Reviewerly/exHarmony huggingface dataset:
PNATS-UHD-1-Long: An Open Video Quality Dataset for Long Sequences for HTTP-based Adaptive Streaming QoE Assessment

The P.NATS Phase 2 competition in ITU-T Study Group 12 resulted in both the ITU-T Rec. P.1204 series of recommendations, and also a large dataset for HTTP-based adaptive streaming QoE assessment that is now made openly available as part of this paper. The presented dataset consists of 3 subjective databases targeting overall quality assessment of a typical HTTP-based Adaptive Streaming session consisting of degradations such as quality switching, initial loading delay, and stalling events using audiovisual content ranging between 2 and 5 minutes. In addition to this, subject bias and consistency in quality assessment of such longer-duration audiovisual contents with multiple degradations are investigated using a subject behaviour model. As part of this paper, the overall test design, subjective test results, sources, encoded audiovisual contents, and a set of analysis plots are made publicly available for further research.
NeRF-QA: Neural Radiance Fields Quality Assessment Database

This short paper proposes a new database - NeRF-QA - containing 48 videos synthesized with seven NeRF based methods, together with their perceived quality scores, resulting from subjective assessment tests; for the videos selection, both real and synthetic, 360 degrees scenes were considered. This database will allow to evaluate the suitability, to NeRF based synthesized views, of existing objective quality metrics, and also the development of new quality metrics.
The impact of anti-money laundering regulations on inclusive finance: Evidence from Sub-Saharan Africa

This study examined the impact of AML regulations on financial inclusion in Sub-Saharan Africa (SSA). Again, the study assessed whether the level of AML regulatory effectiveness determines the impact of AML regulations on financial inclusion. The study employed the Systems Generalized Methods of Moments (SGMM) estimation technique to assess the influence of AML regulations on financial inclusion for a panel of 44 SSA countries from 2012–2019. Data is sourced from the World Development Indicators and the Basel Institute on Governance. The study showed that AML regulations negatively impact accounts ownership and the number of commercial bank branches whereas AML regulations have a positive impact on the number of commercial bank depositors, the number of commercial bank borrowers, and the number of ATMs. Further, the study provided evidence that AML regulations positively impact the number of commercial bank branches and the number of borrowers in low-effectiveness countries (countries with AML regulations below the mean). In contrast, the study reported that AML regulations have a negative impact on accounts ownership for high-effectiveness countries (countries with AML regulations above the mean), while AML regulations have a positive influence on the number of depositors, the number of commercial bank borrowers and number of ATMs above the mean. The findings of the study imply that the impact of AML regulations on financial inclusion depends on the proxy of financial inclusion and also the extent of AML regulations in SSA countries. The uniqueness of this study is its specific focus on assessing the impact of AML regulations on financial inclusion in SSA.
Recommendations for Verifying HDR Subjective Testing Workflows

Over the past few years, there has been an increase in the demand and availability of High Dynamic Range (HDR) displays and content. To ensure the production of high-quality materials, human evaluation is required. However, ascertaining whether the full playback pipeline is indeed HDR-compliant can be challenging. In this paper, we present a set of recommendations for conformance testing to validate various aspects of the testing workflow, including playback, displays, brightness, colours, and viewing environment. We assessed the effectiveness of HDR conversion techniques used in current standards development (3GPP) for making source materials. Additionally, we evaluate HDR display technologies, including OLED and LCD, using both consumer television and a reference monitor.
BLADE: Combining Vocabulary Pruning and Intermediate Pretraining for Scaleable Neural CLIR

Learning sparse representations using pretrained language models enhances the monolingual ranking effectiveness. Such representations are sparse vectors in the vocabulary of a language model projected from document terms. Extending such approaches to Cross-Language Information Retrieval (CLIR) using multilingual pretrained language models poses two challenges. First, the larger vocabularies of multilingual models affect both training and inference efficiency. Second, the representations of terms from different languages with similar meanings might not be sufficiently similar. To address these issues, we propose a learned sparse representation model, BLADE, combining vocabulary pruning with intermediate pre-training based on cross-language supervision. Our experiments reveal BLADE significantly reduces indexing time compared to its monolingual counterpart, SPLADE, on machine-translated documents, and it generates rankings with strengths complementary to those of other efficient CLIR methods.
Research on the reform path of soccer special class for physical education majors in colleges and universities under the vision of intelligent sports

Abstract To promote the reform of soccer special class of physical education majors in colleges and universities, adapt to the needs of the new situation, strengthen the cultivation of students’ teaching practice ability and innovation ability, and meet the needs of social development and students’ employment needs. This paper takes the students, teachers, and teaching situation of the soccer special class as the starting point under the vision of intelligent sports, first, establishes the mathematical model of wheeled robot movement used in the simulation platform, establishes the environmental information model with the lattice method for the possible fields that the robot moves to from the starting point to the endpoint, and introduces the methods of initial population generation and individual coding, discusses the design and The design and implementation of the fitness functions were discussed, and three fitness functions were designed for obstacle avoidance, path minimization, and path smoothing, respectively, and finally, the specific operation process of the five basic operations of the genetic algorithm was introduced. The results of the study show that the ratio of 3:7 hours between theoretical and practical classes is more in line with students’ wishes, and it effectively improves the special skills and comprehensive practical ability of students in soccer special classes.
Lossy Scientific Data Compression With SPERR

As the need for data reduction in high-performance computing (HPC) continues to grow, we introduce a new and highly effective tool to help achieve this goal—SPERR. SPERR is a versatile lossy compressor for structured scientific data; it is built on top of an advanced wavelet compression algorithm, SPECK, and provides additional capabilities valued in HPC environments. These capabilities include parallel execution for large volumes and a compression mode that satisfies a maximum point-wise error tolerance. Evaluation shows that in most settings SPERR achieves the best rate-distortion trade-off among current popular lossy scientific data compressors.
rFaaS: Enabling High Performance Serverless with RDMA and Leases

High performance is needed in many computing systems, from batch-managed supercomputers to general-purpose cloud platforms. However, scientific clusters lack elastic parallelism, while clouds cannot offer competitive costs for high-performance applications. In this work, we investigate how modern cloud programming paradigms can bring the elasticity needed to allocate idle resources, decreasing computation costs and improving overall data center efficiency. Function-as-a-Service (FaaS) brings the pay-as-you-go execution of stateless functions, but its performance characteristics cannot match coarse-grained cloud and cluster allocations. To make serverless computing viable for high-performance and latency-sensitive applications, we present rFaaS, an RDMA-accelerated FaaS platform. We identify critical limitations of serverless - centralized scheduling and inefficient network transport - and improve the FaaS architecture with allocation leases and microsecond invocations. We show that our remote functions add only negligible overhead on top of the fastest available networks, and we decrease the execution latency by orders of magnitude compared to contemporary FaaS systems. Furthermore, we demonstrate the performance of rFaaS by evaluating real-world FaaS benchmarks and parallel applications. Overall, our results show that new allocation policies and remote memory access help FaaS applications achieve high performance and bring serverless computing to HPC.
FedBIAD: Communication-Efficient and Accuracy-Guaranteed Federated Learning with Bayesian Inference-Based Adaptive Dropout

Federated Learning (FL) emerges as a distributed machine learning paradigm without end-user data transmission, effectively avoiding privacy leakage. Participating devices in FL are usually bandwidth-constrained, and the uplink is much slower than the downlink in wireless networks, which causes a severe uplink communication bottleneck. A prominent direction to alleviate this problem is federated dropout, which drops fractional weights of local models. However, existing federated dropout studies focus on random or ordered dropout and lack theoretical support, resulting in unguaranteed performance. In this paper, we propose Federated learning with Bayesian Inference-based Adaptive Dropout (FedBIAD), which regards weight rows of local models as probability distributions and adaptively drops partial weight rows based on importance indicators correlated with the trend of local training loss. By applying FedBIAD, each client adaptively selects a high-quality dropping pattern with accurate approximations and only transmits parameters of non-dropped weight rows to mitigate uplink costs while improving accuracy. Theoretical analysis demonstrates that the convergence rate of the average generalization error of FedBIAD is minimax optimal up to a squared logarithmic factor. Extensive experiments on image classification and next-word prediction show that compared with status quo approaches, FedBIAD provides 2× uplink reduction with an accuracy increase of up to 2.41% even on non-Independent and Identically Distributed (non-IID) data, which brings up to 72% decrease in training time.
Traversing Large Compressed Graphs on GPUs

GPUs can be used effectively for accelerating graph analytics, provided the datasets fit in GPU memory. This is often not the case for large real-world datasets such as social, web, or biological graphs. We propose a graph compression format for static unweighted graphs based on Elias-Fano encoding that is amenable to run-time decompression on massively parallel architectures such as GPUs. We show that we can compress a variety of large graphs by a factor of 1.55x over the commonly used compressed sparse row (CSR) representation. The scheme is particularly beneficial for cases where conventional CSR based approaches do not work at all due to memory capacity constraints, or incur a significant penalty for out-of-core processing. We implement GPU accelerated breadth first search for this graph representation and show that the runtime performance for in-memory compressed graphs is 3.8x-6.5x better than out-of-core implementations for CSR graphs. Further, our implementation is also 1.45x-2x faster than the current state of the art in GPU based compressed graph traversals while maintaining a competitive compression ratio. We also extend our work to other analytics applications such as single source shortest paths and PageRank. Finally, we explore the interplay between graph reordering, graph compression, and performance.
MCR-DL: Mix-and-Match Communication Runtime for Deep Learning

In recent years, the training requirements of many state-of-the-art Deep Learning (DL) models have scaled beyond the compute and memory capabilities of a single processor, and necessitated distribution among processors. Training such massive models necessitates advanced parallelism strategies [1], [2] to maintain efficiency. However, such distributed DL parallelism strategies require a varied mixture of collective and point-to-point communication operations across a broad range of message sizes and scales. Examples of models using advanced parallelism strategies include Deep Learning Recommendation Models (DLRM) [3] and Mixture-of-Experts (MoE) [4], [5]. Communication libraries’ performance varies wildly across different communication operations, scales, and message sizes. We propose MCR-DL: an extensible DL communication framework that supports all point-to-point and collective operations while enabling users to dynamically mix-and-match communication backends for a given operation without deadlocks. MCR-DL also comes packaged with a tuning suite for dynamically selecting the best communication backend for a given input tensor. We select DeepSpeed-MoE and DLRM as candidate DL models and demonstrate a 31% improvement in DS-MoE throughput on 256 V100 GPUs on the Lassen HPC system. Further, we achieve a 20% throughput improvement in a dense Megatron-DeepSpeed model and a 25% throughput improvement in DLRM on 32 A100 GPUs with the Theta-GPU HPC system.
GraphTensor: Comprehensive GNN-Acceleration Framework for Efficient Parallel Processing of Massive Datasets

We present GraphTensor, a comprehensive open-source framework that supports efficient parallel neural network processing on large graphs. GraphTensor offers a set of easy-to-use programming primitives that appreciate both graph and neural network execution behaviors from the beginning (graph sampling) to the end (dense data processing). Our framework runs diverse graph neural network (GNN) models in a destination-centric, feature-wise manner, which can significantly shorten training execution times in a GPU. In addition, GraphTensor rearranges multiple GNN kernels based on their system hyperparameters in a self-governing manner, thereby reducing the processing dimensionality and the latencies further. From the end-to-end execution viewpoint, GraphTensor significantly shortens the service-level GNN latency by applying pipeline parallelism for efficient graph dataset preprocessing. Our evaluation shows that GraphTensor exhibits 1.4× better training performance than emerging GNN frameworks under the execution of large-scale, real-world graph workloads. For the end-to-end services, GraphTensor reduces training latencies of an advanced version of the GNN frameworks (optimized for multi-threaded graph sampling) by 2.4×, on average.
Design of DCT Hardware Accelerator using FPGA for Medical Image Authentication

The biomedical sector has undergone significant changes due to the continuous advancements in technology and computer networks. Multimedia data is now being utilized to transmit crucial biomedical information, including patient reports, diagnoses, and hospital information. To convert multimedia files to transform domain from spatial domain for encryption with a watermark, a Discrete Cosine Transform (DCT) is utilized. This involves incorporating the DCT into the process of transforming the file, allowing it to be modified and encrypted while retaining its original content. To achieve this a hardware accelerator that performs DCT technique is designed and implemented using Vivado HLS. The result shows that the designed architecture possesses high level of resilience, and the image quality is preserved after watermarking.
Long-Form Information Retrieval for Enterprise Matchmaking

Understanding customer requirements is a key success factor for both business-to-consumer (B2C) and business-to-business (B2B) enterprises. In a B2C context, most requirements are directly related to products and therefore expressed in keyword-based queries. In comparison, B2B requirements contain more information about customer needs and as such the queries are often in a longer form. Such long-form queries pose significant challenges to the information retrieval task in B2B context. In this work, we address the long-form information retrieval challenges by proposing a combination of (i) traditional retrieval methods, to leverage the lexical match from the query, and (ii) state-of-the-art sentence transformers, to capture the rich context in the long queries. We compare our method against traditional TF-IDF and BM25 models on an internal dataset of 12,368 pairs of long-form requirements and products sold. The evaluation shows promising results and provides directions for future work.
Human Perception-Oriented Enhancement and Smoothing for Perceptual Video Coding

We propose a human perception-oriented processing method that is readily applicable to human perceptual video coding. While existing methods focus on removing perceptual redundancy, the proposed method aims to enhance image quality in the region of interest while suppressing non-perceivable signals to reduce perceptual redundancy at less interesting regions and smoothing unpleasant noise patterns in motion-blurred regions. To implement the proposed scheme, we propose a DCT domain image enhancement filter, a DCT domain just noticeable difference (JND)-based Gaussian filter, and a DCT domain motion blur smoothing filter. The enhancement filter is based on the proposed signal direction adaptive multiband energy scaling and enhances local image signals without evoking ringing and blocking artifacts. The JND-based Gaussian filter reduces perceptual redundancy using the estimated JND values that partly depend on the distance from an eye-focused point. Motion-blurred image signals are smoothed by the DCT domain Gaussian filter. To demonstrate the effectiveness of the proposed method, it is applied to video sequences before encoding them. The experimental results verify that the proposed method improves perceptual video quality consistently for various sequences and at diverse bandwidths.
MTER: An Efficient Multi-User Threshold Equality Retrieval for Double Auction in Smart Grid Market

With the increasing electricity consumption and the wide application of renewable energy sources, energy auction attracts a lot of attention due to its economic benefits. In recent years, much research employs double auction mechanisms to design a market model for smart grid and shows its paramount importance for energy trading. However, few of them are able to quickly and securely identify the potential winner or winners from all sealed bids and asks throughout the auction process. In this paper, we propose an efficient multi-user threshold equality retrieval (MTER) by a novel non-interactive threshold public key encryption with equality test (T-PKEET) as a building block for bid-ask filtering to help different double auction mechanisms be implemented effectively in the smart grid. In this new T-PKEET primitive, the key used to authorize the equality test is shared among <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> servers so that at least <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> servers are collaborated to compute the test function: a combiner who has a user <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">U</i> ’s ciphertext <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</i> and another user Û’s ciphertext Ĉ wishes to carry out the equality test on ciphertexts <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</i> and Ĉ Preferably, T-PKEET solves a challenge problem of resisting against message recovery attack in PKEET system. We construct a concrete T-PKEET scheme with flexible authorization by issuing tag-based test key shares and ciphertext-based test key shares and prove it to achieve st-IND-CCA2 security against insider adversary in the standard model. The experimental results show that the Trapdoor and Test algorithms of T-PKEET scheme have significant efficiency, whose cost is only 8.46% and 17.78% that of the work closest to ours. Therefore, our construction can effectively facilitate demand responses of the filtering on sealed bids and asks for double auction in smart grid.
QNP Textual Entailment with Polynomial Event Semantics

FraCaS textual entailment corpus has become the standard benchmark for semantics theories, in particular, theories of quantification (Sect. 1 of FraCaS). Here we apply it to polynomial event semantics: the latest approach to combining quantification and Neo-Davidsonian event semantics, maintaining compositionality and the in situ analysis of quantifiers. Although several FraCaS problems look custom-made for the polynomial events semantics, there are challenges: the variety of generalized quantifiers (including ‘many’, ‘most’ and ‘few’); copula, existence, and relative clauses. We address them in this paper.
Towards Knowledge Graph Creation from Greek Governmental Documents

Documents contain textual information, which is of the utmost importance for all the organizations. Document management systems have been used to store vast amounts of unstructured textual data described with minimal metadata, a method that has several limitations. In order to convert hidden knowledge into machine-readable data with rich connections, this paper presents work in progress on the development of the first end-to-end guided approach to construct a Knowledge Graph from Greek governmental documents from the Greek open government portal. The resulted Knowledge graph constitutes a proof-of-concept graph, that illustrates the beneficial semantic relationships between the textual data.
Accelerated Synchronous Model Parallelism Using Cooperative Process for Training Compute-Intensive Models

As deep learning has been recently applied to a wide variety of fields, there is a growing demand for models that can handle large input data, including high-resolution images. Therefore, model parallelism was proposed to train a model whose size exceeds the memory capacity of an accelerator, but this method has a very slow training speed due to bubbles. To solve this problem, GPipe reduced the bubbles by proposing a micro-batch concept, according to which a mini-batch is divided into smaller units. However, the improvement in the training speed is still limited to a certain extent of micro-batch size, because the smaller the micro-batches, the less the computation and input/output (I/O) efficiency. To overcome the limitations, we proposed acceleration through prediction and synchronization steps based on process cooperation to train compute-intensive models. In the prediction step, the input data of all processes for the forward pass are calculated concurrently using the weights shared in the synchronization step in advance, and the results are gathered into each corresponding process via an all-to-all collective operation. This can increase computational efficiency and reduce bubbles by minimizing the idle state of the device. Additionally, the proposed method requires minimal memory because it does not have to store the activations in memory. Thus, compared to the GPipe, the proposed method achieved performance improvements of 15.3%, 34.5%, and 25.8% with the VGG16bn, ResNet50, and InceptionV3 models with four devices, respectively, and the memory used for training was reduced by up to 75.0%.