Reviewerly/exHarmony huggingface dataset:
A reconfigurable HexCell-based systolic array architecture for evolvable hardware on FPGA

Evolvable hardware is a system that modifies its architecture and behavior to adapt with changes of the environment. It is formed by reconfigurable processing elements driven by an evolutionary algorithm. In this paper, we study a reconfigurable HexCell-based systolic array architecture for evolvable systems on FPGA. HexCell is a processing element with a tile-able hexagonal-shaped cell for reconfigurable systolic arrays on FPGAs. The cell has three input ports feed into an internal functional-unit connected to three output ports. The functional-unit is configured using dynamic partial reconfiguration (DPR), and the output ports, in contrast, are configured using virtual reconfiguration circuit (VRC). Our proposed architecture combines the merits of both DPR and VRC to achieve fast reconfiguration and accelerated evolution. A HexCell-based 4 × 4 array was implemented on FPGA and utilized 32.5% look-up tables, 31.3% registers, and 1.4% block RAMs of Artix-7 (XC7Z020) while same-size conventional array consumed 8.7%, 5.1%, and 20.7% of the same FPGA, respectively. As a case study, we used an adaptive image filter as a test application. Results showed that the fitness of the best filters generated by our proposed architecture were generally fitter than those generated by the conventional state-of-the-art systolic array on the selected application. Also, performing 900,000 evaluations on HexCell array was 2.6 × faster than the conventional one.
Automated Object Tracking for Animal Behaviour Studies

It can be an arduous and time consuming task to manually curate video data for tracking the movement of an object. This is particularly challenging when tracking animals whose behaviour is sporadic and unpredictable, but it can provide pertinent information when monitoring health, conditions and treatments. Here, we evaluate the use of machine learning retroactively applied to automatically track specific steers in hours of video with minimal manual interaction. This is approached using the Faster R-CNN Object Detection algorithm with VGG-16 acting as a feature extractor. Performance on a number of video segments is presented and discussed, and the issues encountered are outlined. This highlights a number of guidelines that should be taken under consideration when generating video data to improve object detection performance, and helps define the applicability of the approach to pre-existing data.
Investigating the Impact of Inclusion in Face Recognition Training Data on Individual Face Identification

Modern face recognition systems leverage datasets containing images of hundreds of thousands of specific individuals' faces to train deep convolutional neural networks to learn an embedding space that maps an arbitrary individual's face to a vector representation of their identity. The performance of a face recognition system in face verification (1:1) and face identification (1:N) tasks is directly related to the ability of an embedding space to discriminate between identities. Recently, there has been significant public scrutiny into the source and privacy implications of large-scale face recognition training datasets such as MS-Celeb-1M and MegaFace, as many people are uncomfortable with their face being used to train dual-use technologies that can enable mass surveillance. However, the impact of an individual's inclusion in training data on a derived system's ability to recognize them has not previously been studied. In this work, we audit ArcFace, a state-of-the-art, open source face recognition system, in a large-scale face identification experiment with more than one million distractor images. We find a Rank-1 face identification accuracy of 79.71% for individuals present in the model's training data and an accuracy of 75.73% for those not present. This modest difference in accuracy demonstrates that face recognition systems using deep learning work better for individuals they are trained on, which has serious privacy implications when one considers all major open source face recognition training datasets do not obtain informed consent from individuals during their collection.
Radar Emitter Identification Using Hidden Markov Model

Using the statistical signal processing principle, which is widely used in the field of natural language processing (NLP), a radar emitter identification (REI) approach based on hidden Markov model (HMM) is proposed. Since a radar emitter is modeled by three model parameters of a HMM, the REI can be solved from the solution of the evaluation problem of a HMM. Simulation results show that the HMM based statistical identification method has tolerance to parameter error, which is suitable for radar emitter intelligent identification in complex environment.
Verifying Kinship from RGB-D Face Data

We present a kinship verification (KV) approach based on Deep Learning applied to RGB-D facial data. To work around the lack of an adequate 3D face database with kinship annotations, we provide an online platform where participants upload videos containing faces of theirs and of their relatives. These videos are captured with ordinary smartphone cameras. We process them to reconstruct recorded faces in tridimensional space, generating a normalized dataset which we call Kin3D. We also combine depth information from the normalized 3D reconstructions with 2D images, composing a set of RGBD data. Following approaches from related works, images are organized into four categories according to their respective type of kinship. For the classification, we use a Convolutional Neural Network (CNN) and a Support Vector Machine (SVM) for comparison. The CNN was tested both on a widely used 2D Kinship Verification database (KinFaceW-I and II) and on our Kin3D for comparison with related works. Results indicate that adding depth information improves the model’s performance, increasing the classification accuracy up to 90%. To the extent of our knowledge, this is the first database containing depth information for Kinship Verification. We provide a baseline performance to stimulate further evaluations from the research community.
An Ensemble of Triplet Neural Networks for Differential Diagnostics of Lung Cancer

A new classification subsystem of a lung cancer computer-aided-diagnosis systems is proposed in the paper. Its implementation is based on two main approaches. First, the computed tomography images of segmented suspicious lung nodules are represented by means of five histograms characterizing the shape, inner and outer structures of nodules. This representation significantly reduces the dimensionality of data. Second, an ensemble of triplet neural networks is used to take into account atypical cases of lung cancer and to improve accuracy of the classification subsystem usage. An architecture of the developed triplet network and peculiarities of the triplet network ensemble training process are considered in detail. The corresponding results of numerical experiments with using public dataset LUNA16 show outperforming properties of the proposed classification subsystem.
Intra-hour Forecasting for a 50 MW Photovoltaic System in Uruguay: Baseline Approach

The increased penetration of photovoltaic (PV) generation introduces new challenges for the stability of electricity grids. In this work, machine learning (ML) techniques were implemented to forecast PV power production up to 1-hour ahead with a 10-minute granularity. Three different input combinations were utilised: Model 1 (M1) using the AC power only, Model 2 (M2) using the elevation angle (α), azimuth angle (φ) and AC power and Model 3 (M3) using α, φ, the AC power and satellite observations (SAT) aiming to improve the forecasting performance. Historical PV operational data were used for the training and validation stages of intra-hour PV forecasting models for time t + 10 to 60 minutes ahead. The results obtained over the test set period (15% of the data, i.e. ≈ 110 days) have shown that M2 exhibits the best-performance with a normalised root mean square error (nRMSE) in the range of 7.6% to 14.2%, whereas the skill score (SS) ranged between 6.5% and 30.9% for the 10- to 60-minute ahead, respectively.
Web Service Selection Mechanism in Service-Oriented Architecture Based on Publish–Subscribe Pattern in Fog Environment

Fog computing reduces latency and increases the throughput by processing data near the body sensor network. This paper considers a service-oriented architecture based on publish–subscribe pattern for web service selection in fog environment. With the advancement in fog environment, a large number of web services in fog environment provide same functionality but differ in their quality of service (QoS) parameter. This paper focuses on an approach based on genetic algorithm for the selection of a workflow which comprises different fine-grained web services. This paper only considers the non-functional aspects of web services mentioned in service-level agreement (SLA). In this paper, we propose a fitness function which uses quality of services and user preference in consideration while computing the fitness value. Moreover, genetic algorithm is used as an optimization algorithm to get an optimum workflow considering service-level agreement and user preference assigned to each QoS parameters. The conducted experiment shows that better result is obtained from the new fitness function.
Structured Sparsity Model Based Trajectory Tracking Using Private Location Data Release

Mobile devices have been an integral part of our everyday lives. Users’ increasing interaction with mobile devices brings in significant concerns on various types of potential privacy leakage, among which location privacy draws the most attention. Specifically, mobile users’ trajectories constructed by location data may be captured by adversaries to infer sensitive information. In previous studies, differential privacy has been utilized to protect published trajectory data with rigorous privacy guarantee. Strong protection provided by differential privacy distorts the original locations or trajectories using stochastic noise to avoid privacy leakage. In this article, we propose a novel location inference attack framework, iTracker, which simultaneously recovers multiple trajectories from differentially private trajectory data using the structured sparsity model. Compared with the traditional recovery methods based on single trajectory prediction, iTracker, which takes advantage of the correlation among trajectories discovered by the structured sparsity model, is more effective in recovering multiple private trajectories simultaneously. iTracker successfully attacks the existing privacy protection mechanisms based on differential privacy. We theoretically demonstrate the near-linear runtime of iTracker, and the experimental results using two real-world datasets show that iTracker outperforms existing recovery algorithms in recovering multiple trajectories.
Comparison of Carp Rabin Algorithm and Jaro-Winkler Distance to Determine The Equality of Sunda Languages

In modern times Information Technology (IT) can make it easier for users to create, store and disseminate information to other users who need it. With this, IT also can make a problem with plagiarism. Where plagiarism is a topic that is often discussed, especially on campus. Because it needs attention to avoid this plagiarism. One way to reduce plagiarism is to detect the similarity of sentence texts in a document. There are several similarity detection programs including Turnitin, Eve2, CopyCatchGold, WodCheck, Glatt. One algorithm used to detect text similarity is Jaro Winkler Distance. This algorithm has a good accuracy in matching a relatively short string and can accurately check copies of documents after the tokenizing process. Another algorithm for detecting text similarities is the Rabin Karp algorithm. Rabin-Karp algorithm perform string matching hash value based on the text and the pattern hash value. Although slow in matching single patterns, this algorithm is suitable for long pattern searches. Then knowing the performance of each algorithm is carried out a comparison between these two algorithms.
Automated fault tolerance augmentation in model-driven engineering for CPS

Cyber-Physical Systems are usually subject to dependability requirements such as safety and reliability constraints. Over the last 50 years, a body of efficient fault-tolerance mechanisms has been devised to handle faults occurring at run-time. However, properly implementing those mechanisms is a time-consuming task that requires a great deal of know-how. In this paper, we propose a general framework which allows system designers to decouple functional and non-functional concerns, and express non-functional properties at design time using domain-specific languages. In the spirit of generative programming, functional models are then automatically “augmented” with dependability mechanisms. Importantly, the real-time behavior of the initial models in terms of sampling times and meeting deadlines is preserved. The practicality of the approach is demonstrated with the automated implementation of one prominent software fault-tolerance pattern, namely N-Version Programming, in the CPAL model-driven engineering workflow.
Using LEL and scenarios to derive mathematical programming models. Application in a fresh tomato packing problem

Mathematical programming models are invaluable tools at decision making, assisting managers to uncover otherwise unattainable means to optimize their processes. However, the value they provide is only as good as their capacity to capture the process domain. This information can only be obtained from stakeholders, i.e., clients or users, who can hardly communicate the requirements clearly and completely. Besides, existing conceptual models of mathematical programming models are not standardized, nor is the process of deriving the mathematical programming model from the concept model, which remains ad hoc. In this paper, we propose an agile methodology to construct mathematical programming models based on two techniques from requirements engineering that have been proven effective at requirements elicitation: the language extended lexicon (LEL) and scenarios. Using the pair of LEL + scenarios allows to create a conceptual model that is clear and complete enough to derive a mathematical programming model that effectively captures the business domain. We also define an ontology to describe the pair LEL + scenarios, which has been implemented with a semantic mediawiki and allows the collaborative construction of the conceptual model and the semi-automatic derivation of mathematical programming model elements. The process is applied and validated in a known fresh tomato packing optimization problem. This proposal can be of high relevance for the development and implementation of mathematical programming models for optimizing agriculture and supply chain management related processes in order to fill the current gap between mathematical programming models in the theory and the practice.
Research and Application of Decision Tree Algorithm in QoS-Aware Service for Fault Diagnosis

In recently years, the communication networks envisage the prominent contradiction between the increased requirements for high-quality services and the gradually increased operational problems. However, the existing operation and maintenance face a series of problems: large volume of data, many control links, difficulty of problems localization. We can employ machine to efficiently analyze and deal with these problems. This paper proposes an analysis method for quality of service (QoS)-aware service in the field of operation and maintenance. The proposed method analyzes the correlation between QoS-aware service features and problem solution by mining service scene, operational data, and typical cases. The proposed method is useful for the customer service personnel to locate and solve the problem.
Quantification of spectral losses of Natural soiling and detailed microstructural analysis of Dust collected from Different locations in Dubai, UAE

In this work, we report a field study where glass coupons samples were deployed in four locations in Dubai to analyze the natural soiling over a period of 10 weeks. In the first part of the study, analysis was performed to understand how the solar irradiation is altered by dust soiling. It was observed that soiling causes a strong reduction across all the solar spectrum, and the visible region (400-600)nm wavelength showed the highest spectral soiling losses. Analysis also revealed that coastal industrial sites such as Jabil-Ali has higher dust accumulation and larger particle surface area compared to residential and desert locations. Sites located in the desert (Saih Al-Dahal) showed the lowest accumulation of dust and smaller particle surface area. Compositional microstructural analysis were performed to understand the nature of dust in each location and identify the main mechanisms that cause dust accumulation.
MicPrint

Smartphones are the most commonly used computing platform for accessing sensitive and important information placed on the Internet. Authenticating the smartphone's identity in addition to the user's identity is a widely adopted security augmentation method since conventional user authentication methods, such as password entry, often fail to provide strong protection by itself.
Hand State Combination as Gesture Recognition using Kinect v2 Sensor for Smart Home Control Systems

Smart home technology is evolving along with advanced technology evolvement. One of the technology which is constantly evolving is a human-machine interface. Within this group, a gesture-based interface enables to change this movement becomes a command for computer systems without contacting any surfaces. A gesture is one of the most convenient and natural ways to control a smart home. This research discusses the development of hand gesture recognition using hand state combinations to control various electronic devices for a smart home to facilitate human activities in a home. The system is built by performing hand gesture recognition from a command received by Kinect v2 sensor with hand tracking process to get the desired request based on existing hand state combination. There are three hand states with nine hand state combinations. Each combination means different commands for different electronic devices. This study uses the hand state from Kinect v2 dataset, which are open, close, lasso, untracked, and unknown. Based on the experimental results, the system accuracy reached 87% with an optimal distance of three meters.
Big Data and Interactive Visualization: Overview on Challenges, Techniques and Tools

The data visualization makes for analysts easier to perform the exploratory analysis and research process. Unlike traditional data, other features mark the data in the Big Data context, namely their extremely high volume, their variety in terms of collection sources and types, and the need for speed in their processing. Therefore, the data visualization in this field presents other issues to be considered. In this paper, we are interested in studying the data visualization progress in the Big Data domains. For that, we briefly describe the different concepts, issues, challenges and tools that are related to the Big Data technology. Then, we highlight the data visualization constraints followed by an overview of the most relevant work of data visualization in this field. We also propose a classification that allow to well position oneself when thinking to Big Data visualization strategies. Finally, we look at a promising path to propose an architecture that will cover the multi-level data interactive visualization process in a Big Data context.
An Interactive Augmented Reality Architectural Design Model : A Prototype for Digital Heritage Preservation

We present study on the integration of augmented reality using Microsoft Hololens and architectural design documentation for cultural heritage application by practical evaluation method. Our goal is to understand the potential of AR implementation in architectural narration and documentation. Herein, we outlined our works: 1) Visualization of architectural forms; 2) Data visualization embedded in augmented environment; 3) Basic user interaction mechanism. Our focus of the study is on the methodology and workflows involved in the AR platform. The case study is traditional Balinese architectures that constitute issues of materiality, tectonics, aesthetics and embodied local and specific information, hence the cultural heritage. Our study found that AR and Hololens provide a promising tool for 3D visualization and experiences particularly in cultural heritage application where computer-generated objects are augmented into real and physical objects. Despite latency, limited visual field and interaction methods that are still in development, implementation of AR in the architectural field bring understanding architecture as a medium and interface where space, form, and information are combined.
A New Probabilistic Output Constrained Optimization Extreme Learning Machine

In near decades machine learning approaches have received overwhelming attention from many researchers for solving problems that cannot be ironed out by traditional approaches. However, most of these approaches produces output that is not equivalent to the probability estimates of how credible and reliable the output can be for each prediction. One widely utilized, highly accorded for generalized performance but non-probabilistic machine learning algorithm is the Extreme Learning Machine (ELM). As with other classification systems, ELM generates outputs that cannot be treated as probabilities. Current literature shows approaches attempt to assimilate probabilistic concept in ELM however their outputs are not equivalent to probabilities. Furthermore, these methods invoke two-stage post processing procedures with iterative learning procedures which are against the salient features of ELM that highlight no iterative operations involved in learning. Hence, we want to probe in this paper the ability of ELM to produce probabilistic output from the original architecture of ELM itself while preserving the merits of ELM without the need for a post processing two-stage procedures to convert the output to probability and eliminates iterative learning to compute output weights. Two methodologies of unified probabilistic ELM framework are presented, i.e., Probabilistic Output Extreme Learning Machine (PO-ELM) and Constrained Optimization Posterior Probabilistic Outputs based Extreme Learning Machine (CPP-POELM). The proposed models are evaluated empirically on several benchmark datasets as well as real world power system applications to demonstrate its validity and efficacy in handling pattern classification problems as well as decision making process.