Reviewerly/exHarmony huggingface dataset:
Event Prediction Based on Causality Reasoning

Event prediction is a challenging task which is controversial and highly debatable in the area of text mining. Previous works in utilizing causality reasoning for event prediction have shown a promising outcome in recent years. Many causality-based event prediction systems have been designed where various models, methods, and algorithms are used respectively to (1) extract events from texts, (2) construct causality relationships among events and (3) predict future events based on the causality relationships. In this paper, we first present a brief survey to systematically discuss the existing literature and highlight a common framework of the causality based event prediction systems. We discovered that most systems are not context-aware, which in reality leads to a poor performance. Based on that, we present a revised framework for causality-based event prediction, where we consider the context-related information. Our preliminary experiments suggest that the revised framework improved the overall quality of predicted events.
The Fourier Tracing Approach for Modeling Automotive Radar Sensors

The certification of autonomous driving by means of virtual testing methods is limited by the accuracy of simulated perception sensors, especially radar. This work presents a proof-of-concept implementation of Fourier tracing, a novel approach for synthesizing radar measurements of a virtual scene. Fourier tracing adapts ray tracing techniques from image synthesis to directly simulate a 3D periodogram of the range, range rate, and azimuth domains. Unlike previous approaches, Fourier tracing does not require computationally expensive FFT calculations and is specifically designed to capture the main characteristics of radar measurements, including interference and multi-path reflections. Our findings show that Fourier tracing captures key radar sensor performance parameters.
Development of an IMU-radar sensor board for three-dimensional digital image correlation camera triangulation

In the last few years, advancements made in cameras technology, optically-based systems, and computer-aided methods have made three-dimensional digital image correlation (3D-DIC) a robust tool for structural health monitoring (SHM) and extracting structural deformations and geometry profiles. To perform 3D-DIC measurements, the position of cameras relative to each other must be determined. It is achieved by taking several pictures of calibration objects to determine the camera’s extrinsic parameters (i.e., separation distance and orientation in space). This practice can be very cumbersome and cameras calibration difficult to perform for large-sized structures. This is especially true if data is to be acquired from multiple fields of view. This study describes the design of a MEMS-based sensor board to extend 3D-DIC’s capability and allow for easier calibration and measurement. The suggested system relies on a MEMS-based Inertial Measurement Unit (IMU) for determining the spatial orientation of the cameras (i.e., roll, pitch, and yaw angles) and a 77 GHz radar sensor for measuring the relative distance of the stereo cameras. Both systems are integrated on a commercially available microcontroller unit (MCU) that makes the system suitable for low-power applications. In this research, the efforts for programming the sensor board and the performance of the combined IMU-radar system in comparison with traditional instrumentation are described. To finish, the system is used for calculating the extrinsic parameters of a stereophotogrammetry system and results are compared with data obtained from a traditional calibration.
Collaborative wideband signal decoding using non-coherent receivers

In recent years we are experiencing an important growth of interest for sensing the electromagnetic spectrum and making its access more agile. Emerging initiatives use low-cost receivers in large deployments for sensing the radio spectrum or collecting air-traffic signals at large scale. One of the major drawbacks of low-cost spectrum receivers is their limited sampling rate, which does not allow to decode wideband signals. In order to circumvent the hardware limitations of single receivers, we envision a scenario where non-coherent receivers sample the signal collaboratively to cover a larger bandwidth than the one of the single receiver and then, enable the signal reconstruction and decoding in the backend. We present a methodology to enable the signal reconstruction in the backend by multiplexing in frequency a certain number of noncoherent receivers in order to cover a signal bandwidth that would not otherwise be possible using a single receiver. We propose a method that does not use the knowledge of the modulation scheme, and has been designed to be transparent to the subsequent decoding process. As such, it is equivalent to the reception of the signal by a high-end receiver. We demonstrate and evaluate our approach with two non-coherent receivers which collaboratively sample an aviation signal of almost twice the bandwidth of each receiver. The experimental results show that, using two non-coherent receivers, our method is able to reconstruct and decode correctly more than 80% of data.
Targeting Classical Code to a Quantum Annealer

From a compiler's perspective, a quantum annealer represents a fundamentally different hardware target from a CPU, GPU, or other von Neumann architecture. Quantum annealers are special-purpose computers that use quantum effects to heuristically determine the set of Boolean variables that minimize a quadratic pseudo-Boolean function (an NP-hard problem). Natively programming such systems involves supplying them with a vector of function coefficients and receiving a vector of function-minimizing Booleans in return. The contribution of this work is to demonstrate how to compile conventional code into a minimization problem for solution on a quantum annealer. The resulting code can run either forward (from inputs to outputs) or backward (from outputs to inputs). We show how this capability can be exploited to simplify the expression and solution of problems in the NP complexity class.
On the Network Embedding in Sparse Signed Networks

Network embedding, that learns low-dimensional node representations in a graph such that the network structure is preserved, has gained significant attention in recent years. Most state-of-the-art embedding methods have mainly designed algorithms for representing nodes in unsigned social networks. Moreover, recent embedding approaches designed for the sparse real-world signed networks have several limitations, especially in the presence of a vast majority of disconnected node pairs with opposite polarities towards their common neighbors. In this paper, we propose sign2vec, a deep learning based embedding model designed to represent nodes in a sparse signed network. sign2vec leverages on signed random walks to capture the higher-order neighborhood relationships between node pairs, irrespective of their connectivity. We design a suitable objective function to optimize the learned node embeddings such that the link forming behavior of individual nodes is captured. Experiments on empirical signed network datasets demonstrate the effectiveness of embeddings learned by sign2vec for several downstream applications while outperforming state-of-the-art baseline algorithms.
3D Shape Retrieval using Bag of Word Approaches

After document and image retrieval, shape retrieval is seen as the next step in the search process. Numerous techniques have been proposed to retrieve shapes meta-data considering shapes similarity but these techniques normally suffer from non-interactive running times. A widely used approach, which has its origins in document retrieval, and is successfully been applied for image retrieval, is based on "bag-of-words". Methods using this approach have the potential to support fast, real time retrieval of shapes over the large databases. This research examines these approaches and summarizes the techniques used to convert shapes to their "bag of words" representations. This research also explores the mechanism these techniques adopts to facilitate and enhance interactive search times.
Distributed Composition of Highly-Collaborative Services and Sensors in Tactical Domains

Software systems are often built by composing services distributed over the network. Choreographies are a form of decentralized composition that models the external interaction of the services by specifying peer-to-peer message exchanges from a global perspective. When third-party services are involved, usually black-box services to be reused, actually realizing choreographies calls for exogenous coordination of their interaction. Nowadays, very few approaches address the problem of actually realizing choreographies in an automatic way. These approaches are rather static and are poorly suited to the need of tactical domains, which are highly-dynamic networking environments that bring together services and sensors over military radio networks. In this paper, we describe a method to employ service choreographies in tactical environments, and apply it to a case study in the military domain.
Design of maximum power point tracking (MPPT) on two axes solar tracker based on particle swarm fuzzy

The renewable energy currently becomes a research topic that is constantly being improved. Efforts to find alternative energy sources as a substitute for fossil fuels still continue to be discussed. Indonesia is geographically located on the equator has a big potential for solar energy. It can’t be utilized optimally because still constrained conversion of PV modules that still relatively low. One of the solutions offered is to fit up solar panels with solar tracking system and Maximum Power Point Tracking (MPPT) algorithm. The method of solar tracking system seeks PV panels always perpendicular to the direction of sunlight, whereas MPPT serves to trace the maximum power PV may produce in various climatic conditions. In this study, MPPT-based Fuzzy-Particle Swarm Optimization (PSO) which integrates with fixed and tracking PV panel system to increase PV module power conversion. PSO plays a role in the search for the best fuzzy membership function parameters designed based on the defined objective function that is Mean Square Error (MSE). The result shows that MPPT Fuzzy-PSO solar tracker can increase the power output from PV by 28.84% for 10 hours of operation compared with MPPT Fuzzy-PSO fixed system.
The Parameterized Position Heap of a Trie

Let $$\varSigma $$ and $$\varPi $$ be disjoint alphabets of respective size $$\sigma $$ and $$\pi $$ . Two strings over $$\varSigma \cup \varPi $$ of equal length are said to parameterized match (p-match) if there is a bijection $$f:\varSigma \cup \varPi \rightarrow \varSigma \cup \varPi $$ such that (1) f is identity on $$\varSigma $$ and (2) f maps the characters of one string to those of the other string so that the two strings become identical. We consider the p-matching problem on a (reversed) trie $$\mathcal {T}$$ and a string pattern P such that every path that p-matches P has to be reported. Let N be the size of the given trie $$\mathcal {T}$$ . In this paper, we propose the parameterized position heap for $$\mathcal {T}$$ that occupies O(N) space and supports p-matching queries in $$O(m \log (\sigma + \pi ) + m \pi + pocc ))$$ time, where m is the length of a query pattern P and $$ pocc $$ is the number of paths in $$\mathcal {T}$$ to report. We also present an algorithm which constructs the parameterized position heap for a given trie $$\mathcal {T}$$ in $$O(N (\sigma + \pi ))$$ time and working space.
Vector Space Model for Texts and the tf-idf Measure

In this chapter, we discuss the features that are used for text representation while comparing them in vector space model, such as words or n-grams. We also present the possible values of these features: tf, idf, and tf-idf.
A New Approach to the Phase-Based Video Motion Magnification for Measuring Microdisplacements

Video motion magnification (VMM) techniques have recently gained attention as a low-cost accurate method for measuring microdisplacements. VMM techniques are known to have incorrect frequency responses when handling dynamic measurements consisting of multiple frequencies. New phase-based VMM method utilizing radial fractional steerable 2-D Hilbert transform is proposed and contrasted, in terms of the frequency response, with two other established VMM methods. Typical VMM workflow includes amplifying phase changes between subsequent video frames on many spatial frequencies separately using image pyramids, which are then collapsed forming new, motion-magnified video. In this paper, Hilbert transform is used to extract local phase changes. The modified Kullback-Leibler divergence was calculated between the spectral estimates of the time series of vibrating plate measured using a high-speed video camera and a reference time series synchronously acquired with high-precision air-coupled ultrasonic transducer. Time series consisted of micrometer range multicomponent vibrations (one, two, and three sine frequencies) recorded with different motion magnification factors. The results show that in the majority of cases, the proposed method, out of three considered, resulted in the most uniform frequency response and generated the lowest amount of unwanted frequencies. To conclude, a novel approach to VMM is proposed, which substantially improves frequency response and makes this technique more suitable for the measurements of phenomena consisting of multiple frequencies.
Analyzing and Optimizing Java Code Generation for Apache Spark Query Plan

Big data processing frameworks have received attention because of the importance of high performance computation. They are expected to quickly process a huge amount of data in memory with a simple programming model in a cluster. Apache Spark is becoming one of the most popular frameworks. Several studies have analyzed Spark programs and optimized their performance. Recent versions of Spark generate optimized Java code from a Spark program, but few research works have analyzed and improved such generated code to achieve better performance. Here, two types of problems were analyzed by inspecting generated code, namely, access to column-oriented storage and to a primitive-type array. The resulting performance issues in the generated code and were analyzed, and optimizations that can eliminate inefficient code were devised to solve the issues. The proposed optimizations were then implemented for Spark. Experimental results with the optimizations on a cluster of five Intel machines indicated performance improvement by up to 1.4x for TPC-H queries and by up to 1.4x for machine-learning programs. These optimizations have since been integrated into the release version of Apache Spark 2.3.
Boomerang Connectivity Table Revisited. Application to SKINNY and AES

The boomerang attack is a variant of differential cryptanalysis which regards a block cipher E as the composition of two sub-ciphers, i.e., E = E1 o E0, and which constructs distinguishers for E with probability p2q2 by combining differential trails for E0 and E1 with probability p and q respectively. However, the validity of this attack relies on the dependency between the two differential trails. Murphy has shown cases where probabilities calculated by p2q2 turn out to be zero, while techniques such as boomerang switches proposed by Biryukov and Khovratovich give rise to probabilities greater than p2q2. To formalize such dependency to obtain a more accurate estimation of the probability of the distinguisher, Dunkelman et al. proposed the sandwich framework that regards E as Ẽ1 o Em o Ẽ0, where the dependency between the two differential trails is handled by a careful analysis of the probability of the middle part Em. Recently, Cid et al. proposed the Boomerang Connectivity Table (BCT) which unifies the previous switch techniques and incompatibility together and evaluates the probability of Em theoretically when Em is composed of a single S-box layer. In this paper, we revisit the BCT and propose a generalized framework which is able to identify the actual boundaries of Em which contains dependency of the two differential trails and systematically evaluate the probability of Em with any number of rounds. To demonstrate the power of this new framework, we apply it to two block ciphers SKINNY and AES. In the application to SKINNY, the probabilities of four boomerang distinguishers are re-evaluated. It turns out that Em involves5 or 6 rounds and the probabilities of the full distinguishers are much higher than previously evaluated. In the application to AES, the new framework is used to exclude incompatibility and find high probability distinguishers of AES-128 under the related-subkey setting. As a result, a 6-round distinguisher with probability 2−109.42 is constructed. Lastly, we discuss the relation between the dependency of two differential trails in boomerang distinguishers and the properties of components of the cipher.
Profiling and Tracing Support for Java Applications

We demonstrate the feasibility of undertaking performance evaluations for JVMs using: (1) a hybrid JVM/OS tool, such as async-profiler, (2) OS centric profiling and tracing tools based on Linux perf, and (3) the Extended Berkeley Packet Filter Tracing (eBPF) framework where we demonstrate the rationale behind the standard offwaketime tool, for analysing the causes of blocking latencies, and our own eBPF-based tool bcc-java, that relates changes in microarchitecture performance counter values to the execution of individual JVM and application threads at low overhead.
Breast Cancer Classification with Missing Data Imputation

Missing Data (MD) is a common drawback when applying Data Mining on breast cancer datasets since it affects the ability of the Data mining classifier. This study evaluates the influence of MD on three classifiers: Decision tree C4.5, Support vector machine (SVM), and Multi-Layer Perceptron (MLP). For this purpose, 162 experiments were conducted using KNN imputation with three missingness mechanisms (MCAR, MAR and NMAR), and nine percentages (form 10% to 90%) applied on two Wisconsin breast cancer datasets. The MD percentage affects negatively the classifier performance. MLP achieved the lowest accuracy rates regardless the MD mechanism/percentage.
VeCHArt: Visually Enhanced Comparison of Historic Art Using an Automated Line-Based Synchronization Technique

The analysis of subtle deviations between different versions of historical prints has been a long-standing challenge in art history research. So far, this challenge has required extensive domain knowledge, fine-tuned expert perception, and time-consuming manual labor. In this paper we introduce an explorative visual approach to facilitate fast and accurate support for the task of comparing differences between prints such as engravings and woodcuts. To this end, we have developed a customized algorithm that detects similar stroke-patterns in prints and matches them in order to allow visual alignment and automated deviation highlighting. Our visual analytics system enables art history researchers to quickly detect, document, and categorize qualitative and quantitative discrepancies, and to analyze these discrepancies using comprehensive interactions. To evaluate our approach, we conducted a user study involving both experts on historical prints and laypeople. Using our new interactive technique, our subjects found about 20 percent more differences compared to regular image viewing software as well as “paper-based” comparison. Moreover, the laypeople found the same differences as the experts when they used our system, which was not the case for conventional methods. Informal feedback showed that both laypeople and experts strongly preferred employing our system to working with conventional methods.
Data Visualization as Participatory Research: A Model for Digital Collections to Inspire User-Driven Research

The emerging creative practices of digital collections are increasing in prominence thanks to the access to, and appeal of, data visualizations. Interactive data visualizations, such as a map, timeline, chart, graph, or artist’s rendering of data, enable users to pursue their own research interests within a collection and participate in new areas of research. Use of these data visualizations enhances the scope of the collection and enables a modern and diverse thematic collection to emerge. This article considers the question: Does the addition of data visualizations affect how users participate with a research collection? Impacts on collection development, application of user-centered design, and analysis of users’ exploratory strategies are discussed. Examples of data visualizations used in digital humanities projects and digital collections are presented as case studies.
Biometric Authentication-Based Data Encryption Using ECG Analysis and Diffie–Hellman Algorithm

Data security is becoming the prime concern these days. In the daily and day-to-day activities, data is required to be shared, whether it is a simple chat message or even the crucial business message or mail. In this paper, the unique concept of using the ECG reports as the authentication medium is proposed. First, the ECG report of the person is analyzed using sin charts and compared with the database. Before processing to the next phase, the user has to validate the transaction ID and SHA key generated using the. The next phase of the message transfer is further secured by providing the transaction id and encryption key. After that, the fingerprints of the users are validated again using the SHA-based concept of validation, and then to transfer data, Diffie–Hellman algorithm is used. In the dissertation, the double level security is maintained and implemented by making use of ECG reports and using the fingerprints. From the previous approaches and base papers, we have speed up the comparison process and reduce the time involved by making use of SHA-based analysis, and the double authentication helps us to deal with hackers in the better ways as compared to the previous approaches as now double efforts will be requirement by the hackers to break through the whole process. The uniqueness of the ECG report will act as uniquely validating the identity of two persons.
State Estimation via Communication for Monitoring

Monitoring using sensors is ubiquitous in our environment. In this paper, a state estimation model is proposed for continuous activity monitoring from multimodal and heterogenous sensor data. Each sensor is modeled as an independent agent in the predictive coding framework. It can sample its environment, communicate with other agents, and adapt its internal model to its environment in an unsupervised manner. Using controlled experiments, we show that limitations of each sensor, such as inference inaccuracy and delay, can be overcome through communication with the other sensors. The model is tested for human action recognition on the UTD-MHAD and MHEALTH datasets and for gait freeze recognition from wearable acceleration sensors in Parkinson's disease patients. In both cases, the proposed model yields results comparable to the state-of-the-art. To the best of our knowledge, this is the first work taking advantage of opportunistic communication by a predictive coding agent for estimating the state of its environment.