Reviewerly/exHarmony huggingface dataset:
Research on the design of smart cosmetic based on user experience

This paper proposes a definition of smart cosmetics, addresses the shortcomings of traditional cosmetics in terms of functions and forms, and analyses the consumer stimuli of the products. We used the KANO model to summarize users’ needs and translate functions to explore a feasible way to integrate smart technology into cosmetics, and to verify the feasibility of combining user experience and smart technology through design practice.
Meta-neighbor Aggregated Graph Attention Network for Heterogeneous Graph Representation

Graph representation has been widely adopted in many real-world applications, but it's still a challenge for us to represent the heterogeneous information network (HIN). To fuse the rich semantic information, current works usually employ metapaths to generate low dimensional representation of HIN. However, their methods either ignore the node content information, discard intermediate nodes along the metapaths, unaware of distant neighbors or aggregate some noisy nodes. We propose Meta-neighbor Aggregated Graph Attention Network (MNGAT) to deal with these problems and improve the performance of heterogeneous graph embedding. Firstly, our model utilizes multiple metapaths to generate the candidate meta-neighbor set and chooses the top <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> most frequently appeared neighbors for each target node. Secondly, we use the attention mechanism to assign different weights for each meta-neighbor and leverage the skip connection method to maintain the previous layers' information. Experiments on two real-world datasets demonstrate that our model achieves a better performance over the baselines.
Identification of Similar Gastrointestinal Images through Content Based Image Retrieval System based on Analytical Hierarchical Process

Content based image retrieval (CBIR) is a technique for automatically retrieved images from the large image repository on the basis of image features. Manual operation which is assigned to individual image features as well as equal weight distribution among themselves creates a matter of concern towards getting better performance. Such weight assignment task has been automated in this paper with the help of analytical hierarchical process (AHP). The objective of the study focuses on the proper weightage assignment to features as per the nature of image. The model has been implemented and tested for the gastrointestinal images containing eight different classes taken from <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$K_{vasir}$</tex> medical database. The accuracy of the proposed CBIR system is high in terms of Precision and Recall over manual assignment. Hence, this retrieval system for searching similar gastrointestinal images can assist the physicians in identifying accurate gastrointestinal disease. This system can also become very useful for classifying hyper spectral and multi spectral images.
Deep Learning Methods for Chronic Myeloid Leukaemia Diagnosis

Chronic myeloid leukaemia (CML) is considered as the cancer of the blood or cancer of the bone marrow. Blood consists of three different cells, one that helps to fight diseases and some infections called white blood cells (WBCs), another that can carry oxygen to the tissues of the body called red blood cells (RBCs), and, last but not the least, those which help the formation of blood clots to stop bleeding termed as platelets. Cancer is observed as a disease ‘diversified in character’, which consists of many different subtypes. Blood cancer is the most common toxicant for the human body. It is said to arise when the development of blood cells stops them from working properly. Chronic myeloid leukaemia is regarded as a myeloproliferative disease which is distinguished by the viable presence of Philadelphia chromosome, also known as Ph chromosome correspondingly associated with the BCR-ABL fusion gene. The proportion of patients are asymptomatic as the diagnosis is done after the disease is all through in the entire body. Therefore, the detection of CML requires deep machine learning. The study proposes new approach for diagnosing CML which requires a large training dataset. The significant available CML data source used here is BioGPS, from where the training dataset was collected so as to explore machine learning algorithms. The different machine learning algorithms used here are as naïve Bayes classifier, k-nearest neighbour algorithm, support vector machine algorithm, decision tree method, etc. Deep learning has been used taking into consideration artificial neural networks (ANN). From the variants of ANN, the convolutional neural network (CNN) is the most computational model. An architecture has been designed for deep learning which uses the concept of CNN for detection, extraction, segmentation, and classification of different phases of CML and also the presence of CML identification, which further can help the physicians to identify the present state of the patient for the appropriate treatment. The implementation of the algorithms is done to improve some accuracy and for better performances as compared with the previous forms of artificial intelligence among diverse machine learning methods.
Risk analysis for forecasting cyberattacks against connected and autonomous vehicles

Abstract A security risk analysis was conducted to identify possible cyberattacks against a future transport system consisting of autonomous and connected vehicles. Six scenarios were developed: joyriding, kidnapping, domestic abuse, autopilot manipulation, a large transport accident, and paralysis of the transport system. Even if it were possible to increase the difficulty of conducting such cyberattacks, it might be impossible to eliminate such attacks entirely. Measures that limit the consequences will therefore be necessary. Such measures include safety measures in vehicles to protect their occupants in traffic accidents and measures that make vehicles easier to remove in case they do not function.
Usability Lessons for COVID-19 Diagnostic Tests

As the need for increased and widespread COVID-19 diagnostic testing has arisen, testing has also become decentralized: where testing was originally primarily conducted in clinic or laboratory settings, testing now frequently occurs at community testing sites, pharmacies, workplaces, schools, and in the home. With this paradigm shift comes new usability barriers as companies rapidly adapt laboratory- and point-of-care-based tests for other use cases. This paper outlines the agile methods used to evaluate the usability of diagnostic tests in various phases of the design cycle and under limited access and time constraints, provides an overview of the most commons usability issues found, and lists practical design principles for practice.
Machine-Based Transliterate of Ottoman to Latin-Based Script

In this paper, a machine-based transliterate is presented. The automatic transliteration of Ottoman to the modern Latin Turkish script can open a big window for scientists in fields of history and literature while most of the Turkish people are not familiar with Ottoman script, despite the fact that no concrete solution has been proposed yet for this issue. The proposed method includes several steps since the transliteration process of Ottoman alphabet to Latin base consists of many problems; the first is the basic character mapping which covers the regular pronunciation and orthography mapping. On the other hand, covering other irregular and extraordinary cases is based on rules and normalization. The transliteration system achieved 73.9% accuracy in general.
A New Foreground-Background based Method for Behavior-Oriented Social Media Image Classification

Due to various applications, research on personal traits using information on social media has become an important area. In this paper, a new method for the classification of behavior-oriented social images uploaded on various social media platforms is presented. The proposed method introduces a multimodality concept using skin of different parts of human body and background information, such as indoor and outdoor environments. For each image, the proposed method detects skin candidate components based on R, G, B color spaces and entropy features. The iterative mutual nearest neighbor approach is proposed to detect accurate skin candidate components, which result in foreground components. Next, the proposed method detects the remaining part (other than skin components) as background components based on structure tensor of R, G, B color spaces, and Maximally Stable Extremal Regions (MSER ) concept in the wavelet domain. We then explore Hanman Transform for extracting context features from foreground and background components through clustering and fusion operation. These features are then fed to an SVM classifier for the classification of behavior-oriented images. Comprehensive experiments on 10-class datasets of Normal Behavior-Oriented Social media Image (NBSI) and Abnormal Behavior-Oriented Social media Image (ABSI) show that the proposed method is effective and outperforms the existing methods in terms of average classification rate. Also, the results on the benchmark dataset of five classes of personality traits and two classes of emotions of different facial expressions (FERPlus dataset) demonstrated the robustness of the proposed method over the existing methods.
The Round Complexity of Quantum Zero-Knowledge

We study the round complexity of zero-knowledge for QMA (the quantum analogue of NP). Assuming the quantum quasi-polynomial hardness of the learning with errors (LWE) problem, we obtain the following results: 2-Round statistical witness indistinguishable (WI) arguments for QMA. 4-Round statistical zero-knowledge arguments for QMA in the plain model, additionally assuming the existence of quantum fully homomorphic encryption. This is the first protocol for constant-round statistical zero-knowledge arguments for QMA. 2-Round computational (statistical, resp.) zero-knowledge for QMA in the timing model, additionally assuming the existence of post-quantum non-parallelizing functions (time-lock puzzles, resp.).
Estimation of the Concentration of Some Heavy Metals in Groundwater in Rutba City

Abstract Toxic metal pollutants in groundwater should be identified to prevent future health risks. In this paper, the presence of heavy metals in groundwater in the western region of Iraq was investigated. The heavy metals concentrations, including Ni 2+ , Co 2+ , Zn 2+ , Pb 2+ , Cr 3+ , Cd 2+ , As 3+ and Hg 2+ were explored in twenty selected aquifers near Rutba City and the results were presented as spatial distribution maps. Findings indicate that contamination with the investigated heavy metal ions possesses a serious threat to the study area’s groundwater quality when compared to WHO and IEPA guideline values. Thus, a new approach to remove or adsorb heavy metal ions can be developed for large-scale production and the safe use of these aquifers water. Results revealed that the highest concentrations in mg/L 1 of 2.312 in w19, 1.098 in w2, 5.78 in w17, 0.292 in w9, 3.349 in w5, 0.32 in w13, 0.074 in w11 and 5.622 in w1 for Zn 2+ , Cr 3+ , As3 + , Pb2 + , Ni2 + , Co2 + , Cd2 + and Hg 2+ were recorded, respectively.
A Combinatorial Coordinate System for the Vertices in the Octagonal $$C_4C_8(R)$$ Grid

The octagonal \(C_4C_8\) grid is a tessellation of the plane into regular octagons and squares. It is one of the eight semiregular grids, which have been receiving an increasing amount of research attention as a viable alternative to the traditional square grid.We present an integer-valued combinatorial coordinate system for the vertices in the \(C_4C_8(R)\) grid. We review the existing coordinate systems for this grid proposed in the literature and we provide formulas for the conversion between this coordinate system and the existing ones, including the Cartesian coordinates. Adjacency relation between the vertices can be easily obtained from their coordinates through simple integer arithmetics.
From Virtual Reality to Augmented Reality: Devices, Bodies, Places and Relationships

We can consider augmented reality (AR) a true evolution of virtual reality (VR). AR is an interesting synthesis of those socially structured processes that have led, in a short time, to a maturation of the digital world. In light of all this, we intend analyse the materiality of the devices and tools (Tirino 2017) necessary to create augmented environments in order to shed light on the sociocultural processes that shape the media. We investigate how AR has led to the reaffirmation of the centrality of bodies in the experience (Damasio 1994, 1999). Plural bodies are called to interact in environments increasingly similar to hyperplaces (Bolognari, 2012). In hyperplaces the same body/mind dichotomy tends to blur, opening up to new dynamics of structural coupling with the environment (Maturana, Varela 1972). We are talking about dynamics with obvious consequences on the self and hetero perception of identity. In the light of those new expressive techniques (Montani 2020) and forms of convergent and participatory culture (Jenkins 2006, 2010), we reflect on how AR devices and applications could affect in-person sociability.
Data Storage Encryption With Passphrase Using Hybrid Algorithm

Security of the data is the utmost important in today's world scenario. To achieve completeprivacy of the data stored on various electronic devices like laptops, computers, external hard disk, USB drives etc. data storage encryption is needed to make the data more secure for any organization or any small offices. Encrypting the data provides a way out for the organization to keep a firm hold on their sensitive data or information. Intelligent devices like laptops and PC's are prone to security attacks resulting in the compromising the data. This problem can be solved by employing data encryption. Thought many encryption techniques are being used to make the data secure but a hybrid encryption algorithm should be used to make the data encryption more secure.
The Novel STEM Practice Course and Industrial Robot Educational Platform via University-Middle School Collaboration

Nowadays it has become a trend to combining lessons with science, technology, engineering and mathematics (STEM) with the purpose of cultivating STEM talents for national development. In this paper, we present the novel STEM practice course (NSPC) for middle school students, which is based on the industrial robot educational platform (IREP) and developed via University-Middle school collaboration (UMSC). This course is divided into four STEM related scenario-based activities in order to inspire middle school students’ interest in STEM industries. The course based on IREP and UMSC has been implemented in the Experimental School of Beihang University for five times since 2017 and the result shows that it has a perfect effect on arousing students’ curiosity of STEM occupations, especially the robot field.
Real time Person Re–Identification using Deep Association Metric with Multiple Inputs

While the person re-identification problem could be seen as a challenging task, where every person wearing a certain type of clothing is recalled from the cameras, we are more concerned with distinguishing within these categories to find an exact person. There are many different challenges to solving this problem. Most real-world cameras have an insufficient resolution or are too far away from a detected person to perform high-level feature extraction. In our method, we propose a real-time person re-identification approach with multiple inputs.
Tree-Based Public Key Encryption with Conjunctive Keyword Search

Searchable public key encryption supporting conjunctive keyword search is an important technique in today’s cloud environment. Nowadays, previous schemes usually take advantage of forward index structure, which leads to a linear search complexity. In order to obtain better search efficiency, in this paper, we utilize a tree index structure instead of forward index to realize such schemes. To achieve the goal, we first give a set of keyword conversion methods that can convert the index and query keywords into a group of vectors and then present a novel algorithm for building index tree based on these vectors. Finally, by combining an efficient predicate encryption scheme to encrypt the index tree, a tree-based public key encryption with conjunctive keyword search scheme is proposed. The proposed scheme is proven to be secure against chosen plaintext attacks and achieves a sublinear search complexity. Moreover, both theoretical analysis and experimental result show that the proposed scheme is efficient and feasible for practical applications.
Chapter 12. Language learning and teaching with Swedish FrameNet++

This chapter describes and discusses the use of resources connected to Swedish FrameNet++ (SweFN++) in the context of the teaching and learning of language proficiency and grammatical analysis in Swedish. We illustrate the way in which different resources in the SweFN++ context can be useful for language pedagogy, by employing two examples, the Swedish Constructicon and a semantic role exercise on the intelligent computer assisted language learning (ICALL) platform Lärka. These resources make use of the infrastructure developed within SweFN++ in fundamentally different ways, which are discussed and compared. In addition, we discuss the possibilities for further development of the language pedagogical potential of SweFN++, both in relation to ICALL and to other types of resources and descriptive databases, like corpora, constructicons and framenets.
Prevention of SQL Injection Attacks using Query Hashing Technique

Web applications are a vital part of day-to-day life. Many critical services like shopping, health, banking, data communication and transport are partly or completely dependent on the Internet. Simultaneously, different kinds of the attacks in the network are introduced by various kinds of attacker. One of the important security attacks is SQL injection. It is a web application vulnerability by which an attacker can get unauthorized access to the database of a website. With the help of SQL injection an attacker can take control of any website if attacker is able to get the credentials of the administrator of the website. In some cases, an attacker may access, delete or modify the data in the database that may cause permanent changes in the applications' content or behavior. In this paper, we have proposed a new technique to prevent SQL Injection attacks by comparing the hash value of the generated query with the hashed value of the legitimate query. Our scheme can be easily added to any prebuilt website build using any language or database type as it requires only few changes to be made in the code of the language.
Reconstruct Anomaly to Normal: Adversarially Learned and Latent Vector-Constrained Autoencoder for Time-Series Anomaly Detection

Time-series Anomaly Detection has important applications, such as credit card fraud detection and machine fault detection. Anomaly detection based on the generative model generally detect samples with high reconstruction errors as anomalies. However, some anomalies may get low reconstruction errors, as they can also be well reconstructed due to the strong generalization ability of the model. To ensure the high reconstruction error of anomalies, we propose a novel anomaly detection algorithm named RAN (Reconstruct Anomalies to Normal) based on the Autoencoder. We try to force the reconstruction samples of both normal samples and anomaly samples obey the distribution of normal samples, then the difference between normal sample and its reconstruction sample is small while the difference between anomaly sample and its reconstruction sample is large, and higher reconstruction error for anomaly samples is guaranteed. The Autoencoder constructed by 1D-FCN with different kernel sizes is utilized to extract richer features of time-series data. Imitated anomaly samples are feed to the model to provide more information about anomalies. Then, constraints in the latent space and original data space are added to control the reconstruction process. Extensive experiments on real-life time-series datasets also show that RAN outperforms some state-of-art algorithms.
Algorithms Based On Correlation Filter In Target Tracking: A Survey

As an important field of computer vision, target tracking has been widely used in artificial intelligence fields such as intelligent manufacturing and intelligent transportation. As one of the classics, the single-target tracking algorithm based on correlation filtering is widely praised for its good real-time and robustness. However, it has disadvantages in scale changes and boundary effects, and many researchers have improved it. This article introduces the single-target tracking algorithm based on correlation filtering, and compares its experimental results through the OTB database.
Medicine Information Record Based on Blockchain Technology

Drug's production recording is one of the most critical processes pharmacy industry. The benefit of recording the drugs is to avoid counterfeit drug. By knowing the exact number of drugs produced will make it easier to track. Counterfeit drugs are still circulating today in the market. Counterfeit drugs are very dangerous even cause death for people who consume it. This qualitative research was conducted using Blockchain technology, which has characteristics such as immutable, unchangeable, and peer-to-peer, which will minimize the possibility of counterfeit drugs. Simulations are carried out using the Multichain application to get a clear picture of how drug production can be recorded in the blockchain (Multichain). This research involved one of Indonesia's largest drug industry companies to give input on Blockchain technology (Multichain) for drug production records. The results, blockchain technology (Multichain), can be used to record drug production. So, it is possible to track the drugs record. This research is very important and useful for the drug industry to ensure the quality of drugs production.
Efficient Attentive Knowledge Tracing for Long-Tail Distributed Records

Knowledge Tracing (KT), the most basic task for Intelligent Tutoring Systems, models individual students' knowledge state over time based on their past interactions. In large-scale KT datasets, we observe the length of student interaction records satisfy a long-tail distribution, and propose an efficient self-attentive architecture, EAKT, optimized for this situation to accelerate training. By combining linear self-attention with adaptive window size, EAKT enables the long-range modeling capability for long records and high window utilization for short records. Through experiments on two large-scale KT datasets, we demonstrate that EAKT achieves about 4 times faster with one-third memory cost than the best existing model, while still maintains competitive accuracies.