Reviewerly/exHarmony huggingface dataset:
WeNet: Configurable Neural Network with Dynamic Weight-Enabling for Efficient Inference

Deep Neural Networks (DNN) are widely deployed in resource-limited edge devices. Due to the limitation of computational resources, it is important to meet the timing and energy constraints while maintaining a high level of accuracy. To deploy the same DNN model on different edge devices, one challenge is to train a dynamic neural network with the flexibility of balancing the trade-off between accuracy and efficiency at runtime. In this paper, we present a novel methodology, dynamic Weight-enabling Network (WeNet), where the weights of neural network can be dynamically enabled or disabled to switch between different sub-networks, so that we are able to balance the trade-off between inference time, energy consumption and model accuracy. We extend the methodology to convolutional layers using group convolution and channel shuffling. We also propose a design space exploration approach to search for the optimal sub-network for different scenarios. We thoroughly evaluate our methodology using a number of DNN architectures on different hardware platforms, showing that WeNet provides a large number of energy-efficient operation modes, 73.2 % of which provide better accuracy-efficiency trade-off compared to other methodologies.
FPGA-Patch: Mitigating Remote Side-Channel Attacks on FPGAs using Dynamic Patch Generation

We propose FPGA-Patch, the first-of-its-kind defense that leverages automated program repair concepts to thwart power side-channel attacks on cloud FPGAs. FPGA-Patch generates isofunctional variants of the target hardware by injecting faults and finding transformations that eliminate failure. The obtained variants display different hardware characteristics, ensuring a maximal diversity in power traces once dynamically swapped at run-time. Yet, FPGA-Patch forces the variants to have enough similarity, enabling bitstream compression and minimizing dynamic exchange costs. Considering AES running on AMD/Xilinx FPGA, FPGA-Patch increases the attacker's effort by three orders of magnitude, while preserving the performance of AES and a minimal area overhead of 14.2%.
CARMA: Context-Aware Runtime Reconfiguration for Energy-Efficient Sensor Fusion

Autonomous systems (AS) are systems that can adapt and change their behaviors in response to unanticipated events and include systems such as aerial drones, autonomous vehicles, and ground/aquatic robots. AS require a wide array of sensors, deep learning models, and powerful hardware platforms to perceive the environment and safely operate in real-time. However, in many contexts, some sensing modalities negatively impact perception while increasing the system's overall energy consumption. Since AS are often energy-constrained edge devices, energy-efficient sensor fusion methods have been proposed. However, existing methods either fail to adapt to changing scenario conditions or to optimize system-wide energy efficiency. We propose CARMA, a context-aware sensor fusion approach that uses context to dynamically reconfigure the computation flow on a field-programmable gate array (FPGA) at runtime. By clock gating unused sensors and model sub-components, CARMA significantly reduces the energy used by a multi-sensory object detector without compromising performance. We use a deep learning processor unit (DPU) based reconfiguration approach to minimize the latency of model reconfiguration. We evaluate multiple context identification strategies, propose a novel system-wide energy-performance joint optimization, and evaluate scenario-specific perception performance. Across challenging real-world sensing contexts, CARMA outperforms state-of-the-art methods with up to 1.3× speedup and 73% lower energy consumption.
A Fully-Integrated Energy-Scalable Transformer Accelerator Supporting Adaptive Model Configuration and Word Elimination for Language Understanding on Edge Devices

Efficient natural language processing on the edge is needed to interpret voice commands, which have become a standard way to interact with devices around us. Due to the tight power and compute constraints of edge devices, it is important to adapt the computation to the hardware conditions. We present a Transformer accelerator with a variable-depth adder tree to support different model dimensions, a SuperTransformer model from which Sub Transformers of various sizes can be sampled enabling adaptive model configuration, and a dedicated word elimination unit to prune redundant tokens. We achieve up to 6.9× scalability in network latency and energy between the largest and smallest Sub Transformers, under the same operating conditions. Word elimination can reduce network energy by 16%, with a 14.5% drop in F1 score. At 0.68V and 80MHz, processing a 32-length input with our custom 2-layer Transformer model for intent detection and slot filling takes 0.61ms and 1.6μJ.
Quantifying the Overheads of Modular Multiplication

As security and privacy continue to grow in importance, new techniques, including fully homomorphic encryption (FHE) and post-quantum cryptography (PQC), have emerged to provide new capabilities. Many of these techniques are based on the ring learning with errors problem and operate over rings. Elements of a ring are computed using modular arithmetic, with modular multiplication being a primary component. These components are far more complex than standard integer computing, especially when working with large bit widths. As FHE and PQC become increasingly popular, the need for well-designed and optimized modular multipliers also grows in importance. In this paper, we analyze the power, area, performance, energy, and thermal characteristics of two commonly used modular multipliers: Barrett (bit parallel) and Interleaved (bit parallel). To understand these multipliers' characteristics, this study provides necessary insights into the sources of area, power, frequency, and energy overhead, considering a range of different bit widths (16–256). This paper rigorously analyzes the sub-blocks of modular multipliers and their contributions to overall power, performance, and area (PPA).
Improvement Object Tracking Process Through Filtering Methods

One of the main problems in the object tracking process is the correct estimation of the target position along the entire trajectory. However, in the tracking process, there are variations between the estimated and real positions. So, this article aims to improve the estimation of the trajectory of an object. We treat such variations as Gauss-Markov Colored Measurement Noise (CMN). To mitigate colored noise, we use measurement differentiation and employ modified filtering methods for CMN, KF, and UFIR. The tests were carried out with experimental data obtained from a dataset. We take the measurements got in bounding boxes as a reference. We evaluate the performance of the filters through the parameters estimation error and precision. The modified UFIR showed better results with high precision and low estimation error under high values of coloration factor.
Real-Time Local Map Generation and Collision-Free Trajectory Planning for Autonomous Vehicles in Dynamic Environments

In this paper we propose a method for autonomous navigation in GPS-denied environments that can be deployed in all types of robotic vehicles requiring only a single RGB-D camera. Our algorithm is an integration of localization, mapping and trajectory planning software modules that can handle dynamic environments. Depth measurements and visual odometry are used to (a) create a robocentric Euclidean Signed Distance Fields (ESDF) map in real-time, (b) estimate the position of obstacles surrounding the robot, and (c) calculate the distance and gradient from them. Subsequently, we use an optimization algorithm to plan a collision-free trajectory. We show that a local map can be created faster than with the fixed-size array method used in current optimization methods. Our approach also allows for effective detection of dynamic obstacles by constantly updating the map. As a result, the robot deviates from its initial path only when necessary. We validate our results in simulation by deploying our algorithm in a skid steering rover and a hexacopter in an environment with static and dynamic obstacles.
Color origin and its sedimentary and paleoenvironmental significance of the Permian-Triassic strata in the Ordos Basin, China

The Permian-Triassic strata in the Ordos Basin deposit a colorful set of strata. Although there have been many studies on oil and gas resources here, little attention has been paid to the genesis mechanism of its color. To analyze the color origin of the Permian-Triassic claystones and to clarify the dialectical relationship between rock color and sedimentary environment, petrological and geochemical methods such as polarization microscope, scan electron microscope, X-ray diffractometer, and inductively coupled plasma-mass spectrometer are used. The results indicate that the minerals with different colors, composition, and content serve as colorants in sedimentary rocks, with the predominant dyeing component in dark claystones being the organic matter, whereas red beds primarily acquire their color from hematite and other ferric minerals. In addition, different rock colors correspond to distinct chemical composition variations. The black-gray rocks of the Yanchang and Shihezi Formations have high total organic carbon content, Fe 2+ /Fe 3+ , V/Cr, and low CaO/(MgO × Al 2 O 3 ) and Sr/Cu, whereas the red-brown rocks of the Liujiagou and Heshanggou Formations exhibit the opposite. Therefore, different colors of the rocks stem from their different mineral and chemical compositions, which in turn influenced by the changes in the paleoclimate, paleoenvironment, and tectonic movements. Comprehensive analysis of color, mineral, and chemical composition reveals the evolution process of paleoclimate and paleoenvironment in the Ordos Basin from the late Permian to Triassic, beginning with a warm humid climate characterized by a weak oxidation environment in the Shihezi and Shiqianfeng Formations, transitioning to a hot arid climate in the Liujiagou and Heshanggou Formations, and returning to a warm humid climate with a weak oxidation environment in the Zhifang and Yanchang Formations. This evolution history aligns with the global tectonic and climate evolution. Overall, systematic analysis of sedimentary rock color can provide an important basis for the study of the paleoclimate and paleoenvironment.
Understanding Cloud Systems’ Structure and Organization Using a Machine’s Self-Learning Approach

Abstract In this study, we introduce a self-supervised deep neural network approach to classify satellite images into independent classes of cloud systems. The driving question of the work is to understand whether our algorithm can capture cloud variability and identify distinct cloud regimes. Ultimately, we want to achieve generalization such that the algorithm can be applied to unseen data and thus help automatically extract relevant information important to atmospheric science and renewable energy applications from the ever-increasing satellite data stream. We use cloud optical depth (COD) retrieved from postprocessed high-resolution Meteosat Second Generation (MSG) satellite data as input for the network. The network’s architecture is based on the DeepCluster, version 2, and consists of a convolutional neural network and a multilayer perceptron, followed by a k -means algorithm. We explore the network’s training capabilities by analyzing the centroids and feature vectors found from progressive minimization of the cross-entropy loss function. By making use of additional MSG retrieval products based on multichannel information, we derive the optimum number of classes to determine independent cloud regimes. We test the network capabilities on COD data from 2013 and find that the trained neural network gives insights into the cloud systems’ persistence and transition probability. The generalization on the 2015 data shows good skills of our algorithm with unseen data, but results depend on the spatial scale of cloud systems. Significance Statement This study uses a self-supervised deep neural network to identify distinct cloud systems from cloud optical depth satellite images over central Europe. Satellite-retrieved products support the physical interpretation of the identified cloud classes and help optimize the number of identified classes. The trained neural network gives insights into cloud systems’ persistence and transition probability. The generalization capacity of the deep neural network with unseen data is promising but depends on the spatial scale of cloud systems.
Joint Gaussian mixture model for versatile deep visual model explanation

Explainable AI (XAI) promotes understandability and credibility in the complex decision-making process of machine learning models. Post-hoc explanations for deep neural networks are crucial because they focus on faithful explanation of the learned representations, decision mechanisms, and uncertainty of the model. Explaining deep convolutional neural networks (DCNN) is particularly challenging because of the high dimensionality of deep features and the complexity of model inference. Most post-hoc explanation methods follow one single paradigm of explanation, confining the diversity and consistency of the explanation. This study proposes a joint Gaussian mixture model (JGMM), which is a probabilistic model that jointly models deep inter-layer features and produces faithful and consistent post-hoc explanations. The JGMM explains deep features by Gaussian mixture model and model inference by the posterior distribution of the latent component variables. JGMM enables a versatile explanation framework that unifies the interpretable proxy model and mining global and local explanatory examples. Experiments are performed on various DCNN image classifiers in comparison with other explaining methods. The results show that the JGMM can efficiently produce versatile, consistent, faithful, and understandable explanations.
Sweet Apple, company? or food? Adjective-centric commonsense knowledge acquisition with taxonomy-guided induction

How to enable intelligent machines to possess human commonsense knowledge is one of the central concerns of artificial intelligence. Consequently, a series of commonsense knowledge bases have been designed and constructed by scholars. However, as an important kind of commonsense knowledge, adjective-centric commonsense knowledge (a crucial type of adjective-concept pair such as (blue, sky), (eatable, food)) is far from satisfactory due to the lack of existing knowledge bases and limited acquisition methods. In this paper, we concentrate on automatically constructing large-scale adjective-centric commonsense knowledge bases and propose an effective framework to achieve the goal. The framework mainly contains a filtering module to remove unreasonable inputs, a clustering module, and a conceptualization module to obtain adjective-concept pairs and an evaluation module to assess their plausibility. Extensive automatic and human evaluation results demonstrate the effectiveness of our method, and we finally harvest over 200 k adjective-centric commonsense knowledge, where 81.56% of the implicit commonsense knowledge is not covered by WebChild.1 Moreover, we also show that our mined adjective-centric commonsense knowledge can benefit the downstream query conceptualization task.
Making decisions with evidential probability and objective Bayesian calibration inductive logics

Calibration inductive logics are based on accepting estimates of relative frequencies, which are used to generate imprecise probabilities. In turn, these imprecise probabilities are intended to guide beliefs and decisions — a process called “calibration”. Two prominent examples are Henry E. Kyburg's system of Evidential Probability and Jon Williamson's version of Objective Bayesianism. There are many unexplored questions about these logics. How well do they perform in the short-run? Under what circumstances do they do better or worse? What is their performance relative to traditional Bayesianism? In this article, we develop an agent-based model of a classic binomial decision problem, including players based on variations of Evidential Probability and Objective Bayesianism. We compare the performances of these players, including against a benchmark player who uses standard Bayesian inductive logic. We find that the calibrated players can match the performance of the Bayesian player, but only with particular acceptance thresholds and decision rules. Among other points, our discussion raises some challenges for characterising “cautious” reasoning using imprecise probabilities. Thus, we demonstrate a new way of systematically comparing imprecise probability systems, and we conclude that calibration inductive logics are surprisingly promising for making decisions.
Projective families of distributions revisited

The behaviour of statistical relational representations across differently sized domains has become a focal area of research from both a modelling and a complexity viewpoint. Recently, projectivity of a family of distributions emerged as a key property, ensuring that marginal probabilities are independent of the domain size. However, the formalisation used currently assumes that the domain is characterised only by its size. This contribution extends the notion of projectivity from families of distributions indexed by domain size to functors taking extensional data from a database. This makes projectivity available for the large range of applications taking structured input. We transfer key known results on projective families of distributions to the new setting. This includes a characterisation of projective fragments in different statistical relational formalisms as well as a general representation theorem for projective families of distributions. Furthermore, we prove a correspondence between projectivity and distributions on countably infinite domains, which we use to unify and generalise earlier work on statistical relational representations in infinite domains. Finally, we use the extended notion of projectivity to define a further strengthening, which we call σ-projectivity, and which allows the use of the same representation in different modes while retaining projectivity.
Bayesian denoising of structured sources and its implications on learning-based denoising

Abstract Denoising a stationary process $(X_{i})_{i \in \mathbb{Z}}$ corrupted by additive white Gaussian noise $(Z_{i})_{i \in \mathbb{Z}}$ is a classic, well-studied and fundamental problem in information theory and statistical signal processing. However, finding theoretically founded computationally efficient denoising methods applicable to general sources is still an open problem. In the Bayesian set-up where the source distribution is known, a minimum mean square error (MMSE) denoiser estimates $X^{n}$ from noisy measurements $Y^{n}$ as $\hat{X}^{n}=\mathrm{E}[X^{n}|Y^{n}]$. However, for general sources, computing $\mathrm{E}[X^{n}|Y^{n}]$ is computationally very challenging, if not infeasible. In this paper, starting from a Bayesian set-up, a novel denoising method, namely, quantized maximum a posteriori (Q-MAP) denoiser is proposed and its asymptotic performance is analysed. Both for memoryless sources, and for structured first-order Markov sources, it is shown that, asymptotically, as $\sigma _{z}^{2} $ (noise variance) converges to zero, ${1\over \sigma _{z}^{2}} \mathrm{E}[(X_{i}-\hat{X}^{\mathrm{QMAP}}_{i})^{2}]$ converges to the information dimension of the source. For the studied memoryless sources, this limit is known to be optimal. A key advantage of the Q-MAP denoiser, unlike an MMSE denoiser, is that it highlights the key properties of the source distribution that are to be used in its denoising. This key property leads to a new learning-based denoising approach that is applicable to generic structured sources. Using ImageNet database for training, initial simulation results exploring the performance of such a learning-based denoiser in image denoising are presented.
Spam email detection using a novel multilayer classification-based decision technique

Because of the rapid advancement of technology over the last several years, the number of internet users is growing at an exponential rate, and as a result, email communication has become popular as a means of exchanging information over the internet. Sending data and communicating with peers via email is the most cost-effective method. These email services also cause problems for users by sending electronic junk mail, often known as spam mail. Spam email is a privacy concern that is linked to a slew of commercial and dangerous websites, causing phishing, virus distribution, and a slew of other problems. This study examines several aspects that have been used for email spam classification, as well as offering an overview of a handful of classifiers or algorithms that have been successfully evaluated, as well as exploratory data analysis. The proposed email spam classifier uses three parallel layers of machine learning and deep learning techniques, followed by a decision function to determine whether or not the emails are spam. During testing, it was found that the proposed classifier beats similar systems on the standard dataset with an accuracy of 98.4%.
How to Tame an Unpredictable Emergence? Design Strategies for a Live-Programming System

Programming environments that provide a feeling of liveness help professionals and amateurs alike to approach unfamiliar domains with ease through short feedback loops. Exploration and experimentation are promoted because any change to the program under construction can be observed immediately. However, live-programming systems such as Squeak/Smalltalk struggle with the predictable emergence of adapted program behavior as object communication can be unconstrained and diverse. While programmers wish for immediate effects, it would be helpful to at least know whether anything will happen after some time. In this chapter, we take a closer look at the means available in Squeak to explore and adjust object state and object behavior so that programmers can ensure the system’s responsiveness and hence observe gradual or even induce eventual emergence. We argue that these design strategies are sufficient to architect communication patterns that reward changes with immediate effects. We believe that our work can help programmers to better understand their leverage toward a predictable emergence in systems whose liveness stems from objects and messaging in a space where tools and applications live side by side.
Truncated two‐parameter Poisson–Dirichlet approximation for Pitman–Yor process hierarchical models

Abstract In this paper, we construct an approximation to the Pitman–Yor process by truncating its two‐parameter Poisson–Dirichlet representation. The truncation is based on a decreasing sequence of random weights, thus having a lower approximation error compared to the popular truncated stick‐breaking process. We develop an exact simulation algorithm to sample from the approximation process and provide an alternative MCMC algorithm for the parameter regime where the exact simulation algorithm becomes slow. The effectiveness of the simulation algorithms is demonstrated by the estimation of the functionals of a Pitman–Yor process. Then we adapt the approximation process into a Pitman–Yor process mixture model and devise a blocked Gibbs sampler for posterior inference.