Reviewerly/exHarmony huggingface dataset:
Color Blindness Bartender: An Embodied VR Game Experience

Color blindness is a very common condition, as almost one in ten people have some level of color blindness or visual impairment. However, there are many tasks in daily life that require the abilities of color recognition and visual discrimination. In order to understand the inconvenience that color-blind people experience in daily life, we developed a virtual reality (VR) application that provides the sense of embodiment of a color-blind person. Specifically, we designed a color-based task for users to complete under different types of color blindness in which users make colorful cocktails for customers and need to switch between different color blindness modalities of the application to distinguish different colors. Our application aims to embody users to color blindness in order to raise awareness on such color vision deficiency.
Image Optimization using Improved Gray-Scale Quantization for Content-Based Image Retrieval

Image quantization is an important process in content-based image retrieval (CBIR) systems. In this study, color-image quantization is used to reduce the number of color bins prior to establishing feature extraction step. We modified and applied Improved Grey-Scale (IGS) procedure to obtain optimized feature representations, by mapping a broad dimension of input pixel distributions of an image to a qualified number of outputs (coded pixel values). Thus, to investigate the impact of IGS method using different parameters on system classifications. In order to determine the number of color intensities that are most appropriate for system performance, a set of image-classification tasks were performed using support vector machine classifier on the Coral-1000 dataset. Accordingly, to evaluate the system accuracy, several metrics are considered in terms of precision, recall, and F1 score of each image category in the dataset. Moreover, we present a comparison of three-color optimization methods that generate compact color dimensions while aiming to preserve or enhance image retrieval performance. The results indicate that our IGS-based CBIR system performs better even when color depths are reduced to three bits in each color component.
A new road state information platform based on crowed sensing on challenged network environments

In this paper, a new generation road state information platform based on IoT crowed sensing technology is proposed. Various sensors are attached on vehicle to sample sensor data and to determine the road state in reatime. Those sensor data and road state information are transmitted to the neighbor vehicles and road side server using V2X communication network. Driver can receive the sensor data and road surface state information from the vehicle in opposite direction or road side server and eventually pay attentions to his/her driving before encountering the dangerous location. A prototype system of this proposed system is constructed and evaluated the functions and performance as a preliminary system.
Memory-Aware Denial-of-Service Attacks on Shared Cache in Multicore Real-Time Systems

In this paper, we identify that memory performance plays a crucial role in the feasibility and effectiveness for performing denial-of-service attacks on shared cache. Based on this insight, we introduce new cache DoS attacks, which can be mounted from the user-space and can cause extreme worst-case execution time (WCET) impacts to cross-core victimseven if the shared cache is partitionedby taking advantage of the platforms memory address mapping information and HugePage support. We deploy these enhanced attacks on two popular embedded out-of-order multicore platforms using both synthetic and real-world benchmarks. The proposed DoS attacks achieve up to 111X WCET increases on the tested platforms.
Medical Data Inquiry Using a Question Answering Model

Access to hospital data is commonly a difficult, costly and time-consuming process requiring extensive interaction with network administrators. This leads to possible delays in obtaining insights from data, such as diagnosis or other clinical outcomes. Healthcare administrators, medical practitioners, researchers and patients could benefit from a system that could extract relevant information from healthcare data in real-time. In this paper, we present a question answering system that allows health professionals to interact with a large-scale database by asking questions in natural language. This system is built upon the BERT and SQLOVA models, which translate a user's request into an SQL query, which is then passed to the data server to retrieve relevant information. We also propose a deep bilinear similarity model to improve the generated SQL queries by better matching terms in the user's query with the database schema and contents. This system was trained with only 75 real questions and 455 back-translated questions, and was evaluated over 75 additional real questions about a real health information database, achieving a retrieval accuracy of 78%.
Decentralized Runtime Monitoring Approach Relying on the Ethereum Blockchain Infrastructure

Cloud computing offers a model where resources (storage, applications, etc.) are abstracted and provided “as-aservice” in a remotely accessible manner. Although there are numerous claimed benefits of the Cloud to ensure confidentiality, integrity, and availability of the stored data, the number of security breaches is still on the rise. The lack of security assurance and transparency prevented customers/enterprises from trusting the Cloud Service Providers (CSPs). Unless the customer's security requirements are identified and documented by the CSPs, customers can not be assured that the CSPs will satisfy their requirements. Furthermore, the customer's compensation upon a violation is a manual time intensive process.In this paper we address the aforementioned challenges by proposing a decentralized customer-based monitoring approach running over Ethereum blockchain. The proposed approach allows the customer(s) to validate the compliance of CSP(s) to the contracted services in the Service Level Agreements (SLAs) and “autonomsly” compensate customers in case of security breaches. At the same time, the proposed approach prevents customers from misreporting for financial gain. The approach builds upon the Ethereum blockchain infrastructure in order to securely store monitoring logs and incorporate SLAs as smart contracts. The compliance validation framework is implemented and its functionality is evaluated on Amazon EC2 and Ethereum Blockchain.
A Method for Measuring the Height of Hand Movements Based on a Planar Array of Electrostatic Induction Electrodes

This paper proposes a method based on a planar array of electrostatic induction electrodes, which uses human body electrostatics to measure the height of hand movements. The human body is electrostatically charged for a variety of reasons. In the process of a hand movement, the change of a human body’s electric field is captured through the electrostatic sensors connected to the electrode array. A measurement algorithm for the height of hand movements is used to measure the height of hand movements after the direction of it has been obtained. Compared with the tridimensional array, the planar array has the advantages of less space and easy deployment; therefore, it is more widely used. In this paper, a human hand movement sensing system based on human body electrostatics was established to perform verification experiments. The results show that this method can measure the height of hand movements with good accuracy to meet the requirements of non-contact human-computer interactions.
An image encryption algorithm based on the combination of low - dimensional chaos and high - dimensional chaos

This paper proposes an image encryption algorithm based on the combination of low-dimensional chaos and highdimensional chaos. Some key parameters are given by the encryption operator and the rest are obtained from the original image. These two parts constitute the complete key system of the encryption algorithm. The chaotic sequences generated by Logistic map and improved Logistic map are used to scramble the positions of the original image pixels. Two chaotic sequences generated by Lorenz system are used to diffuse the scrambled image. Simulation experiment shows that this algorithm has great encryption effect, large key space and strong key sensitivity and can effectively resist various attacks. Thus, it has high security and practicability.
Informative Retrieval Framework for Histopathology Whole Slides Images Based on Deep Hashing Network

Histopathology image retrieval is an emerging application for Computer-aided cancer diagnosis. However, the current retrieval methods, especially the methods based on deep hashing, pay less attention to the characteristic of histopathology whole slide images (WSIs). The retrieved results are occasionally occupied by similar images from a few WSIs. The retrieval database cannot be sufficiently utilized. To solve these issues, we proposed an informative retrieval framework based on deep hashing network. Specifically, a novel loss function for the hashing network and a retrieval strategy are designed, which contributes more informative retrieval results without reducing the retrieval precision. The proposed method was verified on the ACDC-LungHP dataset and compared with the state-of-the-art method. The experimental results have demonstrated the effectiveness of our method in the retrieval of large-scale database containing histopathology while slide images.
A Hybrid Adversarial Attack for Different Application Scenarios

Adversarial attack against natural language has been a hot topic in the field of artificial intelligence security in recent years. It is mainly to study the methods and implementation of generating adversarial examples. The purpose is to better deal with the vulnerability and security of deep learning systems. According to whether the attacker understands the deep learning model structure, the adversarial attack is divided into black-box attack and white-box attack. In this paper, we propose a hybrid adversarial attack for different application scenarios. Firstly, we propose a novel black-box attack method of generating adversarial examples to trick the word-level sentiment classifier, which is based on differential evolution (DE) algorithm to generate semantically and syntactically similar adversarial examples. Compared with existing genetic algorithm based adversarial attacks, our algorithm can achieve a higher attack success rate while maintaining a lower word replacement rate. At the 10% word substitution threshold, we have increased the attack success rate from 58.5% to 63%. Secondly, when we understand the model architecture and parameters, etc., we propose a white-box attack with gradient-based perturbation against the same sentiment classifier. In this attack, we use a Euclidean distance and cosine distance combined metric to find the most semantically and syntactically similar substitution, and we introduce the coefficient of variation (CV) factor to control the dispersion of the modified words in the adversarial examples. More dispersed modifications can increase human imperceptibility and text readability. Compared with the existing global attack, our attack can increase the attack success rate and make modification positions in generated examples more dispersed. We’ve increased the global search success rate from 75.8% to 85.8%. Finally, we can deal with different application scenarios by using these two attack methods, that is, whether we understand the internal structure and parameters of the model, we can all generate good adversarial examples.
Adaptive learning of minority class prior to minority oversampling

The minority oversampling techniques have substantiated their appropriateness and utility in the domain of class-imbalance learning. However, this does not affirm the true class of the synthetic minority points. In this work, Adaptive Learning of Minority Class prior to Minority Oversampling (ALMCMO), we work towards bridging this gap by estimating the minority set before oversampling the synthetic points. We estimate a varying and adaptive volume of minority space around the minority points. We aim to guarantee the class-memberships of the synthetic minority points by sampling them from the estimated minority spaces. In our empirical study, we have used six comparing methods, 23 datasets and two classifiers. The results indicate the certain superiority of the proposed method over the six competing schemes.
seIMC: A GSW-Based Secure and Efficient Integer Matrix Computation Scheme With Implementation

As atomic operations, secure matrix-based computations using homomorphic encryption (HE) have attracted much attention in cloud-based machine learning. However, most existing secure matrix computation solutions that focus on HE schemes suffer efficiency loss as the size of the matrix, which greatly limits their applications in the big data environment. To address these issues, this paper proposes seIMC, an integer matrix computation scheme based on the Gentry-Sahai-Waters (GSW) scheme, to cope with privacy protection and secure computation of large-scale data. In detail, we translate the GSW scheme to encrypt an integer matrix modulo $q$ (i.e., a large positive integer), and homomorphically compute matrix addition and multiplication, which is a natural extension of HAO scheme. Besides, the correctness and security analysis of seIMC are shown, and complexity analysis is also given in this study. Furthermore, the proposed schemes are implemented, including public-key encryption and private-key encryption schemes. Compared with existing secure matrix computation schemes, the proposed scheme performs better in execution time. Finally, seIMC is applied to solve the problem of the number of ways in which any two participants make friends through $k$ steps in an encrypted social network. Experiments show that when the cloud server processes an integer matrix of 1000 people with a security level of 90, namely, 1 million data volumes, it only takes approximately 1.9 minutes for each homomorphic matrix multiplication. Hence, the practicality of the proposed seIMC in privacy protection under a big data environment is highly proven.
Signet Ring Cells Detection in Histology Images with Similarity Learning

The detection of signet ring cells in histology images is of great value in clinical practice. However, several reasons such as appearance variations and lack of well-labelled data make it a challenging task. Considering the intrinsic characteristics of signet ring cell images, a dedicated similarity learning network is designed in this paper to help the discovery of distinctive feature representations for ring cells. Specifically, we adapt the region proposal network and add an embedding layer to enable similarity learning for training the model. Experimental results show that similarity learning can strengthen the performance of the state-of-the-art and makes our approach competent for the task of signet ring cell detection.
Implementation a Context-Aware Plant Ecology Mobile Learning System

Mobile devices are becoming ubiquitous methodologies and tools, providing application for learning and teaching field. On the basis of the widespread use of wireless devices and mobile computing technology, this study proposes a context-aware plant ecology learning system (CAPELS) based on context-aware technology; adapting deep neural networks (DNN) and leaf vein and shape identification algorithm which can identify plant leaves, this system automatically provides relevant botanical and growth environment knowledge to the learners. Therefore, during outdoor education, it can assist learners in accurately obtaining the required relevant botanical and growth environment knowledge. The experimental results indicate that students who used CAPELS performed better learning about plant ecology than those who did not. We also delivered questionnaires to those who used CAPELS and analyzed the results by using the partial least squares (PLS) method. The results have shown that CAPELS can encourage student’s learning motivation and thus improve their learning effectiveness. Thus, CAPELS provides a new educational platform for promoting ecology learning approach and effectively improves student learning efficiency and motivation.
Power consumption of a chaos-based stream cipher algorithm

With the huge usage of the Internet of Things (IoT), lots of sensitive data are being transferred between devices. This raises the need for security so that unauthorized parties can’t access the data. IoT devices are known to have limited resources; they have limited battery life, limited memory size and their response time should be minimized. With these speed and memory constraints, classical encryption systems and methods don’t work efficiently with the IoT devices, because they can be too slow, huge or very power consuming. Here comes the need to develop a Lightweight cryptosystem that meets the requirements of the Lightweight device. In this paper, a practical lightweight stream cipher based on stream cipher is proposed, then the power consumption is calculated to show the suitability for limited resources devices. Furthermore, some lightweight measurement tools will be used in order to achieve the research objectives.
Relative view based holistic-separate representations for two-person interaction recognition using multiple graph convolutional networks

In this paper, we focus on recognizing person-person interactions using skeletal data captured from depth sensors. First, we propose a novel and efficient view transformation scheme. The skeletal interaction sequence is re-observed under a new coordinate system, which is invariant to various setups and capturing views of depth cameras as well as the position or facing orientation exchange between two persons. Second, we propose concise and discriminative interaction representations simply composed of the joint locations from two persons. Proposed representations are efficient to describe both the holistic interactive scene and individual poses performed by each subject separately. Third, we introduce the graph convolutional networks(GCN) to directly learn proposed skeletal interaction representations. Moreover, we design a multiple GCN-based model to provide the final class score. Extensive experimental results on three skeletal action datasets NTU RGB+D 60, NTU RGB+D 120 and SBU consistently demonstrate the superiority of our interaction recognition method.
FPGA Implementation of Homotopic Path Planning Method with Automatic Assignment of Repulsion Parameter

In recent times, autonomous robots have become more relevant, aiming not only to be an extension of mobility and human performance but also allowing them to independently solve specific problems such as finding free-collision paths within some defined environments. In order to achieve this, several techniques have been developed, like action-reaction algorithms, sampling-based algorithms, and deterministic algorithms such as the Homotopy Path Planning Method (HPPM). This work presents, for the first time, a complete deterministic collision-free path planning scheme implemented in FPGA, which is mounted on a Scribbler 2 robot from Parallax. Then, an automatic algorithm of the repulsion parameter for the HPPM method is presented, using as a reference the minimum distance between the center of each obstacle with respect to the homotopic ideal path; furthermore, an algorithm is proposed for discriminating dead-end routes and collision risk trajectories, which allows us to obtain a feasible free-collision path that takes into account the robot dimensions. Besides, comparative performance tests have been carried out against other path-finding methods from the low degrees of freedom (low DoF) and sampling-based planners. Our proposal exhibits path calculation times which are 5 to 10 times faster on FPGA implementation, compared to the other methods and 10 to 100 times faster on PC implementation also compared to the rest. Similar results are obtained with regards to memory consumption, namely 20 to 200 times lower on FPGA implementation and 10 to 100 times lower on PC implementation.
Flagging incorrect nucleotide sequence reagents in biomedical papers: To what extent does the leading publication format impede automatic error detection?

Abstract In an idealised vision of science the scientific literature is error-free. Errors reported during peer review are supposed to be corrected prior to publication, as further research establishes new knowledge based on the body of literature. It happens, however, that errors pass through peer review, and a minority of cases errata and retractions follow. Automated screening software can be applied to detect errors in manuscripts and publications. The contribution of this paper is twofold. First, we designed the erroneous reagent checking () benchmark to assess the accuracy of fact-checkers screening biomedical publications for dubious mentions of nucleotide sequence reagents. It comes with a test collection comprised of 1679 nucleotide sequence reagents that were curated by biomedical experts. Second, we benchmarked our own screening software called Seek&amp;Blastn with three input formats to assess the extent of performance loss when operating on various publication formats. Our findings stress the superiority of markup formats (a 79% detection rate on XML and HTML) over the prominent PDF format (a 69% detection rate at most) regarding an error flagging task. This is the first published baseline on error detection involving reagents reported in biomedical scientific publications. The benchmark is designed to facilitate the development and validation of software bricks to enhance the reliability of the peer review process.
A Joint Encryption, Channel Coding and Modulation Scheme Using QC-LDPC Lattice-Codes

We propose a new nonlinear Rao-Nam like symmetric key encryption scheme. In our design, we employ a specific type of coded modulation schemes namely quasi-cyclic low-density parity-check (QC-LDPC) lattice-codes which have low-complexity encoding and decoding algorithms. Due to the application of coded modulation schemes in our design, the proposed scheme performs encryption, encoding and modulation simultaneously. Therefore, we regard the proposed scheme as a joint cryptosystem. The proposed joint cryptosystem withstands all variants of chosen plaintext attacks applied on Rao-Nam like cryptosystems due to its nonlinearity. Moreover, some conditions implying the uniformity of the ciphertexts distribution are introduced through our analysis. Our scheme is efficient and admits small key size. These features are obtained due to several reasons including the quasi-cyclic form of the generator and the parity-check matrices of QC-LDPC lattice-codes, and the simple hardware structure for generating the permutation matrix, the intentional error vector and the nonlinear functions used in our design. The QC-LDPC lattice-codes facilitate high-rate transmission which is suitable for bandlimited AWGN channels. Our simulations indicate that QC-LDPC lattice-codes outperform the error performance of high-order coded modulation schemes based on QAM modulations. Hence, our scheme provides secure, reliable and efficient data transmission in bandlimited AWGN channels.