Reviewerly/exHarmony huggingface dataset:
Continuous black-box optimization with an Ising machine and random subspace coding

A black-box optimization algorithm such as Bayesian optimization finds the extremum of an unknown function by alternating the inference of the underlying function and optimization of an acquisition function. In a high-dimensional space, such algorithms perform poorly due to the difficulty of acquisition function optimization. Herein, we apply Ising machines to overcome the difficulty in the continuous black-box optimization. As an Ising machine specializes in optimization of binary problems, a continuous vector has to be encoded to binary, and the solution by Ising machines has to be translated back. Our method has the following three parts: (1) random subspace coding based on axis-parallel hyperrectangles from continuous vector to binary vector, (2) a quadratic unconstrained binary optimization (QUBO) defined by the acquisition function based on the nonnegative-weighted linear regression model, which is solved by Ising machines, and (3) a penalization scheme to ensure that the solution can be translated back. It is shown with benchmark tests that its performance using the D-Wave Advantage quantum annealer and simulated annealing is competitive with a state-of-the-art method based on the Gaussian process in high-dimensional problems. Our method may open up the possibility of Ising machines and other QUBO solvers, including a quantum approximate optimization algorithm using gated-quantum computers, and may expand its range of application to continuous-valued problems.
Multi-label emotion classification of Urdu tweets

Urdu is a widely used language in South Asia and worldwide. While there are similar datasets available in English, we created the first multi-label emotion dataset consisting of 6,043 tweets and six basic emotions in the Urdu Nastalíq script. A multi-label (ML) classification approach was adopted to detect emotions from Urdu. The morphological and syntactic structure of Urdu makes it a challenging problem for multi-label emotion detection. In this paper, we build a set of baseline classifiers such as machine learning algorithms (Random forest (RF), Decision tree (J48), Sequential minimal optimization (SMO), AdaBoostM1, and Bagging), deep-learning algorithms (Convolutional Neural Networks (1D-CNN), Long short-term memory (LSTM), and LSTM with CNN features) and transformer-based baseline (BERT). We used a combination of text representations: stylometric-based features, pre-trained word embedding, word-based n-grams, and character-based n-grams. The paper highlights the annotation guidelines, dataset characteristics and insights into different methodologies used for Urdu based emotion classification. We present our best results using micro-averaged F1, macro-averaged F1, accuracy, Hamming loss (HL) and exact match (EM) for all tested methods.
Temporal Dynamic Convolutional Neural Network for Text-Independent Speaker Verification and Phonemic Analysis

In the field of text-independent speaker recognition, dynamic models that adapt along the time axis have been proposed to consider the phoneme-varying characteristics of speech. However, a detailed analysis of how dynamic models work depending on phonemes is insufficient. In this paper, we propose temporal dynamic CNN (TDY-CNN) that considers temporal variation of phonemes by applying kernels optimally adapting to each time bin. These kernels adapt to time bins by applying weighted sum of trained basis kernels. Then, an analysis of how adaptive kernels work on different phonemes in various layers is carried out. TDY-ResNet-38(×0.5) using six basis kernels improved an equal error rate (EER), the speaker verification performance, by 17.3% compared to the baseline model ResNet-38(×0.5). In addition, we showed that adaptive kernels depend on phoneme groups and are more phoneme-specific at early layers. The temporal dynamic model adapts itself to phonemes without explicitly given phoneme information during training, and results show the necessity to consider phoneme variation within utterances for more accurate and robust text-independent speaker verification.
Context Awareness in Cultural Heritage Applications: A Survey

Cultural Heritage Institutions fueled by the advances in Information Technology are exploiting new ways to present their content to visitors. The emergence of the Internet of Things paradigm has shifted their efforts into a more personalized and adaptable experience that takes into consideration the visitor profile and behavior, in respect to presented artifacts and environmental parameters. Context-awareness allows the understanding of the situation and the optimization of the delivered cultural User Experience. In this article, we survey context awareness in cultural heritage applications. After presenting the background of Context Awareness and its features to cultural environments, a subset of research projects that incorporate context aware traits in a cultural scenario is reviewed. Each prototype is analysed in respect to several criteria that include context sensing, middleware, and incorporation methods. Finally, based on the review, lessons learnt, and directions for the development of similar prototypes are highlighted.
Deterministic Search on Star Graphs via Quantum Walks

We propose a novel algorithm for quantum spatial search on a star graph using interleaved continuous-time quantum walks and marking oracle queries. Initializing the system in the star's central vertex, we determine the optimal quantum walk times to reach full overlap with the marked state using ⌈(π/4)sqrt[N]-(1/2)⌉ oracle queries, matching the well-known lower bound of Grover's search. We implement the deterministic search in a database of size seven on photonic quantum hardware, and demonstrate the effective scaling of the approach up to size 115. This is the first experimental demonstration of quantum walk-based search on the highly noise-resistant star graph, which provides new evidence for the applications of quantum walk in quantum algorithms and quantum information processing.
Adaptive Image Steganography Using Fuzzy Enhancement and Grey Wolf Optimizer

Adaptive imagesteganography embeds secret messages into areas of cover images with complex features, including rich edges and complex textures. In this article, an adaptive image steganography technique based on the edge and complex texture areas of images is proposed, by comprehensively considering three rules in the design of image steganography. First, the embedding area is composed of the edge and complex texture areas of images, according to the complexity-first rule. Edge detection is realized by an improved fuzzy enhancement function, optimized by the grey wolf optimizer to detect both the weak and strong edges. Second, the minimum average classification error rate is used to assess the choice of the complex texture areas. Third, under the spreading rule, two different average filters and one KerBohme filter are used to design the cost function in the embedding areas. Finally, confidential information is adaptively embedded through syndrome-trellis codes. Experimental results show that the proposed algorithm outperforms seven classical adaptive image steganography algorithms on two steganalytic feature sets. The performance improvement is particularly significant when the payload is large.
Security and Safety in Amazon EC2 Service – A Research on EC2 Service AMIs

There are some multinational companies available in the market to provide cloud services such as Amazon Web Services, Microsoft Azure, and IBM Smart Cloud and so on. Nowadays an organization need to work on different technologies, it need not to install the technologies, it can simply acquire the technology available in online as a service.It is the best practice in the cloud based services that it allows the users to make their own exceptional unprecedented virtual images and share them to with various customers in a comparative cloud. Close to these customer shared virtual pictures, the cloud serviceproviders will in like manner give the virtual pictures that have been preconfigured with open source database and web server to orchestrate our stray pieces. In this paper, we had made an examination to check the general security risks related with the usage of virtual machine pictures from the uninhibitedly available inventories of cloud master affiliations. In adjusted, we had managed the open standard virtual pictures that are existed on the Amazon EC2 association. We analyzed the security issues of the virtual pictures which are available on the Amazon EC2 Cluster as the open AMI (Amazon Machine Images).
Corporate Governance for Complex Cryptocurrencies?

Cryptocurrencies such as Bitcoin or Ethereum are gaining ground not only as alternative modes of payment but also as platforms for financial innovation, particularly through token sales or initial coin offerings (‘ICOs’). All of these ventures are based on decentralized, permissionless blockchain technology, distinguished by their openness to, and the formal equality of, participants. However, recent cryptocurrency crises have shown that these architectures lack robust governance frameworks and are therefore prone to patterns of re-centralization. They are informally dominated by coalitions of powerful players within the cryptocurrency ecosystem who may violate basic rules of the blockchain community without accountability or sanction. This chapter first suggests that cryptocurrency and token-based ecosystems can be fruitfully analysed as <italic>complex systems</italic> that have been studied for decades in complexity theory and have recently gained prominence in financial regulation, too. It applies these insights to three key case studies: the Bitcoin Hard Fork of 2013; the Ethereum hard fork of 2016, following the DAO hack; and the ongoing Bitcoin scaling debate. Second, the chapter argues that complexity-induced uncertainty can be reduced, and elements of stability and order strengthened, by adapting a <italic>corporate governance framework</italic> to blockchain-based organizations: cryptocurrencies, and decentralized applications built on top of them via token sales. The resulting ‘comply-or-explain’ approach combines transparency and accountability with the necessary flexibility that allows blockchain developers to continue to experiment for the sake of innovation. Eventually, however, the coordination of these activities may necessitate the establishment of a self-regulatory institution.
Detecting nonclassicality and non-Gaussianity by the Wigner function and quantum teleportation in photon-added-and-subtracted two modes pair coherent state

We introduce a new state called photon-added-and-subtracted two modes pair coherent state (PAASTMPCS) by simultaneously adding and subtracting photons to the different modes of a pair coherent state. Its nonclassical and non-Gaussian properties are strengthened by the negative values of its Wigner function as the numbers of photons added or substracted increases. Based on the linear entropy criterion, we indicate that the PAASTMPCS is an entangled state. When increasing the numbers of photons added or substracted to a pair coherent state, the degree of entanglement in the PAASTMPCS is enhanced compared with the original pair coherent state. By using a PAASTMPCS as a non-Gaussian entangled resource, the quantum teleportation process is studied in detail. The results show that the number sum and phase difference measurements protocol is more appropriate than the orthogonal quadrature components measurements protocol in the quantum teleportation process of a coherent state.
Preparing for usability testing

This chapter focuses on the preparation for usability testing. Activities include recruiting participants by yourself or through a recruiting company or an online recruiting resource; planning for backups if participants cancel or fail to appear; assigning team roles and responsibilities for testing; developing checklists; writing the moderator's script for in-person or remote testing; preparing video consent forms, consent forms for testing with minors, nondisclosure agreements, and observer forms; creating pretest, posttask, and posttest questionnaires or using standard questionnaires, such as System Usability Scale or Net Promoter Score; and creating or using qualitative feedback methods such as product reaction cards or an interview. The chapter ends with information on testing the test with a walkthrough and a pilot test.
The impact of transformational leadership on organisational performance case study: the University of Jordan

This research aimed to examine the impact of transformational leadership on organisational performance at the University of Jordan. A questionnaire was distributed to a sample of (280) administrative employees at the University of Jordan. Data were analysed using SPSS software. Information about respondents was analysed using descriptive analysis. While multiple regression was used to determine whether the four dimensions of transformational leadership; idealised influence, inspirational motivation, intellectual stimulation and individualised consideration have a significant impact on organisational performance. The findings revealed that the transformational leadership had a significant impact on organisational performance along with idealised influence and intellectual stimulation. However, individualised consideration and inspirational motivation did not have a significant impact on organisational performance according to the respondents' ratings.
VesNet-RL: Simulation-Based Reinforcement Learning for Real-World US Probe Navigation

Ultrasound (US) is one of the most common medical imaging modalities since it is radiation-free, low-cost, and real-time. In freehand US examinations, sonographers often navigate a US probe to visualize standard examination planes with rich diagnostic information. However, reproducibility and stability of the resulting images often suffer from intra- and inter-operator variation. Reinforcement learning (RL), as an interaction-based learning method, has demonstrated its effectiveness in visual navigating tasks; however, RL is limited in terms of generalization. To address this challenge, we propose a simulation-based RL framework for real-world navigation of US probes towards the standard longitudinal views of vessels. A UNet is used to provide binary masks from US images; thereby, the RL agent trained on simulated binary vessel images can be applied in real scenarios without further training. To accurately characterize actual states, a multi-modality state representation structure is introduced to facilitate the understanding of environments. Moreover, considering the characteristics of vessels, a novel standard view recognition approach based on the minimum bounding rectangle is proposed to terminate the searching process. To evaluate the effectiveness of the proposed method, the trained policy is validated virtually on 3D volumes of a volunteer’s in-vivo carotid artery, and physically on custom-designed gel phantoms using robotic US. The results demonstrate that proposed approach can effectively and accurately navigate the probe towards the longitudinal view of vessels.
Evaluation of Vision Transformers for Traffic Sign Classification

Traffic sign recognition is one of the most important tasks in autonomous driving. Camera-based computer vision techniques have been proposed for this task, and various convolutional neural network structures are used and validated with multiple open datasets. Recently, novel Transformer-based models have been proposed for various computer vision tasks and have achieved state-of-the-art performance, outperforming convolutional neural networks in several tasks. In this study, our goal is to investigate whether the success of Vision Transformers can be replicated within the traffic sign recognition area. Based on existing resources, we first extract and contribute three open traffic sign classification datasets. Based on these datasets, we experiment with seven convolutional neural networks and five Vision Transformers. We find that Transformers are not as competitive as convolutional neural networks for the traffic sign classification task. Specifically, there are performance gaps of up to 12.81%, 2.01%, and 4.37% existing for the German, Indian, and Chinese traffic sign datasets, respectively. Furthermore, we propose some suggestions to improve the performance of Transformers.
Dynamic fitness landscape-based adaptive mutation strategy selection mechanism for differential evolution

Differential evolution (DE) is the most efficient evolutionary algorithm widely used to solve continuous or discrete numerical optimization problems. However, the performance of DE highly depends on the choice of mutation strategy. In addition, for a given optimization problem, a different mutation strategy configuration of DE may be more effective than a single mutation strategy in searching for the optimal solution. Based on these observations, a dynamic fitness landscape-based adaptive mutation strategy selection differential evolution (DFLDE) is proposed in this paper. In DFLDE, the optimal mutation strategy selection is based on each optimization problem's dynamic fitness landscape characteristics during the evolutionary process. The CEC2017 benchmark function set is used to evaluate the performance of the proposed DFLDE algorithm. The experimental results indicate that the DFLDE is superior to the other five well-known DE variants in searching for the optimal value, convergence speed, and robustness.
Stay Home! Conducting Remote Usability Evaluations of Novel Real-World Authentication Systems Using Virtual Reality

Evaluating interactive systems often requires researchers to invite user study participants to the lab. However, corresponding evaluations often lack realism and participants are usually recruited from a local area only. In this work, we propose Remote Virtual Reality for simulating Real-world Research (RVR3) to evaluate novel real-world authentication prototypes. A user study (N=25) demonstrates the feasibility of using VR for remote usability research on simulated real-world prototypes. Our remote VR user study provides a glimpse into the usability and social acceptability of two novel authentication systems: Hand Menu and Tap. We build on prior research in this space and discuss the impact RVR3 studies have on the range of possible studies. In summary, our remote VR research method to design, implement, and evaluate interactive real-world prototypes is a next step towards moving human-centred research out of the lab and potentially reaching a more diverse and larger participant sample over time.
Selfee, self-supervised features extraction of animal behaviors

Fast and accurately characterizing animal behaviors is crucial for neuroscience research. Deep learning models are efficiently used in laboratories for behavior analysis. However, it has not been achieved to use an end-to-end unsupervised neural network to extract comprehensive and discriminative features directly from social behavior video frames for annotation and analysis purposes. Here, we report a self-supervised feature extraction (Selfee) convolutional neural network with multiple downstream applications to process video frames of animal behavior in an end-to-end way. Visualization and classification of the extracted features (Meta-representations) validate that Selfee processes animal behaviors in a way similar to human perception. We demonstrate that Meta-representations can be efficiently used to detect anomalous behaviors that are indiscernible to human observation and hint in-depth analysis. Furthermore, time-series analyses of Meta-representations reveal the temporal dynamics of animal behaviors. In conclusion, we present a self-supervised learning approach to extract comprehensive and discriminative features directly from raw video recordings of animal behaviors and demonstrate its potential usage for various downstream applications.
Cascade Learning for Driver Facial Monitoring

As a non-invasive method, vision-based driver monitoring aims to identify risky maneuvers for intelligent vehicles and it has gained an increasing interest over recent years. However, most existing methods tend to design models for specific tasks, such as head pose or gaze estimation, which results in redundant models hampering real time applications. Besides, most driver facial monitoring methods ignore the correlation of different tasks. In this work, we propose a unified framework based on cascade learning for simultaneous facial landmark detection and head pose estimation, as well as simultaneous eye center detection and gaze estimation. In particular, built upon the key idea that facial landmark locations and 3D face model parameters are implicitly correlated, we introduce a cascade regression framework to achieve these two tasks simultaneously. After coarsely extracting the driver’s eye region from the detected facial landmarks, we perform cascade regression for simultaneous eye center detection and gaze estimation. Leveraging the power of cascade learning allows our method to alternatively optimize facial landmark detection, head pose estimation, eye center localization, and gaze prediction. The comparison experiments conducted on benchmark datasets of 300-W, GI4E, BU, MPIIGaze, and driving dataset of SHRP2 demonstrate that our proposed method can achieve state-of-the-art performance with robust effectiveness on the real driver monitoring applications.
The Time-Based Resource-Sharing Model of Working Memory

The time-based resource-sharing model considers working memory as the workspace in which mental representations are built, maintained, and transformed for completing goal-oriented tasks. Its main component is made of an episodic buffer and a procedural system that form an executive loop in which processing and storage share domain-general attentional resources on a temporal basis. Because working memory representations decay with time when attention is diverted, the cognitive load of a given activity is the proportion of time during which it occupies attention and prevents it from counteracting this decay through attentional refreshing. Consequently, recall in working memory tasks is an inverse function of the cognitive load of concurrent processing. Besides this system, an independent domain-specific maintenance system exists for verbal, but not visuospatial, information. Within this framework, working memory development mainly results from increasing processing speed that affects both the duration of the distraction of attention by concurrent tasks and refreshing efficiency.
Multi-scale Spatial Representation Learning via Recursive Hermite Polynomial Networks

Multi-scale representation learning aims to leverage diverse features from different layers of Convolutional Neural Networks (CNNs) for boosting the feature robustness to scale variance. For dense prediction tasks, two key properties should be satisfied: the high spatial variance across convolutional layers, and the sub-scale granularity inside a convolutional layer for fine-grained features. To pursue the two properties, this paper proposes Recursive Hermite Polynomial Networks (RHP-Nets for short). The proposed RHP-Nets consist of two major components: 1) a dilated convolution to maintain the spatial resolution across layers, and 2) a family of Hermite polynomials over a subset of dilated grids, which recursively constructs sub-scale representations to avoid the artifacts caused by naively applying the dilation convolution. The resultant sub-scale granular features are fused via trainable Hermite coefficients to form the multi-resolution representations that can be fed into the next deeper layer, and thus allowing feature interchanging at all levels. Extensive experiments are conducted to demonstrate the efficacy of our design, and reveal its superiority over state-of-the-art alternatives on a variety of image recognition tasks. Besides, introspective studies are provided to further understand the properties of our method.