Reviewerly/exHarmony huggingface dataset:
A novel image encryption scheme based on compressive sensing, elliptic curves and a new jerk oscillator with multistability

Abstract Compression and encryption are the two main steps generally used to ensure fast and secure transmission of digital images over public communication channels. Among the existing crypto compression approaches, those based on compressive sensing and chaotic systems possess good potential. Unfortunately, the present literature presents some limits including speed issue, security issue and reconstruction problem. These problems are mainly due to the poor performances of the measurement matrix exploited. Note that in some cases these measurement matrices are obtained from chaotic sequences with very poor dynamics. To solve this problem, we proposed in this paper, a novel image encryption scheme based on 2D compressive sensing, elliptic curves and a new septic jerk oscillator with multistability. The proposed scheme adopts the compression before encryption topology to reduce the processing time and is divided into two main parts. The first part concerns image compression using compressive sensing. The input image data is divided into 4 blocks. Then, each block is compressed using 2D compressing sensing based on the measurements matrices generated using chaotic sequences coming from the novel septic jerk system. Next, the compressed blocks are combined to form the compressed image. In the second part, to strengthen the security, elliptic curve theory is used to construct a simple and secure <?CDATA $8\times 8$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mn>8</mml:mn> <mml:mo>×</mml:mo> <mml:mn>8</mml:mn> </mml:math> S-box for digital image substitution. This S-box is combined with the septic chaotic solutions to change the pixel’s positions and values of the compressed image. The analysis of simulation test indicates that the proposed cipher has good compression effect, good reconstruction capability even for low compression ratio, is fast and highly secure compared to some recent schemes. As such the proposed work can be exploited to compress and secure data in Wireless Body Area Network (WBAN) applications.
On the energy landscape of symmetric quantum signal processing

Symmetric quantum signal processing provides a parameterized representation of a real polynomial, which can be translated into an efficient quantum circuit for performing a wide range of computational tasks on quantum computers. For a given polynomial $f$, the parameters (called phase factors) can be obtained by solving an optimization problem. However, the cost function is non-convex, and has a very complex energy landscape with numerous global and local minima. It is therefore surprising that the solution can be robustly obtained in practice, starting from a fixed initial guess $\Phi^0$ that contains no information of the input polynomial. To investigate this phenomenon, we first explicitly characterize all the global minima of the cost function. We then prove that one particular global minimum (called the maximal solution) belongs to a neighborhood of $\Phi^0$, on which the cost function is strongly convex under the condition ${\left\lVert f\right\rVert}_{\infty}=\mathcal{O}(d^{-1})$ with $d=\mathrm{deg}(f)$. Our result provides a partial explanation of the aforementioned success of optimization algorithms.
Gravity particle swarm optimization algorithm for solving shop visit balancing problem for repairable equipment

The particle swarm optimization (PSO) algorithm has received much attention from engineering and scientific fields since it was proposed. Nevertheless, when solving complex combinatorial optimization tasks such as the proposed shop visit balancing problem for repairable equipment (SVBPRE), the canonical PSO is still prone to fall into the local optimal stagnation. Therefore, a novel intelligent optimization algorithm, namely gravity particle swarm optimization (GPSO) algorithm, is proposed to remedy the above defects. This algorithm improves the velocity updating strategy of particles in the population, which can effectively improve the global search ability of the algorithm without increasing the time complexity, so that it can jump out of the local optimal position and find the feasible solution in the complex solution space. To verify its performance, many experimental verifications were carried out. Firstly, the effectiveness of the proposed GPSO was verified by comparing with the PSO on the 8 benchmark functions. Secondly, the superiority and advancement of GPSO were proved by comparing with 10 state-of-the-art original optimizers and variant algorithms on 23 benchmark tasks. Finally, based on the constructed shop visit balancing problem model, a series of simulation data cases were generated according to the operation and maintenance data in engineering practice for verification. The results obtained by comparing with 4 commonly used algorithms in engineering demonstrate that the proposed GPSO is superior to other competitors in terms of quality of solutions and has important theoretical significance and application value for solving practical tasks with complex search space.
Cypress

The growing popularity of the serverless platform has seen an increase in the number and variety of applications (apps) being deployed on it. The majority of these apps process user-provided input to produce the desired results. Existing work in the area of input-sensitive profiling has empirically shown that many such apps have input size-dependent execution times which can be determined through modelling techniques. Nevertheless, existing serverless resource management frameworks are agnostic to the input size-sensitive nature of these apps. We demonstrate in this paper that this can potentially lead to container over-provisioning and/or end-to-end Service Level Objective (SLO) violations. To address this, we propose Cypress, an input size-sensitive resource management framework, that minimizes the containers provisioned for apps, while ensuring a high degree of SLO compliance. We perform an extensive evaluation of Cypress on top of a Kubernetes-managed cluster using 5 apps from the AWS Serverless Application Repository and/or Open-FaaS Function Store with real-world traces and varied input size distributions. Our experimental results show that Cypress spawns up to 66% fewer containers, thereby, improving container utilization and saving cluster-wide energy by up to 2.95X and 23%, respectively, versus state-of-the-art frameworks, while remaining highly SLO-compliant (up to 99.99%).
Long-term Visual Tracking: Review and Experimental Comparison

As a fundamental task in computer vision, visual object tracking has received much attention in recent years. Most studies focus on short-term visual tracking which addresses shorter videos and always-visible targets. However, long-term visual tracking is much closer to practical applications with more complicated challenges. There exists a longer duration such as minute-level or even hour-level in the long-term tracking task, and the task also needs to handle more frequent target disappearance and reappearance. In this paper, we provide a thorough review of long-term tracking, summarizing long-term tracking algorithms from two perspectives: framework architectures and utilization of intermediate tracking results. Then we provide a detailed description of existing benchmarks and corresponding evaluation protocols. Furthermore, we conduct extensive experiments and analyse the performance of trackers on six benchmarks: VOTLT2018, VOTLT2019 (2020/2021), OxUvA, LaSOT, TLP and the long-term subset of VTUAV-V. Finally, we discuss the future prospects from multiple perspectives, including algorithm design and benchmark construction. To our knowledge, this is the first comprehensive survey for long-term visual object tracking. The relevant content is available at https://github.com/wangdong-dut/Long-term-Visual-Tracking .
Technology futures: Towards understanding how to design awe-inspiring futures

Future technology can inspire “awe”, the deep feeling of astonishment that can include fear, resulting in often “awe-some” but sometimes also “aw(e)-ful” experiences. We are interested in exploring the potential of future technology beyond any immediate obvious application domain and hence draw from prior work and our own design research (through three case studies) to propose the “technology futures” approach that uses awe as a driver for exploring future technology's potential. Guided by awe's need for sense-making, the approach suggests that experiencing future technology in alternative, playful ways and through a first-hand approach can allow for shared reflections, leading to a more informed view of future technology's potential. Our “technology futures” approach has three stages (envisioning, concretizing, and futuring) towards awe-inspiring futures. We illustrate these stages using three case studies: jogging with a quadcopter, ingestible play, and shape-changing furniture. Furthermore, we use these case studies to articulate a set of strategies to help those who want to engage with the technology futures approach. Ultimately, with our work, we aim to enhance our understanding of how HCI can engage with designing awe-inspiring futures to support reflections around possible futures.
Weight matrix sharing for multi-label learning

Multi-label learning on real-world data is a challenging task due to sparse labels, missing labels, and sparse structures. Some existing approaches are effective in addressing the former two issues. In this paper, we propose a shared weight matrix with low-rank and sparse regularization for multi-label learning (2SML) algorithm to address the issues simultaneously. First, two explicit correlation matrices are constructed from the feature matrix and label matrix. Second, we select informative labels by instance representativeness to learn implicit correlations. Third, a feature manifold and label manifold are employed to guide the shared weight learning process. Extensive experiments are undertaken on multiple benchmark datasets with and without missing labels. The results show that the proposed method outperforms the state-of-the-art methods.
Women political empowerment and vulnerability to climate change: evidence from 169 countries

The objective of this article is to analyze the effect of the political empowerment of women on vulnerability to climate change in 169 countries for the period 1995–2017. The empirical evidence which is based on panel fixed effects regressions shows that (i) the political empowerment of women as well as its components (i.e., civil liberties of women, participation of women in civil society and participation of women in political debates) reduce vulnerability to climate change. (ii) The underlying effect is most pronounced in upper middle income, Latin American, small and fragile countries. (iii) Public spending on education, the effectiveness of governance and education are the real transmission channels through which vulnerability to climate change is affected by women's political empowerment. The findings are robust to alternative estimation methods such as the Tobit, dynamic fixed effects, and generalized method of moments regressions. Policy implications are discussed, inter alia, the need for sampled countries to encourage women's political empowerment in order to reduce risks linked to climate change.
Four-channels optical chaos secure communications with the rate of 400 Gb/s using optical reservoir computing based on two quantum dot spin-VCSELs

In this work, we utilize four parallel reservoirs to model the chaotic dynamics of the output four polarization components (PCs) from a driving QD spin-VCSEL. Here, the four parallel reservoirs are implemented using the four PCs of a reservoir QD spin-VCSEL. High-quality chaos synchronizations of four pairs of PCs can be realized by using the four parallel reservoirs based on a reservoir QD spin-VCSEL. Under these high-quality synchronizations, we successfully implement four-channel secure communications with 4 × 100Gb/s 16 QAM messages under guaranteeing. We further discuss the performances of the bit error ratios ( B E R s ) for four decoding messages under different parameters. We show that all B E R s via different parameters are less than 7 × 10 −3 , denoting that high-quality data-transmissions can be potentially obtained in the system. Moreover, we prove that our proposed multi-channel optical chaotic communication scheme has the same level of security as the traditional schemes. Our findings show that the delay-based optical reservoir computing based on a QD spin-VCSEL provides an effective method for realization of multi-channel optical secure communication. • We propose to use four parallel optical reservoirs to precisely predict the complex chaotic dynamics of the four polarization components output by a driving QD spin-VCSEL, respectively. These four reservoirs are respectively realized by the chaotic four polarization components emitted by a reservoir QD spin-VCSEL. Here, the four polarization components are generated by the ground state and excited state of the driving QD spin-VCSEL or reservoir one. • Wideband chaos synchronization between a polarization component of the driving laser and its corresponding polarization component of the reservoir laser can be well achieved. • Under the high-quality synchronizations, we successfully implement four-channel secure communications with 4×100Gb/s 16 QAM messages under guaranteeing. • We further discuss the performances of the bit error ratios (BERs) for four decoding messages under different parameters. We show that all BERs via different parameters are less than 0.007, denoting that high-quality data-transmissions can be potentially obtained in the system. • We prove that our proposed multi-channel optical chaotic communication scheme has the same level of security as the traditional schemes. • Our findings show that the delay-based optical reservoir computing based on a QD spin-VCSEL provides an effective method for realization of multi-channel optical secure communication.
AsU-OSum: Aspect-augmented unsupervised opinion summarization

Opinion summarization can facilitate user’s decision-making by mining the salient review information. However, due to the lack of sufficient annotated data, most of the early works are based on extractive methods, which restricts the performance of opinion summarization. In this work, we aim to improve the informativeness of opinion summarization to provide better guidance to users. We consider the setting with only reviews without corresponding summaries, and propose an aspect-augmented model for unsupervised abstractive opinion summarization, denoted as AsU-OSum. We first employ an aspect-based sentiment analysis system to extract opinion phrases from reviews. Then, we construct a heterogeneous graph consisting of reviews and opinion clusters as nodes, which is used to enhance the Transformer-based encoder–decoder framework. Furthermore, we design a novel cascaded attention mechanism to prompt the decoder to pay more attention to the aspects that are more likely to appear in summary. During training, we introduce a sentiment accuracy reward that further enhances the learning ability of our model. We conduct comprehensive experiments on the Yelp , Amazon , and Rotten Tomatoes datasets. Automatic evaluation results show that our model is competitive and performs better than the state-of-the-art (SOTA) models on some ROUGE metrics. Human evaluation results further verify that our model can generate more informative summaries and reduce redundancy.
Guidance, Navigation and Control System for Multi-Robot Network in Monitoring and Inspection Operations

This work focuses on the challenges associated with autonomous robot guidance, navigation, and control in multi-robot systems. This study provides an affordable solution by utilizing a group of small unmanned ground vehicles and quadrotors that collaborate on monitoring and inspection missions. The proposed system utilizes a potential fields path planning algorithm to allow a robot to track a moving target while avoiding obstacles in a dynamic environment. To achieve the required performance and provide robust tracking against wind disturbances, a backstepping controller is used to solve the essential stability problem and ensure that each robot follows the specified path asymptotically. Furthermore, the performance is also compared with a proportional-integral-derivative (PID) controller to ensure the superiority of the control system. The system combines a low-cost inertial measurement unit (IMU), a GNSS receiver, and a barometer for UAVs to generate a navigation solution (position, velocity, and attitude estimations), which is then used in the guidance and control algorithms. A similar solution is used for UGVs by integrating the IMU, a GNSS receiver, and encoders. Non-linear complementary filters integrate the measurements in the navigation system to produce high bandwidth estimates of the state of each robotic platform. Experimental results of several scenarios are discussed to prove the effectiveness of the approach.
Using a Proximity-Detection Technology to Nudge for Physical Distancing in a Swedish Workplace During the COVID-19 Pandemic: Retrospective Case Study

The recent COVID-19 pandemic has contributed to the emergence of several technologies for infectious disease management. Although much focus has been placed on contact-tracing apps, another promising new tactic is proximity tracing, which focuses on health-related behavior and can be used for primary prevention. Underpinned by theories on behavioral design, a proximity-detection system can be devised that provides a user with immediate nudges to maintain physical distance from others. However, the practical feasibility of proximity detection during an infectious disease outbreak has not been sufficiently investigated.We aimed to evaluate the feasibility of using a wearable device to nudge for distance and to gather important insights about how functionality and interaction are experienced by users. The results of this study can guide future research and design efforts in this emerging technology.In this retrospective case study, a wearable proximity-detection technology was used in a workplace for 6 weeks during the production of a music competition. The purpose of the technology was to nudge users to maintain their physical distance using auditory feedback. We used a mixed methods sequential approach, including interviews (n=8) and a survey (n=30), to compile the experiences of using wearable technology in a real-life setting.We generated themes from qualitative analysis based on data from interviews and open-text survey responses. The quantitative data were subsequently integrated into these themes: feasibility (implementation and acceptance-establishing a shared problem; distance tags in context-strategy, environment, and activities; understanding and learning; and accomplishing the purpose) and design aspects (a purposefully annoying device; timing, tone, and proximity; and additional functions).This empirical study reports on the feasibility of using wearable technology based on proximity detection to nudge individuals to maintain physical distance in the workplace. The technology supports attention to distance, but the usability of this approach is dependent on the context and situation. In certain situations, the audio signal is frustrating, but most users agree that it needs to be annoying to ensure sufficient behavioral adaption. We proposed a dual nudge that involves vibration followed by sound. There are indications that the technology also facilitates learning how to maintain a greater distance from others, and that this behavior can persist beyond the context of technology use. This study demonstrates that the key value of this technology is that it places the user in control and enables immediate action when the distance to others is not maintained. This study provides insights into the emerging field of personal and wearable technologies used for primary prevention during infectious disease outbreaks. Future research is needed to evaluate the preventive effect on transmission and investigate behavioral changes in detail and in relation to different forms of feedback.
Deep learning for fully-automated nuclear pleomorphism scoring in breast cancer

To guide the choice of treatment, every new breast cancer is assessed for aggressiveness (i.e., graded) by an experienced histopathologist. Typically, this tumor grade consists of three components, one of which is the nuclear pleomorphism score (the extent of abnormalities in the overall appearance of tumor nuclei). The degree of nuclear pleomorphism is subjectively classified from 1 to 3, where a score of 1 most closely resembles epithelial cells of normal breast epithelium and 3 shows the greatest abnormalities. Establishing numerical criteria for grading nuclear pleomorphism is challenging, and inter-observer agreement is poor. Therefore, we studied the use of deep learning to develop fully automated nuclear pleomorphism scoring in breast cancer. The reference standard used for training the algorithm consisted of the collective knowledge of an international panel of 10 pathologists on a curated set of regions of interest covering the entire spectrum of tumor morphology in breast cancer. To fully exploit the information provided by the pathologists, a first-of-its-kind deep regression model was trained to yield a continuous scoring rather than limiting the pleomorphism scoring to the standard three-tiered system. Our approach preserves the continuum of nuclear pleomorphism without necessitating a large data set with explicit annotations of tumor nuclei. Once translated to the traditional system, our approach achieves top pathologist-level performance in multiple experiments on regions of interest and whole-slide images, compared to a panel of 10 and 4 pathologists, respectively.
Self-supervised monocular depth estimation based on combining convolution and multilayer perceptron

There are two mainstream approaches currently used for self-supervised monocular depth estimation. One option is to utilize a complete convolution method to construct the encoder and decoder; however, the local linear operation and the pooling method result in the loss of pixel information in each layer of the feature map, limiting the performance. Another way is to use the transformer and other methods for feature extraction on the encoder side, which are processed at a constant resolution at each stage and have a global receptive field. Therefore, more subtle depth features can be captured, and higher accuracy can be obtained. Unfortunately, the computational cost of self-attention is too large, which increases the memory overhead. With the comprehensive analysis of the advantages and disadvantages of the above two methods, this paper employs a combination of decomposed large kernel convolution and a multilayer perceptron (MLP) to design a new framework — CSMHNet (a hybrid of a Convolution, self-attention, and an MLP network). It cannot only compensate for the disadvantages of convolution static weights and locality but also significantly reduce the memory overhead compared to the transformer architecture while obtaining a more accurate and consistent depth. Experiments on the KITTI dataset demonstrate the effectiveness of our method, which significantly improves the depth prediction accuracy compared with other self-supervised methods.