Reviewerly/exHarmony huggingface dataset:
A review of video-based pig behavior recognition

Video surveillance technology has been applied in many farms and a large amount of video data has been generated. Extracting valuable information about the pig industry from video data is a challenging problem. Pig behavior recognition is one of the research hotspots. In order to facilitate scholars to systematically understand the research status and development trends of this field, the paper summarizes the research on pig segmentation, pig detection and pig behavior recognition. The applications of traditional image processing methods and deep learning methods are introduced. The recognition strategies for different kinds of behavior, such as postural behavior, locomotion behavior, area-related behavior and interactive behavior, are summarized. Finally, some of the existing challenges in the current research are highlighted and future research work envisioned to address these is proposed.
Weakly-Supervised Crowd Counting Learns from Sorting Rather Than Locations

In crowd counting datasets, the location labels are costly, yet, they are not taken into the evaluation metrics. Besides, existing multi-task approaches employ high-level tasks to improve counting accuracy. This research tendency increases the demand for more annotations. In this paper, we propose a weakly-supervised counting network, which directly regresses the crowd numbers without the location supervision. Moreover, we train the network to count by exploiting the relationship among the images. We propose a soft-label sorting network along with the counting network, which sorts the given images by their crowd numbers. The sorting network drives the shared backbone CNN model to obtain density-sensitive ability explicitly. Therefore, the proposed method improves the counting accuracy by utilizing the information hidden in crowd numbers, rather than learning from extra labels, such as locations and perspectives. We evaluate our proposed method on three crowd counting datasets, and the performance of our method plays favorably against the fully supervised state-of-the-art approaches.
Semidefinite Programming Converse Bounds for Quantum Communication

We derive several efficiently computable converse bounds for quantum communication over quantum channels in both the one-shot and asymptotic regime. First, we derive one-shot semidefinite programming (SDP) converse bounds on the amount of quantum information that can be transmitted over a single use of a quantum channel, which improve the previous bound from [Tomamichel/Berta/Renes, Nat. Commun. 7, 2016]. As applications, we study quantum communication over depolarizing channels and amplitude damping channels with finite resources. Second, we find an SDP-strong converse bound for the quantum capacity of an arbitrary quantum channel, which means the fidelity of any sequence of codes with a rate exceeding this bound will vanish exponentially fast as the number of channel uses increases. Furthermore, we prove that the SDP-strong converse bound improves the partial transposition bound introduced by Holevo and Werner. Third, we prove that this SDP strong converse bound is equal to the so-called max-Rains information, which is an analog to the Rains information introduced in [Tomamichel/Wilde/Winter, IEEE Trans. Inf. Theory 63:715, 2017]. Our SDP strong converse bound is weaker than the Rains information, but it is efficiently computable for general quantum channels.
Quantum correlations in separable multi-mode states and in classically entangled light

In this review we discuss intriguing properties of apparently classical optical fields, that go beyond purely classical context and allow us to speak about quantum characteristics of such fields and about their applications in quantum technologies. We briefly define the genuinely quantum concepts of entanglement and steering. We then move to the boarder line between classical and quantum world introducing quantum discord, a more general concept of quantum coherence, and finally a controversial notion of classical entanglement. To unveil the quantum aspects of often classically perceived systems, we focus more in detail on quantum discordant correlations between the light modes and on nonseparability properties of optical vector fields leading to entanglement between different degrees of freedom of a single beam. To illustrate the aptitude of different types of correlated systems to act as quantum or quantum-like resource, entanglement activation from discord, high-precision measurements with classical entanglement and quantum information tasks using intra-system correlations are discussed. The common themes behind the versatile quantum properties of seemingly classical light are coherence, polarization and inter and intra-mode quantum correlations.
URVOS: Unified Referring Video Object Segmentation Network with a Large-Scale Benchmark

We propose a unified referring video object segmentation network (URVOS). URVOS takes a video and a referring expression as inputs, and estimates the object masks referred by the given language expression in the whole video frames. Our algorithm addresses the challenging problem by performing language-based object segmentation and mask propagation jointly using a single deep neural network with a proper combination of two attention models. In addition, we construct the first large-scale referring video object segmentation dataset called Refer-Youtube-VOS. We evaluate our model on two benchmark datasets including ours and demonstrate the effectiveness of the proposed approach. The dataset is released at https://github.com/skynbe/Refer-Youtube-VOS .
The science of deep learning

Scientists today have completely different ideas of what machines can learn to do than we had only 10 y ago. In image processing, speech and video processing, machine vision, natural language processing, and classic two-player games, in particular, the state-of-the-art has been rapidly pushed forward over the last decade, as a series of machine-learning performance records were achieved for publicly organized challenge problems. In many of these challenges, the records now meet or exceed human performance level. A contest in 2010 proved that the Go-playing computer software of the day could not beat a strong human Go player. Today, in 2020, no one believes that human Go players—including human world champion Lee Sedol—can beat AlphaGo, a system constructed over the last decade. These new performance records, and the way they were achieved, obliterate the expectations of 10 y ago. At that time, human-level performance seemed a long way off and, for many, it seemed that no technologies then available would be able to deliver such performance. Systems like AlphaGo benefited in this last decade from a completely unanticipated simultaneous expansion on several fronts. On the one hand, we saw the unprecedented availability of on-demand scalable computing power in the form of cloud computing, and on the other hand, a massive industrial investment in assembling human engineering teams from a globalized talent pool by some of the largest global technology players. These resources were steadily deployed over that decade to allow rapid expansions in challenge problem performance. The 2010s produced a true technology explosion, a one-time–only transition: The sudden public availability of massive image and text data. Billions of people posted trillions of images and documents on social media, as the phrase “Big Data” entered media awareness. Image processing and natural language processing were forever changed by this new data resource … [↵][1]1To whom correspondence may be addressed. Email: donoho{at}stanford.edu. [1]: #xref-corresp-1-1
DE-RRD: A Knowledge Distillation Framework for Recommender System

Recent recommender systems have started to employ knowledge distillation, which is a model compression technique distilling knowledge from a cumbersome model (teacher) to a compact model (student), to reduce inference latency while maintaining performance. The state-of-the-art methods have only focused on making the student model to accurately imitate the predictions of the teacher model. They have a limitation in that the prediction results incompletely reveal the teacher's knowledge. In this paper, we propose a novel knowledge distillation framework for recommender system, called DE-RRD, which enables the student model to learn from the latent knowledge encoded in the teacher model as well as from the teacher's predictions. Concretely, DE-RRD consists of two methods: 1) Distillation Experts (DE) that directly transfers the latent knowledge from the teacher model. DE exploits "experts" and a novel expert selection strategy for effectively distilling the vast teacher's knowledge to the student with limited capacity. 2) Relaxed Ranking Distillation (RRD) that transfers the knowledge revealed from the teacher's prediction with consideration of the relaxed ranking orders among items. Our extensive experiments show that DE-RRD outperforms the state-of-the-art competitors and achieves comparable or even better performance to that of the teacher model with faster inference time.
PFA-GAN: Progressive Face Aging With Generative Adversarial Network

Face aging is to render a given face to predict its future appearance, which plays an important role in the information forensics and security field as the appearance of the face typically varies with age. Although impressive results have been achieved with conditional generative adversarial networks (cGANs), the existing cGANs-based methods typically use a single network to learn various aging effects between any two different age groups. However, they cannot simultaneously meet three essential requirements of face aging -- including image quality, aging accuracy, and identity preservation -- and usually generate aged faces with strong ghost artifacts when the age gap becomes large. Inspired by the fact that faces gradually age over time, this paper proposes a novel progressive face aging framework based on generative adversarial network (PFA-GAN) to mitigate these issues. Unlike the existing cGANs-based methods, the proposed framework contains several sub-networks to mimic the face aging process from young to old, each of which only learns some specific aging effects between two adjacent age groups. The proposed framework can be trained in an end-to-end manner to eliminate accumulative artifacts and blurriness. Moreover, this paper introduces an age estimation loss to take into account the age distribution for an improved aging accuracy, and proposes to use the Pearson correlation coefficient as an evaluation metric measuring the aging smoothness for face aging methods. Extensively experimental results demonstrate superior performance over existing (c)GANs-based methods, including the state-of-the-art one, on two benchmarked datasets. The source code is available at~\url{https://github.com/Hzzone/PFA-GAN}.
The past, present and future of digital contact tracing

The potential of digital contact tracing to slow the spread of a virus had been quietly explored for over a decade before the COVID-19 pandemic thrust the technology into the spotlight. But can it actually be effective in the hard-to-model complexity of real-world social networks?
Management of ICCT underlying Technologies used for Digital Service Innovation

Information Communication and Computation Technology (ICCT) also called as Digital Technology and is considered as a general purpose universal technology due to its ability to solve many problems in the human society related to basic needs, advanced wants, and dreamy desires. In this chapter, initially, we have identified various quality attributes of Digital Service and some prominent published works in digital service innovation. The important underlying technologies of ICCT which are emerging as technologies of 21st century including Artificial intelligence&amp; robotics, Big data &amp; business analytics, Cloud computing&amp; storage, Digital marketing, 3D printing, Internet of Things, Online ubiquitous education, Quantum computing, Information storage technology, and Virtual &amp; Augmented Reality are considered for possible innovations in such industries. The applications of ICCT underlying technologies in some of the prominent service industry sectors are identified and the management of ICCT underlying technology usage strategies for digital service innovation in tertiary sector industries are analysed.
Design Pattern as a Service for Blockchain-Based Self-Sovereign Identity

Self-sovereign identity (SSI) is considered to be a "killer application" of blockchain.However, there is a lack of systematic architecture designs for blockchain-based SSI systems to support methodical development.An aspect of such gap is demonstrated in current solutions, which are considered coarse grained and may increase data security risks.In this paper, we first identify the lifecycles of three major SSI objects (i.e., key, identifier, and credential) and present fine-grained design patterns critical for application development.These patterns are associated with particular state transitions, providing a systematic view of system interactions and serving as a guidance for effective use of these patterns.Further, we present an SSI platform architecture, which advocates the notion of Design-Pattern-as-a-Service.Each design pattern serves as an API by wrapping the respective pattern code to ease application development and improve scalability and security.We implement a prototype and evaluate it on feasibility and scalability.
Spooky action at a global distance: analysis of space-based entanglement distribution for the quantum internet

Recent experimental breakthroughs in satellite quantum communications have opened up the possibility of creating a global quantum internet using satellite links. This approach appears to be particularly viable in the near term, due to the lower attenuation of optical signals from satellite to ground, and due to the currently short coherence times of quantum memories. The latter prevents ground-based entanglement distribution using atmospheric or optical-fiber links at high rates over long distances. In this work, we propose a global-scale quantum internet consisting of a constellation of orbiting satellites that provides a continuous, on-demand entanglement distribution service to ground stations. The satellites can also function as untrusted nodes for the purpose of long-distance quantum-key distribution. We develop a technique for determining optimal satellite configurations with continuous coverage that balances both the total number of satellites and entanglement-distribution rates. Using this technique, we determine various optimal satellite configurations for a polar-orbit constellation, and we analyze the resulting satellite-to-ground loss and achievable entanglement-distribution rates for multiple ground station configurations. We also provide a comparison between these entanglement-distribution rates and the rates of ground-based quantum repeater schemes. Overall, our work provides the theoretical tools and the experimental guidance needed to make a satellite-based global quantum internet a reality.
Adoption of clinical risk prediction tools is limited by a lack of integration with electronic health records

Prognostic risk prediction models aim to estimate the risk of a future outcome based on available clinical parameters.[1][1] There has been an increase in the development of such models given the move towards personalised and precision medicine, since they provide individualised risks for patients.[
Fake It Till You Make It: Guidelines for Effective Synthetic Data Generation

Synthetic data provides a privacy protecting mechanism for the broad usage and sharing of healthcare data for secondary purposes. It is considered a safe approach for the sharing of sensitive data as it generates an artificial dataset that contains no identifiable information. Synthetic data is increasing in popularity with multiple synthetic data generators developed in the past decade, yet its utility is still a subject of research. This paper is concerned with evaluating the effect of various synthetic data generation and usage settings on the utility of the generated synthetic data and its derived models. Specifically, we investigate (i) the effect of data pre-processing on the utility of the synthetic data generated, (ii) whether tuning should be applied to the synthetic datasets when generating supervised machine learning models, and (iii) whether sharing preliminary machine learning results can improve the synthetic data models. Lastly, (iv) we investigate whether one utility measure (Propensity score) can predict the accuracy of the machine learning models generated from the synthetic data when employed in real life. We use two popular measures of synthetic data utility, propensity score and classification accuracy, to compare the different settings. We adopt a recent mechanism for the calculation of propensity, which looks carefully into the choice of model for the propensity score calculation. Accordingly, this paper takes a new direction with investigating the effect of various data generation and usage settings on the quality of the generated data and its ensuing models. The goal is to inform on the best strategies to follow when generating and using synthetic data.
Vehicle Detection from Aerial Images Using Deep Learning: A Comparative Study

This paper addresses the problem of car detection from aerial images using Convolutional Neural Networks (CNNs). This problem presents additional challenges as compared to car (or any object) detection from ground images because the features of vehicles from aerial images are more difficult to discern. To investigate this issue, we assess the performance of three state-of-the-art CNN algorithms, namely Faster R-CNN, which is the most popular region-based algorithm, as well as YOLOv3 and YOLOv4, which are known to be the fastest detection algorithms. We analyze two datasets with different characteristics to check the impact of various factors, such as the UAV’s (unmanned aerial vehicle) altitude, camera resolution, and object size. A total of 52 training experiments were conducted to account for the effect of different hyperparameter values. The objective of this work is to conduct the most robust and exhaustive comparison between these three cutting-edge algorithms on the specific domain of aerial images. By using a variety of metrics, we show that the difference between YOLOv4 and YOLOv3 on the two datasets is statistically insignificant in terms of Average Precision (AP) (contrary to what was obtained on the COCO dataset). However, both of them yield markedly better performance than Faster R-CNN in most configurations. The only exception is that both of them exhibit a lower recall when object sizes and scales in the testing dataset differ largely from those in the training dataset.
Extractive Opinion Summarization in Quantized Transformer Spaces

Abstract We present the Quantized Transformer (QT), an unsupervised system for extractive opinion summarization. QT is inspired by Vector- Quantized Variational Autoencoders, which we repurpose for popularity-driven summarization. It uses a clustering interpretation of the quantized space and a novel extraction algorithm to discover popular opinions among hundreds of reviews, a significant step towards opinion summarization of practical scope. In addition, QT enables controllable summarization without further training, by utilizing properties of the quantized space to extract aspect-specific summaries. We also make publicly available Space, a large-scale evaluation benchmark for opinion summarizers, comprising general and aspect-specific summaries for 50 hotels. Experiments demonstrate the promise of our approach, which is validated by human studies where judges showed clear preference for our method over competitive baselines.
Efficient Chaos-Based Substitution-Box and Its Application to Image Encryption

Chaotic systems are vital in designing contemporary cryptographic systems. This study proposes an innovative method for constructing an effective substitution box using a 3-dimensional chaotic map. Moreover, bouyed by the efficiency of the proposed chaos-based substitution boxes’ effectiveness, we introduce a new chaos-based image cryptosystem that combines the adeptness of Gray codes, a non-linear and sensitive hyper-chaotic system, and the proposed S-box. The generated secret key emanating from the cryptosystem is correlated to the input image to produce a unique key for each image. Extensive experimental outcomes demonstrate the utility, effectiveness, and high performance of the resulting cryptosystem.
Implementation of a 46-node quantum metropolitan area network

Quantum key distribution (QKD) enables secure key exchanges between two remote users. The ultimate goal of secure communication is to establish a global quantum network. The existing field tests suggest that quantum networks are feasible. To achieve a practical quantum network, we need to overcome several challenges, including realising versatile topologies for large scales, simple network maintenance, extendable configuration, and robustness to node failures. To this end, we present a field operation of a quantum metropolitan-area network with 46 nodes and show that all these challenges can be overcome with cutting-edge quantum technologies. In particular, we realise different topological structures and continuously run the network for 31 months, by employing standard equipment for network maintenance with an extendable configuration. We realise QKD pairing and key management with a sophisticated key control center. In this implementation, the final keys have been used for secure communication such as real-time voice telephone, text messaging, and file transmission with one-time pad encryption, which can support 11 pairs of users to make audio calls simultaneously. Combined with inter-city quantum backbone and ground-satellite links, our metropolitan implementation paves the way toward a global quantum network.
Perspective on traveling wave microwave parametric amplifiers

Quantum-limited microwave parametric amplifiers are genuine key pillars for rising quantum technologies and in general for applications that rely on the successful readout of weak microwave signals by adding only the minimum amount of noise allowed by quantum mechanics. In this perspective, after providing a brief overview on the different families of parametric microwave amplifiers, we focus on traveling wave parametric amplifiers (TWPAs), underlining the key achievements of the last years and the present open challenges. We discuss also possible new research directions beyond amplification such as exploring these devices as a platform for multi-mode entanglement generation and for the development of single photon detectors.
Loss aware post-training quantization

Neural network quantization enables the deployment of large models on resource-constrained devices. Current post-training quantization methods fall short in terms of accuracy for INT4 (or lower) but provide reasonable accuracy for INT8 (or above). In this work, we study the effect of quantization on the structure of the loss landscape. We show that the structure is flat and separable for mild quantization, enabling straightforward post-training quantization methods to achieve good results. We show that with more aggressive quantization, the loss landscape becomes highly non-separable with steep curvature, making the selection of quantization parameters more challenging. Armed with this understanding, we design a method that quantizes the layer parameters jointly, enabling significant accuracy improvement over current post-training quantization methods. Reference implementation is available at https://github.com/ynahshan/nn-quantization-pytorch/tree/master/lapq .