Reviewerly/exHarmony huggingface dataset:
Music-Guided Imagination and Digital Voice Assistant – Study Design and First Results on the Application of Voice Assistants for Music-Guided Stress Reduction

In order to relax in everyday life, people use music from a sound carrier. The choice of music is made intuitively and according to one's musical preference. In a therapeutic context, these processes are professionally guided. In so-called receptive music therapy, the patient and therapist listen to music together. The psychological processes triggered by this (e.g. imaginations, memories) are worked through in therapeutic conversation. But professional music therapy is not always feasible, as getting an appointment could take some time, the need for receptive music therapy is urgent at the time the treatment is not available, etc. Furthermore, voice assistants are increasingly dominating everyday life and represent an easy way to perform various tasks with minimal effort. The areas of application for voice assistants are diverse and range from answering simple information questions to processing complex topics and controlling various tasks. Given these considerations of the wide availability and ease of use, a pilot study was designed to combine the effective approaches of music therapy with modern voice assistants. The study entails one interaction with an Alexa Skill allowing a music-imaginative journey in an interactive manner, and a control condition without interactive parts using a CD. 21 participants took part in the study, of which first analyses are presented in the paper. Especially results regarding the analyses of the user experience (AttrakDiff and meCUE) as well as a discussion of the subjective participants' reflections on the interaction regarding the limitations of commercial voice assistants are presented
A Deep Dive into the China’s Gen Z: How They Use and What They Expect for Their Cars

In the next half decade, there will be nearly 40 million potential Gen Z (or Gen Z for short) consumers in the Chinese market who will have a high propensity to buy cars. This study focuses specifically on Chinese Gen Z. It seeks to find out their car-buying preferences, lifestyles, how they understand and use their vehicles, and their daily driving needs, in the context of China. Our goal is to develop potential concepts and collect valuable insights from user research to guide our future product design. As a first step, we identified characteristics of Gen Z in China based on previous studies and industry research reports: Gen Z tends to focus on their feelings and needs, was born and lives on the Internet, and knows they have easy access to all the information they need, enjoys independence but also desires connectedness, uses the technology they are so good at, and loves Chinese culture. Next, we applied a qualitative method combining in-depth interviews and field observation. Twenty participants in China's first- tier cities, such as Beijing, Shanghai, and Shenzhen; second-tier cities, such as Changsha, Hunan, and Chengdu, Sichuan; and third-tier cities, such as Hengyang, Hunan, and Mianyang, Sichuan, had participated. The current study revealed that (a) Gen Z is good at gathering the information they need from KOLs on the Internet to select a suitable car for themselves; (b) a car is assigned multiple roles by Gen Z, such as a friend, a private space, and an assistant; (c) although there is no significant difference between Gen Z and their Millennial counterparts when it comes to car use scenarios, for instance, going to work, shopping, or organizing a road trip, they are more likely to have a connection with friends during a trip, so the connection between their car and the passenger or another car became more important. In addition to the detailed user needs to be highlighted in the scenarios, we also discussed the design possibilities to shape the future smart car based on Gen Z's needs for driver-automation shared control, digital cockpits, and their preferences for the car's interior and exterior decoration.
Automatic Prediction and Linguistic Interpretation of Chinese Directional Complements Based on BERT Model

The Chinese directional complement is one of the trickiest concepts for second language learners due to their derivative meanings. In particular, 出来, 起来, 下来, and 下去 are easy to be confused. This study aims to gain grammatical and educational insights for these complements with a neural network model. This study fine-tuned the Chinese BERT model with Chinese directional complement data composed of sentences containing the above four components used in literature, media, and textbook. By measuring these fine-tuned models’ accuracy, we show how accurately and efficiently the neural network model predicts Chinese directional complements. Furthermore, we interpret and analyze the model’s decision using the Sampling and Occlusion algorithm and visually present which components of the sentence influence the choice for complements.
Frontal-Centers Guided Face: Boosting Face Recognition by Learning Pose-Invariant Features

In recent years, face recognition has made a remarkable breakthrough due to the emergence of deep learning. However, compared with frontal face recognition, plenty of deep face recognition models still suffer serious performance degradation when handling profile faces. To address this issue, we propose a novel Frontal-Centers Guided Loss (FCGFace) to obtain highly discriminative features for face recognition. Most existing discriminative feature learning approaches project features from the same class into a separated latent subspace. These methods only model the distribution at the identity-level but ignore the latent relationship between frontal and profile viewpoints. Different from these methods, FCGFace takes viewpoints into consideration by modeling the distribution at both the identity-level and the viewpoint-level. At the identity-level, a softmax-based loss is employed for a relatively rough classification. At the viewpoint-level, centers of frontal face features are defined to guide the optimization conducted in a more refined way. Specifically, our FCGFace is capable of adaptively adjusting the distribution of profile face features and narrowing the gap between them and frontal face features during different training stages to form compact identity clusters. Extensive experimental results on popular benchmarks, including cross-pose datasets (CFP-FP, CPLFW, VGGFace2-FP, and Multi-PIE) and non-cross-pose datasets (YTF, LFW, AgeDB-30, CALFW, IJB-B, IJB-C, and RFW), have demonstrated the superiority of our FCGFace over the SOTA competitors.
Ensemble of Handcrafted and Deep Learning Model for Histopathological Image Classification

Histopathology is the investigation of tissues to identify the symptom of abnormality. The histopathological procedure comprises gathering samples of cells/tissues, setting them on the microscopic slides, and staining them. The investigation of the histopathological image is a problematic and laborious process that necessitates the expert’s knowledge. At the same time, deep learning (DL) techniques are able to derive features, extract data, and learn advanced abstract data representation. With this view, this paper presents an ensemble of handcrafted with deep learning enabled histopathological image classification (EHCDL-HIC) model. The proposed EHCDL-HIC technique initially performs Weiner filtering based noise removal technique. Once the images get smoothened, an ensemble of deep features and local binary pattern (LBP) features are extracted. For the classification process, the bidirectional gated recurrent unit (BGRU) model can be employed. At the final stage, the bacterial foraging optimization (BFO) algorithm is utilized for optimal hyperparameter tuning process which leads to improved classification performance, shows the novelty of the work. For validating the enhanced execution of the proposed EHCDL-HIC method, a set of simulations is performed. The experimentation outcomes highlighted the betterment of the EHCDL-HIC approach over the existing techniques with maximum accuracy of 94.78%. Therefore, the EHCDL-HIC model can be applied as an effective approach for histopathological image classification.
Digital Identity Management System Using Blockchain

Digital Identity Management is a new identity management model in which the holder of the identity data really have the ownership of their own identity data and have full access control over it without involving any intervenor. This has created in huge amount of user information with the service providers, resulting in two difficulties, user owned private data is saved and left to the care of such third party applications and the user does not have ownership of his own data. Maintenance of such multiple identities will only become more difficult as the digital era unfolds. We attempt to solve this problem by using the Blockchain technology for digital identities. Blockchain is an enabling technology for building Digital Identity that offers a decentralized and secure environment. A blockchain's data is cryptographically connected and distributed across multiple computers, making them nearly impossible to temper with. Hence, Blockchain technology is the best and the secured platform to build Digital Identity. The existing system of identity management is neither secure nor reliable. Therefore, the blockchain can create a path to Digital Identity through decentralized networks, which will assure privacy, trust and security.
On digital and literacy education of European universities during the epidemic period

The paper discusses teaching mode of European universities during the epidemic period and proposes the characteristics of digital education which has realized another ideological change from face-to-face teaching to self-learning and from systematic teaching output to individual learning input. Hence the shift can also be understood as the change from digital education to literacy education centered on flexible learning. Based on shift from digital education to literacy education in European universities the developing trend of literacy education in European universities during the epidemic period are put forward in the construction of lifelong education in Europe.
Ways of Experiencing Technology in a Smart Learning Environment

This paper discusses ways of experiencing digital technology and digital interactions in ‘smart learning journey’ activities. Smart learning journeys can be considered as ad-hoc smart learning environments outside in the real world, offering a wide range of opportunity for empowering local populace engagement in issues relevant to a neighbourhood area. Activities may often be associated with urban citizen communities, enhancing quality of life and life-long learning in urban digitally connected ‘hyperlocal’ spaces. Activities discussed in this paper used freely available smartphone apps and consisted of a series of digitally augmented real-world local features that together formed a journey of points of interest related by topic. Research discussed in this paper investigated how people experienced ‘Technology’ as one of four conceptualised system elements of a smart learning journey activity, the others being Place, Knowledge and Collaboration. Utilising the methodology of phenomenography and analysing participant semi-structured responsive interviews with a structure of awareness analytical framework approach, four categories of Technology experience variation emerged. These were Easy, Helper, Novel and Problematic. These categories were formed by noting the commonality and variation across all interviews at collective level, while retaining the individual participant context. This paper reflects on these categories of experience variation, positioning discussion in further context of the socio-cultural technological, ‘post digital’ world that smart learners may find themselves in future urban smart learning environments.
Parameter Identification of Jiles-Atherton Model Based on Levy Whale Optimization Algorithm

The Jiles-Atherton model is key to researching the hysteresis loop.The focus of scholars across various countries has always been the parameter identification of the Jiles-Atherton model.This paper on the Levy whale optimization algorithm (LWOA), based on the whale optimization algorithm (WOA), proposes to overcome the disadvantage that WOA tends to involve the local optimum.The recommended algorithm uses the Levy flight strategy instead of the encircling prey policy since the former improves the global search.Therefore, the new algorithm is better at stability and calculation accuracy.To substantiate the efficacy of the proposed algorithm, it is tested against six benchmark functions and compared with the WOA, particle swarm optimization (PSO), grey wolf algorithm (GWO), and shuffled frog leaping algorithm (SFLA).In addition, the proposed algorithm is applied to realize two classical engineering problems, such as the tension/compression spring and welded beam design issues.The experimental findings reveal that the proposed algorithm is highly competitive with metaheuristic optimizers and improves the algorithm's performance.To address the poor stability of the J-A model parameter identification, an improved calculation method for parameter k and the reduced parameter ranges of the model parameters a and α were combined with LWOA.The proposed algorithm is called C-LWOA, which is compared with LWOA, PSO, GWO, SFLA, and the cuckoo search (CS) based on the data reported in the literature.Moreover, the simulation results demonstrate that the stability and calculation accuracy of the parameter identification by the C-LWOA was significantly strengthened.Equally important, the calculation error was within 0.2%.Finally, the proposed algorithm was subsequently used to fit the actual measurements of the hysteresis loop of permalloy.
Criss-Cross Attentional Siamese Networks for Object Tracking

Visual object tracking is a hot topic in recent years. In the meanwhile, Siamese networks have attracted extensive attention in this field because of its balanced precision and speed. However, most of the Siamese network methods can only distinguish foreground from the non-semantic background. The fine-tuning and retraining of fully-convolutional Siamese networks for object tracking(SiamFC) can achieve higher precision under interferences, but the tracking accuracy is still not ideal, especially in the environment with more target interferences, dim light, and shadows. In this paper, we propose criss-cross attentional Siamese networks for object tracking (SiamCC). To solve the imbalance between foreground and non-semantic background, we use the feature enhancement module of criss-cross attention to greatly improve the accuracy of video object tracking in dim light and shadow environments. Experimental results show that the maximum running speed of SiamCC in the object tracking benchmark dataset is 90 frames/second. In terms of detection accuracy, the accuracy of shadow sequences is greatly improved, especially the accuracy score of sequence HUMAN8 is improved from 0.09 to 0.89 compared with the original SiamFC, and the success rate score is improved from 0.07 to 0.55.
Multiple Object Trajectory Estimation Using Backward Simulation

This paper presents a general solution for computing the multi-object posterior for sets of trajectories from a sequence of multi-object (unlabelled) filtering densities and a multi-object dynamic model. Importantly, the proposed solution opens an avenue of trajectory estimation possibilities for multi-object filters that do not explicitly estimate trajectories. In this paper, we first derive a general multi-trajectory backward smoothing equation based on random finite sets of trajectories. Then we show how to sample sets of trajectories using backward simulation for Poisson multi-Bernoulli filtering densities, and develop a tractable implementation based on ranked assignment. The performance of the resulting multi-trajectory particle smoothers is evaluated in a simulation study, and the results demonstrate that they have superior performance in comparison to several state-of-the-art multi-object filters and smoothers.
CQACD: A Concept Question-Answering System for Intelligent Tutoring Using a Domain Ontology With Rich Semantics

In this study, a Concept Question Answering system applied to the Computer Domain (CQACD) for intelligent tutoring is proposed. This system is a dialogue-based Intelligent Tutoring System (ITS) that allows the tutor and student with mixed-initiative and natural language to ask each other questions concerning the basic computer knowledge in the <i>Computer Basics</i> course. CQACD is based on constructivist principles and encourages the learner to construct knowledge rather than merely receiving knowledge, which has the following characteristics: (a) this system employs a domain ontology with rich semantic relationships to model the basic computer knowledge and build up a concept-centric knowledge model, (b) uses a limited number of 80 input templates with description logics to acquire the intention of questions posed by students, (c) a textual entailment algorithm with semantic technologies is proposed to match the input template and assess the student&#x2019;s contribution to improve the flexibility of the system, and (d) an ontology-driven dialogue management mechanism is proposed, which can quickly form the conversational content and conversational sequence. The experimental results show that CQACD can replace the teachers&#x2019; tutoring in large classes and can promote the learning of poor students in large classes better than teachers can. The paper reveals that the domain ontology with rich semantic relationships plays an important role in the Concept Question Answer System (CQAS). It can model CQAS&#x2019;s discipline knowledge, provide structured domain knowledge for student model, template design and matching, and provide basic architectural architecture for dialogue management.
Hybrid Segmentation Approach for Different Medical Image Modalities

The segmentation process requires separating the image region into sub-regions of similar properties. Each sub-region has a group of pixels having the same characteristics, such as texture or intensity. This paper suggests an efficient hybrid segmentation approach for different medical image modalities based on particle swarm optimization (PSO) and improved fast fuzzy C-means clustering (IFFCM) algorithms. An extensive comparative study on different medical images is presented between the proposed approach and other different previous segmentation techniques. The existing medical image segmentation techniques incorporate clustering, thresholding, graph-based, edge-based, active contour, region-based, and watershed algorithms. This paper extensively analyzes and summarizes the comparative investigation of these techniques. Finally, a prediction of the improvement involves the combination of these techniques is suggested. The obtained results demonstrate that the proposed hybrid medical image segmentation approach provides superior outcomes in terms of the examined evaluation metrics compared to the preceding segmentation techniques.
Studying the Cost of n-qubit Toffoli Gates

There are several Toffoli gate designs for quantum computers in the literature. Each of these designs is focused on a specific technology or on optimising one or several metrics (T-count, number of qubits, etc.), and therefore has its advantages and disadvantages. While there is some consensus in the state of the art on the best implementations for the Toffoli gate, scaling this gate for use with three or more control qubits is not trivial. In this paper, we analyse the known techniques for constructing an n-qubit Toffoli gate, as well as the existing state-of-the-art designs for the 2-qubit version, which is an indispensable building block for the larger gates. In particular, we are interested in a construction of the temporary logical-AND gate with more than two control qubits. This gate is widely used in the literature due to the T-count and qubit reduction it provides. However, its use with more than two control qubits has not been analysed in detail in any work. The resulting information is offered in the form of comparative tables that will facilitate its consultation for researchers and people interested in the subject, so that they can easily choose the design that best suits their interests. As part of this work, the studied implementations have been reproduced and tested on both quantum simulators and real quantum devices.
Game Analysis of "Blockchain+Supply Chain Finance" Mode in Empowering Small and Micro Enterprises’ Financing

The integration of blockchain and supply chain provides new possibilities for solving the financing difficulties of small and micro enterprises (SMEs). This paper constructs dynamic evolutionary game models between financial institutions and SMEs as well as core firms and SMEs. Furthermore, the following conclusions were drawn by using MATLAB software for numerical simulation based on models combined with the example. Docking with blockchain platform is the dominant strategy of financial institutions. Blockchain can help SMEs make trustworthy decisions by promoting credit split circulation, improving financing efficiency, increasing default cost and reducing financing rate. In addition, through "network cooperation and credit incentive", "joint punishment for breach of trust" and "reasonable revenue sharing", the game equilibrium evolves toward the ideal state that financial institutions dare to lend, core firms and SMEs are "Double Trustworthy". Thus, the financing of SMEs is empowered by blockchain. Finally, according to the results of game analysis, suggestions that blockchain should be used to develop digital supply chain to meet the financing needs of SMEs are proposed.
Image Colorization Algorithm Based on Improved GAN

At present, with deep learning technology applied in the field of machine vision, image colorization has gradually become a hot research direction. The traditional image colorization technology needs human-computer interaction in the application. The operation process of this technology is complex and difficult, it is difficult to achieve large-scale application. Considering the shortcomings of the traditional methods, We propose an image colorization algorithm by designing the improved generation adversarial network (GAN). Aiming at the problem of black-and-white photos' colorization, the self-attention model (SAM) is used to construct the task of image generation. Image details are generated according to the clues of all feature positions, and the spectral normalization method is introduced to improve the quality of the network generated image. The discriminator of the algorithm that we proposed can effectively check the consistency of the features of long-range details in the image. Experimental results show that the algorithm can effectively achieve the batch colorization of black-and-white images.