Reviewerly/exHarmony huggingface dataset:
Cryptology as a Way to Teach Advanced Discrete Mathematics

We present a detailed description of a Cryptography and Computer Security course that has been offered at Knox College for the last 15 years. While the course is roughly divided into two sections, Cryptology and Computer Security, our emphasis here is on the Cryptology section. The course puts the cryptologic material into its historical context and also includes reference to ciphers used and discussed in literature. Through lectures and assignments on cryptanalysis, the course motivates students to learn a variety of topics in advanced discrete mathematics, including discrete probability, elementary statistics, number theory, and abstract algebra. The students have found this approach motivating and we believe our experiences are relevant to instructors teaching this material in a variety of different courses and institutions.
E-commerce brand authenticity perception model of territorial characteristic agricultural products based on fuzzy cognitive map and emotional analysis

Brand authenticity perception is essential for territorial characteristic agricultural product e-commerce studies. From the complexity of consumer perception of brand authenticity, an e-commerce brand authenticity perception (EBAP) analysis model is proposed based on fuzzy cognitive map (FCM) and emotional analysis of online comments. Firstly, LDA model and snowNLP tools extract consumer perception attributes and their emotional inclination. After that, FCM and improved Bonferroni mean (BM) operator are used to accurately analyze the interrelationships between different attributes and comprehensively evaluate the brand authenticity of different enterprises under the same characteristic agricultural product. Finally, the model comparison experiment results show that the model proposed can reflect the “attribute importance” and “emotional inclination” of the e-commerce brand authenticity perception of territorial characteristic agricultural products. Among them, “platform logistics” and “product benefits” are essential in promoting the authenticity of brand-consumer relationships. Meanwhile, “e-commerce aftersales service” is closely related to the positive evaluation of “platform logistics” and “product benefits.” This study expands the methodical approach to brand authenticity perception research; it provides a valuable reference for developing modern fine granularity management of e-commerce brand authenticity for characteristic agricultural products.
Design and simulation of autonomous learning platform for constructive English teaching based on artificial intelligence

With the improvement of message and internet technique, college English education mode is also facing corresponding changes. It is the most important task of discipline research and improvement to actively combine with internet message technique, update and upgrade college English education technique in time, and effectively combine AI with college English education. The era of AI has come, and the improvement of education is inseparable from AI, and the reform of English education is also deeply influenced by it. AI enriches English education resources, changes teachers' education means and stimulates students' enthusiasm for learning. AI, as a new round of education technique reform, injects new vitality into education. By transferring human intelligence to intelligent machines, AI enables intelligent machines to make human-like thinking responses in different application scenarios. This research is based on AI, the design and simulation of autonomous learning platform for constructive English education. According to the research, this means is effective and suitable for being widely used.
Context-Dependent Use of Authority and Empathy in Lifestyle Advices Given By Persuasive Voice Assistants

As smart technology becomes more readily available for the general public, so do the systems that can benefit of such technology, such as recommender systems and Voice Assistants. Persuasive Voice Assistants have a great potential to change people’s lives by promoting changes in behavior that benefit people’s health and lifestyles. Previous studies have focused on finding personality traits that systems can use to optimize persuasive capabilities, but the findings have been contradictory. In this study, we investigate these contradictions by comparing the effect of empathic phrasing versus authoritative phrasing of persuasive recommendations in three lifestyle-related domains. The study results as well as the literature survey strongly indicate that it is close to impossible to isolate general effects, emphasizing the importance of taking all specifics of the user population, individual differences and user context as whole – as well as interactions between these variables – into account when designing persuasive adaptive systems.
ScrutinAI: A visual analytics tool supporting semantic assessments of object detection models

We present ScrutinAI, a Visual Analytics tool to leverage semantic understanding for deep neural network (DNN) prediction analysis, focusing on models for object detection and semantic segmentation. Typical fields of application for such models, e.g., autonomous driving or healthcare, have a high demand for uncovering and mitigating data- and model-inherent shortcomings. Our approach helps analysts and/or auditors use their semantic understanding to identify and investigate potential weaknesses in DNN models. ScrutinAI includes interactive visualizations of the model's inputs and outputs, interactive plots with linked brushing, data filtering with textual queries on descriptive meta data and an interactive similarity based image retrieval feature. The different views and data filtering options enable global and local inspection of DNN predictions. Overall, the tool fosters hypothesis driven knowledge generation which aids in understanding the model's inner reasoning. Insights gained during the analysis process mitigate the "black-box character" of the DNN and thus support model improvement and the generation of a safety argumentation for AI applications. We present two case studies on the investigation of DNN models for pedestrian detection from the automotive domain.
Unsupervised feature selection algorithm based on redundancy learning and sparse regression

In recent years, feature selection methods based on sparse regression have attracted much attention from researchers, and how to select more representative feature is the key point. In this paper, an unsupervised feature selection method based on redundancy learning and sparse regression(RSUFS) is proposed. Firstly, to make the model robust to outliers, this paper uses the l2,1-norm regression model as the loss function to learn the feature weight matrix. Secondly, in order to get exact k top features, l2,0-norm constraint is introduced. At the same time, the cosine similarity between features is taken into account to select more valuable features by reducing the redundancy between features. Finally, an efficient algorithm based on Augmented Lagrangian method is derived to solve the above optimization problem. Comparison experiments are made with some benchmark datasets and seven well-known unsupervised feature selection algorithms and the results show that the given algorithm is effective.
A Representation of Cloth States based on a Derivative of the Gauss Linking Integral

Robotic manipulation of cloth is a complex task because of the infinite-dimensional shape-state space of textiles, which makes their state estimation very difficult. In this paper we introduce the dGLI Cloth Coordinates, a finite low-dimensional representation of cloth states that allows us to efficiently distinguish a large variety of different folded states, opening the door to efficient learning methods for cloth manipulation planning and control. Our representation is based on a directional derivative of the Gauss Linking Integral and allows us to represent spatial as well as planar folded configurations in a consistent and unified way. The proposed dGLI Cloth Coordinates are shown to be more accurate in the representation of cloth states and significantly more sensitive to changes in grasping affordances than other classic shape distance methods. Finally, we apply our representation to real images of a cloth, showing that with it we can identify the different states using a distance-based classifier.
Design Friction and Digital Nudging

People make a vast number of decisions every day and these decisions invoke either a reflective or automatic behavior. Using e.g., nudging to influence a person’s behavior in a positive direction can have beneficial sustainable effects. This study aims to illustrate how digital nudging and design friction can be used to induce a reflective behavior in users of digital services during their decision-making. The field of study is the user’s reflection regarding sustainable choices when ordering meals online. The results provided from this initial case study showed that the participants preferred prototypes with implemented design approaches of digital nudging and design friction over a neutral prototype. Nudging had great impact on the decision-making process and provided feelings such as guilt if the participants did not select the option with the lowest carbon footprint. The design approach also induced a reflective behavior among the participants. Introducing so-called design friction provided a reflective behavior but had no impact on the participants decisions. Results also showed that even detailed information in the neutral prototype provided an informative and reflective behavior, which in some of the cases had an impact on the participants’ decisions.
Energy-Efficient Realtime Motion Planning

Motion planning is a fundamental problem in autonomous robotics with real-time and low-energy requirements for safe navigation through a dynamic environment. More than 90% of computation time in motion planning is spent on collision detection between the robot and the environment. Several motion planning approaches, such as deep learning-based motion planning, have shown significant improvements in motion planning quality and runtime with ample parallelism available in collision detection. However, naive parallelization of collision detection queries significantly increases computation compared to sequential execution. In this work, we investigate the sources of redundant computations in coarsegrained (inter-collision detection) and fine-grained (intracollision detection) parallelism. We find that the physical spatial locality of obstacles results in redundant computation in coarse-grained parallelism. We further show that the primary sources of redundant computation in fine-grained parallelism are easy cases where objects are far apart or significantly overlapping. Based on these insights, we propose MPAccel to improve the energy efficiency of parallelization in motion planning. MPAccel consists of SAS, a Spatially Aware Scheduler for coarse-grained parallelism, and CECDUs, Cascaded Early-exit Collision Detection Units for fine-grained parallelism. SAS results in 7× speedup using 8× parallelization with 6% increase in the computation compared to 3.7× speedup with 83% increase in computation for naive parallelization. CECDU can perform collision detection in 46 -- 154 cycles for a robot with 6 degrees of freedom. We evaluate MPAccel to execute a state-of-the-art learning-based motion planning algorithm. Our simulations suggest MPAccel can achieve real-time motion planning for a robot with 7 degrees of freedom in 0.014ms-0.49ms with an average latency of 0.099ms compared to 1.42ms on a CPU-GPU system.
Q-BEEP: Quantum Bayesian Error Mitigation Employing Poisson Modeling over the Hamming Spectrum

Quantum computing technology has grown rapidly in recent years, with new technologies being explored, error rates being reduced, and quantum processors' qubit capacity growing. However, near-term quantum algorithms are still unable to be induced without compounding consequential levels of noise, leading to non-trivial erroneous results. Quantum Error Correction (in-situ error mitigation) and Quantum Error Mitigation (post-induction error mitigation) are promising fields of research within the quantum algorithm scene, aiming to alleviate quantum errors. IBM recently published an article stating that Quantum Error Mitigation is the path to quantum computing usefulness. A recent work, namely HAMMER, demonstrated the existence of a latent structure regarding post-circuit induction errors when mapping to the Hamming spectrum. However, they assumed that errors occur solely in local clusters, whereas we observe that at higher average Hamming distances this structure falls away. In this work, we show that such a correlated structure is not only local but extends certain non-local clustering patterns which can be precisely described by a Poisson distribution model taking the input circuit, the device run time status (i.e., calibration statistics) and qubit topology into consideration. Using this quantum error characterizing model, we developed an iterative algorithm over the generated Bayesian network state-graph for post-induction error mitigation. Thanks to more precise modeling of the error distribution latent structure and the proposed iterative method, our Q-Beep approach provides state of the art performance and can boost circuit execution fidelity by up to 234.6% on Bernstein-Vazirani circuits and on average 71.0% on QAOA solution quality, using 16 practical IBMQ quantum processors. For other benchmarks such as those in QASMBench, a fidelity improvement of up to 17.8% is attained. Q-Beep is a light-weight post-processing technique that can be performed offline and remotely, making it a useful tool for quantum vendors to adopt and provide more reliable circuit induction results. Q-Beep is maintained at github.com/pnnl/qbeep
Spy in the GPU-box: Covert and Side Channel Attacks on Multi-GPU Systems

The deep learning revolution has been enabled in large part by GPUs, and more recently accelerators, which make it possible to carry out computationally demanding training and inference in acceptable times. As the size of machine learning networks and workloads continues to increase, multi-GPU machines have emerged as an important platform offered on High Performance Computing and cloud data centers. Since these machines are shared among multiple users, it becomes increasingly important to protect applications against potential attacks. In this paper, we explore the vulnerability of Nvidia's DGX multi-GPU machines to covert and side channel attacks. These machines consist of a number of discrete GPUs that are interconnected through a combination of custom interconnect (NVLink) and PCIe connections. We reverse engineer the interconnected cache hierarchy and show that it is possible for an attacker on one GPU to cause contention on the L2 cache of another GPU. We use this observation to first develop a covert channel attack across two GPUs, achieving the best bandwidth of around 4 MB/s. We also develop a prime and probe attack on a remote GPU allowing an attacker to recover the cache access pattern of another workload. This access pattern can be used in any number of side channel attacks: we demonstrate a proof of concept attack that fingerprints the application running on the remote GPU, with high accuracy. We also develop a proof of concept attack to extract hyperparameters of a machine learning workload. Our work establishes for the first time the vulnerability of these machines to microarchitectural attacks and can guide future research to improve their security.
Doppelganger Loads: A Safe, Complexity-Effective Optimization for Secure Speculation Schemes

Speculative side-channel attacks have forced computer architects to rethink speculative execution. Effectively preventing microarchitectural state from leaking sensitive information will be a key requirement in future processor design.
Clifford-based Circuit Cutting for Quantum Simulation

Quantum computing has potential to provide exponential speedups over classical computing for many important applications. However, today's quantum computers are in their early stages, and hardware quality issues hinder the scale of program execution. Benchmarking and simulation of quantum circuits on classical computers is therefore essential to advance the understanding of how quantum computers and programs operate, enabling both algorithm discovery that leads to high-impact quantum computation and engineering improvements that deliver to more powerful quantum systems. Unfortunately, the nature of quantum information causes simulation complexity to scale exponentially with problem size.
Integrated Photonic Convolutional Neural Network Based on Silicon Metalines

Compact and low-power CMOS-compatible hardware can be used for on-chip optical neural networks (ONNs), enabling affordable and portable image classification solutions for applications like autonomous vehicles, healthcare, and optical communication. In this work, we propose a novel one-dimensional Optical Convolutional Neural Network (OCNN) architecture that significantly reduces the number of learnable parameters required for an ONN. Our OCNN achieves an impressive accuracy of over 96% as a pattern classifier, utilizing only 90 learnable parameters, leading to a simpler structure compared to existing on-chip ONNs. Additionally, our OCNN demonstrates scalability and robustness, with an accuracy exceeding 89% in handwritten digit classification. The OCNN’s convolutional layer employs a lenslet 4f system for convolving desired kernels on input images, while an on-chip lens facilitates the desired Fourier Transform effortlessly. The subsequent layer consists of a single metaline layer, implementing a fully connected layer. By parallelizing pre-trained OCNNs, an on-chip deep convolutional neural network (CNN) can be realized, where each OCNN functions as a separate kernel within a conventional CNN.
How can we model climbers’ future visits from their past records?

Outdoor sport climbing is one of the major outdoor sports in Northern Italy due to the vast number of rock climbing places named crags. New crags appear yearly, creating an information overload problem for climbers when they plan where to climb on their forthcoming trips. As such, climbing crags recommender systems address this problem, suggesting crags based on the number of routes similar to those liked by the user in the past. At the same time, people are interested not only in the climbing routes but they have other objectives; for instance, they plan to teach their children to climb while traveling, or prefer to visit the place only if it has a parking space since they travel by car. To better understand and model future climbers’ choices in outdoor sport climbing environments, we first define crags characteristics that primarily impact a user’s behavior. Secondly, we propose to model climbers’ profiles and their preferences for crags’ characteristics as Pearson correlation computed with the number of past users’ visits. Thirdly, we developed and evaluated the recommender system called Visit&Climb, where user tastes are projected into an interactive preferences elicitation panel, which users can further employ to adjust their profile with the sliders. The recommendations are then supplied as the top-visited crags by the most similar user. For the evaluation, we ran several offline experiments: we compared different models (regression-based, matrix factorization, and collaborative filtering) to predict visits recorded in 99 crags by 106 climbers in Arco (Italy). During these experiments, we measured standard metrics such as [email protected], [email protected], and [email protected] for top-k ranking quality. The offline evaluation showed that the Visit&Climb system provides more accurate recommendations than the Baseline model, which utilizes users’ previous records for future prediction. Plus, it delivers a comparable accuracy level to other systems in this domain. Moreover, unlike the other solutions, this developed method visualizes the users’ profiles and allows modification of their tastes, solving a cold-start issue. The recommender system proposed in this work can confidently model climbers’ future visits by their past logs.
Universally Composable Simultaneous Broadcast against a Dishonest Majority and Applications

Simultaneous broadcast (SBC) protocols, introduced in [Chor et al., FOCS 1985], constitute a special class of broadcast channels which, besides consistency, guarantee that all senders broadcast their messages independently of the messages broadcast by other parties. SBC has proved extremely useful in the design of various distributed computing constructions (e.g., multiparty computation, coin flipping, electronic voting, fair bidding). As with any communication channel, it is crucial that SBC security is composable, i.e., it is preserved under concurrent protocol executions. The work of [Hevia, SCN 2006] proposes a formal treatment of SBC in the state-of-the-art Universal Composability (UC) framework [Canetti, FOCS 2001] and a construction secure assuming an honest majority.
The Influence of Culture in Contact Tracing App Design: A Comparative Analysis of Canada’s COVID Alert vs. India’s Aarogya Setu

The 2020 pandemic culminated in the roll-out of contacting tracing apps in many countries to curb the spread of COVID-19. In this paper, we investigated how culture might have influenced the design of these apps by comparing two national apps (COVID Alert and Aarogya Setu) from two different types of culture: individualist (Canada) and collectivist (India), respectively. We found that there was a cultural effect on the design of both apps, starting from their naming. While the Canada’s app was explicitly named “COVID Alert," the Indian app was implicitly named “Aarogya Setu" (meaning "the bridge to health"). Other key attributes on which both apps differ include number of services, information density, privacy design, use of color, use of metaphors, and multimodality. We discuss these differences and the possible rationale behind each app’s design choices using four key cultural dimensions from Hofstede’s and Hall’s cultural frameworks as analytical lenses. The main contribution of the paper is that it is the first to demonstrate the manifestation of national cultures in the interface and functional design of contact tracing apps by focusing on a Western country and an Asian country.
P3DC-shot: Prior-driven discrete data calibration for nearest-neighbor few-shot classification

Nearest-Neighbor (NN) classification has been proven as a simple and effective approach for few-shot learning. The query data can be classified efficiently by finding the nearest support class based on features extracted by pre-trained deep models. However, NN-based methods are sensitive to the data distribution and may produce false prediction if the samples in the support set happen to lie around the distribution boundary of different classes. To solve this issue, we present P3DC-shot, an improved nearest-neighbor based few-shot classification method empowered by prior-driven data calibration. Inspired by the distribution calibration technique which utilizes the distribution or statistics of the base classes to calibrate the data for few-shot tasks, we propose a novel discrete data calibration operation which is more suitable for NN-based few-shot classification. Specifically, we treat the prototypes representing each base class as priors and calibrate each support data based on its similarity to different base prototypes. Then, we perform NN classification using these discretely calibrated support data. Results from extensive experiments on various datasets show our efficient non-learning based method can outperform or at least comparable to SOTA methods which need additional learning steps.