Reviewerly/exHarmony huggingface dataset:
The Simplest Multi-key Linearly Homomorphic Signature Scheme

We consider the problem of outsourcing computation on data authenticated by different users. Our aim is to describe and implement the simplest possible solution to provide data integrity in cloud-based scenarios. Concretely, our multi-key linearly homomorphic signature scheme ( $${\mathsf {mklhs}}$$ ) allows users to upload signed data on a server, and at any later point in time any third party can query the server to compute a linear combination of data authenticated by different users and check the correctness of the returned result. Our construction generalizes Boneh et al.'s linearly homomorphic signature scheme (PKC'09 [7]) to the multi-key setting and relies on basic tools of pairing-based cryptography. Compared to existing multi-key homomorphic signature schemes, our $${\mathsf {mklhs}}$$ is a conceptually simple and elegant direct construction, which trades-off privacy for efficiency. The simplicity of our approach leads us to a very efficient construction that enjoys significantly shorter signatures and higher performance than previous proposals. Finally, we implement $${\mathsf {mklhs}}$$ using two different pairing-friendly curves at the 128-bit security level, a Barreto-Lynn-Scott curve and a Barreto-Naehrig curve. Our benchmarks illustrate interesting performance trade-offs between these parameters, involving the cost of exponentiation and hashing in pairing groups. We provide a discussion on such trade-offs that can be useful to other implementers of pairing-based protocols.
Fast implementations of ARX-based lightweight block ciphers (SPARX, CHAM) on 32-bit processor

Recently, many lightweight block ciphers are proposed, such as PRESENT, SIMON, SPECK, Simeck, SPARX, GIFT, and CHAM. Most of these ciphers are designed with Addition–Rotation–Xor (ARX)-based structure for the resource-constrained environment because ARX operations can be implemented efficiently, especially in software. However, if the word size of a block cipher is smaller than the register size of the target device, it may process inefficiently in the aspect of memory usage. In this article, we present a fast implementation method for ARX-based block ciphers, named two-way operation. Moreover, also we applied SPARX-64/128 and CHAM-64/128 and estimated the performance in terms of execution time (cycles per byte) on a 32-bit Advanced RISC Machines processor. As a result, we achieved a large amount of improvement in execution time. The cycles of round function and key schedule are reduced by 53.31% and 31.51% for SPARX-64/128 and 41.22% and 19.40% for CHAM-64/128.
Unsharp Masking Layer: Injecting Prior Knowledge in Convolutional Networks for Image Classification

Image enhancement refers to the enrichment of certain image features such as edges, boundaries, or contrast. The main objective is to process the original image so that the overall performance of visualization, classification and segmentation tasks is considerably improved. Traditional techniques require manual fine-tuning of the parameters to control enhancement behavior. To date, recent Convolutional Neural Network (CNN) approaches frequently employ the aforementioned techniques as an enriched pre-processing step. In this work, we present the first intrinsic CNN pre-processing layer based on the well-known unsharp masking algorithm. The proposed layer injects prior knowledge about how to enhance the image, by adding high frequency information to the input, to subsequently emphasize meaningful image features. The layer optimizes the unsharp masking parameters during model training, without any manual intervention. We evaluate the network performance and impact on two applications: CIFAR100 image classification, and the PlantCLEF identification challenge. Results obtained show a significant improvement over popular CNNs, yielding 9.49% and 2.42% for PlantCLEF and general-purpose CIFAR100, respectively. The design of an unsharp enhancement layer plainly boosts the accuracy with negligible performance cost on simple CNN models, as prior knowledge is directly injected to improve its robustness.
Single-shot phase detection in a speckle wavemeter for the measurement of femtometric wavelength change

An optical system with instantaneous phase detection is proposed for wavelength change detection in the femtometre scale. Phase measurements are achieved within a few tens of femtometres using a custom made Integrating Sphere (IS) with temperature control. The phase is detected in a single-shot of speckle patterns that are produced using light from a tunable laser after multiple reflections in an IS and combined with a reference beam. It is shown that the proposed optical system produces fringe patterns with phase that is as sensitive as a Wavelength Scanning Interferometer (WSI), with similar optical path difference corresponding to the diameter of the IS. The interference patterns produced by the optical setup show some areas of the interferogram with phase sensitivities that are analogous to the WSI interference, and other areas were vortex generation and annihilation appear independently. Depending on the phase spatial distribution and sensitivity, it is shown here that the phases without vortices can be used to identify particular wavelengths within the femtometre resolution, therefore the sub-femtometer resolution can be attributed to the vortices creation and annihilation.
Low-Rank Multi-Channel Features for Robust Visual Object Tracking

Kernel correlation filters (KCF) demonstrate significant potential in visual object tracking by employing robust descriptors. Proper selection of color and texture features can provide robustness against appearance variations. However, the use of multiple descriptors would lead to a considerable feature dimension. In this paper, we propose a novel low-rank descriptor, that provides better precision and success rate in comparison to state-of-the-art trackers. We accomplished this by concatenating the magnitude component of the Overlapped Multi-oriented Tri-scale Local Binary Pattern (OMTLBP), Robustness-Driven Hybrid Descriptor (RDHD), Histogram of Oriented Gradients (HoG), and Color Naming (CN) features. We reduced the rank of our proposed multi-channel feature to diminish the computational complexity. We formulated the Support Vector Machine (SVM) model by utilizing the circulant matrix of our proposed feature vector in the kernel correlation filter. The use of discrete Fourier transform in the iterative learning of SVM reduced the computational complexity of our proposed visual tracking algorithm. Extensive experimental results on Visual Tracker Benchmark dataset show better accuracy in comparison to other state-of-the-art trackers.
An enriched visit to the botanical garden

The exploitation of Virtual and Augmented Reality in the context of cultural heritage and tourism is increasing. These new technologies enable to integrate the traditional cultural contents and innovative experience modalities. Several cultural sites provide visitors with such systems to create an immersive visiting experience. However, limited attention has been paid so far to the exploitation of such technologies in naturalistic cultural sites. This paper describes the project "This is (not) just a tree", an applied research project aiming to develop an advanced system exploiting Virtual and Augmented Reality technology, which will enable visitors to enjoy closer experiences with plants and their interactions with animals. Special attention will be devoted to involving both stakeholders and target users in the development, thereby applying a co-design approach. Here we report on the participatory design method deployed to gather and organize requirements from stakeholders, the strategies implemented to inquire target users on their preferences, and the resulting implications for the application design.
Breast Cancer Classification on Histopathological Images Affected by Data Imbalance Using Active Learning and Deep Convolutional Neural Network

In this work, we propose an algorithm for training deep neural networks for classification of breast cancer in histopathological images affected by data unbalance with support of active learning. The output of the neural network on unlabeled samples is used to calculate weighted information entropy. It is utilized as uncertainty score for automatic selecting both samples with high and low confidence. A number of low confidence samples that are selected in each iteration is manually labeled by pathologist. A threshold that decays over iteration number is used to decide which high confidence samples should be concatenated with manually labeled samples and then used in fine-tuning of convolutional neural network. The neural network can optionally be trained using weighted cross-entropy loss to better cope with bias towards the majority class.
Using hands as an easy UAV joystick for entertainment applications

This work presents an advanced interface allowing to easily maneuver a Unmanned Aerial Vehicle (UAV), better popularly known as drone, in entertainment settings. The proposal exploits a consumer mobile device, e.g., a smartphone or a tablet, to capture the operator's hand image and movement. The application recognizes a few allowed natural and intuitive movements, and communicates the corresponding commands to the drone. At the best of our knowledge, this is the first attempt in literature to provide a complete natural interface for this task via a mobile device, though in the limited scope of entertainment. A preliminary user study testified the accuracy of the driving control and the quality of the user experience.
TRIVIR

In this paper, we propose TRIVIR, a novel interactive visualization tool powered by an Information Retrieval (IR) engine that implements an active learning protocol to support IR with high recall. The system integrates multiple graphical views in order to assist the user identifying the relevant documents in a collection, including a content-based similarity map obtained with multidimensional projection techniques. Given representative documents as queries, users can interact with the views to label documents as relevant/not relevant, and this information is used to train a machine learning (ML) algorithm which suggests other potentially relevant documents on demand. TRIVIR offers two major advantages over existing visualization systems for IR. First, it merges the ML algorithm output into the visualization, while supporting several user interactions in order to enhance and speed up its convergence. Second, it tackles the problem of vocabulary mismatch, by providing term's synonyms and a view that conveys how the terms are used within the collection. Besides, TRIVIR has been developed as a flexible front-end interface that can be associated with distinct text representations and multidimensional projection techniques. We describe two use cases conducted with collaborators who are potential users of TRIVIR. Results show that the system simplified the search for relevant documents in large collections, based on the context in which the terms occur.
A Correction Method for Heat Wave Distortion in Digital Image Correlation Measurements Based on Background-Oriented Schlieren

Digital image correlation (DIC) is a kind of displacement and strain measurement technique. It can realize non-contact and full-field measurement and is widely used in the testing and research of mechanical properties of materials at high temperatures. However, many factors affect measurement accuracy. As the high temperature environment is complex, the impact of heat waves on DIC is the most significant factor. In order to correct the disturbance in DIC measurement caused by heat waves, this paper proposes a method based on the background-oriented schlieren (BOS) technique. The spot pattern on the surface of a specimen in digital image correlation can be used as the background in the background-oriented schlieren technique. The BOS technique can measure the distortion information of the images caused by heat flow field. The specimen images taken through the heat waves can be corrected using the distortion information. Besides, the characteristics of distortions due to heat waves are also studied in this paper. The experiment results verify that the proposed method can effectively eliminate heat wave disturbances in DIC measurements.
Application and Design of Wireless Community Alarm System Based on nRF24L01 Module

The wireless alarm system is controlled by Microcontroller which is used nRF24L01 wireless communication module to build the wireless alarm system in the residential area. The design in the paper realizes a two-way transmission through the wireless communication module and builds the wireless alarm network by the central monitoring room and users. The characteristics of the design are to be the frequency stability, the reception and the emission integration. The users can send the alarm signals through the user sub-machine and then the host-machine receives the signals and displays the user's address and finally sends out a warning. After many experiments, the wireless alarm system designed the paper achieves correctly and completes entirely all the warning in time.
Lattice-Based Zero-Knowledge SNARGs for Arithmetic Circuits

Succinct non-interactive arguments (SNARGs) enable verifying NP computations with substantially lower complexity than that required for classical NP verification. In this work, we construct a zero-knowledge SNARG candidate that relies only on lattice-based assumptions which are claimed to hold even in the presence of quantum computers. Central to our construction is the notion of linear-targeted malleability introduced by Bitansky et al. (TCC 2013) and the conjecture that variants of Regev encryption satisfy this property. Then, using the efficient characterization of NP languages as Square Arithmetic Programs we build the first quantum-resilient zk-SNARG for arithmetic circuits with a constant-size proof consisting of only 2 lattice-based ciphertexts. Our protocol is designated-verifier, achieves zero-knowledge and has shorter proofs and shorter CRS than the previous such schemes, e.g. Boneh et al. (Eurocrypt 2017).
Occupancy grid for static environment perception in series automotive applications

This paper presents a comprehensive review of state-of-the-art occupancy grid mapping approaches for highway and urban automotive applications. The primary objective is to identify most suitable algorithm candidates for series applications. The occupancy grid functional performance requirements for automotive are given. A new high level grid fusion architecture is introduced. The innovative element is a second stage filtering step allowing better performance in case of highly uncertain inputs. An application of the architectures to an example sensor suite is proposed.
Automatic Text Difficulty Estimation Using Embeddings and Neural Networks

Text difficulty, also called reading difficulty, refers to the complexity of texts on a language level. For many educational applications, such as learning resource recommendation systems, the text difficulty of text is highly relevant information. However, manual annotation of text difficulty is very expensive and not feasible for large collections of texts. For this reason, many approaches to automatic text difficulty estimation have been proposed in the past. All text difficulty estimation models published thus far have one thing in common: they rely on manually engineered feature sets. This is problematic as features are tailored to a specific type of text and do not generalize well to other types and languages. To alleviate this problem we propose a novel approach using neural networks and embeddings to the task of text difficulty classification. Our approach distinguishes between 5 reading levels which correspond to non-overlapping age groups ranging from ages 7 to 16. It performs comparably to existing state-of-the-art approaches in terms of accuracy and Pearson correlation coefficient while being easier and cheaper to adapt to new types of text.
Discriminant component analysis via distance correlation maximization

In the following study, an innovative supervised dimensionality reduction technique is proposed. dCor-based Dimensionality Reduction or dDR technique is based on distance correlation; a powerful correlation measure which is applicable to arbitrary-dimensional random variables. By projecting the samples to a lower dimensional space, dDR maximizes the correlation between explanatory and response variables. The proposed dDR algorithm can be easily implemented and it is computationally efficient. Moreover, it has a closed-form and a simple solution which makes it significantly effective in many different applications. In order to apply the proposed technique on non-linear problems, the kernel version of the dDR is also derived. Extensive analyses and empirical experiments across various visualization, classification, and regression tasks indicate that our algorithm is the method of choice; as it offers statistically superior results in comparison with other state-of-the-art approaches in the literature.
Performance Assessment of a People Tracker for Social Robots

Service robots are becoming more and more pervasive in natural environments where human beings actually carry out daily duties. One of the main enabler in these scenario is the capability of such a system to understand the positions and the motions of people moving in its surroundings. In this paper, we will investigate the effectiveness of a people tracking system that uses a stereo camera as the sensor and then use the gathered data in an estimation algorithm to continuously track their position in the environment. Experiments in a laboratory are carried out to characterise and evaluate the performance of such a system and establish its suitability for an application on an actual service robot.
Research on Food Recognition of Smart Refrigerator Based on SSD Target Detection Algorithm

Nowadays, the growth rate of various diseases is gradually increasing and the incidence rate is younger. Health has become the most important topic for people. For effective prevention of disease from the mouth, people have increased their emphasis on food safety. Refrigerators, as an essential electrical appliance for household food storage, have become the target of mutual competition between many manufacturers. The basis of the intelligent research of refrigerator is to clarify the food inside the refrigerator. Therefore, the focus of this paper is how to effectively and accurately identify the food inside the refrigerator, so that users can know the type, quantity and freshness of the food in the refrigerator without opening the refrigerator. View food lists to prevent double purchases and reduce food waste. By comparing the popular algorithms such as YOLO, SSD, R-CNN, Fast-R-CNN and Faster-R-CNN, combined with the actual internal situation of the refrigerator, it is concluded that the SSD target detection algorithm is the optimal algorithm for identifying the food inside the refrigerator. After determining the optimal algorithm, this paper made a detailed comparison test with the VOC2007 dataset made by SSD300 and SSD512 algorithms. The experimental result is that SSD512 is better for identifying the food inside the refrigerator.
Implementation of Depth-HOG based Human Upper Body Detection On A Mini PC Using A Low Cost Stereo Camera

In this paper, we propose a human upper body detection using a depth image that is implemented on a mini PC using a low cost stereo camera. The camera, named Minoru 3D webcam, produces depth image from its two parallel cameras. A pyramid-based region of interest (RoI) is applied on the depth-image frames to scan the possibility of human upper body existence. Simultaneously, a histogram of oriented gradient (HOG) method is performed in the ROI to extract the HOG feature. The results of the HOG feature extraction are then classified using linear support vector machine (SVM). Our system has been experimentally tested publicly in the campus environment. The detection speed obtained from a computer is 8.53 fps and the Mini PC is 4.26 fps under non-threaded programming. The result of object detection using HOG and SVM Classification method on static image reaches an average accuracy of 78.71%. Testing the system for real implementation has average error accuracy 4.60%. The results of detection for two human objects can achieve an accuracy 71.00%. Moving image testing has an average error of 4.60%.
Modelling of Hypertension Risk Factors Using Logistic Regression to Prevent Hypertension in Indonesia

Abstract Hypertension called as the silent killer, is the number one non-infectious disease that causes death in the world every year. There are 185,857 cases recorded in 2018 in Indonesia. In this study, we model the hypertension risk by considering age, heart rate, hypertension history of family, eating salty foods, and smoking or exposure to cigarette smoke as the influence factors of hypertension risk. A cross-sectional survey was conducted in August 2018 at the Haji Hospital of Surabaya. Logistic regression is used to analyse the influence of various risk factors on hypertension and non-hypertension. In addition, we compare between logit and gompit link functions in logistic regression to build the modelling of hypertension risk factors based on the accuracy of the classification model. By using logit and gompit link functions, we obtain percentage of the classification accuracy are 85.2 % and 81.5 %, respectively. It means that the logit link function is better than the gompit link function for modelling hypertension risk factors. For these link functions, the significant factors that influence hypertension are age and heart rate.
The supply blockchain: integrating blockchain technology within supply chain operations

Blockchain is a distributed system of record that employs a trust protocol built on elements of networking and cryptology. Blockchain can improve the speed of transaction execution and validation across multiple parties. These include digitally validating a transaction that has occurred between two entities and rendering it irrefutable. When one considers the numerous kinds and volume of supply chain transactions that occur daily, this fundamental capability can have profound impacts. Having readily available information that is trustworthy can streamline activities such as purchasing, contracting, and qualifying materials. This chapter surveys some current applications of blockchain by early industry adopters, upon which it explores additional potential applications for supply chain operations. It concludes with a prescriptive approach for how enterprises can determine and identify effective implementation scenarios for blockchain in their supply chain operations.
Screen Content Compression Based on Enhanced Soft Context Formation

The compression of screen content has attracted the interest of researchers in the last years as the market for transferring data from computer displays is growing. It has already been shown that especially those methods can effectively compress screen content which are able to predict the probability distribution of next pixel values. This prediction is typically based on a kind of learning process. The predictor learns the relationship between probable pixel colours and surrounding texture. Recently, an effective method called `soft context formation' (SCF) had been proposed which achieves much lower bitrates for images with less than 8 000 colours than other state-of-the-art compression schemes. This paper presents an enhanced version of SCF. The average lossless compression performance has increased by about 5% in application to images with less than 8 000 colours and about 10% for images with up to 90 000 colours. In comparison to FLIF, FP8v3, and HEVC (HM - 16.20 + SCM - 8.8), it achieves savings of about 33%, 4%, and 11% on average. The improvements compared to the original version result from various modifications. The largest contribution is achieved by the local estimation of the probability distribution for unpredictable colours in stage II of the compression scheme.