Reviewerly/exHarmony huggingface dataset:
Close-set camera style distribution alignment for single camera person re-identification

The purpose of person re-identification (ReID) is to find the same person under different cameras and the basic difficulty lies in the need for large amounts of cross-camera pedestrian annotations. In reality, annotating cross-camera pedestrians is time-consuming especially in large-scale surveillance camera networks. This paper focuses on addressing the ReID problem under single-camera training (SCT) setting, where each person of the training set only appears in one camera. Due to the lack of cross-camera pedestrian annotations, it is difficult to effectively eliminate the camera style interference by narrowing the distance between the image features of the same person. To address this problem, we propose a close-set camera style distribution alignment (C2SDA) framework for SCT ReID. In order to reduce the camera-style interference from both instance- and distribution-levels simultaneously, we first design an instance-distribution camera style alignment module that directly aligns the feature distribution of input images under each camera and then trains the model at instance level with the aligned features. Secondly, we further design an augment close-set camera style distribution module that transforms the camera feature distribution alignment problem from open-set into close-set one, which preserving the discriminative ability of features during the alignment process. Experimental results verify that our framework can significantly improve the ReID performance under SCT setting and surpass the current SOTA methods. The source code is available at https://github.com/HongweiZhang97/CCSDA.
DIAG: A Deep Interaction-Attribute-Generation model for user-generated item recommendation

Most existing recommendation methods assume that all the items are provided by separate producers rather than users. However, it could be inappropriate in some recommendation tasks since users may generate some items. Considering the user–item generation relation may benefit recommender systems that only use implicit user–item interactions. However, it may suffer from a dramatic imbalance. The number of user–item generation relations may be far smaller than the number of user–item interactions because each item is generated by at most one user. At the same time, this item can be interacted with by many users. To overcome the challenging imbalance issue, we propose a novel Deep Interaction-Attribute-Generation (DIAG) model. It integrates the user–item interaction relation, the user–item generation relation, and the item attribute information into one deep learning framework. The novelty lies in the design of a new item–item co-generation network for modeling the user–item generation information. Then, graph attention network is adopted to learn the item feature vectors from the user–item generations and the item attribute information by considering the adaptive impact of one item on its co-generated items. Extensive experiments conducted on two real-world datasets confirm the superiority of the DIAG method.
Time-periodic interaction between a spin-pair: A quantum master equation approach

Quantum master equations describe the dynamics of open quantum systems. Such systems might be subjected to external drives and internal interactions such as direct dipolar couplings. Both the external drives and the internal interactions could be oscillatory functions of time. In particular, a spin pair undergoing magic angle sample spinning experiences such oscillatory interactions in time. In the present work, we analyze a spin pair with a time-periodic interaction by using our recently-proposed fluctuation-regulated quantum master equation [PRA, 97, 063837 (2018)] (FRQME). FRQME helps calculate the first as well as the second-order effects of all time-dependent interactions Hamiltonians. Using FRQME, we find the usual sideband pattern and also have shown the linewidths of the sidebands as a function of the coupling strength, the period of oscillation, and an environmental parameter. We show that at high spinning speed, the sidebands would have a Lorentzian shape. We predict that the dipolar contributions to the sidebands' linewidths are smaller for a shorter correlation time of the environmental fluctuations.
Distilled Neural Networks for Efficient Learning to Rank

Recent studies in Learning to Rank have shown the possibility to effectively distill a neural network from an ensemble of regression trees. This result leads neural networks to become a natural competitor of tree-based ensembles on the ranking task. Nevertheless, ensembles of regression trees outperform neural models both in terms of efficiency and effectiveness, particularly when scoring on CPU. In this paper, we propose an approach for speeding up neural scoring time by applying a combination of Distillation, Pruning and Fast Matrix multiplication. We employ knowledge distillation to learn shallow neural networks from an ensemble of regression trees. Then, we exploit an efficiency-oriented pruning technique that performs a sparsification of the most computationally-intensive layers of the neural network that is then scored with optimized sparse matrix multiplication. Moreover, by studying both dense and sparse high performance matrix multiplication, we develop a scoring time prediction model which helps in devising neural network architectures that match the desired efficiency requirements. Comprehensive experiments on two public learning-to-rank datasets show that neural networks produced with our novel approach are competitive at any point of the effectiveness-efficiency trade-off when compared with tree-based ensembles, providing up to 4x scoring time speed-up without affecting the ranking quality.
Event-Based Kilohertz Eye Tracking using Coded Differential Lighting

Pixels in an event camera operate asynchronously and independently, reporting changes in intensity as events - tuples of (x,y) position, polarity s and timestamp t at microsecond resolution. Event cameras operate at low power (≈ 5mW) and respond to changes in the scene with a latency on the order of microseconds. These properties make event cameras an exciting candidate for eye tracking sensors on mobile platforms such as Augmented/Virtual Reality (AR/VR) headsets, since these systems have hard real-time and power constraints. One proven method for eye tracking and gaze estimation is corneal glint detection. We exploit the fact that corneal glint tracking only requires a sparse set of pixels in the image, by making use of the natural sparsity of event cameras, which only detect changes in the scene. To enhance this effect, we design an illumination scheme, Coded Differential Lighting, which enhances specular reflections, suppresses all other events, and solves the light-to-glint correspondence. This is the first purely event-based corneal glint detection and tracking algorithm, which operates on standard hardware at kHz sampling rate.
Compensation Tracker: Reprocessing Lost Object for Multi-Object Tracking

Tracking by detection paradigm is one of the most popular object tracking methods. However, it is very dependent on the performance of the detector. When the detector has a behavior of missing detection, the tracking result will be directly affected. In this paper, we analyze the phenomenon of the lost tracking object in real-time tracking model on MOT2020 dataset. Based on simple and traditional methods, we propose a compensation tracker to further alleviate the lost tracking problem caused by missing detection. It consists of a motion compensation module and an object selection module. The proposed method not only can re-track missing tracking objects from lost objects, but also does not require additional networks so as to maintain speed-accuracy trade-off of the real-time model. Our method only needs to be embedded into the tracker to work without retraining the network. Experiments show that the compensation tracker can efficaciously improve the performance of the model and reduce identity switches. With limited costs, the compensation tracker successfully enhances the baseline tracking performance by a large margin and reaches 66% of MOTA and 67% of IDF1 on MOT2020 dataset.
How to Protect Ourselves From Overlapping Community Detection in Social Networks

In recent years, overlapping community detection algorithms have been paid more and more attention, which not only reveal the real social relations, but also expose the possible communication channels between communities. Those individuals (or people) in the overlapping area are very important to the communities that can promote communication between two or more communities. On the other hand, from the privacy perspective, some people may not want to be found out in the overlapping areas. With this in mind, we raise a question “Can individuals modify their relationships to avoid the community discovery algorithms locating them into overlapping areas?” If this problem could be solved, these people may not need to worry about being disturbed. In particular, we first give three heuristic hiding strategies, i.e., Random Hiding(RH), Based Degree Hiding(DH) and Betweenness Hiding(BH), as comparison, utilizing the randomly the node, information of node degree and node betweenness centrality, respectively. And then, we propose a novel hiding algorithm by exploiting the importance degree of nodes in communities based on which the corresponding social connections are added or deleted called name <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BIH</i> . Through extensive experiments, we show the effectiveness of the proposed algorithm in moving out a target node from overlapped areas.
Sustainable Management and Policies: The Roles of Stakeholders in the Practice of Inclusive Education in Digital Transformation

Children are a country’s most valuable asset. Educating them and bringing them into society is one of the most fundamental tasks of a nation. The disadvantageous position that individuals with a disability are exposed to shows the importance of the need for inclusive education for the quality of life. It is important to use the merits of digital transformation to provide services and education for all to support sustainability. This research aims to shine a light on the problems related to inclusive education faced in the special education schools and rehabilitation centers and put forward suggestions for digital transformation. As inclusive means inclusion, social interaction, access, and equality for education, the role of digital transformation is examined for the quality of inclusive education. Self-reflection is a qualitative form which was employed in the research involving school managers and teachers who were selected randomly from the Special Education Schools and Rehabilitation Centers that participated in the research. It is revealed that most of the studies focus on inclusive education, the digital divide, social inclusion, online learning, and the implication of inclusive education in digital transformation. Although studies expose inclusive education in digital transformation, most of these have 0.20 quality criteria, showing an intensified need to extend the existing body of knowledge. It is shown that the vast majority of inclusive education aligns with the institution’s inclusive education vision and purpose. Concerning financing, it is difficult to find sponsors among non-governmental organizations to support the institution’s inclusive education-related material, equipment, and security needs, and Ministry of National Education funding is essential. There is a need to improve the inclusive education competencies of administrators and teachers, and in-service training should be provided. Self-improvement opportunities of administrators and teachers of the institution are significantly inadequate and should be improved.
Quantum Technologies for Engineering: the materials challenge

Abstract The materials challenge is often a major hurdle for translating good ideas in science into technologies. This is no different in the arena of quantum technologies which has seen a resurgence of interest in the last decade. This perspective provides a unique insight into the recent collaborative works by research groups in Singapore to surmount key quantum materials and processing bottlenecks that have impeded quantum technologies in the areas of sensing, computing, and communications. We highlight recent important materials related breakthroughs that have made possible novel advancements such as integrated ion traps, light frequency conversion, highly efficient cryogenic contacts to atomically thin quantum devices, and gate defined quantum dots, to name just a few. We also discuss the potential applications and conclude with our perspective on the remaining challenges to be addressed and the prospects enabled by these materials advances for future collaborations and co-developments to advance quantum technologies.
Self-Supervised Multi-Label Transformation Prediction for Video Representation Learning

Self-supervised learning is a promising paradigm to address the problem of manual-annotation through effectively leveraging unlabeled videos. By solving self-supervised pretext tasks, powerful video representations can be discovered automatically. However, recent pretext tasks for videos rely on utilizing the temporal properties of videos, ignoring the crucial supervisory signals from the spatial subspace of videos. Therefore, we present a new self-supervised pretext task called Multi-Label Transformation Prediction (MLTP) to sufficiently utilize the spatiotemporal information in videos. In MLTP, all videos are jointly transformed by a set of geometric and color-space transformations, such as rotation, cropping, and color-channel split. We formulate the pretext as a multi-label prediction task. The 3D-CNN is trained to predict a composition of underlying transformations as multiple outputs. Thereby, transformation invariant video features can be learned in a self-supervised manner. Experimental results verify that 3D-CNNs pre-trained using MLTP yield video representations with improved generalization performance for action recognition downstream tasks on UCF101 ([Formula: see text]) and HMDB51 ([Formula: see text]) datasets.
One size does not fit all: security hardening of MIPS embedded systems via static binary debloating for shared libraries

Embedded systems have become prominent targets for cyberattacks. To exploit firmware’s memory corruption vulnerabilities, cybercriminals harvest reusable code gadgets from the large shared library codebase (e.g., uClibc). Unfortunately, unlike their desktop counterparts, embedded systems lack essential computing resources to enforce security hardening techniques. Recently, we have witnessed a surge of software debloating as a new defense mechanism against code-reuse attacks; it erases unused code to significantly diminish the possibilities of constructing reusable gadgets. Because of the single firmware image update style, static library debloating shows promise to fortify embedded systems without compromising performance and forward compatibility. However, static library debloating on stripped binaries (e.g., firmware’s shared libraries) is still an enormous challenge. In this paper, we show that this challenge is not insurmountable for MIPS firmware. We develop a novel system, named uTrimmer, to identify and wipe out unused basic blocks from shared libraries’ binary code, without causing additional runtime overhead or memory consumption. We propose a new method to identify address-taken blocks/functions, which further help us maintain an inter-procedural control flow graph to conservatively include library code that could be potentially used by firmware. By capturing address access patterns for position-independent code, we circumvent the challenge of determining code-pointer targets and safely eliminate unused code. We run uTrimmer to debloat shared libraries for SPEC CPU2017 benchmarks, popular firmware applications (e.g., Apache, BusyBox, and OpenSSL), and a real-world wireless router firmware image. Our experiments show that not only does uTrimmer deliver functional programs, but also it can cut the exposed code surface and eliminate various reusable code gadgets remarkably. uTrimmer’s debloating capability can compete with the static linking results.
Analysis of Computer Vision Applied in Martial Arts

Computer vision has become an interdisciplinary subject integrating digital image processing, machine learning, computer science, and many other categories. It can provide contactless data collection and technical analysis for many sports. A lot of work in the field of martial arts benefits from computer vision. This paper reviews the applications of computer vision in martial arts. These applications include action recognition, pose estimation, intelligence judgment, etc., which bring more scientific training, more effective technical analysis and wider spread to martial arts. The investigation in this paper inspires future work on the basis of reviewing the existing research.
On Sampling Collaborative Filtering Datasets

We study the practical consequences of dataset sampling strategies on the ranking performance of recommendation algorithms. Recommender systems are generally trained and evaluated on samples of larger datasets. Samples are often taken in a naive or ad-hoc fashion: e.g. by sampling a dataset randomly or by selecting users or items with many interactions. As we demonstrate, commonly-used data sampling schemes can have significant consequences on algorithm performance. Following this observation, this paper makes three main contributions: (1) characterizing the effect of sampling on algorithm performance, in terms of algorithm and dataset characteristics (e.g. sparsity characteristics, sequential dynamics, etc.); (2) designing SVP-CF, which is a data-specific sampling strategy, that aims to preserve the relative performance of models after sampling, and is especially suited to long-tailed interaction data; and (3) developing an oracle, Data-Genie, which can suggest the sampling scheme that is most likely to preserve model performance for a given dataset. The main benefit of Data-Genie is that it will allow recommender system practitioners to quickly prototype and compare various approaches, while remaining confident that algorithm performance will be preserved, once the algorithm is retrained and deployed on the complete data. Detailed experiments show that using Data-Genie, we can discard upto 5x more data than any sampling strategy with the same level of performance.
The Application of Virtual Reality Technology in Interior Design Education: A Case Study Exploring Learner Acceptance

The prototype tools widely used in interior design education are doubted in simulating design outcomes, since students lack the necessary experience to transform the tools into the real interior space accurately. VR (virtual reality) is considered a possible prototype tool for interior design learners. This research has developed a technical approach that uses VR as a prototype tool in interior design education. An experimental study was conducted in an interior design course, focusing on learners' acceptance of the VR prototype and the factors that influence learners' acceptance. A survey administered to students indicates that perceived usefulness (PU) and perceived ease of use (PEOU) are the key factors affecting learners' acceptance, and features of VR such as Interaction and Immersion are mediated through PU and PEOU. The results could provide a reference for the interior design curriculum related to VR learning.
HiKonv: High Throughput Quantized Convolution With Novel Bit-wise Management and Computation

Quantization for Convolutional Neural Network (CNN) has shown significant progress with the intention of reducing the cost of computation and storage with low-bitwidth data inputs. There are, however, no systematic studies on how an existing full-bitwidth processing unit, such as CPUs and DSPs, can be better utilized to carry out significantly higher computation throughput for convolution under various quantized bitwidths. In this study, we propose HiKonv, a unified solution that maximizes the compute throughput of a given underlying processing unit to process low-bitwidth quantized data inputs through novel bitwise parallel computation. We establish theoretical performance bounds using a full-bitwidth multiplier for highly parallelized low-bitwidth convolution, and demonstrate new breakthroughs for high-performance computing in this critical domain. For example, a single 32-bit processing unit can deliver 128 binarized convolution operations (multiplications and additions) under one CPU instruction, and a single <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$27\times 18$</tex> DSP core can deliver eight convolution operations with 4-bit inputs in one cycle. We demonstrate the effectiveness of HiKonv on CPU and FPGA for both convolutional layers or a complete DNN model. For a convolutional layer quantized to 4-bit, HiKonv achieves a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$3.17\times$</tex> latency improvement over the baseline implementation using C++ on CPU. Compared to the DAC-SDC 2020 champion model for FPGA, HiKonv achieves a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2.37\times$</tex> : throughput improvement and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2.61\times$</tex> DSP efficiency improvement, respectively.
Robust Semisupervised Federated Learning for Images Automatic Recognition in Internet of Drones

Air access networks have been recognized as a significant driver of various Internet of Things (IoT) services and applications. In particular, the aerial computing network infrastructure centered on the Internet of Drones has set off a new revolution in automatic image recognition. This emerging technology relies on sharing ground-truth-labeled data between unmanned aerial vehicle (UAV) swarms to train a high-quality automatic image recognition model. However, such an approach will bring data privacy and data availability challenges. To address these issues, we first present a semisupervised federated learning (SSFL) framework for privacy-preserving UAV image recognition. Specifically, we propose a model parameter mixing strategy to improve the naive combination of federated learning and semisupervised learning methods under two realistic scenarios (labels-at-client and labels-at-server), which is referred to as federated mixing (FedMix). Furthermore, there are significant differences in the number, features, and distribution of local data collected by UAVs using different camera modules in different environments, i.e., statistical heterogeneity. To alleviate the statistical heterogeneity problem, we propose an aggregation rule based on the frequency of the client’s participation in training, namely, the FedFreq aggregation rule, which can adjust the weight of the corresponding local model according to its frequency. Numerical results demonstrate that the performance of our proposed method is significantly better than those of the current baseline and is robust to different non-independent and identically distributed(IID) levels of client data.