Reviewerly/exHarmony huggingface dataset:
FARGO: Fast Maximum Inner Product Search via Global Multi-Probing

Maximum inner product search (MIPS) in high-dimensional spaces has wide applications but is computationally expensive due to the curse of dimensionality. Existing studies employ asymmetric transformations that reduce the MIPS problem to a nearest neighbor search (NNS) problem, which can be solved using locality-sensitive hashing (LSH). However, these studies usually maintain multiple hash tables and locally examine them one by one, which may cause additional costs on probing unnecessary points. In addition, LSH is applied without taking into account the properties of the inner product. In this paper, we develop a fast search framework FARGO for MIPS on large-scale, high-dimensional data. We propose a global multi-probing (GMP) strategy that exploits the properties of the inner product to globally examine high quality candidates. In addition, we develop two optimization techniques. First, different with existing transformations that introduce either distortion errors or data distribution imbalances, we design a novel transformation, called random XBOX transformation, that avoids the negative effects of data distribution imbalances. Second, we propose a global adaptive early termination condition that finds results quickly and offers theoretical guarantees. We conduct extensive experiments with real-world data that offer evidence that FARGO is capable of outperforming existing proposals in terms of both accuracy and efficiency.
BANDT: A Border-Aware Network With Deformable Transformers for Visual Tracking

Siamese trackers that predict a tracking box over a regular grid of feature maps based on the sliding-window paradigm have recently drawn widespread attention in the tracking community. However, the sliding-window paradigm lacks the exploration to characterize the explicit border features of the tracking object for accurate box prediction. Furthermore, Siamese trackers do not use an online learning process to improve the model’s discriminative power and a deformable transformer to explore the rich temporal context from the successive frames. To overcome these issues, we propose a border-aware network with deformable transformers (BANDT), which contains classification and regression branches for tracking. The proposed BANDT tracker introduces a border-alignment operation to capture the “border features” from the extreme points of the object’s border to improve the point features of the sliding-window paradigm. The BANDT tracker implements a deformable transformer consisting of an encoder and a decoder to enhance the target representations within a discriminative online learning framework. Specifically, the introduced encoder of the deformable transformer enhances the target representations, while the decoder highlights the potential tracking location. The proposed tracker achieves state-of-the-art performance on seven public datasets. In particular, it achieves 69.6% overlap success and 90.0% precision scores on the UAV123 dataset.
ApproxTrain: Fast Simulation of Approximate Multipliers for DNN Training and Inference

Edge training of deep neural networks (DNNs) is a desirable goal for continuous learning; however, it is hindered by the enormous computational power required by training. Hardware approximate multipliers have shown their effectiveness in gaining resource efficiency in DNN inference accelerators; however, training with approximate multipliers is largely unexplored. To build resource-efficient accelerators with approximate multipliers supporting DNN training, a thorough evaluation of training convergence and accuracy for different DNN architectures and different approximate multipliers is needed. This article presents ApproxTrain, an open-source framework that allows fast evaluation of DNN training and inference using simulated approximate multipliers. ApproxTrain is as user-friendly as TensorFlow (TF) and requires only a high-level description of a DNN architecture along with C/C++ functional models of the approximate multiplier. We improve the speed of the simulation at the multiplier level by using a novel LUT-based approximate floating-point (FP) multiplier simulator on GPU (AMSim). Additionally, a novel flow is presented to seamlessly convert C/C++ functional models of approximate FP multipliers into AMSim. ApproxTrain leverages CUDA and efficiently integrates AMSim into the TensorFlow library to overcome the absence of native hardware approximate multiplier in commercial GPUs. We use ApproxTrain to evaluate the convergence and accuracy performance of DNN training with approximate multipliers for three application domains: image classification, object detection, and neural machine translation. The evaluations demonstrate similar convergence behavior and negligible change in test accuracy compared to FP32 and Bfloat16 multipliers. Compared to CPU-based approximate multiplier simulations in training and inference, the GPU-accelerated ApproxTrain is more than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2500\times $ </tex-math></inline-formula> faster. Based on highly optimized closed-source cuDNN/cuBLAS libraries with native hardware multipliers, the original TensorFlow is, on average, only <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$8\times $ </tex-math></inline-formula> faster than ApproxTrain.
PoE: Poisoning Enhancement Through Label Smoothing in Federated Learning

In federated learning (FL), poisoning attack invades the whole system by manipulating the client data, tampering with the training target, and performing any desired behaviors. Until now, numerous poisoning attacks have been carefully studied, however they are still practically challenged in real-world scenarios from two aspects: (i) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">multiple malicious client selections</i> - poisoning attacks are only successfully launched when the malicious client has been chosen in enough epochs (i.e., more than half of the epochs); (ii) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">long-term poisoning training</i> - the poisoning training usually needs much more epochs than the normal training (i.e., 3 times longer than normal training), both are unavailable in real cases. To address these overlooked problems, we propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Po</i> isoning <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${E}$ </tex-math></inline-formula> nhanced attack (PoE) against FLs, which is a general poisoning reinforcement framework. It is designed to transfer partially predicted probabilities of the source class to the target one. Thus, the inter-class distance between the source class and the target class is narrowed down in feature space for easier attacks. Towards this goal, the attack client uses label smoothing to change the model prediction distribution, dragging the global model in the direction that is favorable for poisoning. Extensive experiments show that PoE can significantly enhance the attack success rate ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim \times8.4$ </tex-math></inline-formula> on average) in practical FLs with normal training epochs. It also achieves state-of-the-art adaptive attack performance against defensive FLs (i.e., robust aggregations). The code of PoE could be downloaded at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Leon022/poisoning_enhancement</uri> .
Assessment of Radiation-Induced Soft Errors on Lightweight Cryptography Algorithms Running on a Resource-Constrained Device

Most safety-critical edge-computing devices rely on lightweight cryptography (LWC) algorithms to provide security at minimum power and performance overhead.LWC algorithms are traditionally embedded as a hardware component, but with the advance of the Internet of Things (IoT), emerging firmware is more likely to support cryptography algorithms to comply with different security levels and industry-standards.This is the first work to present the soft error assessment of five cryptography algorithms executing in a low-power microprocessor running under neutron radiation, considering electronic code book (ECB) and counter (CTR) mode of operation implementations.Results obtained from two neutron radiation tests suggest that: (i) the NOEKEON algorithm gives the best relative soft error reliability, performance, power efficiency and memory footprint utilisation trade-offs between the five algorithms considering both ECB and CTR implementations, and (ii) cryptography solutions based on the counter mode of operation present better FIT rate for silent data corruption (SDC) and crash w.r.t.ECB implementations.
Semi-Decentralized Federated Edge Learning With Data and Device Heterogeneity

Federated edge learning (FEEL) emerges as a privacy-preserving paradigm to effectively train deep learning models from the distributed data in 6G networks. Nevertheless, the limited coverage of a single edge server results in an insufficient number of participating client nodes, which may impair the learning performance. In this paper, we investigate a novel FEEL framework, namely <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">semi-decentralized federated edge learning</i> (SD-FEEL), where multiple edge servers collectively coordinate a large number of client nodes. By exploiting the low-latency communication among edge servers for efficient model sharing, SD-FEEL incorporates more training data, while enjoying lower latency compared with conventional federated learning. We detail the training algorithm for SD-FEEL with three steps, including local model update, intra-cluster, and inter-cluster model aggregations. The convergence of this algorithm is proved on non-independent and identically distributed data, which reveals the effects of key parameters and provides design guidelines. Meanwhile, the heterogeneity of edge devices may cause the straggler effect and deteriorate the convergence speed of SD-FEEL. To resolve this issue, we propose an asynchronous training algorithm with a staleness-aware aggregation scheme, of which, the convergence is also analyzed. The simulations demonstrate the effectiveness and efficiency of the proposed algorithms for SD-FEEL and corroborate our analysis.
Segmentation-Less Extraction of Text and Non-Text Regions From JPEG 2000 Compressed Document Images Through Partial and Intelligent Decompression

JPEG 2000 is a popular image compression technique that uses Discrete Wavelet Transform (DWT) for compression and subsequently provides many rich features for efficient storage and decompression. Though compressed images are preferred for archival and communication purposes, their processing becomes difficult due to the overhead of decompression and re-compression operations which are needed as many times the data needs to operate. Therefore in this research paper, the novel idea of direct operation over the JPEG 2000 compressed documents is proposed for extracting text and non-text regions without using any segmentation algorithm. The technique avoids full decompression of the compressed document in contrast to the conventional methods, where they fully decompress and then process. Moreover, JPEG 2000 features are explored in this research work to partially and intelligently decompress only the selected regions of interest at different resolutions and bitdepths to accomplish segmentation-less extraction of text and non-text regions. Finally Maximally Stable Extremal Regions (MSER) algorithm is used to extract the layout of segmented text and non-text regions for further analysis. Experiments have been carried out on the standard PRImA Layout Analysis Dataset leading to promising results and saving computational resources.
Social Science Contributions to Earthquake Warnings: Commemorating the Work of Dennis S. Mileti

The pioneering work of Dennis Mileti and those with whom he collaborated have established a sound basis in understanding how human beings behave when encountering hazard warnings. His work greatly expanded our understanding of the social environment into which warnings are issued and provided clear guidelines on how warnings should be delivered to optimize behavioral compliance; how warning message content can be construed to motivate protective actions; and why people may fail to comply or delay actions after receipt of a warning. This paper addresses Mileti's work in earthquake warnings, first in the context of earthquake predictions and later, as optimism waned that accurate earthquake predictions were possible, as his insights were applied to newer approaches in the form of earthquake early warnings and operational earthquake forecasting. This paper will first briefly trace the historical development of approaches to warn of a potentially damaging earthquake because it is important to place Mileti's work in the historical context in which it was carried out. It then assesses the manner in which the current methods of earthquake warning for short- and intermediate-term time frames via earthquake early warning and operational earthquake forecasting have benefited from Mileti's very significant observations and insights. His work in evaluating and clarifying the social environment in which warnings are issued is an important and enduring element of his legacy, has significantly influenced how hazard warnings are now formulated, and has greatly shaped the work of those of us who carry on the study of hazard warning systems.
Field features: The impact in learning to rank approaches

Learning to Rank approaches employ Machine Learning techniques for Information Retrieval. Traditionally, the features needed to train a ranking model are naively combined after being extracted from the various fields of the texts. Nevertheless, if not considered carefully, the learning process can make use of strongly correlated features. Moreover, the learned ranking models are not, to date, systematically analyzed in terms of how the field-based features affect their performances. In this work, the impact of using field-based features in Learning to Rank approaches is investigated. Specifically, the Field Learning to Rank technique is proposed to study if the field-based features perform better than the naively combined features. The experiments are conducted employing eight learning to rank approaches on two sizable benchmark datasets: MQ2007 and MQ2008. The models are assessed using three widely adopted Learning to Rank evaluation measures, namely Precision, Mean Average Precision, and Normalized Discounted Cumulative Gain. The results show that the use of field-based features achieve better performance than the naively combined features. Moreover, models aggregated from different fields further improve the ranking results. It is also observed that among the five examined fields, url and title are significantly more effective than wholedoc (full document), body, and anchor to build ranking models. Further, analyses indicate the existence of strong correlations between field features, such as the features from body and wholedoc, title and anchor, or title and url. The proposed Field Learning to Rank technique is shown to have the advantage of avoiding the combination of correlated features. These findings imply that the use of field-based features for training ranking models is valuable for enhancing the effectiveness of Learning to Rank approaches.
Research on Elliptic Curve Crypto System with Bitcoin Curves – SECP256k1, NIST256p, NIST521p and LLL

Very recent attacks like ladder leak demonstrated feasibility to recover private key with side channel attacks using just one bit of secret nonce. ECDSA nonce bias can be exploited in many ways. Some attacks on ECDSA involve complicated Fourier analysis and lattice mathematics. In this paper will enable cryptographers to identify efficient ways in which ECDSA can be cracked on curves NIST256p, SECP256k1, NIST521p and weak nonce, kind of attacks that can crack ECDSA and how to protect yourself. Initially we begin with ECDSA signature to sign a message using private key and validate the generated signature using the shared public key. Then we use a nonce or a random value to randomize the generated signature. Every time we sign, a new verifiable random nonce value is created and way in which the intruder can discover the private key if the signer leaks any one of the nonce value. Then we use Lenstra–Lenstra–Lovasz (LLL) method as a black box, we will try to attack signatures generated from bad nonce or bad random number generator (RAG) on NIST256p, SECP256k1 curves. The analysis is performed by considering all the three curves for implementation of Elliptic Curve Digital Signature Algorithm (ECDSA).The comparative analysis for each of the selected curves in terms of computational time is done with leak of nonce and with Lenstra–Lenstra–Lovasz method to crack ECDSA. The average computational costs to break ECDSA with curves NIST256p, NIST521p and SECP256k1 are 0.016, 0.34, 0.46 respectively which is almost to zero depicts the strength of algorithm. The average computational costs to break ECDSA with curves SECP256K1 and NIST256p using LLL are 2.9 and 3.4 respectively.
Performance characterization and optimization of pruning patterns for sparse DNN inference

Deep neural networks are suffering from over parameterized high storage and high consumption problems. Pruning can effectively reduce storage and computation costs of deep neural networks by eliminating their redundant parameters. In existing pruning methods, filter pruning achieves more efficient inference, while element-wise pruning maintains better accuracy. To make a trade-off between the two endpoints, a variety of pruning patterns has been proposed. This study analyzes the performance characteristics of sparse DNNs pruned by different patterns, including element-wise, vector-wise, block-wise, and group-wise. Based on the analysis, we propose an efficient implementation of group-wise sparse DNN inference, which can make better use of GPUs. Experimental results on VGG, ResNet, BERT and ViT show that our optimized group-wise pruning pattern achieves much lower inference latency on GPU than other sparse patterns and the existing group-wise pattern implementation.
CAM-CAN: Class activation map-based categorical adversarial network

Numerous studies have investigated image classification. In particular, recent methods based on deep learning have exhibited high accuracies. However, various existing state-of-the-art methods based on deep learning show different accuracies depending on the database and environment. Accordingly, different deep learning models need to be used in image classification studies according to the database, environment, and research field. This study investigated a technique to increase the accuracy of the existing deep learning-based models. The proposed method was applied to various existing state-of-the-art methods. In the proposed method, a convolution neural network (CNN) is trained using the classification activation map (CAM) to focus on specific areas in the input image. The CAM image is used as the ground-truth image. Furthermore, the concept of the CAM-based categorical adversarial network (CAM-CAN), in which the CNN is trained based on a generative adversarial network, is proposed in this paper. An action recognition experiment was performed using the self-collected Dongguk thermal image database (DTh-DB) and open database, and the results revealed that the accuracies of the existing state-of-the-art methods significantly increased after applying the proposed method. For instance, the accuracies obtained using the DTh-DB, TPR, PPV, ACC, and F1 with the conventional DenseNet201 model were 80.14%, 75.28%, 96.0%, and 75.91%, respectively. After applying the proposed method, the accuracies increased to 86.53%, 89.90%, 97.64%, and 85.84%, respectively.
TEEFuzzer: A fuzzing framework for trusted execution environments with heuristic seed mutation

With the rapid development of the Internet, data security faces new challenges. As a bridge between the underlying hardware and upper layer applications, the operating system plays a critical role in securing sensitive data. The trusted execution environments (TEEs) are special operating systems aiming at preventing the illegal access and tampering of sensitive data. Thus, TEEs have much stricter security requirements than normal operating systems. Fuzzing is a promising technique that is widely used to identify vulnerabilities in operating systems and applications. However, existing fuzzing frameworks are not directly applicable to TEE-enabled devices due to the specific architecture of TEE-based systems. In this paper, we present the design and implementation of a coverage-guided fuzzing framework for trusted execution environments. Specifically, we build TEEFuzzer, a system that can perform fuzz testing for the open portable trusted executive environment (OP-TEE), which is a widespread TrustZone operating system. Our system contains several purpose-build components, which include a seed generation module and a heuristic seed mutation module to achieve higher coverage, a coverage collection module, and an automatic bug-reproducing module to improve efficiency. With extensive evaluations, 38 crashes have been triggered in OP-TEE. In terms of performance, the average execution speed of TEEFuzzer is 79.4 test cases per second. In summary, we show that fuzzing is a feasible and effective approach to testing trusted execution environments.
User-adaptive verbal calculator for the physically challenged: An assistive technology

Natural barriers necessitate technological support. A user-adaptive verbal calculator (UAVC) is an attempt to let differently abled students and researchers perform calculations with their voices. Automatic speech recognition in real-time mode is used. The UAVC adapts to users’ accents by retraining the speech models with the initial data obtained during usage.