird characters are gems of traditional Chinese art. It is an artistic font and the strokes of the characters are designed as beautiful patterns of flowers, birds, etc. The generation of such characters requires painters' great efforts. Imagine that if we only need to sketch the outline of the ideal flower-bird characters using a pencil, and then we can quickly obtain these artistic characters, which will be of great significance in promoting their development, allowing more people to appreciate and even create this art by computer. Recently, with the development of deep learning and the invention of generative adversarial networks (GANs), some studies on font generation have made new progress. However, there is no research on the generation of flower-bird characters. We provide a solution by designing a GAN-based architecture to generate flower-bird characters. More specifically, a generator inspired by U-Net translates pencil drawings or brush drawings to flower-bird characters, and a patch-level discriminator distinguishes whether the received image is real. In addition to adversarial loss, a valid loss term called structural similarity loss is designed to further drive the network to generate satisfactory images. The quantitative analysis and user perceptual validation show the effectiveness of our method.
A Framework for Distributional Formal Semantics

Formal semantics and distributional semantics offer complementary strengths in capturing the meaning of natural language. As such, a considerable amount of research has sought to unify them, either by augmenting formal semantic systems with a distributional component, or by defining a formal system on top of distributed representations. Arriving at such a unified framework has, however, proven extremely challenging. One reason for this is that formal and distributional semantics operate on a fundamentally different ‘representational currency’: formal semantics defines meaning in terms of models of the world, whereas distributional semantics defines meaning in terms of linguistic co-occurrence. Here, we pursue an alternative approach by deriving a vector space model that defines meaning in a distributed manner relative to formal models of the world. We will show that the resulting Distributional Formal Semantics offers probabilistic distributed representations that are also inherently compositional, and that naturally capture quantification and entailment. We moreover show that, when used as part of a neural network model, these representations allow for capturing incremental meaning construction and probabilistic inferencing. This framework thus lays the groundwork for an integrated distributional and formal approach to meaning.
Manipulating body representations with virtual reality: Clinical implications for anorexia nervosa

Anorexia nervosa patients exhibit distorted body-representations. Specifically, they represent their bodies as larger than reality. Given that this distortion likely exacerbates the condition, there is an obligation to further understand and, if possible, rectify it. In pursuit of this, experimental paradigms are needed which manipulate the spatial content of these representations. In this essay, I discuss how virtual reality technology that implements full-body variants of the rubber-hand illusion may prove useful in this regard, and I discuss some issues related to the clinical application of this method.
CSS Minification via Constraint Solving

Minification is a widely accepted technique that aims at reducing the size of the code transmitted over the web. This article concerns the problem of semantic-preserving minification of Cascading Style Sheets (CSS)—the de facto language for styling web documents—based on merging similar rules. The cascading nature of CSS makes the semantics of CSS files sensitive to the ordering of rules in the file. To automatically identify rule-merging opportunities that best minimise file size, we reduce the rule-merging problem to a problem concerning “CSS-graphs,” i.e., node-weighted bipartite graphs with a dependency ordering on the edges, where weights capture the number of characters. Constraint solving plays a key role in our approach. Transforming a CSS file into a CSS-graph problem requires us to extract the dependency ordering on the edges (an NP-hard problem), which requires us to solve the selector intersection problem. To this end, we provide the first full formalisation of CSS3 selectors (the most stable version of CSS) and reduce their selector intersection problem to satisfiability of quantifier-free integer linear arithmetic, for which highly optimised SMT-solvers are available. To solve the above NP-hard graph optimisation problem, we show how Max-SAT solvers can be effectively employed. We have implemented our rule-merging algorithm and tested it against approximately 70 real-world examples (including examples from each of the top 20 most popular websites). We also used our benchmarks to compare our tool against six well-known minifiers (which implement other optimisations). Our experiments suggest that our tool produced larger savings. A substantially better minification rate was shown when our tool is used together with these minifiers.
Object detection on remote sensing images using deep learning: an improved single shot multibox detector method

Remote sensing images recognition technology has great significance in many aspects, such as military navigation and environmental monitoring. We propose an improved single shot multibox detector approach by combining some strategies, including upsampling, focal loss, and proper calibration of key parameters. Comprehensive experiments on three remote sensing images datasets have demonstrated the effectiveness of the proposed approach in benchmarking with several state-of-the-art object detection methods.
A Task-Oriented Dialogue System for Moral Education

We present a novel and practical dialogue system specifically designed for teachers and parents to solve students’ problems in moral education. Guided by the case-based reasoning theory, we collect the high-quality cases and teaching strategies from heterogeneous sources, and then construct the dedicated knowledge graph to manage the large volume of information in this domain. By leveraging on the latest natural language processing techniques, we finally implement a task-oriented dialogue system to precisely understand user’s problem and subsequently recommend possible solutions. We show the great promise of the system for K-12 education and demonstrate how the system solves the problem raised by the teacher for moral education.