Reviewerly/exHarmony huggingface dataset:
Private Sampling: A Noiseless Approach for Generating Differentially Private Synthetic Data

In a world where artificial intelligence and data science become omnipresent, data sharing is increasingly locking horns with data-privacy concerns. Differential privacy has emerged as a rigorous framework for protecting individual privacy in a statistical database, while releasing useful statistical information about the database. The standard way to implement differential privacy is to inject a sufficient amount of noise into the data. However, in addition to other limitations of differential privacy, this process of adding noise will affect data accuracy and utility. Another approach to enable privacy in data sharing is based on the concept of synthetic data. The goal of synthetic data is to create an as-realistic-as-possible dataset, one that not only maintains the nuances of the original data, but does so without risk of exposing sensitive information. The combination of differential privacy with synthetic data has been suggested as a best-of-both-worlds solutions. In this work, we propose the first noisefree method to construct differentially private synthetic data; we do this through a mechanism called “private sampling.” Using the Boolean cube as benchmark data model, we derive explicit bounds on accuracy and privacy of the constructed synthetic data. The key mathematical tools are hypercontractivity, duality, and empirical processes. A core ingredient of our private sampling mechanism is a rigorous “marginal correction” method, which has the remarkable property that importance reweighting can be utilized to exactly match the marginals of the sample to the marginals of the population.
Two phase learning technique in modular neural network for pattern classification of handwritten Hindi alphabets

Modular neural network overcomes the problem of monolithic structures of artificial neural networks. Generally modular neural network is an integration of smaller sub complete neural network models. Each model works independently on a sub portion of larger size pattern vectors. There are two ways of modularizing the neural network i.e. modularizing learning and modularizing structure. In this present work the modular neural network with modular learning for pattern classification of hand written Hindi alphabets is considered. In the presented approach 24 individual sub neural networks have been considered for first phase computing. In the second phase the collective outputs of first phase is presented as input to global neural network. Thus, the output of second phase presents the desired classification of the given large training set. Neural networks of first phase are trained locally for decomposed input patterns with gradient descent learning. Updated weights of the first phase are mapped to the global neural network. The global neural network is further trained for the collective output patterns of the first phase computing. Two phases of modular neural network i.e. decomposition and replication have been applied to perform the classification task. Simulation results are indicating that the complete neural network is approximated well when updated weights of first phase are combined with new weights of second phase and it generalized well when only updated weights of sub neural network of first phase are mapped to the connection strength of global neural network.
Building the Semantic Layer of the Józef Piłsudski Digital Archive With an Ontology-Based Approach

Using semantic web technologies is becoming an efficient way to overcome metadata storage and data integration problems in digital archives, thus enhancing the accuracy of the search process and leading to the retrieval of more relevant results. In this paper, the results of the implementation of the semantic layer of the Józef Piłsudski Institute of America digital archive are presented. In order to represent and integrate data about the archival collections housed by the institute, the authors developed arkivo, an ontology that accommodates the archival description of records but also provides a reference schema for publishing linked data. The authors describe the application of arkivo to the digitized archival collections of the institute, with emphasis on how these resources have been linked to external datasets in the linked data cloud. They also show the results of an experiment focused on the query answering task involving a state-of-the-art triple store system. The dataset related to the Piłsudski Institute archival collections has been made available for ontology benchmarking purposes.
Drone Vision and Deep Learning for Infrastructure Inspection

This lecture overviews the use of drones for infrastructure inspection and maintenance. Various types of inspection, e.g., using visual cameras, LIDAR or thermal cameras are reviewed. Drone vision plays a pivotal role in drone perception/control for infrastructure inspection and maintenance, because: a) it enhances flight safety by drone localization/mapping, obstacle detection and emergency landing detection; b) performs quality visual data acquisition, and c) allows powerful drone/human interactions, e.g., through automatic event detection and gesture control. The drone should have: a) increased multiple drone decisional autonomy and b) improved multiple drone robustness and safety mechanisms (e.g., communication robustness/safety, embedded flight regulation compliance, enhanced crowd avoidance and emergency landing mechanisms). Therefore, it must be contextually aware and adaptive. Drone vision and machine learning play a very important role towards this end, covering the following topics: a) semantic world mapping b) drone and target localization, c) drone visual analysis for target/obstacle/crowd/point of interest detection, d) 2D/3D target tracking. Finally, embedded on-drone vision (e.g., tracking) and machine learning algorithms are extremely important, as they facilitate drone autonomy, e.g., in communication-denied environments. Primary application area is electric line inspection. Line detection and tracking and drone perching are examined. Human action recognition and co-working assistance are overviewed.The lecture will offer: a) an overview of all the above plus other related topics and will stress the related algorithmic aspects, such as: b) drone localization and world mapping, c) target detection d) target tracking and 3D localization e) gesture control and co-working with humans. Some issues on embedded CNN and fast convolution computing will be overviewed as well.
Python Programming for the Raspberry Pi

Now that we know a little more about the Raspberry Pi, we can learn more about one of the programming languages on the Raspberry Pi when developing your IoT solutions. One of those languages is Python – a very robust and powerful language that you can use to write very powerful applications. Mastering is very easy, and some may suggest it doesn’t require any formal training to use. This is largely true, and thus you should be able to write IoT applications with only a little bit of knowledge about Python.
Active Contours Driven by Cellular Neural Networks for Image Segmentation in Biomedical Application

This paper proposes a novel approach for image segmentation in the context of biomedical applications.The medical images considered for this analysis are both microarray images and images recorded from microfluidics devices.In case of microarray images, microarray spots represented as circular shapes are localised and used further on for the estimation of gene expression levels, based on the average pixel intensities.Considering the microfluidic devices images, the features of cells or clusters of cells are determined by using the proposed image segmentation approach.The novelty of this approach lies in the fact that this is a segmentation procedure which uses an edge-based active contour model (ACM) driven by cellular neural networks (CNNs).Thus, a predefined curve is evolved towards the edges of the image objects (i.e., circular microarray spots and irregular shapes representing clusters of cells).In the curve evolution process, the classic representation of image edges by using the gradient vector is replaced by an edge-based feature template determined by the CNN for each image object.The benefits of the proposed segmentation method are illustrated for both image processing applications, by using specific quality measures for the characterization of object features within the image under analysis.
Face Recognition Based on Fractal Code and Deep Belief Networks

An enhanced algorithm to recognize the human face using bi-dimensional fractal codes and deep belief networks is presented in this work. The proposed method is experimentally robust against variations in the appearance of human face images, despite different disturbances affecting the measurements and the acquisition process such as occlusion, changes in lighting, pose, and expression or the presence or absence of structural components. That is mainly based on fractal codes (IFS) and bi-dimensional subspaces for features extraction and space reduction, combined with a deep belief network (DBN) classifier. The evaluation is performed through comparisons using probabilistic neural network (PNN) and nearest neighbours (KNN) approaches on three well-known databases (FERET, ORL, and FEI). The results suggest the effectiveness and robustness of the proposed approach.
A Cuboid Detection and Tracking System using A Multi RGBD Camera Setup for Intelligent Manipulation and Logistics

Cuboid objects are widely used in logistics and manufacturing, since they are ideal for automatic manipulation. For that, a reliable detection, identification and tracking algorithm of cuboid objects is a key requirement. RGBD cameras are suited for these tasks, however they suffer from limitations such as occlusions from clutter or a small field of view. A multi RGBD camera setup would remedy many of these limitations. In this work, we present a cuboid detection, identification and tracking system using a multi RGBD camera setup. First, we calibrate our multi camera setup using a robot arm and a colored spherical calibration target. By using spatial and color information, a preprocessing pipeline segments then the unified point cloud into locally connected planar segments. Afterwards, custom spatial criteria allows for assigning the resulting segments to cuboid groups. We present a new modified bounding box regression method to estimate cuboid boxes and a novel intersection over union based approach to track cuboids across frames. By applying our approach, the automatic manipulation and transportation in manufacturing industry will become more efficient, which helps to save more time and cost. Our system is tested and analyzed with a recorded dataset, which contents variant constellations. The reliability and accuracy are demonstrated.
Pyramid-based Scatterplots Sampling for Progressive and Streaming Data Visualization

We present a pyramid-based scatterplot sampling technique to avoid overplotting and enable progressive and streaming visualization of large data. Our technique is based on a multiresolution pyramid-based decomposition of the underlying density map and makes use of the density values in the pyramid to guide the sampling at each scale for preserving the relative data densities and outliers. We show that our technique is competitive in quality with state-of-the-art methods and runs faster by about an order of magnitude. Also, we have adapted it to deliver progressive and streaming data visualization by processing the data in chunks and updating the scatterplot areas with visible changes in the density map. A quantitative evaluation shows that our approach generates stable and faithful progressive samples that are comparable to the state-of-the-art method in preserving relative densities and superior to it in keeping outliers and stability when switching frames. We present two case studies that demonstrate the effectiveness of our approach for exploring large data.
The Registration and Turnout Effects of Automatic Voter Registration

Automatic voter registration (AVR) is a popular reform that seeks to register eligible citizens to vote as a matter of course if they engage with government and provide the necessary information. Analysis of early adopting states has generally found some effect of the reform on both registration and turnout, but there has yet to be a comprehensive causal analysis. We use a variety of causal identification strategies to examine the effects of AVR on both registration and turnout. We find it raises registration rates substantially, and while these new registrants are less likely to vote, enough do participate to raise the eligible turnout rate. We also show that the effect of AVR gradually builds the longer it is in place, and that the different types of AVR have significantly different effects on both registration and turnout.
Extensive Analysis Of Internet Of Things Based Health Care Surveillance System Using Rfid Assisted Lightweight Cryptographic Methodology

Internet of Things (IoT) plays a vital role in Smart Applications such as Smart City Maintenance and Control, Health Care, Transportation, Defense Operations and so on. Compare to all of these mentioned applications Health care is the most significant and necessary application to take care with as well as the concern regarding patients and their belongings need to be monitor the patient health related summary from the remote end without any hurdle. This is possible with the help of Internet of Things, in which it enables the bridge between client end and the server end to track the health records clearly without any delay as well as providing the proper security mechanisms to the health records preserved into the server. In this paper, a new Radio Frequency Identification (RFID) enabled Lightweight Cryptographic Method (RFIDLCM) is introduced to provide proper security mechanisms to the health records preserved into the server end. In addition, it must be kept secure against abuse as well as authentication when storing the patient's record as other devices can easily be monitored. It is quite difficult to encrypt voluminous data encryption protocols because of restricted IoT devices. For this reason, Homomorphic encryption models are recommended and in this paper, a new block cipher technique has been proposed for the safe transmission of health information from such Internet of Things devices. The proposed approach is activated with the help of Smart Helath Monitoring Device interconnected with several health related sensors such as RFID Reader, ECG Sensor, Pressure Level Estimation Sensor and Temperature Sensor. All these sensors are integrated together to provide a proper solution to the health care surveillance scheme in an intelligent manner. The resulting emulations of this paper proves the efficiency of the proposed approach in clear manner with graphical representations. The proposed approach of RFIDLCM proves its efficiency with respect to the improvements in prediction accuracy levels, security levels, time concerns and the data transportation efficiency.
Particle Swarm Optimization in Bioinformatics, Image Processing, and Computational Linguistics

Since its inception, particle swarm optimization and its improvement has been an active area of research, and the algorithm has found its application in multifarious domains such as highly constrained engineering problems as well as artificial intelligence. The focal point of this paper is to make the reader aware of the innumerable applications of particle swarm optimization, especially in the field of bioinformatics, digital image processing, and computational linguistics. This review work is designed to serve as a comprehensive look-up guide and to navigate through the algorithm's scope and application in recent times in the aforementioned fields.
Design Of A Decision Support System Of Study Concentration Using Ahp Method

The choice of concentration is a process that is part of a series of systems in higher education institutions. The purpose of this activity is so that students can learn their knowledge more specifically and with direction. Usually the concentration or specialization is determined by the Study Program. However, in some universities or colleges, there are also those who free students to choose. Freedom not only always has a positive impact, but also a negative one. Like what happened at STMIK Sumedang, freedom in choosing to concentrate creates a separate polemic for students. The problem that arises is that students choose to concentrate only because they are following their friends and are also intimidated by the lecturer which results in the decisions that are taken not being objective. Departing from this, the author has the idea to develop a decision support system for concentration selection using the Analitycal Hierarchy Process (AHP) method. With this system, it is hoped that the concentration selection process can be more effective and focused, so that no more students feel lost (in the learning process) after choosing a concentration.
Learning Innovation of Qibla Direction with Mobile-Based App by Adapting Computational Thinking

This research was conducted to obtain the results of the development of Learning to Determine Qibla Direction with Computational Thinking Adaptation using Mobile-Based Applications. The researcher used mixed methods with a sequential exploratory research design that takes two stages (qualitative-quantitative). The R &amp; D stage used 5 (five) steps of the ADDIE method, namely Analysis, Design, Development, Implementation and Evaluation. This research was conducted at the office of the Ministry of Religion of the Thousand Islands Regency, with the result that the learning innovations carried out were appropriate and met expectations. The learning process for training participants and in practice its application was easier and more affordable. The Qibla direction training used in this mobile-based application was applied with a learning pattern that adapts computational thinking skills, which were arranged in four phases, namely: Elaboration, Determination, Calculation, and Evaluation. This learning innovation shows that it can improve participants' understanding of the concept of calculating Qibla Direction which was applied integratively between religion, science and technology as a coherent knowledge.&#x0D; Keywords: Mobile-Based App, Learning Innovation, Adapting Computational Thinking
Target Detection of Terahertz Images Based on Improved Fuzzy C-Means Algorithm

The images obtained by terahertz continuous wave imaging system are easily interfered by background factors, and there are also defects such as low contrast and strong interference fringes, which become obstacles for display and analysis. In order to solve this problem, we propose a clustering algorithm based on improved fuzzy C-means for target detection in this paper. Aiming at the characteristics of terahertz images, the objective function is improved, and the quantum particle swarm algorithm is introduced to optimize it to improve its shortcomings of easyly trapping into local values. The research results show that the new clustering segmentation algorithm is suitable for terahertz continuous wave images with irregular fringe interference. It can better extract the target in the image and has better detection accuracy than the classic image clustering algorithm.
Roadmap of optical computing

This paper presented a roadmap of optical computing, particularly, for optical implementation of convolution, in comparison with supercomputer. Optical computing power presented in this roadmap is predicted based on a simple mathematic equation of convolution where optics can do matrix convolution in full parallelism with single pass of light, which could be 500 times faster than supercomputer( Fugaku), while energy consumption of optical computing might be 1000 times lower than supercomputer (Fugaku), supposing 8K Spatial Light Modulator with extremely high updating rate &lt;22000 frame per second might be obtained in the future. This roadmap indicates a bright future of optical computing for matrix convolution, which might take up 80% of total computing power required in deep-learning neural network.
Rotation Consistency-Preserved Generative Adversarial Networks for Cross-Domain Aerial Image Semantic Segmentation

Due to its wide applications, aerial image semantic segmentation attracts increasing research interest in recent years. As well known, deep semantic segmentation network (DSSN) has been widely used to deal with aerial image segmentation and achieves spectacular success. However, when applying the DSSN trained with the labeled aerial images (i.e., the source domain) to predict the aerial images acquired with different acquisition conditions (i.e., the target domain), the performance often dramatically degrades. To alleviate the negative influence of cross-domain data shift, this paper proposes a domain adaptation approach to deal with cross-domain aerial image semantic segmentation. More precisely, this paper proposes a novel rotation consistency-preserved generative adversarial network (RCP-GAN) to carry out domain adaptation for mapping aerial images in the source domain to the target domain. Furthermore, the mapped aerial imageries with labels are used to train DSSN, which is further used to classify aerial imagery in the target domain. To verify the validity of the presented approach, we give two cross-domain experimental settings including: (I) variation of geographic location; (II) variation of both geographic location and imaging mode. Extensive experiments under two typical cross-domain settings show that our proposed method can effectively address the domain shift problem and outperform the state-of-the-art methods with a large margin.
A Blockchain-Based Distributed Paradigm to Secure Localization Services

In recent decades, modern societies are experiencing an increasing adoption of interconnected smart devices. This revolution involves not only canonical devices such as smartphones and tablets, but also simple objects like light bulbs. Named the Internet of Things (IoT), this ever-growing scenario offers enormous opportunities in many areas of modern society, especially if joined by other emerging technologies such as, for example, the blockchain. Indeed, the latter allows users to certify transactions publicly, without relying on central authorities or intermediaries. This work aims to exploit the scenario above by proposing a novel blockchain-based distributed paradigm to secure localization services, here named the Internet of Entities (IoE). It represents a mechanism for the reliable localization of people and things, and it exploits the increasing number of existing wireless devices and blockchain-based distributed ledger technologies. Moreover, unlike most of the canonical localization approaches, it is strongly oriented towards the protection of the users' privacy. Finally, its implementation requires minimal efforts since it employs the existing infrastructures and devices, thus giving life to a new and wide data environment, exploitable in many domains, such as e-health, smart cities, and smart mobility.