Reviewerly/exHarmony huggingface dataset:
Impact of Frequency of Location Reports on the Privacy Level of Geo-indistinguishability

Abstract Location privacy has became an emerging topic due to the pervasiveness of Location-Based Services (LBSs). When sharing location, a certain degree of privacy can be achieved through the use of Location Privacy-Preserving Mechanisms (LPPMs), in where an obfuscated version of the exact user location is reported instead. However, even obfuscated location reports disclose information which poses a risk to privacy. Based on the formal notion of differential privacy, Geo-indistinguishability has been proposed to design LPPMs that limit the amount of information that is disclosed to a potential adversary observing the reports. While promising, this notion considers reports to be independent from each other, thus discarding the potential threat that arises from exploring the correlation between reports. This assumption might hold for the sporadic release of data, however, there is still no formal nor quantitative boundary between sporadic and continuous reports and thus we argue that the consideration of independence is valid depending on the frequency of reports made by the user. This work intends to fill this research gap through a quantitative evaluation of the impact on the privacy level of Geo-indistinguishability under different frequency of reports. Towards this end, state-of-the-art localization attacks and a tracking attack are implemented against a Geo-indistinguishable LPPM under several values of privacy budget and the privacy level is measured along different frequencies of updates using real mobility data.
A Novel Parallel Ant Colony Optimization Algorithm for Warehouse Path Planning

In order to improve the working efficiency of automated guided vehicles (AGVs) and the processing efficiency of fulfilling orders in intelligent warehouses, a novel parallel ant colony optimization algorithm for warehouse path planning is proposed. Through the interaction of pheromones among multiple subcolonies, the coevolution of multiple subcolonies is realized and the operational capability of the algorithm is improved. Then, a multiobjective function with the object of the shortest path and the minimum number of turns of the AGV is established. And the path satisfying this objective function is obtained by the proposed algorithm. In addition, the path is further smoothed by reducing the number of intermediate nodes. The results show that the stability and convergence rate of the algorithm are faster and more stable, compared to other algorithms, in generating paths for different complexity maps. The smoothing treatment of the path significantly reduces the number of turns and the path length in the AGV driving process.
A Randomly Accessible Lossless Compression Scheme for Time-Series Data

We detail a practical compression scheme for lossless compression of time-series data, based on the emerging concept of generalized deduplication. As data is no longer stored for just archival purposes, but needs to be continuously accessed in many applications, the scheme is designed for low-cost random access to its compressed data, avoiding decompression. With this method, an arbitrary bit of the original data can be read by accessing only a few hundred bits in the worst case, several orders of magnitude fewer than state-of-the-art compression schemes. Subsequent retrieval of bits requires visiting at most a few tens of bits. A comprehensive evaluation of the compressor on eight real-life data sets from various domains is provided. The cost of this random access capability is a loss in compression ratio compared with the state-of-the-art compression schemes BZIP2 and 7z, which can be as low as 5% depending on the data set. Compared to GZIP, the proposed scheme has a better compression ratio for most of the data sets. Our method has massive potential for applications requiring frequent random accesses, as the only existing approach with comparable random access cost is to store the data without compression.
Incorporating symbolic domain knowledge into graph neural networks

Our interest is in scientific problems with the following characteristics: (1) Data are naturally represented as graphs; (2) The amount of data available is typically small; and (3) There is significant domain-knowledge, usually expressed in some symbolic form (rules, taxonomies, constraints and the like). These kinds of problems have been addressed effectively in the past by symbolic machine learning methods like Inductive Logic Programming (ILP), by virtue of 2 important characteristics: (a) The use of a representation language that easily captures the relation encoded in graph-structured data, and (b) The inclusion of prior information encoded as domain-specific relations, that can alleviate problems of data scarcity, and construct new relations. Recent advances have seen the emergence of deep neural networks specifically developed for graph-structured data (Graph-based Neural Networks, or GNNs). While GNNs have been shown to be able to handle graph-structured data, less has been done to investigate the inclusion of domain-knowledge. Here we investigate this aspect of GNNs empirically by employing an operation we term vertex-enrichment and denote the corresponding GNNs as VEGNNs. Using over 70 real-world datasets and substantial amounts of symbolic domain-knowledge, we examine the result of vertex-enrichment across 5 different variants of GNNs. Our results provide support for the following: (a) Inclusion of domain-knowledge by vertex-enrichment can significantly improve the performance of a GNN. That is, the performance of VEGNNs is significantly better than GNNs across all GNN variants; (b) The inclusion of domain-specific relations constructed using ILP improves the performance of VEGNNs, across all GNN variants. Taken together, the results provide evidence that it is possible to incorporate symbolic domain knowledge into a GNN, and that ILP can play an important role in providing high-level relationships that are not easily discovered by a GNN.
Watch out! Motion is Blurring the Vision of Your Deep Neural Networks

The state-of-the-art deep neural networks (DNNs) are vulnerable against adversarial examples with additive random-like noise perturbations. While such examples are hardly found in the physical world, the image blurring effect caused by object motion, on the other hand, commonly occurs in practice, making the study of which greatly important especially for the widely adopted real-time image processing tasks (e.g., object detection, tracking). In this paper, we initiate the first step to comprehensively investigate the potential hazards of the blur effect for DNN, caused by object motion. We propose a novel adversarial attack method that can generate visually natural motion-blurred adversarial examples, named motion-based adversarial blur attack (ABBA). To this end, we first formulate the kernel-prediction-based attack where an input image is convolved with kernels in a pixel-wise way, and the misclassification capability is achieved by tuning the kernel weights. To generate visually more natural and plausible examples, we further propose the saliency-regularized adversarial kernel prediction, where the salient region serves as a moving object, and the predicted kernel is regularized to achieve naturally visual effects. Besides, the attack is further enhanced by adaptively tuning the translations of object and background. A comprehensive evaluation on the NeurIPS'17 adversarial competition dataset demonstrates the effectiveness of ABBA by considering various kernel sizes, translations, and regions. The in-depth study further confirms that our method shows more effective penetrating capability to the state-of-the-art GAN-based deblurring mechanisms compared with other blurring methods. We release the code to this https URL.
AMM: Attentive Multi-field Matching for News Recommendation

Personalized news recommendation is a critical technology to help users find interested news, and how to precisely match users' interests and candidate news lies in the core of news recommendation. Existing studies generally learn user's interest vector by aggregating his/her browsed news and then match it with the candidate news vector, which may lose the textual semantic matching signals for recommendation. In this paper, we propose an Attentive Multi-field Matching (AMM) framework for news recommendation which captures the semantic matching representations between each browsed news and candidate news, and then aggregates them as final user-news matching signal. In addition, our method incorporates multi-field information and designs a within-field and cross-field matching mechanism, which leverages complementary information from different fields (e.g., titles, abstracts and bodies) and obtain the multi-field matching representations. To achieve a comprehensive semantic understanding, we employ the most popular language model BERT to learn the matching representation of each browsed-candidate news pair, and incorporate the attention mechanism in aggregating procedure to characterize the importance of each matching representation for the final user-news matching signal. Experiments on the real world datasets validate the effectiveness of AMM.
Performance Evaluation of Deep Learning Classification Network for Image Features

Deep learning (DL) has emerged as a powerful image processing technique that learns the features of the data and produces state-of-the-art prediction results. The decade from 2010 to 2020 is a real revival of DL, which has come to a turning point in history. In image classification, many deep learning networks have been proposed by scholars, and each of them has its own strengthness. It is very important and efficient for the researchers and the developers to know the performance of these networks, especially for the beginners, so as to give them a transplant instruction by an objective evaluation index. In this paper, we constructed different data sets from three aspects, texture, shape, and measurement scale to test the performance of nine mainstream image classification networks. Cross-contrast experiments were performed to analyze the sensitivity of factors which influence the stability of image classification networks. Experimental results shown that in the 27 image datasets generated by the three image factors, the classification performance of AlexNet, GoogleNet, VggNet, and DenseNet is better. The perfomance comparison of these networks are showed and discussed in details. Code and pretrained models are available at https://github.com/liqiang12689/image-classification-finall.
Math Word Problem Solving with Explicit Numerical Values

In recent years, math word problem solving has received considerable attention and achieved promising results, but previous methods rarely take numerical values into consideration.Most methods treat the numerical values in the problems as number symbols, and ignore the prominent role of the numerical values in solving the problem.In this paper, we propose a novel approach called NumS2T, which enhances math word problem solving performance by explicitly incorporating numerical values into a sequence-to-tree network.In addition, a numerical properties prediction mechanism is used to capture the category and comparison information of numerals and measure their importance in global expressions.Experimental results on the Math23K and APE datasets demonstrate that our model achieves better performance than existing state-of-the-art models.
WARP : Revisiting GFN for Lightweight 128-Bit Block Cipher

In this article, we present WARP, a lightweight 128-bit block cipher with a 128-bit key. It aims at small-footprint circuit in the field of 128-bit block ciphers, possibly for a unified encryption and decryption functionality. The overall structure of WARP is a variant of 32-nibble Type-2 Generalized Feistel Network (GFN), with a permutation over nibbles designed to optimize the security and efficiency. We conduct a thorough security analysis and report comprehensive hardware and software implementation results. Our hardware results show that WARP is the smallest 128-bit block cipher for most of typical hardware implementation strategies. A serialized circuit of WARP achieves around 800 Gate Equivalents (GEs), which is much smaller than previous state-of-the-art implementations of lightweight 128-bit ciphers (they need more than 1, 000 GEs). While our primary metric is hardware size, WARP also enjoys several other features, most notably low energy consumption. This is somewhat surprising, since GFN generally needs more rounds than substitution permutation network (SPN), and thus GFN has been considered to be less advantageous in this regard. We show a multi-round implementation of WARP is quite low-energy. Moreover, WARP also performs well on software: our SIMD implementation is quite competitive to known hardware-oriented 128-bit lightweight ciphers for long input, and even much better for small inputs due to the small number of parallel blocks. On 8-bit microcontrollers, the results of our assembly implementations show that WARP is flexible to achieve various performance characteristics.
Stereo camera calibration for large field of view digital image correlation using zoom lens

We propose a calibration method for stereo camera with a large field of view (FOV) using zoom lenses. We adopted Magill’s formula, using intrinsic parameters with small object distances to calculate intrinsic parameters with large object distances. We employed a regular sized calibration board to calibrate extrinsic parameters in the small FOV, using zoom lenses to reduce the FOV, and then calculated extrinsic parameters in the large FOV. Verification experiments showed 3D reconstruction error was approximately 1 mm when the FOV is about 6 × 6 m. The proposed calibration method will broaden the application range for 3D digital image correlation in scientific and engineering fields.
EnhancedTouchX

EnhancedTouchX, a bracelet-type interpersonal body area network device, not only detects but also quantifies interpersonal hand-to-hand touch interactions. Without any wired connection, it can identify the direction and gestures of a touch. The developed device can connect to an external device via Bluetooth Low Energy for monitoring and logging where, when, how long, who, and how the touch interactions occurred. These daily augmented touch interactions provided by such contextual information would offer a variety of applications to facilitate social interactions. Our experiment, conducted with several pairs of participants, demonstrates that the devices can identify the direction of a touch (from one initiating the touch (active touch) to the one being touched (passive touch)) with 95% accuracy. In addition, the devices are also capable of identifying four types of touch gestures with 85% accuracy using a simple threshold classifier.
Core-reviewer recommendation based on Pull Request topic model and collaborator social network

Pull Request (PR) is a major contributor to external developers of open-source projects in GitHub. PR reviewing is an important part of open-source software developments to ensure the quality of project. Recommending suitable candidates of reviewer to the new PRs will make the PR reviewing more efficient. However, there is not a mechanism of automatic reviewer recommendation for PR in GitHub. In this paper, we propose an automatic core-reviewer recommendation approach, which combines PR topic model with collaborators in the social network. First PR topics will be extracted from PRs by the latent Dirichlet allocation, and then the collaborator–PR network will be constructed with the connection between collaborators and PRs, and the influence of each collaborator will be calculated via the improved PageRank algorithm which combines with HITS. Finally, the relationship between topics and collaborators will also be built by the history of PR reviewing. When a new PR presents, a collaborator will be chosen as a core reviewer according to the influence of collaborators and the relationship between the new PR and collaborators. The experiment results show in the matching score calculation processing, the influence of collaborators shows higher than that with the expert, and the recommendation precision is better than 70%.
Memory-Augmented Temporal Dynamic Learning for Action Recognition

Human actions captured in video sequences contain two crucial factors for action recognition, i.e., visual appearance and motion dynamics. To model these two aspects, Convolutional and Recurrent Neural Networks (CNNs and RNNs) are adopted in most existing successful methods for recognizing actions. However, CNN based methods are limited in modeling long-term motion dynamics. RNNs are able to learn temporal motion dynamics but lack effective ways to tackle unsteady dynamics in long-duration motion. In this work, we propose a memory-augmented temporal dynamic learning network, which learns to write the most evident information into an external memory module and ignore irrelevant ones. In particular, we present a differential memory controller to make a discrete decision on whether the external memory module should be updated with current feature. The discrete memory controller takes in the memory history, context embedding and current feature as inputs and controls information flow into the external memory module. Additionally, we train this discrete memory controller using straight-through estimator. We evaluate this end-to-end system on benchmark datasets (UCF101 and HMDB51) of human action recognition. The experimental results show consistent improvements on both datasets over prior works and our baselines.
CBIR-CNN: Content-Based Image Retrieval on Celebrity Data Using Deep Convolution Neural Network

Background: Finding a region of interest in an image and content-based image analysis has been a challenging task for the last two decades. With the advancement in image processing, computer vision and a huge amount of image data generation, the Content-Based Image Retrieval System (CBIR) has attracted several researchers as a common technique to manage this huge amount of data. It is an approach of searching user interest based on visual information present in an image. The requirement of high computation power and huge memory limits the deployment of the CBIR technique in real-time scenarios. Objective: In this paper, an advanced deep learning model is applied to the CBIR on facial image data. We designed a deep convolution neural network architecture where activation of the convolution layer is used for feature representation and included max-pooling as a feature reduction technique. Furthermore, our model uses partial feature mapping as image descriptor to incorporate the property that facial image contains repeated information. Methods: Existing CBIR approaches primarily consider colour, texture and low-level features for mapping and localizing image segments. While deep learning has shown high performance in numerous fields of research, its application in CBIR is still very limited. Human face contains significant information to be used in a content driven task and applicable to various applications of computer vision and multimedia systems. In this research work, a deep learning-based model has been discussed for Content-Based Image Retrieval (CBIR). In CBIR, there are two important things 1) classification and 2) retrieval of image based on similarity. For the classification purpose, a fourconvolution layer model has been proposed. For the calculation of the similarity, Euclidian distance measure has been used between the images. Results: The proposed model is completely unsupervised, and it is fast and accurate in comparison to other deep learning models applied for CBIR over the facial dataset. The proposed method provided satisfactory results from the experiment, and it outperforms other CNN-based models such as VGG16, Inception V3, ResNet50, and MobileNet. Moreover, the performance of the proposed model has been compared with pre-trained models in terms of accuracy, storage space and inference time. Conclusion: : The experimental analysis over the dataset has shown promising results with more than 90% classification accuracy.
The differential spectrum of a class of power functions over finite fields

<p style='text-indent:20px;'>Functions with good differential-uniformity properties have important applications in coding theory and sequence design in addition to the applications in cryptography. The differential spectrum of a cryptographic function is useful for estimating its resistance to some variants of differential cryptanalysis. The objective of this paper is to determine the differential spectrum of the power function <inline-formula><tex-math id="M1">\begin{document}$ x^{p^{2k}-p^k+1} $\end{document}</tex-math></inline-formula> over <inline-formula><tex-math id="M2">\begin{document}$ \mathbb F_{p^n} $\end{document}</tex-math></inline-formula>, where <inline-formula><tex-math id="M3">\begin{document}$ p $\end{document}</tex-math></inline-formula> is an odd prime, <inline-formula><tex-math id="M4">\begin{document}$ n, k, e $\end{document}</tex-math></inline-formula> are integers with <inline-formula><tex-math id="M5">\begin{document}$ \gcd(n,k) = e $\end{document}</tex-math></inline-formula> and <inline-formula><tex-math id="M6">\begin{document}$ \frac{n}{e} $\end{document}</tex-math></inline-formula> being odd. In particular, when <inline-formula><tex-math id="M7">\begin{document}$ n $\end{document}</tex-math></inline-formula> is odd and <inline-formula><tex-math id="M8">\begin{document}$ e = 1 $\end{document}</tex-math></inline-formula>, our result includes a recent one (IEEE Trans. Inform. Theory 65(10): 6819-6826) as a special case.
Aligning Videos in Space and Time

In this paper, we focus on the task of extracting visual correspondences across videos. Given a query video clip from an action class, we aim to align it with training videos in space and time. Obtaining training data for such a fine-grained alignment task is challenging and often ambiguous. Hence, we propose a novel alignment procedure that learns such correspondence in space and time via cross video cycle-consistency. During training, given a pair of videos, we compute cycles that connect patches in a given frame in the first video by matching through frames in the second video. Cycles that connect overlapping patches together are encouraged to score higher than cycles that connect non-overlapping patches. Our experiments on the Penn Action and Pouring datasets demonstrate that the proposed method can successfully learn to correspond semantically similar patches across videos, and learns representations that are sensitive to object and action states.
Building A Platform for Machine Learning Operations from Open Source Frameworks

Machine Learning Operations (MLOps) aim to establish a set of practices that put tools, pipelines, and processes to build fast time-to-value machine learning development projects. The lifecycle of machine learning project development encompasses a set of roles, stacks of software frameworks and multiple types of computing resources. Such complexity makes MLOps support usually bundled with commercial cloud platforms that is referred as vendor lock. In this paper, we provide an alternative solution that devises a MLOps platform with open source frameworks on any virtual resources. Our MLOps approach is driven by the development roles of machine learning models. The tool chain of our MLOps connects to the typical CI/CD workflow of machine learning applications. We demonstrate a working example of training and deploying a machine learning model for the application of detecting software repository code vulnerability.