Reviewerly/exHarmony huggingface dataset:
External Behavior of a Logic Program and Verification of Refactoring

Abstract Refactoring is modifying a program without changing its external behavior. In this paper, we make the concept of external behavior precise for a simple answer set programming language. Then we describe a proof assistant for the task of verifying that refactoring a program in that language is performed correctly.
Big Nihilism: Generation Z, Surveillance Capitalism, and the Emerging Digital Technocracy

The promise of an open cyberspace driven by an empowered generation of “digital natives” has collapsed, due to the corporate capture of the internet and the psychosocial immiseration of youngsters caused by the instrumental manipulation of them at the screen interface. Certain strands in the twentieth-century philosophy of technology can throw light on these developments in terms of (1) Martin Heidegger’s suggestion that the expanding influence of ‘Technik’ tends toward the treatment of persons as exploitable things, or “standing reserve,” and (2) Jacques Ellul’s contention that humans would in time and of necessity assimilate themselves to vast and autonomous technological system. Young people are now being largely shaped by “social physics,” as big data–derived fodder for the creation of a hive mind in the interests of technocratic social control and corporate profiteering.
Reduced precision floating-point optimization for Deep Neural Network On-Device Learning on microcontrollers

Enabling On-Device Learning (ODL) for Ultra-Low-Power Micro-Controller Units (MCUs) is a key step for post-deployment adaptation and fine-tuning of Deep Neural Network (DNN) models in future TinyML applications. This paper tackles this challenge by introducing a novel reduced precision optimization technique for ODL primitives on MCU-class devices, leveraging the State-of-Art advancements in RISC-V RV32 architectures with support for vectorized 16-bit floating-point (FP16) Single-Instruction Multiple-Data (SIMD) operations. Our approach for the Forward and Backward steps of the Back-Propagation training algorithm is composed of specialized shape transform operators and Matrix Multiplication (MM) kernels, accelerated with parallelization and loop unrolling. When evaluated on a single training step of a 2D Convolution layer, the SIMD-optimized FP16 primitives result up to 1.72× faster than the FP32 baseline on a RISC-V-based 8+1-core MCU. An average computing efficiency of 3.11 Multiply and Accumulate operations per clock cycle (MAC/clk) and 0.81 MAC/clk is measured for the end-to-end training tasks of a ResNet8 and a DS-CNN for Image Classification and Keyword Spotting, respectively – requiring 17.1 ms and 6.4 ms on the target platform to compute a training step on a single sample. Overall, our approach results more than two orders of magnitude faster than existing ODL software frameworks for single-core MCUs and outperforms by 1.6× previous FP32 parallel implementations on a Continual Learning setup.
Impact of Quality and Distance on the Perception of Point Clouds in Mixed Reality

Point Cloud (PC) streaming has recently attracted research attention as it has the potential to provide six degrees of freedom (6DoF), which is essential for truly immersive media.PCs require high-bandwidth connections, and adaptive streaming is a promising solution to cope with fluctuating bandwidth conditions.Thus, understanding the impact of different factors in adaptive streaming on the Quality of Experience (QoE) becomes fundamental.Mixed Reality (MR) is a novel technology and has recently become popular.However, quality evaluations of PCs in MR environments are still limited to static images.In this paper, we perform a subjective study on four impact factors on the QoE of PC video sequences in MR conditions, including quality switches, viewing distance, and content characteristics.The experimental results show that these factors significantly impact QoE.The QoE decreases if the sequence switches to lower quality and/or is viewed at a shorter distance, and vice versa.Additionally, the end user might not distinguish the quality differences between two quality levels at a specific viewing distance.Regarding content characteristics, objects with lower contrast seem to provide better quality scores.
A Subjective Dataset for Multi-Screen Video Streaming Applications

In modern-era video streaming systems, videos are streamed and displayed on a wide range of devices. Such devices vary from large-screen UHD and HDTVs to medium-screen Desktop PCs and Laptops to smaller-screen devices such as mobile phones and tablets. It is well known that a video is perceived differently when displayed on different devices. The viewing experience for a particular video on smaller screen devices such as smartphones and tablets, which have high pixel density, will be different with respect to the case where the same video is played on a large screen device such as a TV or PC monitor. Being able to model such relative differences in perception effectively can help in the design of better quality metrics and in the design of more efficient and optimized encoding profiles, leading to lower storage, encoding, and transmission costs. However, to the best of our knowledge, open-source datasets providing subjective scores for the same content when viewed on multiple devices with different screen sizes do not exist, thus limiting a proper evaluation of the existing quality metrics for such multi-screen video streaming applications. This paper addresses this research gap by presenting a new, open-source dataset consisting of subjective ratings for various encoded video sequences of different resolutions and bitrates (quality) when viewed on three devices of varying screen sizes: TV, Tablet, and Mobile. Along with the subjective scores, an evaluation of some of the most famous and commonly used open-source objective quality metrics is also presented. It is observed that the performance of the metrics varies a lot across different device types, with the recently standardized ITU-T P.1204.3 Model, on average, outperforming their full-reference counterparts. The dataset consisting of the videos, along with their subjective and objective scores, is available freely on Github <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Explainable Data-Driven QoE Modelling with XAI

Data-driven QoE modelling using Machine Learning (ML) allows to reduce the modelling bias and to continuously integrate new QoE results into the QoE model, which can improve its generalizability. The downside is that the majority of ML models are black-box models, which prevent to obtain insights about QoE influence factors and their fundamental relationships that are highly relevant for researchers and providers of services and networks. However, recent advances in the field of eX-plainable Artificial Intelligence (XAI) resolve these issues. Thus, XAI allows to benefit from data-driven QoE modelling to obtain generalizable QoE models, and at the same time to understand what QoE factors are relevant and how they affect the QoE score. In this work, we showcase the feasibility of explainable data-driven QoE modelling for video streaming, since video streaming QoE has been well researched, and thus, allows us to validate our results. Finally, we discuss opportunities and challenges of deploying XAI for QoE modelling.
Immersive and Interactive Subjective Quality Assessment of Dynamic Volumetric Meshes

Dynamic point cloud delivery can provide the required interactivity and realism to six degrees of freedom (6DoF) interactive applications.However, dynamic point cloud rendering imposes stringent requirements (e.g., frames per second (FPS) and quality) that current hardware cannot handle.A possible solution is to convert point cloud into meshes before rendering on the head-mounted display (HMD).However, this conversion can induce degradation in quality perception such as a change in depth, level of detail, or presence of artifacts.This paper, as one of the first, presents an extensive subjective study of the effects of converting point cloud to meshes with different quality representations.In addition, we provide a novel in-session content rating methodology, providing a more accurate assessment as well as avoiding post-study bias.Our study shows that both compression level and observation distance have their influence on subjective perception.However, the degree of influence is heavily entangled with the content and geometry at hand.Furthermore, we also noticed that while end users are clearly aware of quality switches, the influence on their quality perception is limited.As a result, this has the potential to open up possibilities in bringing the adaptive video streaming paradigm to the 6DoF environment.
qTask: Task-parallel Quantum Circuit Simulation with Incrementality

Incremental quantum circuit simulation has emerged as an important tool for simulation-driven quantum applications, such as circuit synthesis, verification, and analysis. When a small portion of the circuit is modified, the simulator must incrementally update state amplitudes for reasonable turnaround time and productivity. However, this type of incrementality has been largely ignored by existing research. To fill this gap, we introduce a new incremental quantum circuit simulator called qTask. qTask leverages a task-parallel decomposition strategy to explore both inter- and intra-gate operation parallelisms from partitioned data blocks. Our partitioning strategy effectively narrows down incremental update to a small set of partitions affected by circuit modifiers. We have demonstrated the promising performance of qTask on QASMBench benchmarks. Compared to two state-of-the-art simulators, Qulacs and Qiskit, qTask is respectively 1.46 × and 1.71× faster for full simulation and 5.77× and 9.76× faster for incremental simulation.
Dynasparse: Accelerating GNN Inference through Dynamic Sparsity Exploitation

Graph Neural Network (GNN) inference is used in many real-world applications. Data sparsity in GNN inference, including sparsity in the input graph and the GNN model, offer opportunities to further speed up inference. Also, many pruning techniques have been proposed for model compression that increase the data sparsity of GNNs.We propose Dynasparse, a comprehensive hardware-software codesign on FPGA to accelerate GNN inference through dynamic sparsity exploitation. For this, we decouple the GNN computation kernels from the basic computation primitives, and explore hardware-software codesign as follows: 1) Hardware design: We propose a novel unified accelerator design on FPGA to efficiently execute various computation primitives. We develop a customized soft processor that is tightly coupled with the accelerator to execute a runtime system. Moreover, we develop efficient hardware mechanisms to profile the data sparsity and perform on-the-fly data format transformation to prepare the input data for various computation primitives; 2) Software design: We develop a runtime system that works synergistically with the accelerator to perform dynamic kernel-to-primitive mapping based on data sparsity. We implement Dynasparse on a state-of-the-art FPGA platform, Xilinx Alveo U250, and evaluate the design using widely used GNN models (GCN, GraphSAGE, GIN and SGC). For the above GNN models and various input graphs, the proposed accelerator and dynamic kernel-to-primitive mapping reduces the inference latency by 3.73× on the average compared with the static mapping strategies employed in the state-of-the-art GNN accelerators. Compared with state-of-the-art CPU (GPU) implementations, Dynasparse achieves up to 56.9× (2.37×) speedup in end-to-end latency. Compared with state-of-the-art FPGA implementations, Dynasparse achieves 2.7× speedup in accelerator execution latency.
SCONNA: A Stochastic Computing Based Optical Accelerator for Ultra-Fast, Energy-Efficient Inference of Integer-Quantized CNNs

Convolutional Neural Networks (CNNs) are used extensively for artificial intelligence applications due to their record-breaking accuracy. For efficient and swift hardware-based acceleration, CNNs are typically quantized to have integer input/weight parameters. The acceleration of a CNN inference task uses convolution operations that are typically transformed into vector-dot-product (VDP) operations. Several photonic microring resonators (MRRs) based hardware architectures have been proposed to accelerate integer-quantized CNNs with remarkably higher throughput and energy efficiency compared to their electronic counterparts. However, the existing photonic MRR-based analog accelerators exhibit a very strong trade-off between the achievable input/weight precision and VDP operation size, which severely restricts their achievable VDP operation size for the quantized input/weight precision of 4 bits and higher. The restricted VDP operation size ultimately suppresses computing throughput to severely diminish the achievable performance benefits. To address this shortcoming, we for the first time present a merger of stochastic computing and MRR-based CNN accelerators. To leverage the innate precision flexibility of stochastic computing, we invent an MRR-based optical stochastic multiplier (OSM). We employ multiple OSMs in a cascaded manner using dense wavelength division multiplexing, to forge a novel Stochastic Computing based Optical Neural Network Accelerator (SCONNA). SCONNA achieves significantly high throughput and energy efficiency for accelerating inferences of high-precision quantized CNNs. Our evaluation for the inference of four modern CNNs at 8-bit input/weight precision indicates that SCONNA provides improvements of up to 66.5×, 90× and 91× in frames-per-second (FPS), FPS/W and FPS/W/mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> respectively, on average over two photonic MRR-based analog CNN accelerators from prior work, with Top-1 accuracy drop of only up to 0.4% for large CNNs and up to 1.5% for small CNNs. We developed a transaction-level, event-driven python-based simulator for the evaluation of SCONNA and other accelerators (https://github.com/uky-UCAT/SC_ONN_SIM.git).
QoS-Aware and Cost-Efficient Dynamic Resource Allocation for Serverless ML Workflows

Machine Learning (ML) workflows are increasingly deployed on serverless computing platforms to benefit from their elasticity and fine-grain pricing. Proper resource allocation is crucial to achieve fast and cost-efficient execution of serverless ML workflows (specially for hyperparameter tuning and model training). Unfortunately, existing resource allocation methods are static, treat functions equally, and rely on offline prediction, which limit their efficiency. In this paper, we introduce CE-scaling – a Cost-Efficient autoscaling framework for serverless ML work-flows. During the hyperparameter tuning, CE-scaling partitions resources across stages according to their exact usage to minimize resource waste. Moreover, it incorporates an online prediction method to dynamically adjust resources during model training. We implement and evaluate CE-scaling on AWS Lambda using various ML models. Evaluation results show that compared to state-of-the-art static resource allocation methods, CE-scaling can reduce the job completion time and the monetary cost by up to 63% and 41% for hyperparameter tuning, respectively; and by up to 58% and 38% for model training.
Engineering Massively Parallel MST Algorithms

We develop and extensively evaluate highly scalable distributed-memory algorithms for computing minimum spanning trees (MSTs). At the heart of our solutions is a scalable variant of Borůvka’s algorithm. For partitioned graphs with many local edges we improve this with an effective form of contracting local parts of the graph during a preprocessing step. We also adapt the filtering concept of the best practical sequential algorithm to develop a massively parallel Filter-Borůvka algorithm that is very useful for graphs with poor locality and high average degree. Our experiments indicate that our algorithms scale well up to at least 65 536 cores and are up to 800 times faster than previous distributed MST algorithms.
A Deep Learning Framework for Semantic Segmentation of Nucleus for Acute Lymphoblastic Leukemia Detection

Acute lymphoblastic leukemia is a form of blood cancer in which the bone marrow overproduces immature white blood cells. A novel semantic segmentation of nucleus for detection of Acute lymphoblastic leukemia is proposed here. The input images are obtained from public database ‘‘ALLIDB2’’. Resizing, SMOTE and Augmentation are carried out as preprocessing. After pre-processing, segmentation of nucleus is performed by SegNet and ResUNet. The performance of SegNet and ResUNet are compared. The segmented images are given as input to the classification models. Using Xception, Inception-v3 and ResNet50 models, the segmented images are classified as healthy and blast cells. It is found that Inception-v3 performs better than Xception and ResNet50 with an accuracy of 93.74%. This will be helpful to detect Acute lymphoblastic leukemia at the earliest.
Bitcoin under the microscope

This paper explores and describes historical on-chain transaction data recorded on the Bitcoin blockchain, constructs a panel of all individual Bitcoin users, and computes their balances in the cross-section and over time. We run clustering algorithms to combine addresses that belong to the same user into wallets and we find that using wallets over addresses as the unit of analysis allows for more economically meaningful interpretations of user behavior. We identify and divide wallets into user categories – miners, exchanges, services, retail wallets and receiving-only addresses – and observe varying activity levels and balances in the cross-section and over time, corresponding to their intended role in the Bitcoin network. Our findings also suggest heterogeneity in financial performance across user categories with miners exhibiting higher realized returns relative to exchanges and retail users.
Personalized Showcases: Generating Multi-Modal Explanations for Recommendations

Existing explanation models generate only text for recommendations but still struggle to produce diverse contents. In this paper, to further enrich explanations, we propose a new task named personalized showcases, in which we provide both textual and visual information to explain our recommendations. Specifically, we first select a personalized image set that is the most relevant to a user's interest toward a recommended item. Then, natural language explanations are generated accordingly given our selected images. For this new task, we collect a large-scale dataset from Google Maps and construct a high-quality subset for generating multi-modal explanations. We propose a personalized multi-modal framework which can generate diverse and visually-aligned explanations via contrastive learning. Experiments show that our framework benefits from different modalities as inputs, and is able to produce more diverse and expressive explanations compared to previous methods on a variety of evaluation metrics.
A Symmetric Dual Encoding Dense Retrieval Framework for Knowledge-Intensive Visual Question Answering

Knowledge-Intensive Visual Question Answering (KI-VQA) refers to answering a question about an image whose answer does not lie in the image. This paper presents a new pipeline for KI-VQA tasks, consisting of a retriever and a reader. First, we introduce DEDR, a symmetric dual encoding dense retrieval framework in which documents and queries are encoded into a shared embedding space using uni-modal (textual) and multi-modal encoders. We introduce an iterative knowledge distillation approach that bridges the gap between the representation spaces in these two encoders. Extensive evaluation on two well-established KI-VQA datasets, i.e., OK-VQA and FVQA, suggests that DEDR outperforms state-of-the-art baselines by 11.6% and 30.9% on OK-VQA and FVQA, respectively.
A Probabilistic Transformation of Distance-Based Outliers

The scores of distance-based outlier detection methods are difficult to interpret, and it is challenging to determine a suitable cut-off threshold between normal and outlier data points without additional context. We describe a generic transformation of distance-based outlier scores into interpretable, probabilistic estimates. The transformation is ranking-stable and increases the contrast between normal and outlier data points. Determining distance relationships between data points is necessary to identify the nearest-neighbor relationships in the data, yet most of the computed distances are typically discarded. We show that the distances to other data points can be used to model distance probability distributions and, subsequently, use the distributions to turn distance-based outlier scores into outlier probabilities. Over a variety of tabular and image benchmark datasets, we show that the probabilistic transformation does not impact outlier ranking (ROC AUC) or detection performance (AP, F1), and increases the contrast between normal and outlier score distributions (statistical distance). The experimental findings indicate that it is possible to transform distance-based outlier scores into interpretable probabilities with increased contrast between normal and outlier samples. Our work generalizes to a wide range of distance-based outlier detection methods, and, because existing distance computations are used, it adds no significant computational overhead.
Coupling of support vector machine and acoustic models to characterize the droplet size distribution of emulsions using ultrasonic techniques

The application of ultrasonic techniques to characterize emulsions concerning the droplet size distribution (DSD) has attracted interest in recent years, mainly due to the advantage of being a non-intrusive technique able to characterize concentrated and opaque emulsions. These characterizations occur by adjusting sound attenuation spectroscopy with the acoustic models. The most used models for this purpose are simplifying more complete models, such as ECAH, mainly due to their mathematical complexity and convergence problems. However, most of these simplified models have restrictions related to the range of applications in the wave propagation regime. Therefore, the objective of this work was to couple the acoustic models to the support vector machines (SVM), which have numerous advantages linked to their successful adaptability to nonlinear data, to acting on this limitation, thus improving the determination of the emulsion DSD. This coupling proved to be satisfactory since the SVM classified all data correctly.