Reviewerly/exHarmony huggingface dataset:
Identification of Cellular Signal Measurements Using Machine Learning

Spectrum awareness has a plethora of civilian and defense applications, such as spectrum resource management, adaptive transmissions, interference detection, and identification of threat signals. This article proposes an identification neural network (INN)-based model that identifies cellular signals from three different radio access technologies, namely global system for mobile (GSM) communications, universal mobile telecommunications service, and long-term evolution. The proposed INN identifies whether or not the measured power spectral density belongs to a certain cellular signal type. Two data collection approaches (DCAs) are considered: in-band and multiple-band. The over-the-air measurements for the two DCAs show that with low computational complexity, the proposed INN model provides an identification accuracy between 93% and 100%, with a false alarm (FA) rate between 0% and 10%.
Revisiting the Adversarial Robustness-Accuracy Tradeoff in Robot Learning

Adversarial training (i.e., training on adversarially perturbed input data) is a well-studied method for making neural networks robust to potential adversarial attacks during inference. However, the improved robustness does not come for free but rather is accompanied by a decrease in overall model accuracy and performance. Recent work has shown that, in practical robot learning applications, the effects of adversarial training do not pose a fair trade-off but inflict a net loss when measured in holistic robot performance. This work revisits the robustness-accuracy trade-off in robot learning by systematically analyzing if recent advances in robust training methods and theory in conjunction with adversarial robot learning, are capable of making adversarial training suitable for real-world robot applications. We evaluate three different robot learning tasks ranging from autonomous driving in a high-fidelity environment amenable to sim-to-real deployment to mobile robot navigation and gesture recognition. Our results demonstrate that, while these techniques make incremental improvements on the trade-off on a relative scale, the negative impact on the nominal accuracy caused by adversarial training still outweighs the improved robustness by an order of magnitude. We conclude that although progress is happening, further advances in robust learning methods are necessary before they can benefit robot learning tasks in practice.
On Model Transmission Strategies in Federated Learning With Lossy Communications

Recently, federated learning (FL) has received tremendous attention in both academia and industry, in which decentralized clients collaboratively complete model training by exchanging model updates with a parameter server through the Internet. Its distributed nature well utilizes the localized data and preserves clients’ privacy, but also incurs heavy communication overhead. Existing studies on model update have mostly focused on the bandwidth constraint of the communication channels. Today's Internet however is highly unreliable. Simply using Transmission Control Protocol (TCP) would lead to low network utilization under frequent losses. In this paper, we closely examine the optimal transmission strategies in FL over the realistic lossy Internet. We systematically integrate model compression, forward error correction (FEC) and retransmission towards Federated Learning with Lossy Communications (FedLC). We derive the convergence rate of FedLC under non-convex loss with the optimal transmission. We then decompose this non-convex problem and present effective practical solutions. Public datasets are exploited for performance evaluation by varying the packet loss rate from 10% to 50%. In a fixed training time budget, FedLC can improve model accuracy by 3.91% on average or reduce the communication traffic by 34.27%-47.57% in comparison with state-of-the-art baselines.
Universal and Accurate Sketch for Estimating Heavy Hitters and Moments in Data Streams

In computer networks, traffic measurement is a module in a network probe to measure flow-level statistics from an IP packet stream, which are the basis for network performance monitoring and malicious activity detection. This module extracts the flow IDs from incoming IP packets, classifies packets into flows, and counts the number of packets (or bytes) for each flow. It is a great challenge to measure the per-flow statistics for a high-speed network device, using only the size-limited SRAM on its line cards. Therefore, many algorithms using sublinear memory have been proposed, such as CountMin and CountSketch. However, most of previous algorithms are designed for specific measurement tasks. To obtain multiple types of statistics, people have to deploy multiple sketches, which demands more resources of a network device. It is useful to design a universal sketch that can track not only the top- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> largest individual flows (called heavy hitters) but also the overall traffic distribution statistics (called moments). Prior work named UnivMon successfully tackled this ambitious quest. However, it incurs large and variable per-packet processing overhead, which may result in a significant throughput bottleneck in high-rate packet stream, given that each packet requires 33 hashes and 32 memory accesses on average and many times of that in the worst case. To address this performance issue, we fundamentally redesign the solution architecture from hierarchical sampling to new progressive sampling and from CountSketch to new GenericCM, which ensure that per-packet overhead is a small constant (5 hashes and 8 memory accesses in the worst case), making it more suitable for online operations, especially for hardware pipeline implementation. This new design also makes effort to reduce memory footprint or equivalently improve measurement accuracy under the same memory. Our experiments show that our solution reduces measurement error by roughly 98.1% for second-order moment and by 91.5% for entropy, when given the same 0.2MB memory as UnivMon.
Semi-Supervised Crowd Counting With Spatial Temporal Consistency and Pseudo-Label Filter

Semi-supervised crowd counting (SSCC) aims to learn a crowd counting model with limited labeled images and a large number of unlabeled images. Previous works leverage unlabeled images by pseudo-labeling and spatial consistency regularization paradigms, which frequently adopt teacher-student frameworks. However, their performances are readily degraded due to the inconsistent and unreliable pseudo density map in complex crowd scenes. Here, we argue that the SSCC performance can be significantly improved by reducing the over-fitting of the incorrect pseudo labels, and a novel spatial-temporal consistency framework, named STC-Crowd, is proposed. Under different spatial perturbations, spatial consistency enables the counting model to output consistent predictions for the same crowd image. Temporal consistency generates similar feature embedding over adjacent training stages, alleviating the inconsistent issues of pseudo density maps generated for the same image over time. To store the temporal feature embedding of different density levels for temporal consistency, a dynamic temporal knowledge memory (DTKM) is deliberately designed, and considerably reduces the storage cost. Besides, a pseudo-label filter (PLF) mechanism is used to alleviate the negative impact of incorrect pseudo density maps, by reducing the supervision weights of unreliable pseudo labels with high uncertainty. Extensive experiments on four benchmark datasets show that our method obtains competent performance against leading SSCC methods, and especially works better on limited labeled images.
MSE Design of Nearly Constant Velocity Kalman Filters for Tracking Targets With Deterministic Maneuvers

When tracking maneuvering targets with a nearly constant velocity (NCV) Kalman filter with white noise acceleration, the selection of the process noise variance is complicated by the fact that the process noise errors are modeled as white Gaussian in the Kalman filter, while target maneuvers are deterministic or highly correlated in time. In recent years, the deterministic maneuver index was introduced for NCV Kalman filters and used to develop a relationship between the anticipated maximum acceleration of the target and the process noise variance that minimizes the maximum mean squared error (MSE) in position. Lower bounds on the process noise variance that prevent the position MSE from exceeding the measurement noise variance were also expressed in terms of the maximum acceleration and deterministic maneuver index. In this article, those results are summarized and codified for practical application by the target tracking community. The design methods for NCV Kalman filters with discrete white noise acceleration and continuous white noise acceleration are presented for sustained and brief maneuvers. The application of the design methods to radar tracking of maneuvering targets is also addressed. The effectiveness of the design methods is illustrated via Monte Carlo simulations.
MCoR-Miner: Maximal Co-Occurrence Nonoverlapping Sequential Rule Mining

The aim of sequential pattern mining (SPM) is to discover potentially useful information from a given sequence. Although various SPM methods have been investigated, most of these focus on mining all of the patterns. However, users sometimes want to mine patterns with the same specific prefix pattern, called co-occurrence pattern. Since sequential rule mining can make better use of the results of SPM, and obtain better recommendation performance, this paper addresses the issue of maximal co-occurrence nonoverlapping sequential rule (MCoR) mining and proposes the MCoR-Miner algorithm. To improve the efficiency of support calculation, MCoR-Miner employs depth-first search and backtracking strategies equipped with an indexing mechanism to avoid the use of sequential searching. To obviate useless support calculations for some sequences, MCoR-Miner adopts a filtering strategy to prune the sequences without the prefix pattern. To reduce the number of candidate patterns, MCoR-Miner applies the frequent item and binomial enumeration tree strategies. To avoid searching for the maximal rules through brute force, MCoR-Miner uses a screening strategy. To validate the performance of MCoR-Miner, eleven competitive algorithms were conducted on eight sequences. Our experimental results showed that MCoR-Miner outperformed other competitive algorithms, and yielded better recommendation performance than frequent co-occurrence pattern mining. All algorithms and datasets can be downloaded from <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/wuc567/Pattern-Mining/tree/master/MCoR-Miner</uri> .
Graph Wedgelets: Adaptive Data Compression on Graphs Based on Binary Wedge Partitioning Trees and Geometric Wavelets

We introduce graph wedgelets - a tool for data compression on graphs based on the representation of signals by piecewise constant functions on adaptively generated binary graph partitionings. The adaptivity of the partitionings, a key ingredient to obtain sparse representations of a graph signal, is realized in terms of recursive wedge splits adapted to the signal. For this, we transfer adaptive partitioning and compression techniques known for 2D images to general graph structures and develop discrete variants of continuous wedgelets and binary space partitionings. We prove that continuous results on best <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$m$</tex-math></inline-formula> -term approximation with geometric wavelets can be transferred to the discrete graph setting and show that our wedgelet representation of graph signals can be encoded and implemented in a simple way. Finally, we illustrate that this graph-based method can be applied for the compression of images as well.
Blockchain-Based Authentication of GNSS Civil Navigation Message

The civil navigation message (CNAV) of the global navigation satellite system (GNSS) is transmitted in an open channel without authentication and integrity protection mechanism. Therefore, the CNAV is facing the threat of interception and tampering, and it is vulnerable to spoofing attacks. To guarantee the authenticity and integrity of CNAV, we propose a cryptographic antispoofing scheme of CNAV in GNSS based on consortium blockchain using domestic cryptographic algorithms. In this scheme, we design a CNAV authentication model based on satellites, ground stations, and key management center, and implement the authentication protocol to achieve the purpose of preventing CNAV in GNSS from being tampered with. On the basis of the authentication of the entities, the scheme further realizes the functions of information source authentication and integrity protection of the legitimate senders. On the basis of satisfying security, the protocol has lower computing and communication cost, simplifies the process of reauthentication of information source, and has great practicability for receivers that use civil navigation message to achieve navigation and positioning functions.
A Formal Semantics for P-Code

Abstract Decompilation is currently a widely used tool in reverse engineering and exploit detection in binaries. Ghidra, developed by the National Security Agency, is one of the most popular decompilers. It decompiles binaries to high P-Code, from which the final decompilation output in C code is generated. Ghidra allows users to work with P-Code, so users can analyze the intermediate representation directly. Several projects make use of this to build tools that perform verification, decompilation, taint analysis and emulation, to name a few. P-Code lacks a formal semantics, and its documentation is limited. It has a notoriously subtle semantics, which makes it hard to do any sort of analysis on P-Code. We show that P-Code, as-is, cannot be given an executable semantics. In this paper, we augment P-Code and define a complete, executable, formal semantics for it. This is done by looking at the documentation and the decompilation results of binaries with known source code. The development of a formal P-Code semantics uncovered several issues in Ghidra, P-Code, and the documentation. We show that these issues affect projects that rely on Ghidra and P-Code. We evaluate the executability of our semantics by building a P-Code interpreter that directly uses our semantics. Our work uncovered several issues in Ghidra and allows Ghidra users to better leverage P-Code.
Traffic Sign Classification Using Deep and Quantum Neural Networks

Quantum Neural Networks (QNNs) are an emerging technology that can be used in many applications including computer vision. In this paper, we presented a traffic sign classification system implemented using a hybrid quantum-classical convolutional neural network. Experiments on the German Traffic Sign Recognition Benchmark dataset indicate that currently QNN do not outperform classical DCNN (Deep Convolutuional Neural Networks), yet still provide an accuracy of over 90% and are a definitely promising solution for advanced computer vision.
Towards automated check-worthy sentence detection using Gated Recurrent Unit

People are exposed to a lot of information daily, which is a mix of facts, opinions, and false claims. The rate at which information is created and spread has necessitated an automated fact-checking mechanism. In this work, we focus on the first step of the fact-checking system, which is to identify whether a given sentence is factual. We propose a glove embedding-based gated recurrent unit pipeline for check-worthy sentence detection, referred to as G2CW framework. It detects whether a given sentence has check-worthy content in it or not; furthermore, if it has check-worthy content, whether it is important or not, from a fact-checking perspective. We evaluate our proposed framework on two datasets: a standard ClaimBuster dataset commonly used by the research community for this problem and a self-curated IndianClaim dataset. Our G2CW framework outperforms prior work with 0.92 as F1-score. Furthermore, our G2CW framework, when trained on the ClaimBuster dataset, performs the best on the IndianClaims dataset.
Single-shot N-step Phase Measuring Profilometry based on algebraic addition and subtraction

A single-shot N-step phase measuring profilometry based on algebraic addition and subtraction (single-shot N-PMP-AS) is proposed, where simpler and more accurate algebraic addition and subtraction rather than multiplication operations are introduced to filter out the fundamental frequency spectrum. Unlike the multiplicative operation that incorporates the background information into the fundamental frequency components, the background information of the proposed method exists only in the zero-frequency components, while the effective information exists in the positive first-order spectrum, so it is more resistant to high-frequency noise. In addition, the wrapped phase derived by the proposed method is only related to the height of the object itself, and there is no need to solve the phase for the reference plane and the measured object separately, so it possesses a more concise mathematical model compared to the traditional PMP. The proposed method not only inherits the characteristics of phase-shifting and high measuring accuracy of traditional PMP, but also only captures the single frame deformed pattern in real-time measuring. Experiments show that the proposed method has a higher measuring accuracy and a more flexible way of solving the phase than the Fourier transform profilometry and other transformations of the computer-generated moiré profilometry.
A novel 5-bit S-box design for lightweight cryptography algorithms

Cryptography is one of the techniques to secure communication and data transfer over the network. It performs well on resource-rich devices (PC, servers, smartphones, etc.). However, it may not fit or, if forcefully fitted, perform poorly on the resource-constrained Internet of Things (IoT) devices (e.g., Radio Frequency Identification (RFID) tags, sensors). For these reasons, there is a need for a lightweight version of cryptography, called lightweight cryptography (LWC). While designing any cryptography algorithm, a substitution box (S-box) is a core and the only component that offers a nonlinear functionality between inputs and outputs. Various researchers propose various S-box designs for different applications. Still, very few of them maintain the trade-offs among cost, performance and security, especially when considered resource-constrained IoT devices. First, the article discusses various S-boxes used in the popular LWC algorithms by their input–output bit-size (3/4/5/6/8 bit) and highlights their strengths and limitations. Then, it focuses on the proposed 5-bit S-box design. The novel design uses a chaotic mapping theory to offer a random behaviour of the element in the proposed S-box. The experimental results from ASIC implementation reveal two essential characteristics of the proposed S-box, cost and performance, and further, compare it with 4/5-bit S-box competitors. Finally, the article demonstrates the security strength of the proposed 5-bit S-box through various cryptanalysis such as bijective, nonlinearity, linearity, differential cryptanalysis, differential style boomerang attack, avalanche effect, bit independence criterion, etc. Also, a comparison is carried out to exhibit the superiority of the proposed 5-bit S-box over its 5-bit competitors.
Turn on, Tune in, Listen up: Maximizing Side-Channel Recovery in Time-to-Digital Converters

Voltage fluctuation sensors measure minute changes in an FPGA power distribution network, allowing attackers to extract information from concurrently executing computations. Previous voltage fluctuation sensors make assumptions about the co-tenant computation and require the attacker have a priori access or system knowledge to tune the sensor parameters statically. We present the open-source design of the Tunable Dual-Polarity Time-to-Digital Converter, which introduces three dynamically tunable parameters that optimize signal measurement, including the transition polarity, sample window, frequency, and phase. We show that a properly tuned sensor improves co-tenant classification accuracy by 2.5× over prior work and increases the ability to identify the co-tenant computation and its microarchitectural implementation. Across 13 varying applications, our techniques yield an 80% classification accuracy that generalizes beyond a single board. Finally, our sensor improves the ability of a correlation power analysis attack to rank correct subkey values by 2×.
A memory-related vulnerability detection approach based on vulnerability model with Petri Net

With the continuous development of information technology, software vulnerabilities have become a critical threat to information security. Post-release detection of memory leaks, double free and use after free is one of the most challenging research problems in software vulnerability analysis. To tackle this challenge, we introduce a vulnerability model based on Petri Net. We consider the characteristics and causes of vulnerabilities, modeling is conducted from the subject and environment of vulnerabilities. Based on this vulnerability model, we propose a memory-related vulnerability detection framework based on vulnerability model (MRVD-VM) and its vulnerability detection algorithm based on vulnerability mode (VDA-VM). The results of experiments on Juliet Test Suite 1.2 for C_CPP show that MRVD-VM significantly outperforms three state-of-the-art baseline tools, including Cppcheck, Flawfinder, and Splint, in detecting memory leaks, double free and use after free.
Quantifying excess heavy metal concentrations in drainage basins using conservative mixing models

High concentrations of heavy metals and other pollutants in river sediments can have detrimental effects on the ecosystem and humans. The composition of river sediments throughout drainage basins therefore provides important information for environmental monitoring. An obvious first step for using river sediment compositions for monitoring is to quantify natural baseline concentrations. Once baselines have been quantified, is it straightforward to compare them to observations to identify excesses generated by, for example, anthropogenic inputs. In this study a new strategy for mapping element concentrations along rivers from discrete geochemical observations upstream is presented. We demonstrate our approach in a case study of the Clyde drainage basin in western Scotland, UK. First, continuous baselines are generated using simple forward models that conservatively mix source region concentrations along drainage networks. 1185 measurements of elemental concentrations from first-order streams are used to parameterise the source region. The calculated baselines are then compared to concentrations measured at 60 localities along the main channel of the Clyde river. For a range of major and trace elements (e.g., Mg, Sr, K, Mn), the downstream observations are in close agreement with baseline concentrations predicted by conservative mixing models. However, some heavy metal concentrations (Pb, Cu, Zn) tend to exceed predicted baseline concentrations. Therefore, the second part of our approach calculates element concentrations in source areas required to match the observed Pb, Cu and Zn concentrations measured along the river. An inverse approach is used to ‘unmix’ the observed concentrations utilising, again, a conservative mixing model. Model resolution is determined by the spatial distribution of the data. Resultant calculated natural baselines and heavy metal concentrations along the river can easily be compared to estimate excesses. We tentatively suggest that anthropogenic input to sediment composition along the river is equivalent to annual fluxes of 9.7, 1.5 and 5.7 t (106 g) of Pb, Cu and Zn, respectively.