Reviewerly/exHarmony huggingface dataset:
Topology-theoretic approach to address attribute linkage attacks in differential privacy

Differential Privacy (DP) is well-known for its strong privacy guarantee. Briefly speaking, DP algorithms guarantee that the statistical information of the data is roughly preserved, and at the same time, individual privacy is protected with guarantees. However, when there are correlations among the attribute in the dataset, only relying on DP is not sufficient to defend against the attribute linkage attack, which is a well-known privacy attack aiming at deducing individuals’ private information. In the attribute linkage attack, the adversary can leverage prior knowledge about the victim, combined with accessing the published dataset, to infer sensitive information about a victim. In this paper, we study the attribute linkage attack in DP settings, and argue that enhancing DP can give users a higher level of privacy guarantees. Our contributions are ➀ we show that the attribute linkage attack can be initiated with high probability under the protection of DP, ➁ we propose a variant of DP called APL-Free ϵ-DP to provide a higher level of privacy guarantees, ➂ we design an algorithm APLKiller which satisfies the APL-Free ϵ-DP. Finally, experiments show that our algorithm not only eliminates the attribute linkage attack, and at the same time, it has a better ability to extract useful information from the data.
CCFD-Net: A novel deep learning model for credit card fraud detection

Credit card fraud can cause billions of dollars in financial losses to merchants and consumers each year. If fraud can be detected in time and corresponding measures can be taken, financial losses can be significantly alleviated and other derivative frauds can be prevented. Although traditional machine learning methods can achieve good precision and recall in credit card fraud detection, they cannot avoid the false positives effectively. In this paper, we propose a novel Credit Card Fraud Detection model called CCFD-Net that employs a modified residual network architecture. Based on a realworld dataset from Vesta's e-commerce transactions, we conduct comparative analysis on predictive models to evaluate and verify the effectiveness of the proposed method. The paper explores a hybrid architecture of 1D-Conv and the residual neural network (Res-net), evaluates the performance of different machine learning models based on K-fold cross-validation. The results prove the effectiveness and robustness of the model in credit card fraud detection. In practice, our proposed model can identify more fraudulent transactions than other compared models, and performs best on the evaluation metrics. We publicly share our full implementation with the dataset and trained models at https://github.com/zhenguonie/2021_CCFD_Net.
Optimal Path-Planning With Random Breakdowns

We propose a model for path-planning based on a single performance metric that accurately accounts for the potential (spatially inhomogeneous) cost of breakdowns and repairs. These random breakdowns (or system faults) happen at a known, spatially inhomogeneous rate. Our model includes breakdowns of two types: total, which halt all movement until an in-place repair is completed, and partial, after which the movement continues in a damaged state toward a repair depot. We use the framework of piecewise-deterministic Markov processes to describe the optimal policy for all starting locations. We also introduce an efficient numerical method that uses hybrid value-policy iterations to solve the resulting system of Hamilton-Jacobi-Bellman PDEs. Our method is illustrated through a series of computational experiments that highlight the dependence of optimal policies on the rate and type of breakdowns, with one of them based on Martian terrain data.
Object Depth Measurement and Filtering from Monocular Images for Unmanned Aerial Vehicles

The flight safety of low-altitude small fixed-wing unmanned aerial vehicles (UAVs) is often threatened by obstacles such as buildings. This requires UAVs to have the ability to autonomously measure the depth of objects ahead. However, existing depth measurement methods based on multiview geometry and handcrafted features still have problems in accuracy and scene suitability. This paper proposes an object depth measurement and filtering method for UAVs by using monocular images. Firstly, the length of the line segment between feature points instead of the pixel position of feature point is used to solve object depth, which reduces the adverse effect of feature matching error. Meanwhile, in order to adapt to UAV platforms, height and attitude changes are both considered in the modeling process. Moreover, the sequence of object depth values corresponding to the image sequence is filtered by an extended Kalman filter to reduce oscillations. The effectiveness of the whole scheme is verified by visual simulation. Results show that the proposed method achieves better accuracy than other depth measurement methods based on multiview geometry.
Evaluating Human Understanding of a Mixed Reality Interface for Autonomous Robot-Based Change Detection

Online change detection performed by mobile robots has incredible potential to impact safety and security applications. While robots are superior to humans at detecting changes, humans are still better at interpreting this information and will be responsible for making critical decisions in these contexts. For these reasons, robot-to-human communication of change detection is a fundamental requirement for successful human-robot teams operating in such scenarios. In this work we seek to improve this communication, and present the results of a study that evaluates the interpretability of autonomous robot-based change detections conveyed via mixed reality to untrained human participants. Our results show that humans are able to identify changes and understand the visualizations employed without prior training. Our analysis of the limitations of this initial study should be constructive to future work in this domain.
Optimized threshold implementations: securing cryptographic accelerators for low-energy and low-latency applications

Threshold implementations have emerged as one of the most popular masking countermeasures for hardware implementations of cryptographic primitives. In this work, we provide three TI optimization techniques: First, a generic construction for $$d+1$$ TI sharing achieves the minimal number of output shares for any n-input Boolean function of degree $$t=n-1$$ and for any d. Next, we present a methodology for finding minimal number of output shares in $$d+1$$ TI when $$t<n-1$$ . Third, a heuristic for minimizing the number of output shares for higher-order $$td + 1$$ TI for any n, any t and $$d \le 2$$ is proposed. In addition, we describe an optimization for the secure AES schedule which achieves maximum throughput for a serial implementation. Then, we demonstrate the applicability of our results on $$d+1$$ and $$td+1$$ TI versions, for first- and second-order secure, low-latency and low-energy implementations of the PRINCE block cipher. We show the fastest and the most energy efficient known TI-protected implementations of PRINCE.
Subword Speech Recognition for Agglutinative Languages

The field of large vocabulary continuous speech recognition has advanced in recent years. Most research has used phonemes and words as speech recognition units. In this work, we introduce and develop syllable-based subword modeling for speech recognition and compare it with word-based speech recognition. Our method suggests adding an additional syllable layer between phone and word. The proposed method tested for the Azerbaijani language. The speech database was collected using mobile devices. The suggested method is very effective for agglutinative language structure. Because syllable count is less than word count, our approach reduces the number of out-of-vocabulary words significantly. Experimental results show that our syllable-based speech recognition method reduces the word error rate by 5%. The suggested method can be applied to other agglutinative languages also, especially for Turkic groups of languages. Experiments show that the proposed method can greatly improve the system accuracy, and also outperform commonly used word-based methods.
SIP-GAN: Generative Adversarial Networks for SIP traffic generation

Generative adversarial networks (GANs) are one of the major ML techniques for data augmentation and classification, in the field of image processing, computer vision and natural language processing. However, in the field of data networks and protocols the use of GANs for data generation and classification (at packet level) is very limited or relatively new. Although, GANs specific properties and characteristics can be highly relevant in this context (unsupervised technique). This limitation, is even more critical if we consider network protocols or communication oriented protocols such as SIP VoIP To address this problem, we propose “SIP-GAN” an extension and adaptation of GANs model for SIP, aiming to process and generate SIP traffic at packet level. The proposed generic model includes an encoder, a generator, and a decoder The encoder extracts information from pcap data, associates and converts these SIP data into a GAN image representation. The generator is based on a DCGAN model, that generates new SIP dataset from each extracted image. The decoder combines the generated images and reconstruct a valid pcap file (SIP file). A specific testbed, with a formal and practical analysis, demonstrate the validity of the generated data, from the SIP-GAN model. Also, the experimental and performance results are globally satisfactory, showing the relevance of our proposed SIP-GAN based traffic generator in this context.
Puzzle Battle 2.0: A Revisited Serious Game in VR During Pandemic’s Period

Puzzle Battle 2.0 is a Serious Game in Virtual Reality created within the 3D Lab Factory. It is the second version of puzzle game created in 2017 to teach art history. The goal was to complete all the proposed paintings to collect them in a personal museum. The COVID-19 epidemic has generated an increase in the earnings of the videogame sector but also brought many experiences designed to be enjoyed in specific places to be unusable. This article proposes a redesign of the original project to make available even at a distance an experience that until now had only been used in a VR Corner.
Effatá: Obstacle Identification System to help the Blind in Urban Displacement

This paper presents Effatá, a product developed to help blind people walk safer and with greater ease on urban roads. The system uses a Deep Learning algorithm to detect obstacles in the user's vicinity through image detection on a Raspberry Pi 4 connected to two cameras. Once detected, the obstacle's relative position and distance to the user is estimated and informed through 3D sound via bone conduction (open-ear) headphones. The detection cycle takes 0.7 secs. Initial feedback from users, indicates that this solution has proven to be of great aid for blind people when walking through unknown sidewalks. The result product provides the user with a better understanding of surrounding objects, providing a greater sense of safety and independence.
Exploiting appearance transfer and multi-scale context for efficient person image generation

Pose guided person image generation means to generate a photo-realistic person image conditioned on an input person image and a desired pose. This task requires spatial manipulation of the source image according to the target pose. However, convolutional neural networks (CNNs) are inherently limited to geometric transformations due to the fixed geometric structures in their building modules, i.e., convolution, pooling and unpooling, which cannot handle large motion and occlusions caused by large pose transform. This paper introduces a novel two-stream context-aware appearance transfer network to address these challenges. It is a three-stage architecture consisting of a source stream and a target stream. Each stage features an appearance transfer module, a multi-scale context module and two-stream feature fusion modules. The appearance transfer module handles large motion by finding the dense correspondence between the two-stream feature maps and then transferring the appearance information from the source stream to the target stream. The multi-scale context module handles occlusion via contextual modeling, which is achieved by atrous convolutions of different sampling rates. Both quantitative and qualitative results indicate the proposed network can effectively handle challenging cases of large pose transform while retaining the appearance details. Compared with state-of-the-art approaches, it achieves comparable or superior performance using much fewer parameters while being significantly faster.
Forecasting of Solar Power Volatility using GJR-GARCH method

The rapid increase in solar power plant installation creating considerable difficulties for power system operation and control, due to the highly stochastic nature of solar energy. To ensure a smooth solar panel output, an accurate short-time forecast plays a crucial role in grid energy management. In this paper, a problem of hours ahead solar plant power prediction and forecasting is considered. This accurate prediction can significantly improve the electricity cost by planning the extra power generation for supplying loads. For a case study, a meteorological dataset for a real operated solar power plant is used to develop and verify the forecasting model. A detailed time series analysis of the solar power output (per second data) has been used for model development and validation. Additionally, after investigating different techniques, a Glosten-Jagannathan-Runkle Generalized Auto Regressive Conditional Heteroskedasticity (GJR-GARCH) based forecasting has been proposed. The developed model predicts solar power with high accuracy with a confidence interval of 99%. The results and verification with a real data set prove the capability of applying this method in industrial applications. These predicted models are utilized to generate battery control signals ensuring a smooth power flow to the grid or directly send to the grid for ensuring peak energy savings.
Weight-Aware Cache for Application-Level Proportional I/O Sharing

Virtualization technology has enabled server consolidation where multiple servers are co-located on a single physical machine to improve resource utilization. In such systems, proportional I/O sharing is critical to meet the SLO (Service-Level Objectives) of the applications running in each virtual instance. However, previous studies focus on block-level I/O proportionality without considering the upper-layer I/O caches, which handle I/O requests on behalf of the underlying storage devices, thereby failing to achieve application-level proportional I/O sharing. To overcome this limitation, we propose a new cache management scheme, Weight-aware Cache (WaC), which reflects the I/O weights on cache allocation and reclamation. Specifically, WaC prioritizes higher-weighted applications in the lock acquisition process of cache allocation by re-ordering the lock waiting queue based on I/O weight. Additionally, WaC keeps the number of cache entries of each application proportional to its I/O weight, through weight-aware cache reclamation. To verify the efficacy of our scheme, we implement and evaluate WaC on both the page cache and bcache. The experimental results demonstrate that our scheme improves I/O proportionality with negligible overhead in various cases.
Study of Statistical Text Representation Methods for Performance Improvement of a Hierarchical Attention Network

To effectively process textual data, many approaches have been proposed to create text representations. The transformation of a text into a form of numbers that can be computed using computers is crucial for further applications in downstream tasks such as document classification, document summarization, and so forth. In our work, we study the quality of text representations using statistical methods and compare them to approaches based on neural networks. We describe in detail nine different algorithms used for text representation and then we evaluate five diverse datasets: BBCSport, BBC, Ohsumed, 20Newsgroups, and Reuters. The selected statistical models include Bag of Words (BoW), Term Frequency-Inverse Document Frequency (TFIDF) weighting, Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA). For the second group of deep neural networks, Partition-Smooth Inverse Frequency (P-SIF), Doc2Vec-Distributed Bag of Words Paragraph Vector (Doc2Vec-DBoW), Doc2Vec-Memory Model of Paragraph Vectors (Doc2Vec-DM), Hierarchical Attention Network (HAN) and Longformer were selected. The text representation methods were benchmarked in the document classification task and BoW and TFIDF models were used were used as a baseline. Based on the identified weaknesses of the HAN method, an improvement in the form of a Hierarchical Weighted Attention Network (HWAN) was proposed. The incorporation of statistical features into HAN latent representations improves or provides comparable results on four out of five datasets. The article presents how the length of the processed text affects the results of HAN and variants of HWAN models.
Noiseless Attenuation for Continuous-Variable Quantum Key Distribution over Ground-Satellite Uplink

Satellite-based quantum key distribution (QKD) has lately received considerable attention due to its potential to establish a secure global network. Associated with its application is a turbulent atmosphere that sets a notable restriction to the transmission efficiency, which is especially challenging for ground-to-satellite uplink scenarios. Here, we propose a novel noiseless attenuation (NA) scheme involving a zero-photon catalysis operation for source preparation to improve the performance of continuous-variable (CV) QKD over uplink. Numerical analysis shows that the NA-based CV-QKD, under attenuation optimization, outperforms the traditional CV-QKD, which is embodied in extending the allowable zenith angle while improving the effective communication time. Attributing to characteristics of the attenuation optimization, we find that the NA-involved source preparation improves the security bound by relatively reducing the amount of information available to eavesdroppers. Taking the finite-size effect into account, we achieve a tighter bond of security, which is more practical compared with the asymptotic limit.
Low Precision Networks for Efficient Inference on FPGAs

Block Floating Point (BFP) is a type of quantization that combines high dynamic range with low-cost inference. BFP can be implemented efficiently on FPGA hardware and, at low precision, halves the logic footprint versus blocked FP16 while maintaining accuracy. Moving to very low precision halves the logic footprint again and retraining allows the recovery of any accuracy lost in transition. This paper describes our approach to achieving target accuracy and FPGA resource usage in a low-precision end-to-end AI solution. We go on to investigate the effects of retraining with our software model that replicates the low-level implementation of BFP on FPGA. Our solution allows efficacy testing for the quantization of custom networks and provides accuracy indications and resource usage for the final application. Using our solution, we were able to quantize ResNet 50, SSD300 and UNet to int5/4bfp precision without losing accuracy while reducing FPGA resources and improving performance.
mRAT-SQL+GAP: A Portuguese Text-to-SQL Transformer

The translation of natural language questions to SQL queries has attracted growing attention, in particular in connection with transformers and similar language models. A large number of techniques are geared towards the English language; in this work, we thus investigated translation to SQL when input questions are given in the Portuguese language. To do so, we properly adapted state-of-the-art tools and resources. We changed the RAT-SQL+GAP system by relying on a multilingual BART model (we report tests with other language models), and we produced a translated version of the Spider dataset. Our experiments expose interesting phenomena that arise when non-English languages are targeted; in particular, it is better to train with original and translated training datasets together, even if a single target language is desired. This multilingual BART model fine-tuned with a double-size training dataset (English and Portuguese) achieved 83% of the baseline, making inferences for the Portuguese test dataset. This investigation can help other researchers to produce results in Machine Learning in a language different from English. Our multilingual ready version of RAT-SQL+GAP and the data are available, open-sourced as mRAT-SQL+GAP at: https://github.com/C4AI/gap-text2sql
Spatial-Context-Aware Deep Neural Network for Multi-Class Image Classification

Multi-label image classification is a fundamental but challenging task in computer vision. Over the past few decades, solutions exploring relationships between semantic labels have made great progress. However, the underlying spatial-contextual information of labels is under-exploited. To tackle this problem, a spatial-context-aware deep neural network is proposed to predict labels taking into account both semantic and spatial information. This proposed framework is evaluated on Microsoft COCO and PASCAL VOC, two widely used benchmark datasets for image multi-labelling. The results show that the proposed approach is superior to the state-of-the-art solutions on dealing with the multi-label image classification problem.
Panoramic image mosaic method based on image segmentation and Improved SIFT algorithm

Abstract Aiming at the problem of large amount of calculation in extracting image feature points in panoramic image mosaic by SIFT algorithm, a panoramic image mosaic algorithm based on image segmentation and Improved SIFT is proposed in this paper. The algorithm fully considers the characteristics of panoramic image stitching. Firstly, the stitched image is divided into blocks, and the maximum overlapping block of image pairs is extracted by using mutual information. The SIFT key points are extracted by SIFT algorithm, and the dog is filtered before the spatial extreme value detection of SIFT algorithm to eliminate the feature points with small intensity value; When establishing the feature descriptor, the 128 dimension of the original algorithm is reduced to 64 dimensions to reduce the amount of calculation. In the feature point registration process, the feature descriptor is reduced to 32 dimensions, the feature point pairs are roughly extracted by the optimal node first BBF algorithm, and the feature point pairs are registered and screened by RANSAC; Finally, the image transformation matrix is obtained to realize panoramic image mosaic. The experimental results show that the proposed algorithm not only ensures the panoramic mosaic effect, but also extracts the feature points in 11% of the time of the traditional SIFT algorithm, and the feature point registration speed is 27.17% of the traditional SIFT algorithm.