Reviewerly/exHarmony huggingface dataset:
A Data and Model Parallelism based Distributed Deep Learning System in a Network of Edge Devices

With the emergence of edge computing along with its local computation advantage over the cloud, methods for distributed deep learning (DL) training on edge nodes have been proposed. The increasing scale of DL models and large training dataset poses a challenge to run such jobs in one edge node due to resource constraints. However, the proposed methods either run the entire model in one edge node, collect all training data into one edge node, or still involve the remote cloud. To handle the challenge, we propose a fully distributed training system that realizes both Data and Model Parallelism over a network of edge devices (called DMP). It clusters the edge nodes to build a training structure by taking advantage of the feature that distributed edge nodes sense data for training. For each cluster, we propose a heuristic and a Reinforcement Learning (RL) based algorithm to handle the problem of how to partition a DL model and assign the partitions to edge nodes for model parallelism to minimize the overall training time. Taking advantage of the feature that geographically close edge nodes sense similar data, we further propose two schemes to avoid transferring duplicated data to the first-layer edge node as training data without compromising accuracy. Our container-based emulation and real edge node experiments show that our systems reduce up to 44% training time while maintaining the accuracy comparing with the state-of-the-art approaches. We also open sourced our source code.
Finding BERT Errors Using Activation Vectors

The internal workings of deep learning models are opaque and are considered as black boxes due to their nested and non-linear structure. This opaque nature of the deep neural networks makes it difficult to interpret the reason behind their output, thus reducing trust and verifiability of the system where these models are applied. This paper explains a systematic approach to identify the clusters with most misclassifications or false label annotations. For this research, we extracted the activation vectors from a deep learning model, DNABERT, and visualized them using t-SNE to decode the reason behind the results that are produced. We applied K-means in a hierarchical fashion on the activation vectors for a set of training instances. We analyzed cluster mean activation vectors to find any patterns in the errors across K-means clusters. The cluster analysis revealed that the predictions were uniform, or nearly 100 percent same, in clusters of similar activation vectors. It was found that two clusters containing most of their objects belonging to the same true class tend to be closer together than clusters of opposite classes. The means of objects of the same true label are closer if two clusters have the same predicted labels rather than opposite predicted labels, showing that the activation vectors reflect both predicted and true classes. We propose a heuristic to find the clusters with a high number of misclassifications or incorrect label annotations using the between clusters and within clusters mean vector analysis. This can aid in identifying misclassifications of DNA sequences or problems with sequence tagging.
An Empirical Study of Bugs in Quantum Machine Learning Frameworks

Quantum computing has emerged as a promising domain for the machine learning (ML) area, offering significant computational advantages over classical counterparts. With the growing interest in quantum machine learning (QML), ensuring the correctness and robustness of software platforms to develop such QML programs is critical. A necessary step for ensuring the reliability of such platforms is to understand the bugs they typically suffer from. To address this need, this paper presents the first comprehensive study of bugs in QML frameworks. We inspect 391 real-world bugs collected from 22 open-source repositories of nine popular QML frameworks. We find that 1) 28% of the bugs are quantum-specific, such as erroneous unitary matrix implementation, calling for dedicated approaches to find and prevent them; 2) We manually distilled a taxonomy of five symptoms and nine root cause of bugs in QML platforms; 3) We summarized four critical challenges for QML framework developers. The study results provide researchers with insights into how to ensure QML framework quality and present several actionable suggestions for QML framework developers to improve their code quality.
When Edge Meets FaaS: Opportunities and Challenges

The proliferation of edge devices and the rapid growth of IoT data have called forth the edge computing paradigm. Function-as-a-service (FaaS) is a promising computing paradigm to realize edge computing. This paper explores the feasibility and advantages of FaaS-based edge computing. It also studies the research challenges that should be addressed in the design of such systems, which are 1) the quick decomposing and recomposing of applications, 2) the trade-off between performance and isolation of sandbox mechanisms, and 3) distributed scheduling. The challenges are illustrated by evaluating existing FaaS-based edge platforms, AWS IoT Greengrass, and OpenFaaS.
Deep Learning-based Transceiver with One-bit ADC Over Fading Channel

To tackle the power consumption challenges in terahertz band wireless communication, this study proposes a deep learning-driven approach for transceiver design that utilizes one-bit quantization and oversampling at the receiver. The solution also involves implementing Faster-than-Nyquist (FTN) transmission on a fading channel. Our approach employs a convolutional autoencoder (AE) to enable the transmission of higher-order modulation over a one-bit fading channel while utilizing pilots. By exploiting the AE transceiver, it is evident that performance in quantized communication has significantly improved for QPSK, 16-QAM, and 64-QAM modulation levels, approaching the theoretical lower bound for the corresponding modulation over additive white Gaussian noise (AWGN) channel. Furthermore, the study has explored how to use the robust error-correcting capabilities of the AE transceiver to boost spectral efficiency by increasing FTN rates without dire Bit-error-rate sacrifice.
Soft Body Pose-Invariant Evasion Attacks against Deep Learning Human Detection

Evasion attacks on deep neural networks (DNN) use manipulated data to let targets evade detection and/or classification across a wide range of DNNs. Most existing evasion attacks focus on planar images (e.g., photo, satellite imaging) and ignore the distortion of evasion in practical attacks (e.g., object rotation, deformation). Here, we build evasion patterns for soft-body human stakeholders, where patterns are designed to take into account body rotation, fabric stretch, printability, and lighting variations. We show that these are effective and robust to different human poses. This poses a significant threat to safety of autonomous vehicles and adversarial training should consider this new area.
APNet: Attention Mechanism with Point Sampling Loss Network for Remote Sensing Images Semantic Segmentation

With the rapid development of remote sensing technology, high-resolution remote sensing images (HRRSIs) are becoming increasingly prevalent in various applications. HRRSIs contain a large amount of information on ground objects, exhibiting both diversity and complexity. The semantic segmentation of HRRSIs is a rapidly evolving field, emerging with the development of remote sensing technology. However, HRRSIs are typically captured from high altitudes, have a wide imaging range, and present a number of challenges, including foreground-background imbalance, multi-scale ground objects, large intra-class variance and small inter-class variance, all of which contribute to the difficulty of semantic segmentation. To address these challenges, this paper proposes an end-to-end semantic segmentation model named APNet, short for Attention Mechanism with Point Sampling Loss Network, which uses attention mechanism, multi-scale feature fusion, and a point sampling loss function. APNet aims to address the aforementioned challenges and improve the accuracy of semantic segmentation. We conducted comparative experiments with several classic methods on the LoveDA dataset. The results demonstrate the effectiveness of our proposed method in improving the results of semantic segmentation.
Reinforcement Learning with Neural Network-based Deterministic Game Tree Approximation

The goal of reinforcement learning is to learn a policy that maximizes a reward function. In some environments with complete information, search algorithms are highly useful in simulating action sequences in a game tree. However, in many practical environments, such effective search strategies are not applicable since their state transition information may not be available. This paper proposes a novel method to approximate a game tree that enables a reinforcement learning agent to use search strategies even in incomplete information environments. With an approximated game tree, the agent predicts all possible states multiple steps into the future and evaluates the states to determine the best action sequences with the highest return. Our proposal differs from deep reinforcement learning in that it uses deep learning for not only the state evaluation but also game tree approximation. This allows the agent to perform better in completing complex tasks as well as learning tasks in sparse reward environments.
Creating Robust Deep Neural Networks with Coded Distributed Computing for IoT

The increasing interest in serverless computation and ubiquitous wireless networks has led to numerous connected devices in our surroundings. Such IoT devices have access to an abundance of raw data, but their inadequate resources in computing limit their capabilities. With the emergence of deep neural networks (DNNs), the demand for the computing power of IoT devices is increasing. To overcome inadequate resources, several studies have proposed distribution methods for IoT devices that harvest the aggregated computing power of idle IoT devices in an environment. However, since such a distributed system strongly relies on each device, unstable latency, and intermittent failures, the common characteristics of IoT devices and wireless networks, cause high recovery overheads. To reduce this overhead, we propose a novel robustness method with a close-to-zero recovery latency for DNN computations. Our solution never loses a request or spends time recovering from a failure. To do so, first, we analyze how matrix computations in DNNs are affected by distribution. Then, we introduce a novel coded distributed computing (CDC) method, the cost of which, unlike that of modular redundancies, is constant when the number of devices increases. Our method is applied at the library level, without requiring extensive changes to the program, while still ensuring a balanced work assignment during distribution.
Real-time hybrid quantum-classical computations for trapped ions with Python control-flow

In recent years, the number of hybrid algorithms that combine quantum and classical computations has been continuously increasing. These two approaches to computing can mutually enhance each others’ performances thus bringing the promise of more advanced algorithms that can outmatch their pure counterparts. In order to accommodate this new class of codes, a proper environment has to be created, which enables the interplay between the quantum and classical hardware.For many of these hybrid processes the coherence time of the quantum computer arises as a natural time constraint, making it crucial to minimize the classical overhead. For ion-trap quantum computers however, this is a much less limiting factor than with superconducting technologies, since the relevant timescale is on the order of seconds instead of microseconds. In fact, we show that the operating time-scales of trapped-ion quantum computers are compatible with the execution speed of the Python programming language, enabling us to develop an interpreted scheme for real-time control of quantum computations. In particular, compilation of all instructions in advance is not necessary, unlike with superconducting qubits. This keeps the implementation of hybrid algorithms simple and also lets users benefit from the rich environment of existing Python libraries.In order to show that this approach of interpreted quantum-classical computations (IQCC) is feasible, we bring real-world examples and evaluate them in realistic benchmarks.
ESDI: Entanglement Scheduling and Distribution in the Quantum Internet

Quantum entanglement distribution between remote nodes is key to many promising quantum applications. Existing mechanisms have mainly focused on improving throughput and fidelity via entanglement routing or single-node scheduling. This paper considers entanglement scheduling and distribution among many source-destination pairs with different requests over an entire quantum network topology. Two practical scenarios are considered. When requests do not have deadlines, we seek to minimize the average completion time of the communication requests. If deadlines are specified, we seek to maximize the number of requests whose deadlines are met. Inspired by optimal scheduling disciplines in conventional single-queue scenarios, we design a general optimization framework for entanglement scheduling and distribution called ESDI, and develop a probabilistic protocol to implement the optimized solutions in a general buffered quantum network. We develop a discrete-time quantum network simulator for evaluation. Results show the superior performance of ESDI compared to existing solutions.
Location Inference under Temporal Correlation

Location Based Services (LBSs) have become increasingly popular in the past decade, allowing mobile users to access location-dependent information and services. To protect user privacy while using LBSs, various Location Privacy Protection Mechanisms (LPPMs) have been proposed that obfuscate users' true locations through random perturbation. However, adversaries can still exploit the temporal correlation between a user's locations in multiple LBS queries to improve inference accuracy. In this paper, we introduce a novel location inference attack that strikes a good balance between inference accuracy and computational complexity by effectively exploiting temporal correlation. Simulation studies using synthetic and real datasets confirm the advantages of our proposed attack.
Graph deep learning hashtag recommender for reels

Recommendation systems are algorithms that attempt to provide consumers with relevant suggestions for items, such as movies, videos, or reels (micro videos) to watch, hashtags for their posts, songs to listen to, or items to purchase. In many businesses, recommender systems are essential because they can create enormous amounts of revenue and make the platform stand out when compared to others. In this project, we focus on providing users with hashtag recommendations for the reels they want to post, based on their preferences for the reels and hashtags, creating a personalized recommender for each user. Users add hashtags to posts which, in their opinion, properly depict the content in the post. Thus, we should consider both users’ preferences for the content of the post and their preferences for the hashtags. Most methods that implement hashtag recommendations model interactions between users and hashtags or hashtags and posts, unlike this scenario, where we design a hashtag recommendation system based on users, reels, and hashtags. The dataset was built using web scraping, which downloaded the reels from TikTok. We extracted video and audio features from the videos and text features from the hashtags used by users to annotate the reel. We built GNNMCL, a personalized hashtag recommender to recommend hashtags to users based on their previous posts, taking into account both their preferences for the content of the post and their understanding of hashtags. Our GNNMCL graph deep learning model performed better than existing approaches by achieving a NDCG score of 0.9156.
Depicting Instant Messaging Encrypted Traffic Characteristics through an Empirical Study

Instant Messaging Applications (IMAs), such as Discord and WhatsApp, have become one of the main communication tools for mobile device users. Network traffic analysis is a method of monitoring network activity to identify operational and security issues. There is limited research on network traffic analysis of IMAs on mobile devices due to the challenges of end-to-end encryption, user privacy, and dynamic port usage. In this paper, we design, develop and evaluate a framework to generate end-to-end IMA traffic on mobile devices, employ feature selection and conduct traffic analysis that can cope with encrypted traffic while identifying different IMAs. Results show a performance evaluation workbench as well as highlight the key characterictis of six popular IMAs.
Personalized Tweet Recommendation Using Users’ Image Preferences

In the era of information explosion, the vast amount of data on social media platforms can overwhelm users. As a result, personalized recommendation systems have become increasingly important to help users navigate and make sense of this data. We propose a novel technique to use users’ image preferences to recommend tweets. We extract vital information by analyzing images liked by users and use it to recommend tweets from Twitter.
Role of Liver Registration in Hepatocellular Carcinoma Treatment Planning

This paper describes a new non-rigid approach to register liver images from two different imaging modalities. The deformation is a key challenge in medical image registration. We have proposed a diffeomorphism-based method to tackle this problem using an optimized framework. A non stationary velocity field is used to minimize the effect of forces that are derived from the image gradients. Furthermore, we propose a similarity energy function, based on the gray scale distribution, to limit the fluctuations while approaching the local minima. The proposed method is evaluated on both private and public datasets; the results show that the values of mean square error (MSE), normalized cross-correlation (NCC), structural similarity (SS), mutual information (MI), feature similarity index (FSIM), and mean absolute error (MAE) are 1.3136, 0.9962, 0.9897, 0.883, 0.9922, and 1.52± 2.09, respectively. Both qualitative and quantitative evaluation show promising registration accuracy reflecting the potential of the proposed method.
Spam SMS Classification Using Machine Learning

Over the past few years, the use of emails and text messages has drastically increased. Short Message Service (SMS) on cellphone providers and related apps, like Whatsapp, is one of the best and fastest ways to communicate among users. SMSs are used and sent globally for personal and business purposes. However, alongside safe SMSs, the users may receive fraudulent Spam SMSs, which could cause security issues and inconvenient for the users. Numerous Spam messages are being sent daily for both personal and professional benefits. Accurately identifying Spam SMS is a challenge. The objective of this research is to build a model utilizing machine learning and deep learning to understand the semantics of SMSs and classify them to either Spam or non-Spam (Ham). We used a pre-trained BERT model and combined it with several machine learning and deep learning models. The results indicated that BERT+SVC and BERT+BiLSTM performed the best with 99.10% and 99.19% accuracies respectively on the test dataset.
Grandma Karl is 27 years old – research agenda for pseudonymization of research data

Accessibility of research data is critical for advances in many research fields, but textual data often cannot be shared due to the personal and sensitive information which it contains, e.g names or political opinions. General Data Protection Regulation (GDPR) suggests pseudonymization as a solution to secure open access to research data, but we need to learn more about pseudonymization as an approach before adopting it for manipulation of research data. This paper outlines a research agenda within pseudonymization, namely need of studies into the effects of pseudonymization on unstructured data in relation to e.g. readability and language assessment, as well as the effectiveness of pseudonymization as a way of protecting writer identity, while also exploring different ways of developing context-sensitive algorithms for detection, labelling and replacement of personal information in unstructured data. The recently granted project on pseudonymization ‘Grandma Karl is 27 years old’ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> addresses exactly those challenges. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1.</sup> https://spraakbanken.gu.se/en/projects/mormor-karl
Improved Knowledge Distillation for Crowd Counting on IoT Devices

Manual crowd counting for real-world problems is impossible or results in wildly inaccurate estimations. Deep learning is one area that has been applied to address this issue. Crowd counting is a computationally intensive task. Therefore, many crowd counting models employ large-scale deep convolutional neural networks (CNN) to achieve higher accuracy. However, these are typically at the cost of performance and inference speed. This makes such approaches difficult to apply in real-world settings, e.g., on Internet-of-Things (IoT) devices. To tackle this problem, one method is to compress models using pruning and quantization or use of lightweight model backbones. However, such methods often result in a significant loss of accuracy. To address this, some studies have explored knowledge distillation methods to extract useful information from large state-of-the-art (teacher) models to guide/train smaller (student) models. However, knowledge distillation methods suffer from the problem of information loss caused by hint-transformers. Furthermore, teacher models may have a negative impact on student models. In this work, we propose a method based on knowledge distillation that uses self-transformed hints and loss functions that ignore outliers to tackle real-world and challenging crowd counting tasks. Based on our approach, we achieve a MAE of 77.24 and a MSE of 276.17 using the JHU-CROWD++ [1] test set. This is comparable to state-of-the-art deep crowd counting models, but at a fraction of the original model size and complexity, thus making the solution suitable for IoT devices. The source code is available at https://github.com/huangzuo/effcc_distilled.