Reviewerly/exHarmony huggingface dataset:
Quantum Image Segmentation Based on Grayscale Morphology

The classical image segmentation algorithm based on grayscale morphology can effectively segment images with uneven illumination, but with the increase of the image data, the real-time problem will emerge.In order to solve this problem, a quantum image segmentation algorithm is proposed in this paper, which can use quantum mechanism to simultaneously perform morphological operations on all pixels in a grayscale image, and then quickly segment the image into a binary image.In addition, several quantum circuit units, including dilation, erosion, bottom hat transformation, top hat transformation, etc., are designed in detail, and then they are combined together to construct the complete quantum circuits for segmenting the NEQR images.For a 2 n × 2 n image with q grayscale levels, the complexity of our algorithm can be reduced to O(n 2 + q), which is an exponential speedup than the classic counterparts.Finally, the experiment is conducted on IBM Q to show the feasibility of our algorithm in the noisy intermediate-scale quantum (NISQ) era.
Safety for Whom? Investigating How Platforms Frame and Perform Safety and Harm Interventions

This article reports on a thematic content analysis of 486 newsroom posts published between 2016 and 2021 by five prominent digital platforms (Facebook, Tinder, YouTube, TikTok, and Twitter). We aimed to understand how these platforms frame and define the issues of harm and safety, and to identify the interventions they publicly report introducing to address these issues. We found that platforms respond to and draw upon external controversies and media panics to selectively construct matters of concern related to safety and harm. They then reactively propose solutions that serve as justification for further investment in and scaling up of automated, data-intensive surveillance and verification technologies. We examine four key themes in the data: locating harm with bad actors and discrete content objects (Theme 1), framing surveillance and policing as solutions to harm (Theme 2), policing “borderline” content through suppression strategies (Theme 3), and performing diversity and inclusion (Theme 4).
A Multi-Mode 8k-MAC HW-Utilization-Aware Neural Processing Unit With a Unified Multi-Precision Datapath in 4-nm Flagship Mobile SoC

This article presents an 8k-multiply-accumulate (MAC) neural processing unit (NPU) in 4-nm mobile system-on-chip (SoC). The unified multi-precision MACs support from integer (INT)4/8/16 to floating point (FP)16 data with high area and energy efficiency. When the NPU meets some layers having low hardware (HW) utilization, such as depthwise convolution or shallow layers with a few input channels, the NPU reconfigures the computational flow to enhance the utilization up to four times after getting basic tensor information from a compiler, such as operation types and shapes. The NPU supports a dynamic operation mode to cover extremely low-power to low-latency requirements. The NPU achieves 4.26 tera FP operations per second (TFLOPS)/W and 11.59 tera operations per second (TOPS)/W for DeepLabV3 (FP16) and MobileNetEdgeTPU (INT8), respectively, as well as high area efficiency (1.72 TFLOPS/mm2 and 3.45 TOPS/mm2).
A hybrid decision support system with golden cut and bipolar q-ROFSs for evaluating the risk-based strategic priorities of fintech lending for clean energy projects

Abstract In the last decade, the risk evaluation and the investment decision are among the most prominent issues of efficient project management. Especially, the innovative financial sources could have some specific risk appetite due to the increasing return of investment. Hence, it is important to uncover the risk factors of fintech investments and investigate the possible impacts with an integrated approach to the strategic priorities of fintech lending. Accordingly, this study aims to analyze a unique risk set and the strategic priorities of fintech lending for clean energy projects. The most important contributions to the literature can be listed as to construct an impact-direction map of risk-based strategic priorities for fintech lending in clean energy projects and to measure the possible influences by using a hybrid decision making system with golden cut and bipolar q-rung orthopair fuzzy sets. The extension of multi stepwise weight assessment ratio analysis (M-SWARA) is applied for weighting the risk factors of fintech lending. The extension of elimination and choice translating reality (ELECTRE) is employed for constructing and ranking the risk-based strategic priorities for clean energy projects. In this process, data is obtained with the evaluation of three different decision makers. The main superiority of the proposed model by comparing with the previous models in the literature is that significant improvements are made to the classical SWARA method so that a new technique is created with the name of M-SWARA. Hence, the causality analysis between the criteria can also be performed in this proposed model. The findings demonstrate that security is the most critical risk factor for fintech lending system. Moreover, volume is found as the most critical risk-based strategy for fintech lending. In this context, fintech companies need to take some precautions to effectively manage the security risk. For this purpose, the main risks to information technologies need to be clearly identified. Next, control steps should be put for these risks to be managed properly. Furthermore, it has been determined that the most appropriate strategy to increase the success of the fintech lending system is to increase the number of financiers integrated into the system. Within this framework, the platform should be secure and profitable to persuade financiers.
Quantum Annealing in the NISQ Era: Railway Conflict Management

We are in the Noisy Intermediate-Scale Quantum (NISQ) devices' era, in which quantum hardware has become available for application in real-world problems. However, demonstrating the usefulness of such NISQ devices are still rare. In this work, we consider a practical railway dispatching problem: delay and conflict management on single-track railway lines. We examine the issue of train dispatching consequences caused by the arrival of an already delayed train to the network segment being considered. This problem is computationally hard and needs to be solved almost in real-time. We introduce a quadratic unconstrained binary optimization (QUBO) model of this problem, compatible with the emerging quantum annealing technology. The model's instances can be executed on present-day quantum annealers. As a proof-of-concept, we solve selected real-life problems from the Polish railway network using D-Wave quantum annealers. As a reference, we also provide solutions calculated with classical methods, including those relevant to the community (linear integer programming) and a sophisticated algorithm based on tensor networks for solving Ising instances. Our preliminary results illustrate the degree of difficulty of real-life railway instances for the current quantum annealing technology. Moreover, our analysis shows that the new generation of quantum annealers (the advantage system) perform much worse on those instances than its predecessor.
Research on 3D Phenotypic Reconstruction and Micro-Defect Detection of Green Plum Based on Multi-View Images

Rain spots on green plum are superficial micro-defects. Defect detection based on a two-dimensional image is easily influenced by factors such as placement position and light and is prone to misjudgment and omission, which are the main problems affecting the accuracy of defect screening of green plum. In this paper, using computer vision technology, an improved structure from motion (SFM) and patch-based multi-view stereo (PMVS) algorithm based on similar graph clustering and graph matching is proposed to perform three-dimensional sparse and dense reconstruction of green plums. The results show that, compared with the traditional algorithm, the running time of this algorithm is lower, at only 26.55 s, and the mean values of camera optical center error and pose error are 0.019 and 0.631, respectively. This method obtains a higher reconstruction accuracy to meet the subsequent plum micro-defect detection requirements. Aiming at the dense point cloud model of green plums, through point cloud preprocessing, the improved adaptive segmentation algorithm based on the Lab color space realizes the effective segmentation of the point cloud of green plum micro-defects. The experimental results show that the average running time of the improved adaptive segmentation algorithm is 2.56 s, showing a faster segmentation speed and better effect than the traditional K-means and K-means++ algorithms. After clustering the micro-defect point cloud, the micro-defect information of green plums was extracted on the basis of random sample consensus (RANSAC) plane fitting, which provides a theoretical model for further improving the accuracy of sorting the appearance quality of green plums.
An Analysis of the Algebraic Group Model

The algebraic group model (AGM), formalized by Fuchsbauer, Kiltz, and Loss, has recently received significant attention. One of the appealing properties of the AGM is that it is viewed as being (strictly) weaker than the generic group model (GGM), in the sense that hardness results for algebraic algorithms imply hardness results for generic algorithms, and generic reductions in the AGM (namely, between the algebraic formulations of two problems) imply generic reductions in the GGM. We highlight that as the GGM and AGM are currently formalized, this is not true: hardness in the AGM may not imply hardness in the GGM, and a generic reduction in the AGM may not imply a similar reduction in the GGM.
Cybersickness, Cognition, &amp; Motor Skills: The Effects of Music, Gender, and Gaming Experience

Recent research has attempted to identify methods to mitigate cybersickness and examine its aftereffects. In this direction, this paper examines the effects of cybersickness on cognitive, motor, and reading performance in VR. Also, this paper evaluates the mitigating effects of music on cybersickness, as well as the role of gender, and the computing, VR, and gaming experience of the user. This paper reports two studies. In this study, 39 participants performed an assessment four times, once before the rides (baseline), and then once after each ride (3 rides). In each ride either Calming, or Joyful, or No Music was played. During each ride, linear and angular accelerations took place to induce cybersickness in the participants. In each assessment, while immersed in VR, the participants evaluated their cybersickness symptomatology and performed a verbal working memory task, a visuospatial working memory task, and a psychomotor task. While responding to the cybersickness questionnaire (3D UI), eye-tracking was conducted to measure reading time and pupillometry. The results showed that Joyful and Calming music substantially decreased the intensity of nausea-related symptoms. However, only Joyful music significantly decreased the overall cybersickness intensity. Importantly, cybersickness was found to decrease verbal working memory performance and pupil size. Also, it significantly decelerated psychomotor (reaction time) and reading abilities. Higher gaming experience was associated with lower cybersickness. When controlling for gaming experience, there were no significant differences between female and male participants in terms of cybersickness. The outcomes indicated the efficiency of music in mitigating cybersickness, the important role of gaming experience in cybersickness, and the significant effects of cybersickness on pupil size, cognition, psychomotor skills, and reading ability.
Weakly supervised histopathology image segmentation with self-attention

Accurate segmentation in histopathology images at pixel-level plays a critical role in the digital pathology workflow. The development of weakly supervised methods for histopathology image segmentation liberates pathologists from time-consuming and labor-intensive works, opening up possibilities of further automated quantitative analysis of whole-slide histopathology images. As an effective subgroup of weakly supervised methods, multiple instance learning (MIL) has achieved great success in histopathology images. In this paper, we specially treat pixels as instances so that the histopathology image segmentation task is transformed into an instance prediction task in MIL. However, the lack of relations between instances in MIL limits the further improvement of segmentation performance. Therefore, we propose a novel weakly supervised method called SA-MIL for pixel-level segmentation in histopathology images. SA-MIL introduces a self-attention mechanism into the MIL framework, which captures global correlation among all instances. In addition, we use deep supervision to make the best use of information from limited annotations in the weakly supervised method. Our approach makes up for the shortcoming that instances are independent of each other in MIL by aggregating global contextual information. We demonstrate state-of-the-art results compared to other weakly supervised methods on two histopathology image datasets. It is evident that our approach has generalization ability for the high performance on both tissue and cell histopathology datasets. There is potential in our approach for various applications in medical images.
Amorphica: 4-Replica 512 Fully Connected Spin 336MHz Metamorphic Annealer with Programmable Optimization Strategy and Compressed-Spin-Transfer Multi-Chip Extension

Combinatorial optimization (CO) is vital for making wiser decisions and planning in our society. Annealing computation is a promising CO approach derived from an analogy to physical phenomena (Fig. 2.3.1). It represents a CO problem as an energy function, a quadratic form of {1, -1} vectors, where each binary element is called a (pseudo) spin. The spin vector is initialized randomly and is updated stochastically to find minimum energy states by gradually reducing the (pseudo) temperature. Local-connection annealers (quantum [1] and non-quantum [2-4]) have been constrained to spin models having only local inter-spin couplings. This restriction, however, severely limits their CO applications even with the help of clever graph embedding algorithms. Full-connection annealers [5], [6], considered here, have been proposed to address this drawback, permitting handling of arbitrary topologies and densities of inter-spin couplings, even if they are irregular.
From statistical methods to deep learning, automatic keyphrase prediction: A survey

Keyphrase prediction aims to generate phrases (keyphrases) that highly summarizes a given document. Recently, researchers have conducted in-depth studies on this task from various perspectives. In this paper, we comprehensively summarize representative studies from the perspectives of dominant models, datasets and evaluation metrics. Our work analyzes up to 167 previous works, achieving greater coverage of this task than previous surveys. Particularly, we focus highly on deep learning-based keyphrase prediction, which attracts increasing attention of this task in recent years. Afterwards, we conduct several groups of experiments to carefully compare representative models. To the best of our knowledge, our work is the first attempt to compare these models using the identical commonly-used datasets and evaluation metric, facilitating in-depth analyses of their disadvantages and advantages. Finally, we discuss the possible research directions of this task in the future.
Real-Time Memory Efficient Multitask Learning Model for Autonomous Driving

Developing a self-driving system is a challenging task that requires a high level of scene comprehension with real-time inference, and it is safety-critical. This study proposes a real-time memory efficient multitask learning-based model for joint object detection, drivable area segmentation, and lane detection tasks. To accomplish this research objective, the encoder-decoder architecture efficiently utilized to handle input frames through shared representation. Comprehensive experiments conducted on a challenging public Berkeley Deep Drive(BDD100 K) dataset. For further performance comparisons, a private dataset consisting of 30 K frames was collected and annotated for the three aforementioned tasks. Experimental results demonstrated the superiority of the proposed method's over existing baseline approaches in terms of computational efficiency, model power consumption and accuracy performance. The performance results for object detection, drivable area segmentation and lane detection tasks showed the highest 77.5 mAP50, 91.9 mIoU and 33.8 mIoU results on BDD100 K dataset respectively. In addition, the model achieved 112.29 fps processing speed improving both performance and inference speed results of existing multi-tasking models.
A Deep Learning-Based Innovative Technique for Phishing Detection in Modern Security with Uniform Resource Locators

Organizations and individuals worldwide are becoming increasingly vulnerable to cyberattacks as phishing continues to grow and the number of phishing websites grows. As a result, improved cyber defense necessitates more effective phishing detection (PD). In this paper, we introduce a novel method for detecting phishing sites with high accuracy. Our approach utilizes a Convolution Neural Network (CNN)-based model for precise classification that effectively distinguishes legitimate websites from phishing websites. We evaluate the performance of our model on the PhishTank dataset, which is a widely used dataset for detecting phishing websites based solely on Uniform Resource Locators (URL) features. Our approach presents a unique contribution to the field of phishing detection by achieving high accuracy rates and outperforming previous state-of-the-art models. Experiment results revealed that our proposed method performs well in terms of accuracy and its false-positive rate. We created a real data set by crawling 10,000 phishing URLs from PhishTank and 10,000 legitimate websites and then ran experiments using standard evaluation metrics on the data sets. This approach is founded on integrated and deep learning (DL). The CNN-based model can distinguish phishing websites from legitimate websites with a high degree of accuracy. When binary-categorical loss and the Adam optimizer are used, the accuracy of the k-nearest neighbors (KNN), Natural Language Processing (NLP), Recurrent Neural Network (RNN), and Random Forest (RF) models is 87%, 97.98%, 97.4% and 94.26%, respectively, in contrast to previous publications. Our model outperformed previous works due to several factors, including the use of more layers and larger training sizes, and the extraction of additional features from the PhishTank dataset. Specifically, our proposed model comprises seven layers, starting with the input layer and progressing to the seventh, which incorporates a layer with pooling, convolutional, linear 1 and 2, and linear six layers as the output layers. These design choices contribute to the high accuracy of our model, which achieved a 98.77% accuracy rate.
Security-Informed Safety Analysis of Autonomous Transport Systems Considering AI-Powered Cyberattacks and Protection

The entropy-oriented approach called security- or cybersecurity-informed safety (SIS or CSIS, respectively) is discussed and developed in order to analyse and evaluate the safety and dependability of autonomous transport systems (ATSs) such as unmanned aerial vehicles (UAVs), unmanned maritime vehicles (UMVs), and satellites. This approach allows for extending and integrating the known techniques FMECA (Failure Modes, Effects, and Criticality Analysis) and IMECA (Intrusion MECA), as well as developing the new SISMECA (SIS-based Intrusion Modes, Effects, and Criticality Analysis) technique. The ontology model and templates for SISMECA implementation are suggested. The methodology of safety assessment is based on (i) the application and enhancement of SISMECA considering the particularities of various ATSs and roles of actors (regulators, developers, operators, customers); (ii) the development of a set of scenarios describing the operation of ATS in conditions of cyberattacks and physical influences; (iii) AI contribution to system protection for the analysed domains; (iv) scenario-based development and analysis of user stories related to different cyber-attacks, as well as ways to protect ATSs from them via AI means/platforms; (v) profiling of AI platform requirements by use of characteristics based on AI quality model, risk-based assessment of cyberattack criticality, and efficiency of countermeasures which actors can implement. Examples of the application of SISMECA assessment are presented and discussed.
Face-Sensitive Image-to-Emotional-Text Cross-modal Translation for Multimodal Aspect-based Sentiment Analysis

Aspect-level multimodal sentiment analysis, which aims to identify the sentiment of the target aspect from multimodal data, recently has attracted extensive attention in the community of multimedia and natural language processing.Despite the recent success in textual aspect-based sentiment analysis, existing models mainly focused on utilizing the object-level semantic information in the image but ignore explicitly using the visual emotional cues, especially the facial emotions.How to distill visual emotional cues and align them with the textual content remains a key challenge to solve the problem.In this work, we introduce a facesensitive image-to-emotional-text translation (FITE) method, which focuses on capturing visual sentiment cues through facial expressions and selectively matching and fusing with the target aspect in textual modality.To the best of our knowledge, we are the first that explicitly utilize the emotional information from images in the multimodal aspect-based sentiment analysis task.Experiment results show that our method achieves state-of-the-art results on the Twitter-2015 and Twitter-2017 datasets.The improvement demonstrates the superiority of our model in capturing aspect-level sentiment in multimodal data with facial expressions 1 .
Challenges in High-Performance Computing

High-Performance Computing, HPC, has become one of the most active computer science fields. Driven mainly by the need for high processing capabilities required by algorithms from many areas, such as Big Data, Artificial Intelligence, Data Science, and subjects related to chemistry, physics, and biology, the state-of-art algorithms from these fields are notoriously demanding computer resources. Therefore, choosing the right computer system to optimize their performance is paramount. This article presents the main challenges of future supercomputer systems, highlighting the areas that demand the most of HPC servers; the new architectures, including heterogeneous processors composed of artificial intelligence chips, quantum processors, the adoption of HPC on cloud servers; and the challenges of software developers when facing parallelizing applications. We also discuss challenges regarding non-functional requirements, such as energy consumption and resilience.