Reviewerly/exHarmony huggingface dataset:
A linear space adjustment by mapping data into an intermediate space and keeping low level data structures

One of the most important assumptions in machine learning tasks is the fact that training data points and test data points are extracted from the same distribution. However, this paper assumes the situation in which this fact does no longer hold. Therefore, a task named space adjustment, through which the distribution of the data points in the training-data space and the distribution of the data points in the test-data space become identical, is inevitable. Hereby, authors propose a linear mapping for the space adjustment task in the paper. It considers four approaches for preserving localities among data samples during the space adjustment. Each approach is defined based on a different locality concept. Considering all locality concepts in an objective function, authors transform the space adjustment into an optimisation problem. The paper proposes to optimise the corresponding objective function by an iterative approach. Empirical study shows that the proposed method outperforms the baseline methods. To do experiments, authors employ a large number of real-world datasets.
Projection Mapping System To A Widely Dynamic Sphere With Circumferential Markers

Image projection on spheres and their surroundings have been researched for all-around display and motion visualization. However, wide-range projection onto a dynamic sphere can suffer from tracking errors and projection latency. We propose a high-speed projection mapping system for dynamic spheres, and circumferential markers for sphere posture estimation. Marker detection based on ellipse appearance can easily start tracking, and is robust against interactive occlusion; therefore, the system enables projection mapping and rotational visualization for a dynamically moving sphere. We experimentally confirmed sufficient pose estimation accuracy of the circumferential markers compared with initialized dot markers, and sufficient tracking ability against initialization, occlusion, and depth-direction movement. Demonstrations showed accurate, rotation-visualized projection onto a dynamic sphere, which is useful for sports practice applications.
Uncertainty Based Camera Model Selection

The quality and speed of Structure from Motion (SfM) methods depend significantly on the camera model chosen for the reconstruction. In most of the SfM pipelines, the camera model is manually chosen by the user. In this paper, we present a new automatic method for camera model selection in large scale SfM that is based on efficient uncertainty evaluation. We first perform an extensive comparison of classical model selection based on known Information Criteria and show that they do not provide sufficiently accurate results when applied to camera model selection. Then we propose a new Accuracy-based Criterion, which evaluates an efficient approximation of the uncertainty of the estimated parameters in tested models. Using the new criterion, we design a camera model selection method and fine-tune it by machine learning. Our simulated and real experiments demonstrate a significant increase in reconstruction quality as well as a considerable speedup of the SfM process.
Extracting Arabic Composite Names Using Genitive Principles of Arabic Grammar

Named Entity Recognition (NER) is a basic prerequisite of using Natural Language Processing (NLP) for information retrieval. Arabic NER is especially challenging as the language is morphologically rich and has short vowels with no capitalisation convention. This article presents a novel rule-based approach that uses linguistic grammar-based techniques to extract Arabic composite names from Arabic text. Our approach uniquely exploits the genitive Arabic grammar rules; in particular, the rules regarding the identification of definite nouns (معرفة) and indefinite nouns (نكرة) to support the process of extracting composite names. Based on domain knowledge and Arabic Genitive Rules (AGR), the developed approach formalises a set of syntactical rules and linguistic patterns that initially use genitive patterns to classify definiteness within phrases and then extracts proper composite names from the unstructured text. The developed novel approach does not place any constraints on the length of the Arabic composite name and our initial experimentation demonstrated high recall and precision results when the NER algorithm was applied to a financial domain corpus.
Building One-Shot Semi-Supervised (BOSS) Learning Up to Fully Supervised Performance

Reaching the performance of fully supervised learning with unlabeled data and only labeling one sample per class might be ideal for deep learning applications. We demonstrate for the first time the potential for building one-shot semi-supervised (BOSS) learning on CIFAR-10 and SVHN up to attain test accuracies that are comparable to fully supervised learning. Our method combines class prototype refining, class balancing, and self-training. A good prototype choice is essential and we propose a technique for obtaining iconic examples. In addition, we demonstrate that class balancing methods substantially improve accuracy results in semi-supervised learning to levels that allow self-training to reach the level of fully supervised learning performance. Our experiments demonstrate the value with computing and analyzing test accuracies for every class, rather than only a total test accuracy. We show that our BOSS methodology can obtain total test accuracies with CIFAR-10 images and only one labeled sample per class up to 95% (compared to 94.5% for fully supervised). Similarly, the SVHN images obtains test accuracies of 97.8%, compared to 98.27% for fully supervised. Rigorous empirical evaluations provide evidence that labeling large datasets is not necessary for training deep neural networks. Our code is available at https://github.com/lnsmith54/BOSS to facilitate replication.
Neural Estimators for Conditional Mutual Information Using Nearest Neighbors Sampling

The estimation of mutual information (MI) or conditional mutual information (CMI) from a set of samples is a long-standing problem. A recent line of work in this area has leveraged the approximation power of artificial neural networks and has shown improvements over conventional methods. One important challenge in this new approach is the need to obtain, given the original dataset, a different set where the samples are distributed according to a specific product density function. This is particularly challenging when estimating CMI. In this paper, we introduce a new technique, based on k nearest neighbors (k-NN), to perform the resampling and derive high-confidence concentration bounds for the sample average. Then the technique is employed to train a neural network classifier and the CMI is estimated accordingly. We propose three estimators using this technique and prove their consistency, make a comparison between them and similar approaches in the literature, and experimentally show improvements in estimating the CMI in terms of accuracy and variance of the estimators.
SPCOR: a secure and privacy‐preserving protocol for mobile‐healthcare emergency to reap computing opportunities at remote and nearby

This study proposes a secure and privacy-preserving protocol for outsourcing health data processing operations during the emergency in the mobile healthcare network. The proposed protocol provides a practical solution to utilise smartphone resources at both remote and nearby for processing the overwhelming personal health information (PHI) of a user in healthcare emergency opportunistically and securely. The patients with symptoms matching with those of the user in an emergency are considered as opportunities to minimise the privacy disclosure of the user. Opportunities at both remote and nearby are exploited with the help of a base station in the 4G network. Moreover, novel and efficient outsourced privacy access control schemes are developed to minimise the power drain of the user in an emergency without compromising his privacy. The outsourced privacy access control is facilitated through the design of innovative schemes for outsourced attribute-based access mechanism and an outsourced privacy-preserving scalar product computation. Detailed performance evaluations through implementations on Raspberry Pi 3B + board and simulations using NS3 network simulator and Scyther tool confirm the efficiency of the proposed protocol in providing highly reliable PHI processing and transmissions with reasonably low delay and energy consumption while maintaining user privacy.
PGO: Describing Property Graphs in RDF

RDF and Property Graphs are data models that are being used to represent Knowledge Graphs. The definition of methods to transform RDF data into Property graph data is fundamental to allow interoperability among the systems using these models. Although both models are based on a graph structure, they have special features that complicate the definition of data transformation methods. This article presents an ontology-based approach to transform (automatically) property graphs into RDF graphs. The ontology, called PGO, defines a set of terms that allows describing the elements of a property graph. The algorithm corresponding to the transformation method is described, and some properties of the method are discussed (complexity, data preservation, and monotonicity). The results of an experimental evaluation are also presented.
Low-Frequency Guided Self-Supervised Learning For High-Fidelity 3d Face Reconstruction In The Wild

In this paper, we propose a low-frequency guided self-supervised learning method for high-fidelity 3D face reconstruction from an in-the-wild image. Unlike other self-supervised methods only using the color difference between the original image and the estimated image, we add low-frequency albedo information to enhance the self-supervised learning for more realistic albedo while insensitive to the non-skin regions. Specifically, based on a PCA albedo model, we first train a Boosting Network (B-Net) to provide illumination and intact albedo distribution. Then with above information, we learn an image-to-image non-linear Facial Albedo Network (FAN) by self-supervision to produce a high-fidelity albedo. We further propose a Detail Recovering Network (DRN) to recover geometric details such as wrinkles. FAN and DRN permit to reconstruct 3D faces with high-fidelity albedo and geometry details. Finally, experimental results demonstrate the effectiveness of the proposed method.
Enabling Elasticity Control Functions for Cloud-Network Slice-Defined Domains

The NECOS (Novel Enablers for Cloud Slicing) platform controls the entire lifecycle of end-to-end cloud-network slice instances, so that new services and applications can run in a broader perspective for quality-guaranteed and highly isolated multi-tenancy. Elasticity plays a vital role in ensuring the efficiency of the NECOS platform. According the state of the art, mainstream reactive elasticity solutions are not suitable for dealing with cloud-network slicing-defined systems by the fact that they cannot trigger time-burdensome vertical elasticity on deriving resource-depleting conditions. This gap can be filled by introducing in the elaSticity cLOud-neTwork Slices (SLOTS) solution, which participates in the NECOS platform in the form of new building blocks that interwork with others to add new patterns of cloud- and networking-type resources under resource-depleting conditions. The unique contributions made by SLOTS are as follow, and go beyond the state–of-the-art by: (i) fully supporting cloud-network slice-defined systems; (ii) offering a hybrid elasticity approach; (iii) provide a sophisticated mechanism to enable a slice solidarity approach, and (iv) providing a real lab-premised large-scale testbed for a SLOTS prototyping assessment. Analysis on the evaluation results suggest that SLOTS performs well by accomplishing a higher number of elasticity events under resource-critical conditions, while slightly adding computing overhead, when compared with a widely-used stochastic-based representing solution.
Latent Representation Learning and Manipulation for Privacy-Preserving Sensor Data Analytics

The rapid deployment of sensor systems in homes and work environments, and new applications of machine learning at the edge have posed an enormous and unprecedented threat to privacy. In this paper we investigate the ability of existing deep neural network models trained in an adversarial fashion to enhance privacy without sacrificing data utility, and propose a re-identification attack that could effectively hinder them from hiding private data. This highlights an important problem with anonymization techniques that rely on adversarial training. To address this problem, we perform deterministic and probabilistic arithmetic operations in the learned latent variable space of a variational autoencoder and show empirically that it makes the anonymized data less susceptible to the proposed re-identification attack. Our experiments on a Human Activity Recognition data set confirm that the proposed deterministic manipulation of the latent variables outperforms the state-of-the-art anonymizing autoencoders, reducing the accuracy of a gender identification model by an additional 16% without noticeably affecting the accuracy of the activity recognition model. Moreover, the accuracy of the gender re-identification model is further reduced by 16.25% on average thanks to the proposed probabilistic manipulation.
Stability and Scalability of Blockchain Systems

The blockchain paradigm, introduced in the Bitcoin whitepaper [10], enables distributed consensus over a peer-to-peer network. Each peer constantly mines new information called blocks. Thus, blocks in the network are created over time. Each peer that creates (mines) a block also creates references to one or more previously created blocks. Peers also communicate blocks in order to synchronize their information sets; i.e., the sets of blocks and references the peers are aware of.
Growing Artificial Neural Networks

Pruning is a legitimate method for reducing the size of a neural network to fit in low SWaP hardware, but the networks must be trained and pruned offline. We propose an algorithm, Artificial Neurogenesis (ANG), that grows rather than prunes the network and enables neural networks to be trained and executed in low SWaP embedded hardware. ANG accomplishes this by using the training data to determine critical connections between layers before the actual training takes place. Our experiments use a modified LeNet-5 as a baseline neural network that achieves a test accuracy of 98.74% using a total of 61,160 weights. An ANG grown network achieves a test accuracy of 98.80% with only 21,211 weights.
A Survey on Hardware Implementation of Cryptographic Algorithms Using Field Programmable Gate Array

In the past recent years the idea towards secure data communication is increasing day by day. The secure communication is being achieved by applying various cryptographic algorithms on the data which is to be transferred over wireless networks. The different cryptographic algorithms that are generally practiced in the current cyber world are Advanced Encryption Standard (AES), Data Encryption Standard (DES), RSA algorithm, Message Digest 5 (MD5), Secure Hash Algorithm (SHA). All these algorithms are highly secured with sound and complex mathematical computations that makes the hacker tedious to breach the data which is protected by these algorithm. The hardware implementation of algorithms enhances the speed, efficiency and reliability of security standards. In this work the Field Programmable Gate Array (FPGA) implementation of various cryptographic algorithms is discussed in details. The main motivation behind the FPGA implementations of Security algorithms is to increase the speed and decrease delays of software implementations. There are millions of logic gates that are clustered in FPGA, this brings new innovations to existing algorithms. This paper surveyss the parameters such as throughput, operating frequency, number of slice registers used and number of clock cycles of FPGA that have the major role in execution process of cryptographic algorithms. Comparative analysis on hardware implementation of security algorithms on different FPGA's is also done.
Small UAV Flight Planning in Urban Environments

This work proposes a fast algorithm for generating obstacle-free and wind-efficient flight paths at a constant above-ground-level altitude in urban environments, because a fast flight path planning algorithm is an essential function or service needed for enabling small Unmanned Aerial Vehicle (sUAV) to operate in urban environments within Class G airspace. The proposed method first converts the 3D path planning problem to a 2D problem by constructing an obstacle map at a given above-ground-level altitude. A quad-tree decomposition is then used to build a search space in terms of obstacle occupancy and wind difference. The wind cost of traveling through each cell is defined based on energy consumption under various wind conditions. A repulsive potential is also adopted to make sure the flight plans stay away from obstacles. The Theta* search algorithm, a variant of A* algorithm, is applied to mitigate the path angle change constraints introduced by grid-based graphs. With the Theta* and post smoothing techniques, an obstacle-free, wind efficient, and constant above-ground-level flight plan can be quickly generated for small UAV operations in urban environments while meeting the lateral path angle constraints. The results showed that the path planning algorithm is efficient and can be finished within several seconds. With a proper choice of wind coefficient, the proposed path planning algorithm outperforms the multiple-shooting trajectory optimization method even in an obstacle-free environment. With the flexibility of incorporating other geo-related costs and the efficiency in computation, the proposed algorithm shows potential for real-time flight path planning in complex urban environments.
Open Set Semantic Segmentation With Statistical Test And Adaptive Threshold

Semantic segmentation in the open world is prerequisite when deploying a well-trained segmentation model in real scenarios, where objects of unseen classes during model training may often appear in future new images to be segmented by the model. However, such open set semantic segmentation task has been rarely explored before. In this study, making use of the large number of pixel-level prediction uncertainties for each image, we proposed applying the non-parametric statistical test to detect whether objects of unseen classes appear in a new image, and an adaptive threshold method to automatically segment each pixel into either one of the known classes or the unknown class. Experiments on the natural image dataset showed that the proposed method significantly outperforms multiple strong baseline methods.
RISCuer: a reliable multi-UAV search and rescue testbed

We present the Robotics Intelligent Systems & Control (RISC) Lab multiagent testbed for reliable search and rescue and aerial transport in outdoor environments. The system consists of a team of three multirotor unmanned aerial vehicles (UAVs), which are capable of autonomously searching, picking up, and transporting randomly distributed objects in an outdoor field. The method involves vision based object detection and localization, passive aerial grasping with our novel design, GPS based UAV navigation, and safe release of the objects at the drop zone. Our cooperative strategy ensures safe spatial separation between UAVs at all times and we prevent any conflicts at the drop zone using communication enabled consensus. All computation is performed onboard each UAV. We describe the complete software and hardware architecture for the system and demonstrate its reliable performance using comprehensive outdoor experiments, and by comparing our results with some recent, similar works.
Efficient Graph Query Processing over Geo-Distributed Datacenters

Graph queries have emerged as one of the fundamental techniques to support modern search services, such as PageRank web search, social networking search and knowledge graph search. As such graphs are maintained globally and very huge (e.g., billions of nodes), we need to efficiently process graph queries across multiple geographically distributed datacenters, running geo-distributed graph queries. Existing graph computing frameworks may not work well for geographically distributed datacenters, because they implement a Bulk Synchronous Parallel model that requires excessive inter-datacenter transfers, thereby introducing extremely large latency for query processing. In this paper, we propose GeoGraph --a universal framework to support efficient geo-distributed graph query processing based on clustering datacenters and meta-graph, while reducing the inter-datacenter communication. Our new framework can be applied to many types of graph algorithms without any modification. The framework is developed on the top of Apache Giraph. The experiments were conducted by applying four important graph queries, i.e., shortest path, graph keyword search, subgraph isomorphism and PageRank. The evaluation results show that our proposed framework can achieve up to 82% faster convergence, 42% lower WAN bandwidth usage, and 45% less total monetary cost for the four graph queries, with input graphs stored across ten geo-distributed datacenters.
Active stylus‐touch discrimination scheme based on anomaly detection algorithm

Abstract This paper proposes an anomaly detection (AD) algorithm that can discriminate stylus‐touch based on capacitive touch screen panel. The digital value acquired from an analog‐to‐digital converter (ADC) are transferred to an autoencoder including an encoder and a decoder. While the encoder classifies only two classes of a no‐touch and a finger‐touch, the decoder reconstructs the similar sequence to the input one according to the encoder's decision. Because the touch sequences caused by the stylus are not trained, the large difference between input and output sequences is used to discriminate the stylus‐touch from finger‐touch and no‐touch. The proposed method is evaluated by means of an 8‐inch capacitive touch panel, an AD touch detection board, and a stylus board. At the sequence length of 16 touch samples, the measured bit error rate (BER) of less than 10 −6 for each touch case is equivalent to the previous support vector machine (SVM) scheme whereas the number of multipliers is dramatically reduced to 16, compared with 400 of the previous SVM method.