Reviewerly/exHarmony huggingface dataset:
DATA++: An Automated Tool for Intelligent Data Augmentation Using Wikidata

In the present, technology has become a big influence that impacts the lives of many humans, with artificial intelligence being one of the most influential elements. Creative feature engineering is an important part of machine learning methodology that supports and manipulates existing data to make it work more efficiently by modifying dimensions of data. Pulling useful information from external sources and combining them, however, are cumbersome since data engineers need to manually find external data sources and process them. Therefore, the ability to modify and enrich existing data automatically, using external open data sources could prove crucial to data engineers and scientists looking to enrich their datasets. In this paper, we propose a method that automatically augments a given structured dataset, by inferencing relevant dimension from an external data source with respect to the target attribute. Specifically, our proposed algorithm first creates bloom filters for every instance of data items. Such filters are then used to retrieve relevant information from the linked open data source, which is later processed into additional columns in the target dataset. A case study of three real-world datasets using Wikidata as the external data source is used to empirically validate our proposed method on both regression and classification tasks. The experimental results show that the datasets augmented by our proposed algorithm yield correlation improvement of 23.11 % on average for the regression task, and ROC improvement of 86.50% for the classification task.
An architecture for automatic multimodal video data anonymization to ensure data protection

To perform a data protection concept for our mobile sensor platform (MODISSA), we designed and implemented an anonymization pipeline. This pipeline contains plugins for reading, modifying, and writing different image formats, as well as methods to detect the regions that should be anonymized. This includes a method to determine head positions and an object detector for the license plates, both based on state of the art deep learning methods. These methods are applied for all image sensors on the platform, no matter if they are panoramic RGB, thermal IR, or grayscale cameras. In this paper we focus on the whole face anonymization process. We determine the face region to anonymize on the basis of body pose estimates from OpenPose what proved to lead to robust results. Our anonymization pipeline achieves nearly human performance, with almost no human resources spent. However, to gain perfect anonymization a quick additional human interactive postprocessing step can be performed. We evaluated our pipeline quantitatively and qualitatively on urban example data recorded with MODISSA.
Efficient RSA Crypto Processor Using Montgomery Multiplier in FPGA

With the advancement of technology in data communication, security plays a major role in protecting user’s data from adversaries. Cryptography is a technique which consists of various algorithms to provide secure communication during data transfer. One of the most widely used and highly secure algorithm is RSA (Rivest, Adi Shamir and Leonard Adleman). This research is going to focus on designing an efficient crypto processor for RSA on Nexys4, a ready-to-use FPGA (Field Programmable Gate Array) board. There are various techniques in implementing RSA algorithm in hardware platforms. Primary concentration is to implement the algorithm in a high performant manner, and it will be achieved using Montgomery multiplication technique. RSA is a public key cryptography system which involves generation of public key for encryption and private key for decryption. Building blocks of RSA includes: Two multiplier blocks, two blocks to verify the primality of random numbers, one GCD (Greatest Common Divisor) block to check the validity of a public key, two modular exponential blocks – one is for generating encrypted or cipher message and another is to retrieve the original message from the cipher text. Simulation and synthesis of these blocks is achieved and verified using Xilinx Vivado Design Suite.
T-TATIL

Graphic contents are important resources to communicate information and their use is fundamental in the teaching process. Visually impaired people do not have easy access to this resource owing to their impairment. They mainly use tactile graphics to explore visual contents. In this article, we propose the development of a tool for tablets intended to aid visually impaired people in interpreting tactile graphics. After studying different exploration strategies of tactile graphics adopted by visually impaired people, we propose an application that utilizes the benefit of multimodal interactions. Through a case study, three blind students tested the prototype of the application, demonstrating that the solution is a promising approach to aid in the exploration of tactile graphics.
Refinement of Convolutional Neural Network Based Cell Nuclei Detection Using Bayesian Inference

Cytological samples provide useful data for cancer diagnostics but their visual analysis under a microscope is tedious and time-consuming. Moreover, some scientific tests indicate that various pathologists can classify the same sample differently or the same pathologist can classify the sample differently if there is a long interval between subsequent examinations. We can help pathologists by providing tools for automatic analysis of cellular structures. Unfortunately, cytological samples usually consist of clumped structures, so it is difficult to extract single cells to measure their morphometric parameters. To deal with this problem, we are proposing a nuclei detection approach, which combines convolutional neural network and Bayesian inference. The input image is preprocessed by the stain separation procedure to extract a blue dye (hematoxylin) which is mainly absorbed by nuclei. Next, a convolutional neural network is trained to provide a semantic segmentation of the image. Finally, the segmentation results are post processed in order to detect nuclei. To do that, we model the nuclei distribution on a plane using marked point process and apply the Besag's iterated conditional modes to find the configuration of ellipses that fit the nuclei distribution. Thanks to this we can represent clusters of occluded cell nuclei as a set of an overlapping ellipses. The accuracy of the proposed method was tested on 50 cytological images of breast cancer. Reference data was generated by the manual labeling of cell nuclei in images. The effectiveness of the proposed method was compared with the marker-controlled watershed. We applied our method and marker controlled watershed to detect nuclei in the semantic segmentation maps generated by the convolutional neural network. The accuracy of nuclei detection is measured as the number of true positive (TP) detections and false positive (FP) detections. It was recorded that the method can detect correctly 93.5% of nuclei (TP) and at the same time it generates only 6.1% of FP. The proposed approach has led to better results than the marker-controlled watershed both in the number of correctly detected nuclei and in the number of false detections.
The Augmented Reality in the Teaching-Learning Process of Children from 3 to 5 Years Old

Augmented reality (AR) is a technology that allows the perception of the real world. It provides the opportunity for the human being to interact with the objects already created in an environment of a supposed reality. The aim of the RA is to study the digital processing of images, the creation of patterns and the recognition of markers in 3D modeling designs. Through an analysis in a sample of children aged between 3 to 5 years, the advantages, disadvantages that the RA has over them in their cognitive advances with the experience of the objects are identified. Observation, experimental and inductive methods were used, as a result of the project, it was found that children love learning in a more practical, playful and positive way. The use of AR serves to motivate them and encourage them to learn in a meaningful, creative and animated way so that they can grasp and adopt learning as part of their environment. The augmented reality, therefore, offers the possibility of improving the processes of education in children with or without learning problems in the city of Babahoyo.
Implementation of a Print DDA Program at Old Dominion University Libraries

After feedback from faculty, students, and staff on the viability of starting a print demand driven acquisitions service, Old Dominion University Libraries (ODU) in collaboration with ProQuest Coutts and Ex-Libris implemented a print DDA pilot project in the fall of 2017. The project planning team addressed and resolved a variety of challenges including software development, record duplication, and order fulfillment. As a result of their efforts, Print DDA is now considered one of the Libraries most efficient options for satisfying the informational and research demands of ODU faculty and students. This article examines all aspects of planning and implementing print DDA as well as the adjustments made by the planning team to improve the service.
A Three-Layered Approach for Designing Smart Contracts in Collaborative Processes

In collaborative environments, where enterprises interact each other’s without a centralised authority that ensures trust among them, the ability of providing cross-organisational services must be enabled also between mutually untrusting participants. Blockchain platforms and smart contracts have been proposed to implement collaborative processes. However, current solutions are platform-dependent and deploy on-chain the whole process, thus increasing the execution costs of smart contracts if deployed on permissionless blockchain. In this paper, we propose an approach that includes criteria to identify trust-demanding objects and activities in collaborative processes, a model to describe smart contracts in a platform-independent way and guidelines to deploy them in a blockchain. To this aim, a three-layered model is used to describe: (i) the collaborative process, represented in BPMN, where the business expert is supported to add annotations that identify trust-demanding objects and activities; (ii) Abstract Smart Contracts based on trust-demanding objects and activities only and specified by means of descriptors, that are independent from any blockchain technology; (iii) Concrete Smart Contracts, that implement abstract ones and are deployed over a specific blockchain, enabling the creation of a repository where a single descriptor is associated with multiple implementations. The flexibility and reduced costs of the approach, due to the smart contracts abstraction and the use of blockchain only when necessary, are discussed with a case study on remote monitoring services in the digital factory.
Multiple lexicalisation (a Java based study)

We consider the possibility of making the lexicalisation phase of compilation more powerful by avoiding the need for the lexer to return a single token string from the input character string. This has the potential to empower language design by softening the boundaries between lexical and phrase level specification. The large number of lexicalisations makes it impractical to parse each one individually, but it is possible to share the parsing of common subparts, reducing the number of tokens parsed from the product of the token numbers associated with the components to their sum. We report total numbers of lexicalisations of example Java strings, and the impact on these numbers of various lexical disambiguation strategies, and we introduce a new generalised parsing technique that can efficiently parse multiple lexicalisations of character string simultaneously. We then use this technique on Java, reporting on the number of lexicalisations that correspond to syntactically correct Java strings and the degree to which the standard Java lexer is safe in the sense that it does not remove all the syntactically correct lexicalisations of an input character string. Our multi-lexer parser is an alternative to scannerless parsing of a character level grammar, retaining the separation between grammar terminals and the corresponding lexical tokens. This has the advantages of allowing the parser to use terminal level lookahead and keeping lexical level disambiguation separate from the context free grammar.
Vehicle Detection and Tracking in Remote Sensing Satellite Vidio based on Dynamic Association

Since remote sensing video satellites can continuously observe a certain target area and obtain multitemporal remote sensing images, it makes the surveillance of thousands of moving objects on the wide area possible. Vehicles are a kind of important and typical objects for remote sensing detection and tracking. In the paper, we propose an efficient method to detect and track vehicles in multi-temporal remote sensing images including two stages: Vehicle detection stage and tracking stage. In the vehicle detection stage, we use background subtraction and combine road prior information to improve accuracy and efficiency and reduce search space. In the tracking stage, we improve the traditional association matching method, which apply more dynamic association methods and more practical state judgment rule. In addition, we divide tracking objects into groups to further improve the accuracy. Our method is evaluated on remote sensing video dataset. According to experiment result, the proposed method can detect and tracking vehicle objects and correct the misdirected objects by the dynamic association structure. In the stable tracking stage, tracking quality is 96%. The experimental results show effectiveness and robustness of the proposed method in detection and tracking of vehicle objects from multi-temporal remote sensing images.
Managing the doubt in fuzzy clustering by means of interval-valued fuzzy sets

In this work we study how the outliers can distort a partitional clustering process. We present a new algorithm to avoid this distortion. It is based on the minimization of a new objective functions, which is an extension of the one of the Fuzzy Clusters Means algorithm. The main novelty is the use of interval values to calculate the membership degrees of each datum to each cluster. We show the performance of our proposal over different datasets and we present its advantages in image segmentation.
3D Multi-poses Face Expression Recognition Based on Action Units

This paper presents a new 3D multi-poses facial expression recognition method based on action units. Firstly, automatic landmark detection is carried out according to the detected position of nose tips. Then a new feature descriptor is created to describe facial features involving geometric, topological and texture information. AUs are detected by classifying the fused features, and further used for recognizing the facial expressions. The method achieves good performance on Bosphorus database.
Wind turbine segmentation performing kNN-clustering on superpixel segmentations

A growing number of offshore wind farms have been deployed in recent years, comprising up to hundreds of wind turbines. The high number of wind turbines and the effort to reach an offshore wind turbine motivate the use of unmanned aerial vehicles (UAV) or drones for remote visual inspection. The images taken by the drones need to be inspected to detect and classify various kinds of damages. To leverage the effort for the detection and classification of damages, it is a good practice to segment the wind turbines from the background in a first step. In this paper, we compare the speed and segmentation accuracy of a k-Nearest-Neighbor classifier when applied to pixels or superpixels. Pixel-by-pixel classification is compared to superpixel classification using two different kinds of superpixel representations: Superpixels gained by Single Linear Iterative Clustering (SLIC) and Boundary Aware Superpixel Segmentation (BASS). Our results clearly show that superpixel based segmentation increases the segmentation speed by a relevant factor while the segmentation accuracy is at least on the same level or even improves in one dataset.
AuthCropper

As surveillance systems are popular, the privacy of the recorded video becomes more important. On the other hand, the authenticity of video images should be guaranteed when used as evidence in court. It is challenging to satisfy both (personal) privacy and authenticity of a video simultaneously, since the privacy requires modifications (e.g., partial deletions) of an original video image while the authenticity does not allow any modifications of the original image. This paper proposes a novel method to convert an encryption scheme to support partial decryption with a constant number of keys and construct a privacy-aware authentication scheme by combining with a signature scheme. The security of our proposed scheme is implied by the security of the underlying encryption and signature schemes. Experimental results show that the proposed scheme can handle the UHD video stream with more than 17 fps on a real embedded system, which validates the practicality of the proposed scheme.
Improving Whole-Brain Neural Decoding of fMRI with Domain Adaptation

In neural decoding, there has been a growing interest in machine learning on functional magnetic resonance imaging (fMRI). However, the size discrepancy between the whole-brain feature space and the training set poses serious challenges. Simply increasing the number of training examples is infeasible and costly. In this paper, we propose a domain adaptation framework for whole-brain fMRI (DawfMRI) to improve whole-brain neural decoding on target data leveraging source data. DawfMRI consists of two steps: (1) source and target feature adaptation, and (2) source and target classifier adaptation. We evaluate its four possible variations, using a collection of fMRI datasets from OpenfMRI. The results demonstrated that appropriate choices of source domain can help improve neural decoding accuracy for challenging classification tasks. The best-case improvement is $$10.47\%$$ (from $$77.26\%$$ to $$87.73\%$$ ). Moreover, visualising and interpreting voxel weights revealed that the adaptation can provide additional insights into neural decoding.
A hybrid methodology for the day-ahead PV forecasting exploiting a Clear Sky Model or Artificial Neural Networks

PhotoVoltaic (PV) plants can bring a positive impact on the sustainability of electric grids. Its variability and uncertainty, however, leads to challenging technical problems related to the efficient and secure operations of power systems. In this paper, a novel hybrid methodology for the day-ahead PV power forecasting is proposed. The methodology is able to use a Clear Sky Model (CSM) or an Artificial Neural Network (ANN), according to the day-ahead weather forecasting. Selection among these two methods is performed by a threshold on the Cloud Cover Index (CCI) determined by linear regression. The method has been tested and validated on a real PV plant.
A GPU Accelerated Path Planner for Multiple Unmanned Aerial Vehicles

Unmanned aerial vehicles (UAV's) have experienced an increased usage in the execution of surveillance and reconnaissance tasks, primary reasons being their versatility, low cost, elimination of human risk, and potential autonomous capabilities. This task requires the aircraft to overfly specified points of interest in an efficient manner whilst avoiding terrain and dangerous regions. To accomplish this autonomously, a path planning module capable of calculating and determining the most appropriate route must be implemented. It must be capable of providing a solution in a robust and timely manner to allow for live flight path updating. This paper proposes a flight planner for a reconnaissance scenario in which multiple UAV's are required to overfly numerous points of interest (POI) in a given geographical area. The approach in this paper is presented as a three step solution; the set up and formatting of input data, solving the single source shortest point problem for each POI using Bellman Ford, and the distribution and assignment of the appropriate path for each UAV using the Genetic Algorithm. It was shown that the acceleration of this process, achieved by using a Graphics Processing Unit (GPU) allowed for an average speed-up of 11x allowing for rapid path planning.
Reducing the risks of communication failures through software models

In the development process, software models support communication among members of the development team. Communication failures caused by software models may generate inconsistencies in the system, impairing its quality. Aiming to reduce risks of communication failures through software models, researchers can use the Directives of Communicability (DCs). The DCs were created based on Semiotic Engineering Theory and Grice's Cooperative Principle to improve the quality of communication among software development team members through software models. The aim of this paper is to present an empirical study assessing the use of DCs in the production of three software models. The results indicated that the DCs achieved the intended potential to reduce communication failures of what software models meant to those who produced them. The DCs also contributed to the quality of artifacts produced by their consumers, given that communication failures through software models can cause inconsistencies.
WITHDRAWN: Bayesian Deep Convolutional Neural Network for SMS Quality Analysis

This article has been withdrawn at the request of the author(s) and/or editor. The Publisher apologizes for any inconvenience this may cause. The full Elsevier Policy on Article Withdrawal can be found at https://www.elsevier.com/about/our-business/policies/article-withdrawal
Design and Implementation of Real-Time Collision Detection ASIC for Machine Tools

With the rapid development of technology, mechanical automatic anti-collision systems have become more important. Whether in self-driving vehicles, precision instruments, robots or machine tools‥etc, will have to use this technology. Instant collision detection technology requires complex structures and algorithms to handle large data operations. Previous collision detection was limited by the execution speed of the CPU and was pre-simulated. It takes a lot of time and it is difficult to achieve instant detection. In this paper, the collision detection algorithm is used to implement the triangle collision detection block in the anti-collision detection system that requires a lot of operations on the integrated circuit. The goal of collision detection is to determine whether the two objects in the three-dimensional space intersect. It will effectively reduce the operation of the entire collision detection system to achieve instant collision detection system. In order to improve the computation time of the hardware, this paper simplifies the projection theorem, and the selection strategy of the separation axis will effectively reduce the computation time of the hardware part. The VLSI implementation result under TSMC TN40G CMOS technology reveal that our architecture requires 1961.386K gates while operating at freqency 400MHz.