Reviewerly/exHarmony huggingface dataset:
FeatureBox: Feature Engineering on GPUs for Massive-Scale Ads Systems

Deep learning has been widely deployed for online ads systems to predict click-through rate (CTR). Practitioners frequently re-train CTR models to test their new extracted features. As the CTR model training relies on a large number of raw input data logs, the feature extraction step takes a significant portion of the training time. In this paper, we propose FeatureBox, a novel end-to-end training framework that pipelines the feature extraction and the training on GPU servers to save the intermediate I/O of the feature extraction. We rewrite computation-intensive feature extraction operators as GPU operators and leave the memory-intensive operator on CPUs. We introduce a layer-wise operator scheduling algorithm to schedule these heterogeneous operators. We present a light-weight GPU memory management algorithm that supports dynamic GPU memory allocation with minimal overhead. We experimentally evaluate FeatureBox and compare it with the previous in-production feature extraction framework on two ads applications. The results confirm the effectiveness of our proposed method.
An Empirical Study on Characterizing Natural Disasters in Class Imbalanced Social Media Data using Weak Supervision

Supervised learning has proven to be successful in classifying both class balanced and imbalanced data when a strong supervision signal is available. However, generating the supervision signal (eg: ground truth labels) is expensive and a major bottleneck of supervised learning. To curtail this, we rely on the theory of noisy learning and weak supervision to generate supervision signals. In this work, we utilize a noisy labeled dataset to train several class balanced and imbalanced machine learning models and compare the results to observe how efficient the models trained on silver standard dataset are in identifying ground truth labels. We demonstrate the approach on a natural disasters application which contains data from three different natural disasters. Our results demonstrate that theory of noisy learning can be utilized to build models via weak supervision for both class balanced and imbalanced data from social media sources for natural disasters application.
Using Transformers for Identification of Persuasion Principles in Phishing Emails

It is important to learn about attackers and their attacking strategies so that better and more effective defense systems can be built. During the reconnaissance stage, attackers intend to probe potential targets through various techniques including social engineering attacks. Phishing through email is a well-known, cheap, easy, and surprisingly effective technique for obtaining the needed information. This type of attack targets individuals and thus utilizes weaknesses that might exist in each person. Given the uniqueness of each individual’s personality, attackers make sure the right persuasion principle technique is employed for each targeted individual. This paper describes efforts to build machine-learning transformers, the emerging technique in language modeling, with the goal of building classifiers that take into account different types of persuasion principles. More specifically, the paper describes efforts to build machine-learning transformers based on BERT, RoBERTa, and DistilBERT and captures their classification results. The results show that these transformers are accurate enough to build a classification of phishing emails with respect to persuasion techniques. Furthermore, we report that the RoBERTa model is able to train faster than BERT and DistilBERT models.
Decomposed Transformer with Frequency Attention for Multivariate Time Series Anomaly Detection

Unsupervised anomaly detection for multivariate time series has been an active area of research due to its enormous potential for industrial applications. Existing works have made extraordinary progress in time series representation, reconstruction and forecasting. However, the intricate temporal patterns of individual signals and the relationships between different signals are rarely explicitly considered, which limits the performance. To this end, we propose a novel approach based on Transformer and signal decomposition for time series anomaly detection. Specifically, we provide a multi-view embedding method to capture temporal and correlational features of signals. To take full advantage of temporal patterns, we design a frequency attention module to extract periodic oscillation features. Then, the signals are decomposed into seasonal, trend and remainder components for further representation separately. Additionally, we employ a optimization strategy to improve feature representation. Extensive experiments on various public benchmarks demonstrate that our method has achieved the state-of-the-art performance.
An Empirical Comparison of DistilBERT, Longformer and Logistic Regression for Predictive Coding

In recent years, transformer-based, large-scale language models have greatly advanced deep learning in NLP tasks. These models allow transfer learning to be performed on NLP in similar methods to what was previously done for computer vision. Two of the latest models are DistilBERT and Longformer -- the former is a distilled version of BERT, which enables faster training and inferencing on off-cloud servers, and the latter offers capability of working with long texts. In this paper, we study empirical comparisons of the effectiveness of the two deep learning methods along with a logistical regression method for text classification, using three real-world datasets from legal document reviews. The study shows that Longformer performs better (up to 10%) than or at par with the other two methods. Cross-dataset evaluation is leveraged as well to validate the performance of Longformer as a viable method when labeled data is not available.
IEEE Big Data Cup 2022: Privacy Preserving Matching of Encrypted Images with Deep Learning

Smart sensors, devices and systems deployed in smart cities have brought improved physical protections to their citizens. Enhanced crime prevention, and fire and life safety protection are achieved through these technologies that perform motion detection, threat and actors profiling, and real-time alerts. However, an important requirement in these increasingly prevalent deployments is the preservation of privacy and enforcement of protection of personal identifiable information. Thus, strong encryption and anonymization techniques should be applied to the collected data. In this IEEE Big Data Cup 2022 challenge, different masking, encoding and homomorphic encryption techniques were applied to the images to protect the privacy of their contents. Participants are required to develop detection solutions to perform privacy preserving matching of these images. In this paper, we describe our solution which is based on state-of-the-art deep convolutional neural networks and various data augmentation techniques. Our solution achieved 1 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> place at the IEEE Big Data Cup 2022: Privacy Preserving Matching of Encrypted Images Challenge.
Preserving Privacy Integration and Mining for Big Temporal Co-occurrence Patterns

Privacy policy, terms of use, public consent, reusable data, and transparency are trending words associated with the world wide web data such that privacy is now the responsibility of all stakeholders. Although privacy is a concern, integrating publicly available data may be for social good. For instance, integrating emergency calls, substance use, and overdose antagonist drug may help inform policies relating to emergency resources allocation, substance overdose antagonist drug distribution, and spiral effect of reducing overdose death. Hence, in this paper, we examine privacy preserving integration of public open data in the hierarchy of time and space. Our experimental result on four open datasets demonstrate the effectiveness of temporal and location hierarchy model in preserving privacy integration of big temporal co-occurrence data.
Gradient Boosted Trees for Privacy-Preserving Matching of Encrypted Images

A huge amount of data of various types is stored on servers and transferred over the Internet. This may pose a threat to the privacy of individuals. Encryption algorithms are developed to protect privacy. However, not all encryption mechanisms are effective. The easiest way of proving such ineffectiveness is by presenting a method of breaking the encryption scheme. In this paper, one method for matching original and encrypted images is proposed. The method was developed under the Privacy-preserving Matching of Encrypted Images shared task in the IEEE BigData 2022 Cup, and achieved second place with an accuracy of 0.6915, trailing the winning solution by only 0.0031. The proposed solution is based on Gradient Boosted Trees as implemented in CatBoost, and feature extraction of pixel value aggregates regardless of their positions. This is done using Arnold’s cat map obfuscation scheme in the encryption algorithm. Arnold’s cat map shuffles pixel positions, but leaves the image histogram unchanged. The method described in this paper almost solves two of the subtasks in the competition, reaching 0.98 accuracy for both of them; however, it does not propose a solution to the third subtask.
Sequential Recommendation with Auxiliary Item Relationships via Multi-Relational Transformer

Sequential Recommendation (SR) models user dynamics and predicts the next preferred items based on the user history. Existing SR methods model the ‘was interacted before’ item-item transitions observed in sequences, which can be viewed as an item relationship. However, there are multiple auxiliary item relationships, e.g., items from similar brands and with similar contents in real-world scenarios. Auxiliary item relationships describe item-item affinities in multiple different semantics and alleviate the long-lasting cold start problem in the recommendation. However, it remains a significant challenge to model auxiliary item relationships in SR.To simultaneously model high-order item-item transitions in sequences and auxiliary item relationships, we propose a Multi-relational Transformer capable of modeling auxiliary item relationships for SR (MT4SR). Specifically, we propose a novel self-attention module, which incorporates arbitrary item relationships and weights item relationships accordingly. Second, we regularize intra-sequence item relationships with a novel regularization module to supervise attentions computations. Third, for inter-sequence item relationship pairs, we introduce a novel inter-sequence related items modeling module. Finally, we conduct experiments on four benchmark datasets and demonstrate the effectiveness of MT4SR over state-of-the-art methods and the improvements on the cold start problem. The code is available in https://github.com/zfan20/MT4SR.
Heterogeneity-Aware Adaptive Federated Learning Scheduling

Federated learning (FL) is becoming an important distributed machine learning approach that considers privacy and security concerns while training a shared model across various clients with localized data. One of the key challenges in FL is heterogeneity in both hardware resources and local datasets due to the nature of incorporating diverse clients. Given the resource heterogeneity, the availability of participating clients is not stable over time and their resource usage patterns become dynamic. This leads to resource wastage and straggler issues. Additional challenges are introduced due to data heterogeneity, causing model biasness and poor model performance. However, most existing FL systems are not well suited to heterogeneous environments because those approaches are not adaptive to various and dynamically changing resource usage patterns and accuracy trends during training process. To this end, we propose a heterogeneity-aware scheduling which is adaptive to the accuracy trends and various resource usage patterns. Our proposed scheduling provides different scheduling knobs for achieving different goals such as resource-efficient fast training, resource fairness, accuracy fairness, and high model performance. To the best of our knowledge, this is the first effort to mitigate effects of resource and data heterogeneity while providing adaptive scheduling based on dynamically changing resource usage patterns and accuracy trends.
PMU-Spill: Performance Monitor Unit Counters Leak Secrets in Transient Executions

The processor's Performance Monitor Unit (PMU) allows the recording of architectural and microarchitectural events for profiling purposes. In this study, we reveal a security issue caused by the fact that current PMU implementations are capable of recording some events that happened during transient executions. We propose the PMU -Spill attack, a new kind of attack that enables attackers to maliciously leak the secret data in transient executions. We demonstrate on real hardware that PMU -Spill attack can leak the secret data stored in Intel Software Guard Extensions (SGX). In addition, we perform a thorough study to reveal all the vulnerable PMU counters and find that 20 of them can be used to achieve PMU -Spill attack. Our experiments suggest that the throughput of PMU -Spill attack is up to 575.3 bytes per second (Bps) with an average error rate of 1.89% when leaking the SGX-protected secret data.
Contrast Enhancement of Lung CT Scan Images using Multi-Level Modified Dualistic Sub-Image Histogram Equalization

Image enhancement is one of the most important phases of automated in lung cancer detection. Lung cancer is a worldwide fatal disease that claims victims among both men and women. Computed Tomography (CT) is the most effective predominantly used medical imaging modality for diagnosing uncontrolled lung cell growth. The detection of lung cancer from a CT scan using visual perception requires extensive visual abilities and expertise. The proposed Multi-Level Modified Dualistic Sub Image Histogram Equalization (ML-DSIHE) separates the histogram of a CT image into two parts using the mid-point of the intensity scale. Then ML-DSIHE iteratively divides the bifurcated histogram based on the median and then histogram equalization independently on those partitioned histogram. The proposed ML-DSIHE offers a solution for over-enhancement and under-enhancement of background, loss of foreground Gray-Level components, and noise amplification. The M-DSIHE is confirmed to be a novel enhancement method and thus greater aids in the early detection of lung cancer improved accuracy. The ML-DSIHE is found to the accuracy of early detection of lung cancer. Moreover, this enhancement technique is computationally simple and thus takes less computational time.
Оценка возможностей классических компьютеров при реализации симуляторов квантовых алгоритмов

Modern quantum devices have severe limitations in the number of qubits, which limit the width and depth of the quantum circuit and have strong noise processes that make it difficult to obtain correct results.It is also necessary to design quantum circuits for a particular quantum device taking into account the coupling between qubits and to apply quantum error mitigation.It is possible to avoid these problems using classical computers to simulate quantum computation.Classical computers are used both for quick testing of hypotheses before running on quantum devices and for solving real-world problems.
Semantic Segmentation In Learned Compressed Domain

Most machine vision tasks (e.g., semantic segmentation) are based on images encoded and decoded by image compression algorithms (e.g., JPEG). However, these decoded images in the pixel domain introduce distortion, and they are optimized for human perception, making the performance of machine vision tasks suboptimal. In this paper, we propose a method based on the compressed domain to improve segmentation tasks. i) A dynamic and a static channel selection method are proposed to reduce the redundancy of compressed representations that are obtained by encoding. ii) Two different transform modules are explored and analyzed to help the compressed representation be transformed as the features in the segmentation network. The experimental results show that we can save up to 15.8% bitrates compared with a state-of-the-art compressed domain-based work while saving up to about 83.6% bitrates and 44.8% inference time compared with the pixel domain-based method.
Non-Perturbative Treatment of Open-System Multi-Time Expectation Values in Gaussian Bosonic Environments

We determine the conditions for the equivalence between the multi-time expectation values of a general finite-dimensional open quantum system when interacting with, respectively, an environment undergoing a free unitary evolution or a discrete environment under a free evolution fixed by a proper Gorini-Kossakowski-Lindblad-Sudarshan generator. We prove that the equivalence holds if both environments are bosonic and Gaussian and if the one- and two-time correlation functions of the corresponding interaction operators are the same at all times. This result leads to a non-perturbative evaluation of the multi-time expectation values of operators and maps of open quantum systems interacting with a continuous set of bosonic modes by means of a limited number of damped modes, thus setting the ground for the investigation of open-system multi-time quantities in fully general regimes.
An Automated System for Employee Recruitment Management

Recruitment of employees is an important process in the human resource management of a company. Currently, most of the recruitment process is done manually in many companies. This manual process may be time-consuming and possibly may be erroneous in employing inappropriate individuals. This may result in the loss of time, money, and efficiency of a company. As a solution to the above problem, we are considering developing an automated process for recruitment. The scope of the system is to cover not only the recruitment process but also to provide job seekers a platform to identify their current skills, help them identify the current skill trends that are required by companies, and provide the ability to automatically generate their resumes through the system. On the other hand, employers will save a lot of time and money since the system will automate the processes such as skill matching of the employee and the company, shortlisting of resumes, and scheduling interviews. The platform involves features such as online mock interview hosting, automated scheduling, and a pre-interview quiz with a monitoring background. To achieve the above components, machine learning algorithms are used along with other technologies such as web scraping.
Can we predict useful comments in source codes? - Analysis of findings from Information Retrieval in Software Engineering Track @ FIRE 2022

The Information Retrieval in Software Engineering (IRSE) track aims to develop solutions for automated evaluation of code comments in a machine learning framework. In this track, there is a binary classification task to classify comments as useful and not useful. The dataset consists of 9048 code comments and surrounding code snippet pairs extracted from open source github C based projects. Overall 34 experiments have been submitted by 11 teams from various universities and software companies. The submissions have been evaluated quantitatively using the F1-Score and qualitatively based on the type of features developed, the supervised learning model used and their corresponding hyper-parameters. The best performing architectures mostly have employed transformer architectures coupled with a software development related embedding space.
Coverage Path Planning and Precise Localization for Autonomous Lawn Mowers

In this paper, we present a concept for automatic path planning and high-precision localization for autonomous lawn mowers. In particular, two objectives contribute to the increased efficiency of the presented approach compared to classical automatic lawn mowing techniques. First, the standard chaotic control of the mower is replaced by an efficient planning strategy for traversing the area without gaps and with as few overlaps as possible. Second, the conventional boundary wires become unnecessary as high-precision localization based on multi-sensor fusion allows for keeping the virtual boundaries. The whole concept is implemented and tested on an industrial-grade lawn mower. The advantages of intelligent path planning over chaotic strategies are shown, and the localization performance is validated using real-world data.
Deep Learning Based Malicious Drone Detection Using Acoustic and Image Data

Drones have been studied in a variety of industries. Drone detection is one of the most important task. The goal of this paper is to detect the target drone using the microphone and a camera of the detecting drone by training deep learning models. For evaluation, three methods are used: visual-based, audio-based, and the decision fusion of both features. Image and audio data were collected from the detecting drone, by flying two drones in the sky at a fixed distance of 20m. CNN (Convolutional Neural Network) was used for audio, and YOLOv5 was used for computer vision. From the result, the decision fusion of audio and vision-based features showed the highest accuracy among the three evaluation methods.
CRC-Aided Short Convolutional Codes and RCU Bounds for Orthogonal Signaling

We extend earlier work on the design of convolutional code-specific CRC codes to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$Q$</tex> -ary alphabets, with an eye toward <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$Q$</tex> -ary orthogonal signaling. Starting with distance-spectrum optimal, zero-terminated, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$Q$</tex> -ary convolutional codes, we design <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$Q$</tex> -ary CRC codes so that the CRC/convolutional concatenation is distance-spectrum optimal. The <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$Q$</tex> -ary code symbols are mapped to a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$Q$</tex> -ary orthogonal signal set and sent over an AWGN channel with noncoherent reception. We focus on <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$Q=4$</tex> , rate-1/2 convolutional codes in our designs. The random coding union bound and normal approximation are used in earlier works as benchmarks for performance for distance-spectrum-optimal convolutional codes. We derive a saddlepoint approximation of the random coding union bound for the coded noncoherent signaling channel, as well as a normal approximation for this channel, and compare the performance of our codes to these limits. Our best design is within 0.6 dB of the RCU bound at a frame error rate of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−4</sup> .