Reviewerly/exHarmony huggingface dataset:
ADA-GP: Accelerating DNN Training By Adaptive Gradient Prediction

Neural network training is inherently sequential where the layers finish the forward propagation in succession, followed by the calculation and back-propagation of gradients (based on a loss function) starting from the last layer. The sequential computations significantly slow down neural network training, especially the deeper ones. Prediction has been successfully used in many areas of computer architecture to speed up sequential processing. Therefore, we propose ADA-GP, which uses gradient prediction adaptively to speed up deep neural network (DNN) training while maintaining accuracy. ADA-GP works by incorporating a small neural network to predict gradients for different layers of a DNN model. ADA-GP uses a novel tensor reorganization method to make it feasible to predict a large number of gradients. ADA-GP alternates between DNN training using backpropagated gradients and DNN training using predicted gradients. ADA-GP adaptively adjusts when and for how long gradient prediction is used to strike a balance between accuracy and performance. Last but not least, we provide a detailed hardware extension in a typical DNN accelerator to realize the speed up potential from gradient prediction. Our extensive experiments with fifteen DNN models show that ADA-GP can achieve an average speed up of 1.47 × with similar or even higher accuracy than the baseline models. Moreover, it consumes, on average, 34% less energy due to reduced off-chip memory accesses compared to the baseline accelerator.
ReCon: Efficient Detection, Management, and Use of Non-Speculative Information Leakage

In a speculative side-channel attack, a secret is improperly accessed and then leaked by passing it to a transmitter instruction. Several proposed defenses effectively close this security hole by either delaying the secret from being loaded or propagated, or by delaying dependent transmitters (e.g., loads) from executing when fed with tainted input derived from an earlier speculative load. This results in a loss of memory-level parallelism and performance.
Explaining Unsolvability of Planning Problems in Hybrid Systems with Model Reconciliation

A recent problem of interest in Explainable AI Planning is that of explaining the unsolvability of planning problems. Though there has been a lot of research on generating explanations of solutions to planning problems, explaining the absence of solutions remains a largely open and understudied problem. Model reconciliation has been a popular approach for generating explanations for such problems in recent literature, which involves an AI agent and a human planner, who have different models of the planning domain and each explains to the other the differences they have in their domain representations and attempt to arrive at a consensus. More often than not, it is assumed that the AI agent has a correct and complete view of the domain, of which the human only has a partial view. Through reconciliation, the human domain is updated to be consistent with what the AI agent has. Most of the works in this direction are targeted toward classical planning problems on domains represented typically as discrete state transition systems or variants. In this paper, we provide an approach towards model reconciliation for planning problems in hybrid systems represented as a mix of discrete and continuous domains. We assume that the agent has a complete model of the environment, while the human has a partial or erroneous model and expects a plan for the planning problem when there is none. The explanation problem is presented as a process of continuous reconciliation between these two entities (agent and human) to make the human domain consistent with that of the agent. To this effect, we use a mix of graph traversal and path analysis, along with Linear programming to carry out the reconciliation process. In particular, we use the concept of Irreducible Infeasible Sets (IIS) to generate explanations. Experimental results on 2 representative hybrid domains show the efficacy of our approach.
MEGA Evolving Graph Accelerator

Graph Processing is an emerging workload for applications working with unstructured data, such as social network analysis, transportation networks, bioinformatics and operations research. We examine the problem of graph analytics over evolving graphs, which are graphs that change over time. The problem is challenging because it requires evaluation of a graph query on a sequence of graph snapshots over a time window, typically to track the progression of a property over time. In this paper, we introduce MEGA, a hardware accelerator designed for efficiently evaluating queries over evolving graphs. MEGA leverages CommonGraph, a recently proposed software approach for incrementally processing evolving graphs that gains efficiency by avoiding the need to process expensive deletions by converting them into additions. MEGA supports incremental event-based streaming of edge additions as well as execution of multiple snapshots concurrently to support evolving graphs. We propose Batch-Oriented-Execution (BOE), a novel batch-update scheduling technique that activates snapshots that share batches simultaneously to achieve both computation and data reuse. We introduce optimizations that pack compatible batches together, and pipeline batch processing. To the best of our knowledge, MEGA is the first graph accelerator for evolving graphs that evaluates graph queries over multiple snapshots simultaneously. MEGA achieves 24 × -120 × speedup over CommonGraph. It also achieves speedups ranging from 4.08 × to 5.98 × over JetStream, a state-of-the-art streaming graph accelerator.
TT-GNN: Efficient On-Chip Graph Neural Network Training via Embedding Reformation and Hardware Optimization

Training Graph Neural Networks on large graphs is challenging due to the need to store graph data and move them along the memory hierarchy. In this work, we tackle this by effectively compressing graph embedding matrix such that the model training can be fully enabled with on-chip compute and memory resources. Specifically, we leverage the graph homophily property and consider using Tensor-train to represent the graph embedding. This allows nodes with similar neighborhoods to partially share the feature representation.
SUPPORTING ENERGY-BASED LEARNING WITH AN ISING MACHINE SUBSTRATE: A CASE STUDY ON RBM

Nature apparently does a lot of computation constantly. If we can harness some of that computation at an appropriate level, we can potentially perform certain type of computation (much) faster and more efficiently than we can do with a von Neumann computer. Indeed, many powerful algorithms are inspired by nature and are thus prime candidates for nature-based computation. One particular branch of this effort that has seen some recent rapid advances is Ising machines. Some Ising machines are already showing better performance and energy efficiency for optimization problems. Through design iterations and co-evolution between hardware and algorithm, we expect more benefits from nature-based computing systems in the future. In this paper, we make a case for an augmented Ising machine suitable for both training and inference using an energy-based machine learning algorithm. We show that with a small change, the Ising substrate accelerates key parts of the algorithm and achieves non-trivial speedup and efficiency gain. With a more substantial change, we can turn the machine into a self-sufficient gradient follower to virtually complete training entirely in hardware. This can bring about 29x speedup and about 1000x reduction in energy compared to a Tensor Processing Unit (TPU) host.
CryptoMMU: Enabling Scalable and Secure Access Control of Third-Party Accelerators

Due to increasing energy and performance gaps between general-purpose processors and hardware accelerators (e.g., FPGA or ASIC), clear trends for leveraging accelerators arise in various fields or workloads, such as edge devices, cloud systems, and data centers. Moreover, system integrators desire higher flexibility to deploy custom accelerators based on their performance, power, and cost constraints, where such integration can be as early as (1) at the design time when third-party intellectual properties (IPs) are used, (2) at integration/upgrade time when third-party discrete chip accelerators are used, or (3) during runtime as in reconfigurable logic.
SecureLoop: Design Space Exploration of Secure DNN Accelerators

Deep neural networks (DNNs) are gaining popularity in a wide range of domains, ranging from speech and video recognition to healthcare. With this increased adoption comes the pressing need for securing DNN execution environments on CPUs, GPUs, and ASICs. While there are active research efforts in supporting a trusted execution environment (TEE) on CPUs, the exploration in supporting TEEs on accelerators is limited, with only a few solutions available [18, 19, 27]. A key limitation along this line of work is that these secure DNN accelerators narrowly consider a few specific architectures. The design choices and the associated cost for securing these architectures do not transfer to other diverse architectures.
QuCT: A Framework for Analyzing Quantum Circuit by Extracting Contextual and Topological Features

In the current Noisy Intermediate-Scale Quantum era, quantum circuit analysis is an essential technique for designing high-performance quantum programs. Current analysis methods exhibit either accuracy limitations or high computational complexity for obtaining precise results. To reduce this tradeoff, we propose QuCT, a unified framework for extracting, analyzing, and optimizing quantum circuits. The main innovation of QuCT is to vectorize each gate with each element, quantitatively describing the degree of the interaction with neighboring gates. Extending from the vectorization model, we propose two representative downstream models for fidelity prediction and unitary decomposition. The fidelity prediction model performs a linear transformation on all gate vectors and aggregates the results to estimate the overall circuit fidelity. By identifying critical weights in the transformation matrix, we propose two optimizations to improve the circuit fidelity. In the unitary decomposition model, we significantly reduce the search space by bridging the gap between unitary and circuit via gate vectors. Experiments show that QuCT improves the accuracy of fidelity prediction by 4.2 × on 5-qubit and 18-qubit quantum devices and achieves 2.5 × fidelity improvement compared to existing quantum compilers [19, 55]. In unitary decomposition, QuCT achieves 46.3 × speedup for 5-qubit unitary and more than hundreds of speedup for 8-qubit unitary, compared to the state-of-the-art method [87].
ACRE: Accelerating Random Forests for Explainability

As machine learning models become more widespread, they are being increasingly applied in applications that heavily impact people’s lives (e.g., medical diagnoses, judicial system sentences, etc.). Several communities are thus calling for ML models to be not only accurate, but also explainable. To achieve this, recommendations must be augmented with explanations summarizing how each recommendation outcome is derived. Explainable Random Forest (XRF) models are popular choices in this space, as they are both very accurate and can be augmented with explainability functionality, allowing end-users to learn how and why a specific outcome was reached. However, the limitations of XRF models hamper their adoption, the foremost being the high computational demands associated with training such models to support high-accuracy classifications, while also annotating them with explainability meta-data.
Phantom: Exploiting Decoder-detectable Mispredictions

Violating the Von Neumann sequential processing principle at the microarchitectural level is commonplace to reach high performing CPU hardware — violations are safe as long as software executes correctly at the architectural interface. Speculative execution attacks exploit these violations and queue up secret-dependent memory accesses allowed by long speculation windows due to the late detection of these violations in the pipeline. In this paper, we show that recent AMD and Intel CPUs speculate very early in their pipeline, even before they decode the current instruction. This mechanism enables new sources of speculation to be triggered from almost any instruction, enabling a new class of attacks that we refer to as Phantom. Unlike Spectre, Phantom speculation windows are short since the violations are detected early. Nonetheless, Phantom allows for transient fetch and transient decode on all recent x86-based microarchitectures, and transient execution on AMD Zen 1 and 2. We build a number of exploits using these new Phantom primitives and discuss why mitigating them is difficult in practice.
Radar Signal Recognition Method based on Transfer Learning

Radar signal recognition has a guiding role for electronic warfare systems. The traditional radar signal recognition method confirms the target model through signal feature comparison, which is suitable for electromagnetic environments with single radar signal and low signal feature complexity. With the continuous development of radar technology, traditional radar signal recognition methods have been difficult to adapt to target recognition in complex electromagnetic environments. In order to improve the accuracy of radar signal recognition in complex electromagnetic environments, this paper proposed a radar signal recognition method based on transfer learning. At first, the proposed method performed pre-processing operations on the initial signal data collected by the sensor, including normalization, noise truncation, feature extraction, etc., and established a database for the processed signal data. Afterwards, a feedforward BP neural network model was trained to classify the signal data. Finally, on the basis of the transfer learning, pulse characteristic parameters (radio frequency, etc.) were added to the output layer of the feedforward BP neural network, it served as a new input layer to train deeper network models, making it adaptive and deviation-resistant. The feasibility of the proposed method was verified with six sets of different sample data. The results showed that the average recognition accuracy of the method in this paper reached 93.3%, compared with the traditional ANN-based method, the average recognition accuracy is improved by 12%, which verifies the reliability of the proposed method.
Ghost in the Machine : Discourses with AI

AI systems analyze vast amounts of data, uncover patterns, and make decisions - emulating a semblance of intelligence despite lacking qualia and embodiment that form the basis of the human condition. In this paper, we expound on "Ghost in the Machine", an interactive installation that delves into our pervasive tendency to anthropomorphize AI, ascribing human-like qualities, intentions, and even consciousness. Participants engage in dialogue with the AI as it collaboratively materializes the AI's thoughts in moving image and generative sound. The installation attempts to forge embodiment for an amorphous AI, revealing errors in its comprehension, represented by the metaphor of hallucinations.
Exploring the Eco-Digital: Performative Sensing with Plants and Data

Artistic practices influenced by philosophical understandings that extend ‘beyond the human’, such as posthuman and post-Anthropocene approaches, partially connect on a qualitative level with ancient understandings of an expanded nature that engulfs both human and nonhuman kin. Preserved in Indigenous epistemes, such as mātauranga Māori (Māori knowledge), this research brings selected aspects of this complex knowledge system into contact with artistic practice generated with networked technologies, to manifest speculative methods of data visualization and signal flow. While there are many different approaches of the eco-digital currently at play, this research looks at practices engaging plants as co-composers. Articulating various situational assemblages that entangle humans, plants, and data, this paper explores theoretical positions that cross-pollinate natural ecology and digital technology. We do this through the prism of two iterative works by the authors: Contact/Sense (2019) and Nga manawataki o te koiora: Biorhythms (2022).
The Most Expensive Museum in the World: Three Player Cooperative Game Between VR and PC Platforms Investigating Empathy between Players and Historical Characters.

The paper describes an art installation based on a local cooperative cross platform VR/PC game for three players. The story is based on the historical story of a nuclear power plant, which was built, but never turned on because of a public referendum. The game mechanics are built upon the interaction of three playable historical characters: engineer, activist, and politician. The main goal was to foster empathy in the players by replaying the game from each character’s perspective. We present the story implementation, custom-made interfaces, physical setup, and an evaluation of recordings from a four-month display in a public gallery.
AI Nüshu: An Exploration of Language Emergence in Sisterhood Through the Lens of Computational Linguistics

This paper presents “AI Nüshu," an emerging language system inspired by Nüshu (women’s scripts), the unique language created and used exclusively by ancient Chinese women who were thought to be illiterate under a patriarchal society. In this interactive installation, two artificial intelligence (AI) agents are trained in the Chinese dictionary and the Nüshu corpus. By continually observing their environment and communicating, these agents collaborate towards creating a standard writing system to encode Chinese. It offers an artistic interpretation of the creation of a non-western script from a computational linguistics perspective, integrating AI technology with Chinese cultural heritage and a feminist viewpoint.
Cryptographic Competitions

Abstract Competitions are widely viewed as the safest way to select cryptographic algorithms. This paper surveys procedures that have been used in cryptographic competitions, and analyzes the extent to which those procedures reduce security risks.
Reimagining Animation Making through Style Transfer

"Dissolution” and “Cunabula” are experimental animation projects that explore the potential of artistic style transfer with neural networks in animation filmmaking. The objective is to discover novel production methods using fast, arbitrary, and stroke-adjustable style transfer networks that allow for unconventional technical approaches during the production process and the creation of unique and unexplored aesthetics in moving images powered by deep learning. These projects exemplify a creative approach to leveraging existing neural style transfer techniques to create compelling animations that go beyond mere passive pastiches of certain artistic styles, thereby advancing the aesthetics of contemporary animation.
Visually Reporting Geographic Data Insights as Integrated Visual and Textual Representations

Abstract Geographic information volunteered by the public is usually also of public interest. However, just publishing the data is not enough to make the data accessible and usable for the public. The raw data might need to be abstracted and interpreted, as well as visually presented to be understandable to non-experts. To address this, we propose interactive visual reporting solutions that leverage natural language and visualizations for geo-related data. We present these reports as interactive documents, but also in other media such as virtual reality environments. First, we have studied the interplay of textual and visual content in such reports. To ease the creation of content, we have developed solutions for authoring interactive documents with a close linking of textual contents and visually presented data. Moreover, we propose automatic report generation approaches that specifically support the exploration of the geo-related data starting from an explanatory summary.
Two Worlds in One Network: Fusing Deep Learning and Random Forests for Classification and Object Detection

Abstract Neural networks have demonstrated great success; however, large amounts of labeled data are usually required for training the networks. In this work, a framework for analyzing the road and traffic situations for cyclists and pedestrians is presented, which only requires very few labeled examples. We address this problem by combining convolutional neural networks and random forests, transforming the random forest into a neural network, and generating a fully convolutional network for detecting objects. Because existing methods for transforming random forests into neural networks propose a direct mapping and produce inefficient architectures, we present neural random forest imitation—an imitation learning approach by generating training data from a random forest and learning a neural network that imitates its behavior. This implicit transformation creates very efficient neural networks that learn the decision boundaries of a random forest. The generated model is differentiable, can be used as a warm start for fine-tuning, and enables end-to-end optimization. Experiments on several real-world benchmark datasets demonstrate superior performance, especially when training with very few training examples. Compared to state-of-the-art methods, we significantly reduce the number of network parameters while achieving the same or even improved accuracy due to better generalization.
Development of new interactive methods suitable for teaching semiconductor relays

In this article, the issues of improving the quality of education require special attention in all countries of the world, the problems arising in this regard are noted. The article says that there are different approaches to teaching in educational processes, it is necessary to completely abandon traditional approaches or to integrate these approaches with innovative, modern approaches. According to this, proposals were made for modern students to use exactly the methods of teaching suitable for them. This article is written based on existing pedagogical technologies and interactive teaching methods that serve to improve the quality of education and are more suitable for teaching students, and a proposal is made to create new interactive methods suitable for teaching technical sciences.