This work presents an Intrusion Prevention System (IPS) called the Embedded Process Prediction Intrusion Prevention System (EPPIPS) to detect cyber-attacks by predicting what harm the attacks could cause to the physic...
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Traffic congestion is one of the most common problems in the transportation system. In urban planning and construction, traffic congestion increases the difficulty of control and scheduling, hindering the pace of urba...
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Incredible progress has been made in human action recognition(HAR),significantly impacting computer vision applications in sports ***,identifying dynamic and complex movements in sports like badminton remains challeng...
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Incredible progress has been made in human action recognition(HAR),significantly impacting computer vision applications in sports ***,identifying dynamic and complex movements in sports like badminton remains challenging due to the need for precise recognition accuracy and better management of complex motion *** learning techniques like convolutional neural networks(CNNs),long short-term memory(LSTM),and graph convolutional networks(GCNs)improve recognition in large datasets,while the traditional machine learning methods like SVM(support vector machines),RF(random forest),and LR(logistic regression),combined with handcrafted features and ensemble approaches,perform well but struggle with the complexity of fast-paced sports like *** proposed an ensemble learning model combining support vector machines(SVM),logistic regression(LR),random forest(RF),and adaptive boosting(AdaBoost)for badminton action *** data in this study consist of video recordings of badminton stroke techniques,which have been extracted into spatiotemporal *** three-dimensional distance between each skeleton point and the right hip represents the spatial *** temporal features are the results of Fast Dynamic Time Warping(FDTW)calculations applied to 15 frames of each video *** weighted ensemble model employs soft voting classifiers from SVM,LR,RF,and AdaBoost to enhance the accuracy of badminton action *** E2 ensemble model,which combines SVM,LR,and AdaBoost,achieves the highest accuracy of 95.38%.
In this paper, we introduce a novel class of fast, beam search-based adversarial attack (BEAST) for Language Models (LMs). BEAST employs interpretable parameters, enabling attackers to balance between attack speed, su...
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In this paper, we introduce a novel class of fast, beam search-based adversarial attack (BEAST) for Language Models (LMs). BEAST employs interpretable parameters, enabling attackers to balance between attack speed, success rate, and the readability of adversarial prompts. The computational efficiency of BEAST facilitates us to investigate its applications on LMs for jailbreaking, eliciting hallucinations, and privacy attacks. Our gradient-free targeted attack can jailbreak aligned LMs with high attack success rates within one minute. For instance, BEAST can jailbreak Vicuna-7B-v1.5 under one minute with a success rate of 89% when compared to a gradient-based baseline that takes over an hour to achieve 70% success rate using a single Nvidia RTX A6000 48GB GPU. BEAST can also generate adversarial suffixes for successful jailbreaks that can transfer to unseen prompts and unseen models such as GPT-4-Turbo. Additionally, we discover a unique outcome wherein our untargeted attack induces hallucinations in LM chatbots. Through human evaluations, we find that our untargeted attack causes Vicuna-7B-v1.5 to produce ∼15% more incorrect outputs when compared to LM outputs in the absence of our attack. We also learn that 22% of the time, BEAST causes Vicuna to generate outputs that are not relevant to the original prompt. Further, we use BEAST to generate adversarial prompts in a few seconds that can boost the performance of existing membership inference attacks for LMs. We believe that our fast attack, BEAST, has the potential to accelerate research in LM security and privacy. Copyright 2024 by the author(s)
The potential of the metaverse in the field of education is an area of increasing interest, with many researchers exploring the space to increase the ease and efficacy of student education while reducing time and labo...
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Despite the significance of Sign Language education, access to resources with immediate feedback remains a challenge. This study aims to assess the effectiveness of an online learning tool offering real-time video fee...
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Electromagnetic pulse(EMP)is a kind of transient electromagnetic phenomenon with short rise time of the leading edge and wide spectrum,which usually disrupts communications and damages electronic equipment and *** is ...
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Electromagnetic pulse(EMP)is a kind of transient electromagnetic phenomenon with short rise time of the leading edge and wide spectrum,which usually disrupts communications and damages electronic equipment and *** is challenging for an EMP sensor to measure a wideband electromagnetic pulse without distortion for the whole ***,analyzing the distortion of EMP measurement is crucial to evaluating the sensor distortion characteristics and correcting the measurement *** fidelity is usually employed to evaluate the distortion of an ***,this metric depends on specific signal waveforms,thus is unsuitable for evaluating and analyzing the distortion of EMP *** this paper,an associated-hermite-function based distortion analysis method including system transfer matrices and distortion rates is proposed,which is general and independent from individual *** system transfer matrix and distortion rate can be straightforwardly calculated by the signal orthogonal transformation coefficients using associated-hermite *** of a sensor *** is then visualized via the system transfer matrix,which is convenient in quantitative analysis of the *** of a current probe,a coaxial pulse voltage probe and a B-field sensor were performed,based on which the feasibility and effectiveness of the proposed distortion analysis method is successfully verified.
In blockchain networks, transactions can be transmitted through channels. The existing transmission methods depend on their routing information. If a node randomly chooses a channel to transmit a transaction, the tran...
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In blockchain networks, transactions can be transmitted through channels. The existing transmission methods depend on their routing information. If a node randomly chooses a channel to transmit a transaction, the transmission may be aborted due to insufficient funds(also called balance) or a low transmission rate. To increase the success rate and reduce transmission delay across all transactions, this work proposes a transaction transmission model for blockchain channels based on non-cooperative game *** balance, channel states, and transmission probability are fully considered. This work then presents an optimized channel transaction transmission algorithm. First, channel balances are analyzed and suitable channels are selected if their balance is sufficient. Second, a Nash equilibrium point is found by using an iterative sub-gradient method and its related channels are then used to transmit transactions. The proposed method is compared with two state-of-the-art approaches: Silent Whispers and Speedy Murmurs. Experimental results show that the proposed method improves transmission success rate, reduces transmission delay,and effectively decreases transmission overhead in comparison with its two competitive peers.
The threat of chip-level tampering and its detection has been widely researched. Hardware Trojan insertions are prominent examples of such tamper events. Altering the placement and routing of a design or removing a pa...
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The threat of chip-level tampering and its detection has been widely researched. Hardware Trojan insertions are prominent examples of such tamper events. Altering the placement and routing of a design or removing a part of a circuit for side-channel leakage/fault sensitivity amplification are other instances of such attacks. While semi-and fully-invasive physical verification methods can confidently detect such stealthy tamper events, they are costly, time-consuming, and destructive. On the other hand, virtually all proposed non-invasive side-channel methods suffer from noise and, therefore, have low confidence. Moreover, they require activating the tampered part of the circuit (e.g., the Trojan trigger) to compare and detect the modifications. In this work, we introduce a non-invasive post-silicon tamper detection technique applicable to different classes of tamper events at the chip level without requiring the activation of the malicious circuit. Our method relies on the fact that physical modifications (regardless of their physical, activation, or action characteristics) alter the impedance of the chip. Hence, characterizing the impedance can lead to the detection of the tamper events. To sense the changes in the impedance, we deploy known RF tools, namely, scattering parameters, in which we inject sine wave signals with high frequencies to the power distribution network (PDN) of the system and measure the "echo" of the signal. The reflected signals in various frequency bands reveal different tamper events based on their impact size on the die. To validate our claims, we performed measurements on several proof-ofconcept tampered hardware implementations realized on FPGAs manufactured with a 28 nm technology. We further show that deploying the Dynamic Time Warping (DTW) distance can distinguish between tamper events and noise resulting from manufacturing process variation of different chips/boards. Based on the acquired results, we demonstrate that stealthy hardwa
We show that we can harness two recent experimental developments to build a compact hardware emulator for the classical Heisenberg model in statistical physics. The first is the demonstration of spin-diffusion lengths...
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We show that we can harness two recent experimental developments to build a compact hardware emulator for the classical Heisenberg model in statistical physics. The first is the demonstration of spin-diffusion lengths in excess of microns in graphene even at room temperature. The second is the demonstration of low-barrier magnets (LBMs) whose magnetization can fluctuate rapidly even at subnanosecond rates. Using experimentally benchmarked circuit models, we show that an array of LBMs driven by an external current source has a steady-state distribution corresponding to a classical system with an energy function of the form E=−(1/2)∑i,jJij(m^i⋅m^j). This may seem surprising for a nonequilibrium system, but we show that it can be justified by a Lyapunov function corresponding to a system of coupled Landau–Lifshitz–Gilbert (LLG) equations. The Lyapunov function we construct describes LBMs interacting through the spin currents they inject into the spin-neutral substrate. We suggest ways to tune the coupling coefficient Jij so that it can be used as a hardware solver for optimization problems involving continuous variables represented by vector magnetizations, similar to the role of the Ising model in solving optimization problems with binary variables. Finally, we train a Heisenberg xor gate based on a network of four coupled stochastic LLG equations, illustrating the concept of probabilistic computing with a programmable Heisenberg model.
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