Reviewerly/exHarmony huggingface dataset:
Region NMS-based deep network for gigapixel level pedestrian detection with two-step cropping

Pedestrian detection has emerged as a fundamental technology for autonomous cars, robotics, pedestrian search, and other applications. Although a lot of progress has been made in pedestrian detection, it is still a challenging problem in wide field-of-view surveillance videos due to the random distribution and dynamic characteristics of pedestrians. Especially in large-scale high-resolution images, there are many pedestrians and vast scale variance, so it is difficult to accurately detect all pedestrians. In order to solve this problem, sliding window is used to crop all original images to obtain pre-detection results firstly in this paper. Then, the original images are cropped again with the object as the center utilizing the label files shared in the same scene to get multi-scale images. Finally, a region NMS algorithm, a fusion strategy about the results of small images mapped into large images, is proposed to remove the redundant shredded boxes caused by cropping image. We verify the effectiveness of the proposed method with Faster R-CNN, Cascade R-CNN, IterDet and Scale-aware Fast R-CNN models on PANDA dataset.
Quantum efficiency, purity and stability of a tunable, narrowband microwave single-photon source

Abstract We demonstrate an on-demand source of microwave single photons with 71–99% intrinsic quantum efficiency. The source is narrowband (300 kHz) and tuneable over a 600 MHz range around 5.2 GHz. Such a device is an important element in numerous quantum technologies and applications. The device consists of a superconducting transmon qubit coupled to the open end of a transmission line. A π -pulse excites the qubit, which subsequently rapidly emits a single photon into the transmission line. A cancellation pulse then suppresses the reflected π -pulse by 33.5 dB, resulting in 0.005 photons leaking into the photon emission channel. We verify strong antibunching of the emitted photon field and determine its Wigner function. Non-radiative decay and 1/ f flux noise both affect the quantum efficiency. We also study the device stability over time and identify uncorrelated discrete jumps of the pure dephasing rate at different qubit frequencies on a time scale of hours, which we attribute to independent two-level system defects in the device dielectrics, dispersively coupled to the qubit. Our single-photon source with only one input port is more compact and scalable compared to standard implementations.
Narrative event segmentation in the cortical reservoir

Recent research has revealed that during continuous perception of movies or stories, humans display cortical activity patterns that reveal hierarchical segmentation of event structure. Thus, sensory areas like auditory cortex display high frequency segmentation related to the stimulus, while semantic areas like posterior middle cortex display a lower frequency segmentation related to transitions between events. These hierarchical levels of segmentation are associated with different time constants for processing. Likewise, when two groups of participants heard the same sentence in a narrative, preceded by different contexts, neural responses for the groups were initially different and then gradually aligned. The time constant for alignment followed the segmentation hierarchy: sensory cortices aligned most quickly, followed by mid-level regions, while some higher-order cortical regions took more than 10 seconds to align. These hierarchical segmentation phenomena can be considered in the context of processing related to comprehension. In a recently described model of discourse comprehension word meanings are modeled by a language model pre-trained on a billion word corpus. During discourse comprehension, word meanings are continuously integrated in a recurrent cortical network. The model demonstrates novel discourse and inference processing, in part because of two fundamental characteristics: real-world event semantics are represented in the word embeddings, and these are integrated in a reservoir network which has an inherent gradient of functional time constants due to the recurrent connections. Here we demonstrate how this model displays hierarchical narrative event segmentation properties beyond the embeddings alone, or their linear integration. The reservoir produces activation patterns that are segmented by a hidden Markov model (HMM) in a manner that is comparable to that of humans. Context construction displays a continuum of time constants across reservoir neuron subsets, while context forgetting has a fixed time constant across these subsets. Importantly, virtual areas formed by subgroups of reservoir neurons with faster time constants segmented with shorter events, while those with longer time constants preferred longer events. This neurocomputational recurrent neural network simulates narrative event processing as revealed by the fMRI event segmentation algorithm provides a novel explanation of the asymmetry in narrative forgetting and construction. The model extends the characterization of online integration processes in discourse to more extended narrative, and demonstrates how reservoir computing provides a useful model of cortical processing of narrative structure.
A new nonparametric interpoint distance-based measure for assessment of clustering

A new interpoint distance-based measure is proposed to identify the optimal number of clusters present in a data set. Designed in nonparametric approach, it is independent of the distribution of given data. Interpoint distances between the data members make our cluster validity index applicable to univariate and multivariate data measured on arbitrary scales, or having observations in any dimensional space where the number of study variables can be even larger than the sample size. Our proposed criterion is compatible with any clustering algorithm, and can be used to determine the unknown number of clusters or to assess the quality of the resulting clusters for a data set. Demonstration through synthetic and real-life data establishes its superiority over the well-known clustering accuracy measures of the literature.
Exposure Trajectory Recovery From Motion Blur

Motion blur in dynamic scenes is an important yet challenging research topic. Recently, deep learning methods have achieved impressive performance for dynamic scene deblurring. However, the motion information contained in a blurry image has yet to be fully explored and accurately formulated because: (i) the ground truth of dynamic motion is difficult to obtain; (ii) the temporal ordering is destroyed during the exposure; and (iii) the motion estimation from a blurry image is highly ill-posed. By revisiting the principle of camera exposure, motion blur can be described by the relative motions of sharp content with respect to each exposed position. In this paper, we define exposure trajectories, which represent the motion information contained in a blurry image and explain the causes of motion blur. A novel motion offset estimation framework is proposed to model pixel-wise displacements of the latent sharp image at multiple timepoints. Under mild constraints, our method can recover dense, (non-)linear exposure trajectories, which significantly reduce temporal disorder and ill-posed problems. Finally, experiments demonstrate that the recovered exposure trajectories not only capture accurate and interpretable motion information from a blurry image, but also benefit motion-aware image deblurring and warping-based video extraction tasks. Codes are available on https://github.com/yjzhang96/Motion-ETR.
MDS Constacyclic Codes and MDS Symbol-Pair Constacyclic Codes

Symbol-pair codes are used to protect against symbol-pair errors in high density data storage systems. One of the most important tasks in symbol-pair coding theory is to design MDS codes. MDS symbol-pair codes are optimal in the sense that such codes attain the Singleton bound. In this paper, a new class of MDS symbol-pair codes with code-length 5p and optimal pair distance of 7 is established. It is shown that for any prime p ≡ 1 (mod 5), we can always construct four p-ary MDS symbol-pair cyclic codes of length 5p of largest possible pair distance 7. We also completely determined all MDS symbol-pair and MDS b-symbol codes of length ps and 2ps over Fpm + uFpm by filling in some missing cases, and rectifying some gaps in Type 3 codes of recent papers. As an applications of our results, we use MAGMA to provide many examples of new MDS codes over Fpm and Fpm + uFpm.
Integration Between Cascade Region-Based Convolutional Neural Network and Bi-Directional Feature Pyramid Network for Live Object Tracking and Detection

The current target tracking and detection algorithms often have mistakes and omissions when the target is occluded or small. To overcome the defects, this paper integrates bi-directional feature pyramid network (BiFPN) into cascade region-based convolutional neural network (R-CNN) for live object tracking and detection. Specifically, the BiFPN structure was utilized to connect between scales and fuse weighted features more efficiently, thereby enhancing the network’s feature extraction ability, and improving the detection effect on occluded and small targets. The proposed method, i.e., Cascade R-CNN fused with BiFPN, was compared with target detection algorithms like Cascade R-CNN and single shot detection (SSD) on a video frame dataset of wild animals. Our method achieved a mean average precision (mAP) of 91%, higher than that of SSD and Cascade R-CNN. Besides, it only took 0.42s for our method to detect each image, i.e., the real-time detection was realized. Experimental results prove that the proposed live object tracking and detection model, i.e., Cascade R-CNN fused with BiFPN, can adapt well to the complex detection environment, and achieve an excellent detection effect.
Human voice pitch measures are robust across a variety of speech recordings: methodological and theoretical implications

Fundamental frequency (fo), perceived as voice pitch, is the most sexually dimorphic, perceptually salient and intensively studied voice parameter in human nonverbal communication. Thousands of studies have linked human fo to biological and social speaker traits and life outcomes, from reproductive to economic. Critically, researchers have used myriad speech stimuli to measure fo and infer its functional relevance, from individual vowels to longer bouts of spontaneous speech. Here, we acoustically analysed fo in nearly 1000 affectively neutral speech utterances (vowels, words, counting, greetings, read paragraphs and free spontaneous speech) produced by the same 154 men and women, aged 18-67, with two aims: first, to test the methodological validity of comparing fo measures from diverse speech stimuli, and second, to test the prediction that the vast inter-individual differences in habitual fo found between same-sex adults are preserved across speech types. Indeed, despite differences in linguistic content, duration, scripted or spontan--eous production and within-individual variability, we show that 42-81% of inter-individual differences in fo can be explained between any two speech types. Beyond methodological implications, together with recent evidence that inter-individual differences in fo are remarkably stable across the lifespan and generalize to emotional speech and nonverbal vocalizations, our results further substantiate voice pitch as a robust and reliable biomarker in human communication.
Privacy-Preserving Image Classification With Deep Learning and Double Random Phase Encoding

With the emergence of cloud computing, large amounts of private data are stored and processed in the cloud. On the other hand, data owners (users) may not want to reveal data information to cloud providers to protect their privacy. Therefore, users may upload encrypted data to the cloud or third-party platforms, such as Google Cloud, Amazon Web Service, and Microsoft Azure. Conventionally, data must be decrypted before being analyzed in the cloud, which raises privacy concerns. Moreover, decryption of big data such as images requires enormous computation resources, which is unsuitable for energy-constrained devices, particularly Internet of Things (IoT) devices. Data privacy in most popular applications, such as image query or classification, can be preserved if encrypted images can be directly classified on the cloud or IoT devices without decryption. This paper proposes a high-speed double random phase encoding (DRPE) technique of encrypting images into white-noise images. DRPE-encrypted images are then uploaded and stored in the cloud. Images that are encrypted without being decrypted are classified using deep convolutional neural networks in the cloud. The simulation results indicated the feasibility and good performance of the proposed approach. The proposed privacy-preserving image classification method can be useful in data-sensitive fields, such as medicine and transportation.
RALSA: the R analyzer for large-scale assessments

Abstract This paper presents the R Analyzer for Large-Scale Assessments (), a newly developed package for analyzing data from studies using complex sampling and assessment designs. Such studies are, for example, the IEA’s Trends in International Mathematics and Science Study and the OECD’s Programme for International Student Assessment. The package covers all cycles from a broad range of studies. The paper presents the architecture of the package, the overall workflow and illustrates some basic analyses using it. The package is open-source and free of charge. Other software packages for analyzing large-scale assessment data exist, some of them are proprietary, others are open-source. However, is the first comprehensive package, designed for the user experience and has some distinctive features. One innovation is that the package can convert SPSS data from large scale assessments into native data sets. It can also do so for PISA data from cycles prior to 2015, where the data is provided in tab-delimited text files along with SPSS control syntax files. Another feature is the availability of a graphical user interface, which is also written in and operates in any operating system where a full copy of can be installed. The output from any analysis function is written into an MS Excel workbook with multiple sheets for the estimates, model statistics, analysis information and the calling syntax itself for reproducing the analysis in future. The flexible design of allows for the quick addition of new studies, analysis types and features to the existing ones.
3DMeT: 3D Medical Image Transformer for Knee Cartilage Defect Assessment

While convolutional neural networks (CNNs) are dominating the area of computer-aided 3D medical image diagnosis, they are incapable of capturing global information due to the intrinsic locality of convolution. Transformers, another type of neural network empowered with self-attention mechanism, are good at representing global relations, yet computationally expensive and do not generalize well on small datasets. Applying Transformers on 3D medical images has two major problems: 1) medical 3D volumes are bigger in size than natural images which makes training process computationally impractical, 2) and 3D medical image datasets are usually smaller than natural image datasets since medical images are expensive to collect. In this paper, we propose the 3D Medical image Transformer (3DMeT) to address these two issues. 3DMeT introduces 3D convolutional layers to perform block embedding instead of the original linear embedding to cut the computational cost. Additionally, we propose a teacher-student training strategy to address the data-hungry issue by adapting convolutional layers’ weights from a CNN teacher. We conduct experiments on knee images, results demonstrate that the 3DMeT (70.2) confidently outperforms the 3DCNNs (65.3) and Vision Transformer (58.7).
Verifying BQP Computations on Noisy Devices with Minimal Overhead

With the development of delegated quantum computation, clients will want to ensure confidentiality of their data and algorithms and the integrity of their computations. While protocols for blind and verifiable quantum computation exist, they suffer from high overheads and from oversensitivity: when running on noisy devices, imperfections trigger the same detection mechanisms as malicious attacks, resulting in perpetually aborted computations. We introduce the first blind and verifiable protocol for delegating bounded-error quantum polynomial (BQP) computations to a powerful server, with repetition as the only overhead. It is composable and statistically secure with exponentially low bounds and can tolerate a constant amount of global noise.Received 14 January 2021Revised 31 May 2021Accepted 8 July 2021DOI:https://doi.org/10.1103/PRXQuantum.2.040302Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasMeasurement-based quantum computingQuantum computationQuantum cryptographyQuantum Information
Incomplete-view oriented kernel learning method with generalization error bound

Multi-view learning (MVL) is a promising direction and most MVL methods work under the assumption that data are complete in all views. However, this assumption is often violated in practice due to the difficulties of high cost, equipment failure, and so on. Besides, the amount of data with missing views is particularly huge. Therefore, it is challenging yet valuable to fully leverage the large-scale incomplete-view data. Enlightened by the reduced support vector machine (RSVM) and multi-view privileged support vector machine (PSVM-2V), this paper proposes an efficient kernel method called reduced PSVM-2V (RPSVM-2V). It can not only provide a novel solution to process incomplete-view data, but also can be adjusted to address large-scale complete-view learning problem efficiently. In addition, the idea of replacing the full kernel with a smaller rectangular reduced kernel is extended to develop another two MVL methods, i.e., reduced SVM-2K (RSVM-2K) and reduced multiple kernel learning method (RMKL). Furthermore, we analyze the generalization error bounds of RPSVM-2V and two extensions by using Rademacher complexity. The comprehensive experiments demonstrate that our proposed models can achieve comparable performance with less time and memory cost. The spectral analysis has further verified the effectiveness of the reduced kernel used in our models.
Web Application Penetration Testing Using SQL Injection Attack

A web application is a very important requirement in the information and digitalization era. With the increasing use of the internet and the growing number of web applications, every web application requires an adequate security level to store information safely and avoid cyber attacks. Web applications go through rapid development phases with short turnaround times, challenging to eliminate vulnerabilities. The vulnerability on the web application can be analyzed using the penetration testing method. This research uses penetration testing with the black-box method to test web application security based on the list of most attacks on the Open Web Application Security Project (OWASP), namely SQL Injection. SQL injection allows attackers to obtain unrestricted access to the databases and potentially collecting sensitive information from databases. This research randomly tested several websites such as government, schools, and other commercial websites with several techniques of SQL injection attack. Testing was carried out on ten websites randomly by looking for gaps to test security using the SQL injection attack. The results of testing conducted 80% of the websites tested have a weakness against SQL injection attacks. Based on this research, SQL injection is still the most prevalent threat for web applications. Further research can explain detailed information about SQL injection with specific techniques and how to prevent this attack.
Characterizing and Mitigating Self-Admitted Technical Debt in Build Systems

Technical Debt is a metaphor used to describe the situation in which long-term software artifact quality is traded for short-term goals in software projects. In recent years, the concept of self-admitted technical debt (SATD) was proposed, which focuses on debt that is intentionally introduced and described by developers. Although prior work has made important observations about admitted technical debt in source code, little is known about SATD in build systems. In this paper, we set out to better understand the characteristics of SATD in build systems. To do so, through a qualitative analysis of 500 SATD comments in the Maven build system of 291 projects, we characterize SATD by location and rationale (reason and purpose). Our results show that limitations in tools and libraries, and complexities of dependency management are the most frequent causes, accounting for 50% and 24% of the comments. We also find that developers often document SATD as issues to be fixed later. As a first step towards the automatic detection of SATD rationale, we train classifiers to detect the two most frequently occurring reasons and the four most frequently occurring purposes of SATD in the content of comments in Maven build systems. The classifier performance is promising, achieving an F1-score of 0.71-0.79. Finally, within 16 identified 'ready-to-be-addressed' SATD instances, the three SATD submitted by pull requests and the five SATD submitted by issue reports were resolved after developers were made aware. Our work presents the first step towards understanding technical debt in build systems and opens up avenues for future work, such as tool support to track and manage SATD backlogs.
Joint Motion Correction and Super Resolution for Cardiac Segmentation via Latent Optimisation

In cardiac magnetic resonance (CMR) imaging, a 3D high-resolution segmentation of the heart is essential for detailed description of its anatomical structures. However, due to the limit of acquisition duration and respiratory/cardiac motion, stacks of multi-slice 2D images are acquired in clinical routine. The segmentation of these images provides a low-resolution representation of cardiac anatomy, which may contain artefacts caused by motion. Here we propose a novel latent optimisation framework that jointly performs motion correction and super resolution for cardiac image segmentations. Given a low-resolution segmentation as input, the framework accounts for inter-slice motion in cardiac MR imaging and super-resolves the input into a high-resolution segmentation consistent with input. A multi-view loss is incorporated to leverage information from both short-axis view and long-axis view of cardiac imaging. To solve the inverse problem, iterative optimisation is performed in a latent space, which ensures the anatomical plausibility. This alleviates the need of paired low-resolution and high-resolution images for supervised learning. Experiments on two cardiac MR datasets show that the proposed framework achieves high performance, comparable to state-of-the-art super-resolution approaches and with better cross-domain generalisability and anatomical plausibility. The codes are available at https://github.com/shuowang26/SRHeart.
Clustering Effect in Simon and Simeck

Simon and Simeck are two lightweight block ciphers with a simple round function using only word rotations and a bit-wise AND operation. Previous work has shown a strong clustering effect for differential and linear cryptanalysis, due to the existence of many trails with the same inputs and outputs.