Reviewerly/exHarmony huggingface dataset:
Analyzing the Direct and Transitive Impact of Vulnerabilities onto Different Artifact Repositories

In modern-day software development, a vast amount of public software libraries enable the reuse of existing implementations for reoccurring tasks and common problems. While this practice does yield significant benefits in productivity, it also puts an increasing amount of responsibility on library maintainers. If a security flaw is contained in a library release, then it may directly affect thousands of applications that are depending on it. Given the fact that libraries are often interconnected, meaning they are depending on other libraries for certain sub-tasks, the impact of a single vulnerability may be large, and is hard to quantify. Recent studies have shown that developers in fact struggle with upgrading vulnerable dependencies, despite ever-increasing support by automated tools, which are often publicly available. With our work, we aim to improve on this situation by providing an in-depth analysis on how developers handle vulnerability patches and dependency upgrades. To do so, we contribute a miner for artifact dependency graphs supporting different programming platforms, which annotates the graph with vulnerability information. We execute our application and generate a data set for the artifact repositories Maven Central, NuGet.org, and the NPM Registry, with the resulting graph being stored in a Neo4j graph database. Afterwards, we conduct an extensive analysis of our data, which is aimed at understanding the impact of vulnerabilities for the three different repositories. Finally, we summarize the resulting risks and derive possible mitigation strategies for library maintainers and software developers based on our findings. We found that NuGet.org, the smallest artifact repository in our sample, is subject to fewer security concerns than Maven Central or the NPM Registry. However, for all repositories, we found that vulnerabilities may influence libraries via long transitive dependency chains and that a vulnerability in a single library may affect thousands of other libraries transitively.
DEF-Net: A Face Aging Model by Using Different Emotional Learnings

Face aging has attracted widespread attention in recent years, but most studies are based on the same emotional situation. Is the same person’s aging in different emotional situations the same? To solve the above confusion, this paper proposes a novel face aging model DEF-Net, which consists of two parts: different emotional learnings (Emotion-Net) and face aging (Age-Net). Given a target emotion category, DEF-Net first assists the image from the original dataset to learn the emotion features through Emotion-Net and the generated dataset is used as the inputs of Age-Net. At the same time, multiple loss functions are used to ensure that the crucial information of the original image is not lost. Secondly, Age-Net, which has been pre-trained on the original dataset, began to adopt the generated dataset to learn the aging distribution under different emotions. Designed loss functions are utilized to ensure that the realistic target images generated by Age-Net do not lose the learned emotional characteristics. Finally, extensive experiments are used to verify the performance of DEF-Net. Compared with other state-of-the-art methods: (1) DEF-Net can learn different facial emotions across different datasets and generate corresponding realistic aging images; (2) the results achieved by our DEF-Net are demonstrated to be better than those by the model that performs face aging first and then learns different emotional characteristics.
Machine Learning Based Assembly of Fragments of Ancient Papyrus

The task of assembling fragments in a puzzle-like manner into a composite picture plays a significant role in the field of archaeology as it supports researchers in their attempt to reconstruct historic artifacts. In this article, we propose a method for matching and assembling pairs of ancient papyrus fragments containing mostly unknown scriptures. Papyrus paper is manufactured from papyrus plants and therefore portrays typical thread patterns resulting from the plant’s stems. The proposed algorithm is founded on the hypothesis that these thread patterns contain unique local attributes such that nearby fragments show similar patterns reflecting the continuations of the threads. We posit that these patterns can be exploited using image processing and machine learning techniques to identify matching fragments. The algorithm and system which we present support the quick and automated classification of matching pairs of papyrus fragments as well as the geometric alignment of the pairs against each other. The algorithm consists of a series of steps and is based on deep-learning and machine learning methods. The first step is to deconstruct the problem of matching fragments into a smaller problem of finding thread continuation matches in local edge areas (squares) between pairs of fragments. This phase is solved using a convolutional neural network ingesting raw images of the edge areas and producing local matching scores. The result of this stage yields very high recall but low precision. Thus, we utilize these scores in order to conclude about the matching of entire fragments pairs by establishing an elaborate voting mechanism. We enhance this voting with geometric alignment techniques from which we extract additional spatial information. Eventually, we feed all the data collected from these steps into a Random Forest classifier in order to produce a higher order classifier capable of predicting whether a pair of fragments is a match. Our algorithm was trained on a batch of fragments which was excavated from the Dead Sea caves and is dated circa the 1st century BCE. The algorithm shows excellent results on a validation set which is of a similar origin and conditions. We then tried to run the algorithm against a real-life set of fragments for which we have no prior knowledge or labeling of matches. This test batch is considered extremely challenging due to its poor condition and the small size of its fragments. Evidently, numerous researchers have tried seeking matches within this batch with very little success. Our algorithm performance on this batch was sub-optimal, returning a relatively large ratio of false positives. However, the algorithm was quite useful by eliminating 98% of the possible matches thus reducing the amount of work needed for manual inspection. Indeed, experts that reviewed the results have identified some positive matches as potentially true and referred them for further investigation.
Optical double-image encryption based on Gaussian apertured reality-preserving fractional Mellin transform

A novel optical double-image encryption scheme based on Gaussian apertured reality-preserving fractional Mellin transform (GARPFrMT) is proposed. In terms of optical image encryption, GARPFrMT has several attractive advantages: (1) The GAPRFrMT can be used to scramble the value of two images simultaneously; (2) GARPFrMT is constructed in diffraction domain to ensure that the ciphertext is real; (3) Owing to the nonlinearity, this transform can ensure that the proposed encryption system can be kept from the potential insecurity; and (4) A Gaussian lens is applied to improve intensity distributions of the output light. Experimental results demonstrate that the proposed algorithm is feasible and robust by a lot of attack analysis, i.e. sensitivity attack analysis (>99.60%), differential attack analysis (NPCR >99.6072 and UACI >33.5681%), entropy values (almost 7.99928), statistical attack (correlation coefficient values are almost 0), noise attack, cropping attack and NIST analysis.
Trail as Heritage: Safeguarding Location-Specific and Transient Indigenous Knowledge

The importance of Indigenous Knowledge (IK) in understanding the environment and informing scientific studies has gained prominence with the increased attention on environmental sustainability. Researchers have partnered with indigenous communities towards leveraging technology to preserve these important IK. However, there still remains a gap in the understanding of how indigenous community members use technology to engage with, and safeguard their IK. We conducted an interview-based study with museum experts and members of an indigenous community in Kenya to understand how community members seek, preserve, and disseminate location-dependent IK. We augmented our findings through a year-long observation of organic interactions on six Facebook Pages that are specifically geared towards discussing aspects of IK. The findings illustrate the importance of location in providing context, and identifying disappearing IK. We also highlight how community members seek and share the IK especially on Facebook. We conclude by describing research and design opportunities for identifying and preserving IK in accordance with community wishes.
E-commerce products recognition based on a deep learning architecture: Theory and implementation

Nowadays, the number of goods in the market is increasing rapidly. In order to realize the efficient circulation of e-business products, it is necessary to apply the state-of-the-art image recognition technologies to improve the service quality. In this paper, on the basis of the deep learning towards the characteristics of product image, we formulate a novel deep architecture for searching products in e-business context. Specifically, we have designed a product image recognition platform, which consists of various actual cases. We have analyzed the application of this platform. Meanwhile, this work also discusses the theoretical basis of deep learning technology in time series forecasting, and sums up the commodity sales forecasting as multivariable time series. Using the historical sales data of an e-commerce online store, we also formulate the mechanism of constructing the LSTM network model under the well-known Tensorflow framework. We evaluate our method by comparing it with diverse AR models, it can be concluded that LSTM network model is simple and with convenient data input. Besides, it can be shown that good marketing decisions and reasonable inventory management is significant in products purchasing. • In this paper, on the basis of the deep learning towards the characteristics of product image, we formulate a novel deep architecture for searching products in e-business context. • Using the historical sales data of an e-commerce online store, we also formulate the mechanism of constructing the LSTM network model under the well-known Tensorflow framework. • We evaluate our method by comparing it with diverse AR models, it can be concluded that LSTM network model is simple and with convenient data input. Besides, it can be shown that good marketing decisions and reasonable inventory management is significant in products purchasing.
Restoring Healthy Online Discourse by Detecting and Reducing Controversy, Misinformation, and Toxicity Online

Healthy online discourse is becoming less and less accessible beneath the growing noise of controversy, mis- and dis-information, and toxic speech. While IR is crucial in detecting harmful speech, researchers must work across disciplines to develop interventions, and partner with industry to deploy them rapidly and effectively. In this position paper, we argue that both detecting online information disorders and deploying novel, real-world content moderation tools is crucial in promoting empathy in social networks, and maintaining free expression and discourse. We detail our insights in studying different social networks such as Parler and Reddit. Finally, we discuss the joys and challenges as a lab-grown startup working with both academia and other industrial partners in finding a path toward a better, more trustworthy online ecosystem.
Tracking of Objects in Video Sequences

The paper considers the issues of trajectory tracking of a large number of objects on video images in modes close to real time. To implement such support, a combination of algorithms based on the YOLO v3 convolutional neural network (CNN), doubly stochastic filters and pseudo-gradient procedures for aligning image fragments is proposed. The obtained numerical performance characteristics show the consistency of such a combination and the possibility of its application in real video processing systems.
Parsimonious hidden Markov models for matrix-variate longitudinal data

Hidden Markov models (HMMs) have been extensively used in the univariate and multivariate literature. However, there has been an increased interest in the analysis of matrix-variate data over the recent years. In this manuscript we introduce HMMs for matrix-variate balanced longitudinal data, by assuming a matrix normal distribution in each hidden state. Such data are arranged in a four-way array. To address for possible overparameterization issues, we consider the eigen decomposition of the covariance matrices, leading to a total of 98 HMMs. An expectation-conditional maximization algorithm is discussed for parameter estimation. The proposed models are firstly investigated on simulated data, in terms of parameter recovery, computational times and model selection. Then, they are fitted to a four-way real data set concerning the unemployment rates of the Italian provinces, evaluated by gender and age classes, over the last 16 years.
Adversarial network integrating dual attention and sparse representation for semi-supervised semantic segmentation

Semantic segmentation is the important task of assigning a semantic label to each pixel. However, semantic segmentation based on the deep neural network usually requires massive annotations consumption to acquire better performance. To avoid the problem, some algorithms based on weakly-supervised and semi-supervised conditions have been proposed and achieved gradually improving performance in recent years. In this paper, we propose a novel semi-supervised adversarial network to alleviate the shortage of labeled data, which only requires a few labeled images to get competitive performance. The model is composed of two parts: the segmentation network and the discriminator network. The first part aims to semantically generate a segmented result that has the same size as the input color image. The discriminator network is designed in a fully convolutional manner to distinguish the predicted probability maps depending on the ground truth distribution. In particular, the probability maps are regarded as focal attention maps, which are fed back to the segmentation network to make the model converge faster, and the process can induce the model to focus on pixels that are hard to segment. To enhance the representation ability of image features, sparse representation and dual attention are adopted in the segmentation network. The sparse representation module aims to emphasize the object edges and locations by learning the convolutional sparse representation of the input color images, and the dual attention module can exploit the semantic interdependencies in two different dimensions. Moreover, the semi-supervised mechanism is introduced to the network, in which the adaptive parameter T that controls the sensitivity of the self-taught phase is proposed, and the training dataset is split into two parts for fully-supervised learning and semi-supervised learning. Specifically, the first part is unlabeled data, which is applied to provide supervised signals for semi-supervised training. The labeled data drawn from the other part is utilized for fully-supervised learning. Our semi-supervised adversarial framework can improve the learning ability and achieve higher performance, also, provide a novel approach to tackle the semantic segmentation task. Finally, comprehensive experiments on the PASCAL VOC 2012 and Cityscapes datasets are conducted to verify the effectiveness of the proposed model, which achieves the expected performance.
Achieving fault tolerance against amplitude-damping noise

With the intense interest in small, noisy quantum computing devices comes the push for larger, more accurate -- and hence more useful -- quantum computers. While fully fault-tolerant quantum computers are, in principle, capable of achieving arbitrarily accurate calculations using devices subjected to general noise, they require immense resources far beyond our current reach. An intermediate step would be to construct quantum computers of limited accuracy enhanced by lower-level, and hence lower-cost, noise-removal techniques. This is the motivation for our work, which looks into fault-tolerant encoded quantum computation targeted at the dominant noise afflicting the quantum device. Specifically, we develop a protocol for fault-tolerant encoded quantum computing components in the presence of amplitude-damping noise, using a 4-qubit code and a recovery procedure tailored to such noise. We describe a universal set of fault-tolerant encoded gadgets and compute the pseudothreshold for the noise, below which our scheme leads to more accurate computation. Our work demonstrates the possibility of applying the ideas of quantum fault tolerance to targeted noise models, generalizing the recent pursuit of biased-noise fault tolerance beyond the usual Pauli noise models. We also illustrate how certain aspects of the standard fault tolerance intuition, largely acquired through Pauli-noise considerations, can fail in the face of more general noise.
Empirical Evaluation of Typical Sparse Fast Fourier Transform Algorithms

Computing the Sparse Fast Fourier Transform(sFFT) has emerged as a critical topic for a long time. The sFFT algorithms decrease the runtime and sampling complexity by taking advantage of the signal's inherent characteristics that a large number of signals are sparse in the frequency domain. More than ten sFFT algorithms have been proposed, which can be classified into many types according to filter, framework, method of location, method of estimation. In this paper, the technology of these algorithms is completely analyzed in theory. The performance of them is thoroughly tested and verified in practice. The techniques involved in different sFFT algorithms include the following contents: five operations of signal, three methods of frequency bucketization, five methods of location, four methods of estimation, two problems caused by bucketization, three methods to solve these two problems, four algorithmic frameworks. All the above technologies and methods are introduced in detail and examples are given to illustrate the above research. After theoretical research, we make experiments for computing the signals of different SNR, length, sparsity by a standard testing platform and record the run time, percentage of the signal sampled and L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> ,L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> ,L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> error with eight different typical sFFT algorithms. The result of experiments satisfies the inferences obtained in theory. It helps us to optimize these algorithms and use them selectively.
Joint Resource Allocation for Efficient Federated Learning in Internet of Things Supported by Edge Computing

Federated learning (FL) and edge computing are both important technologies to support the future Internet of Things (IoT). Despite that the network supported by edge computing has great potential to promote FL, it is more challenging to achieve efficient FL due to more complex resource coupling in it. Focus on this problem, we formulate a problem which minimizes the weighted sum of system cost and learning cost by jointly optimizing bandwidth, computation frequency, transmission power allocation and subcarrier assignment. In order to solve this mixed-integer non-linear problem, we first decouple the bandwidth allocation subproblem from the original problem and obtain a closed-form solution. Further considering the remaining joint optimization problem of computation frequency, transmission power and subcarrier, an iterative algorithm with polynomial time complexity is designed. In an iteration, the latency and computation frequency optimization subproblem and transmission power and subcarrier optimization subproblem are solved using the proposed algorithms in turn. The iterative algorithm is repeated until convergence. Finally, to verify the performance of the algorithm, we compare the proposed algorithm with five baselines. Numerical results show the significant performance gain and the robustness of the proposed algorithm.
Object Tracking and Geo-Localization from Street Images

Object geo-localization from images is crucial to many applications such as land surveying, self-driving, and asset management. Current visual object geo-localization algorithms suffer from hardware limitations and impractical assumptions limiting their usability in real-world applications. Most of the current methods assume object sparsity, the presence of objects in at least two frames, and most importantly they only support a single class of objects. In this paper, we present a novel two-stage technique that detects and geo-localizes dense, multi-class objects such as traffic signs from street videos. Our algorithm is able to handle low frame rate inputs in which objects might be missing in one or more frames. We propose a detector that is not only able to detect objects in images, but also predicts a positional offset for each object relative to the camera GPS location. We also propose a novel tracker algorithm that is able to track a large number of multi-class objects. Many current geo-localization datasets require specialized hardware, suffer from idealized assumptions not representative of reality, and are often not publicly available. In this paper, we propose a public dataset called ARTSv2, which is an extension of ARTS dataset that covers a diverse set of roads in widely varying environments to ensure it is representative of real-world scenarios. Our dataset will both support future research and provide a crucial benchmark for the field.
AOPDet: Automatic Organized Points Detector for Precisely Localizing Objects in Aerial Imagery

With the development of deep convolutional neural networks, detecting rotating objects in remote-sensing images is of great significance in various fields. Existing rotating object detectors most suffer the problem of ambiguous supervision caused by inappropriate rotating object representations. This problem may result in fuzzy object localization and further lead to misclassification. In this article, we propose an Automatic Organized Points Detector (AOPDet), which derives precise localization results by applying a novel rotating object representation called nonsequential corners representation. To achieve the proposed representation, an Automatic Organization Mechanism (AOM) technique is designed to guide the model to organize points to object corners automatically. An Automatic-Organized-Points-specific (AOP-specific) head structure is also designed and equipped in the model to better focus on the rotating object detection task. On public aerial datasets, experiments show that the AOPDet achieves 17.0 mAP higher than the compared baseline model, reaching the state-of-the-art (SOTA) level. Detailed ablation experiments and error analysis strongly reveal the effectiveness of the proposed model.