Reviewerly/exHarmony huggingface dataset:
Reality Anchors: Bringing Cues from Reality to Increase Acceptance of Immersive Technologies in Transit

Immersive technologies allow us to control and customise how we experience reality, but are not widely used in transit due to safety, social acceptability, and comfort barriers. We propose that cues from reality can create reference points in virtuality, which we call Reality Anchors, will reduce these barriers. We used simulated public transportation journeys in a lab setting to explore Reality Anchors using speculative methods in two studies. Our first study (N=20) explored how elements of reality like objects, furniture, and people could be used as anchors, demonstrating that visibility of other passengers and personal belongings could reduce barriers. Our second study (N=19) focused on journey types that emerged from the first study - self-managed vs. externally managed journeys - revealing that self-managed journeys increased the need for anchors. We conclude that Reality Anchors can reduce concerns associated with immersive technology use in transit, especially for self-managed journeys.
Output-Directed Dynamic Quantization for DNN Acceleration

Quantization is an effective technique for reducing the number of computations and improving the performance of deep neural networks (DNNs). Weight quantization is popular because weights can be trained beforehand. However, weight quantization only targets the kernel weights and ignores the sensitivity of input features, which can lead to reduced accuracy. Fine-grained input quantization has gained attention as a way to speed up DNNs while maintaining accuracy. Existing approaches determine computation precision based on input sensitivity but do not effectively reduce computations for insensitive outputs or retain the precision of sensitive outputs. These limitations motivate us to develop an output-directed dynamic quantization method named ODQ in this paper. ODQ is a two-stage DNN quantization scheme designed to improve performance, reduce energy consumption, and maintain and often improve accuracy, compared with existing quantization methods. Specifically, inputs and weights go through sensitivity prediction and result generation. The high-order 2 bits of input and weight are used to predict output sensitivity. Result generation is performed only for predicted sensitive outputs. We designed an FPGA accelerator to optimize ODQ quantization performance for DNNs. We implement a prototype of ODQ and evaluate its performance using several state-of-the-art DNNs. Compared with a state-of-the-art input-directed quantization approach, ODQ achieves a 67.6% performance speedup and a 66.9% energy saving, with minimal accuracy degradation (≤ 0.6%).
Embracing Uncertainty for Equity in Resource Allocation in ML Training

To reduce the Deep Learning (DL) model training time and hence resource consumption, it is critical to avoid stragglers. However, the dynamics and uncertainty features of resource availability pose a challenge to avoiding stragglers caused. To handle this challenge, we propose a Straggler-Avoiding job Scheduling approach (SAS), which smartly ensures that the tasks of a job receive resources with similar dynamics and uncertainty so that the tasks can complete at approximately the same time. Specifically, SAS uses an ML method to predict available resource amounts with probability in future times, groups nodes with similar available resource amounts and probabilities, and then assigns each job to one node group with the objective of minimizing job completion time (JCT). To reduce the decision making time, we also propose a reinforcement learning (RL) based scheduling approach (SAS-RL) that assigns each job to a node group. In addition, we propose a distributed parameter server (PS) load reassignment method to handle PS stragglers. Our trace-driven real experiments show that SAS reduce up to 45% JCT and 63% stragglers compared with existing job schedulers, and our PS load reassignment reduces up to 48% JCT compared with the previous PS load distribution scheme.
Communication Optimizations for State-vector Quantum Simulator on CPU+GPU Clusters

Simulating the behavior of quantum circuits on classical computers are one of the widely used approaches for current quantum computing device and quantum algorithm research. State-vector simulators keep all the quantum states in main memory, thus consuming a large amount of memory and resulting in great memory access and communication overhead far longer than calculation time. Many classical simulators, such as QuEST, are designed by serial execution of quantum gates without using the data locality, and all amplitudes need to be exchanged. Such a gate-unaware full data communication scheme forces powerful computation such as GPUs to spend a lot of time on waiting for data transmission. In this paper, we propose a gate-aware on-demand communication quantum simulation framework to optimize communication overhead. A quantum circuit partition method through gate fusion is first proposed to avoid unnecessary communications. Moreover, a on-demand data communication scheme is proposed to optimize data transfer among computation nodes. Based on these designs, a prototype has been implemented on QuEST. We evaluated on an 8-node cluster with four NVIDIA Tesla V100 GPUs on each node and our designs can achieve 3.0x-15.6x speedup compared to QuEST for 32-34-qubit quantum systems. Moverover, it can scale to simulate 37-qubit quantum system instead of 34-qubit quantum system for QuEST.
Learning From Your Neighbours: Mobility-Driven Device-Edge-Cloud Federated Learning

Federated learning (FL) in large-scale wireless networks is implemented in a hierarchical way by introducing edge servers as relays between the cloud server and devices, where devices are dispersed within multiple clusters coordinated by edges. However, the devices are usually mobile users with unpredictable mobile trajectories, whose effects on the model training process are still less studied. In this work, we propose a new MobIlity-Driven feDerated LEarning framework, namely MIDDLE in wireless networks, which can relieve unbalanced and biased model updates by leveraging the new model aggregation opportunities on mobile devices due to their mobility across edges. Specifically, mobile devices can have different models while traversing across edges, and adequately aggregate these models on the device. By theoretical analysis, we can show that this on-device model aggregation can reduce the bias of model updating on edges and cloud, and then accelerate the convergence of model training in FL. Then, we define a model similarity utility to measure the difference in gradient updates among various models, which guides the adaptive on-device model aggregation and in-edge device selection to facilitate the comprehensive information sharing between edges. Extensive experiment results validate that MIDDLE can achieve 1.51 × −6.85 × speedup on the model training, compared with the state-of-the-art model training approaches in hierarchical FL.
ITIF: Integrated Transformers Inference Framework for Multiple Tenants on GPU

Transformer models, which have gained prominence in recent years, serve as the backbone for a wide range of deep learning applications, from natural language processing to computer vision. These models have become indispensable for various inference tasks, but their implementation often faces challenges related to GPU utilization and system throughput. Current GPU-based inference frameworks typically treat each model individually, leading to suboptimal resource management and reduced performance. In response to these limitations, we introduce ITIF: Integrated Transformers Inference Framework for multi-tenants with a shared backbone. By allowing multiple tenants to share a single backbone Transformer model on a single GPU, ITIF consolidates operators from diverse multi-tenant inference models, which in turn optimizes GPU utilization and system throughput. Our proposed framework, ITIF, represents a significant step towards improving the efficiency of deep learning, particularly for large-scale cloud providers hosting numerous models with a shared backbone. In our experiments, ITIF improved performance by up to 2.40 × compared with the baselines.
Marlin: A Concurrent and Write-Optimized B+-tree Index on Disaggregated Memory

Memory disaggregation architecture can achieve higher resource utilization, independent scaling of CPUs and memory. Disaggregated memory systems manage memory resources and locate data by distributed index. However, existing distributed indexes suffer from high synchronization overhead of write, due to naive node lock, thus resulting in high tail latency, low throughput, and poor scalability.
DAG-Aware Optimization for Geo-Distributed Data Analytics

Geo-distributed data analytics has been proposed to analyze geographically distributed data. Existing studies have achieved significant reductions in execution time and data transfer cost ($) of data analytics jobs by optimizing task placement. Given a directed acyclic graph (DAG)-style job, however, they mainly optimize each stage independently, and they tend to distribute tasks and intermediate data across all locations, potentially inflating execution time and data transfer cost of descendent stages and the whole job.
On Optimizing Traffic Scheduling for Multi-replica Containerized Microservices

Containerized deployment of microservices has been becoming prevalent, as it provides flexible deployment and elastic resource configuration. For high concurrency and fault tolerance, multiple container replicas are often deployed for each microservice component, but this may induce heavy cross-machine traffic and degrades the performance of microservice applications. Traffic localization tries to put containers with heavy communication traffic on the same machine to reduce cross-machine traffic. However, it is still very common to have the containers with heavy traffic on different machines, especially under multi-replica deployment, due to the insufficient resources of a physical machine. To this end, we develop a network-aware scheduling system OptTraffic, which realizes optimized traffic scheduling for containerized microservices. OptTraffic estimates the traffic between each pair of containers in a lightweight manner by combining a simple math calculation with coarse-grained monitoring, then it proposes an efficient traffic allocation algorithm and leverages dynamic scheduling with multiple optimizations to minimize the cross-machine traffic without sacrificing resource usage balance. Experiments show that under multi-replica deployment, OptTraffic can save up to 47% of the network bandwidth, while reducing the P99 latency by 28%-45%, compared to Kubernetes and existing traffic localization designs for real-world microservice applications.
PSRA-HGADMM: A Communication Efficient Distributed ADMM Algorithm

Among distributed machine learning algorithms, the global consensus alternating direction method of multipliers (ADMM) has attracted much attention because it can effectively solve large-scale optimization problems. However, the high communication cost slows its convergence and limits scalability. To solve the problem, we propose a hierarchical grouping ADMM algorithm (PSRA-HGADMM) with a novel Ring-Allreduce communication model in this paper. Firstly, we optimize the parameter exchange of the ADMM algorithm and implement the global consensus ADMM algorithm in the decentralized architecture. Secondly, to improve the communication efficiency of the distributed system, we propose a novel Ring-Allreduce communication model (PSR-Allreduce) based on the idea of parameter server architecture. Finally, a Worker-Leader-Group generator (WLG) framework is designed to solve the problem of inconsistency of cluster nodes. This framework combines hierarchical parameter aggregation and adopts the grouping strategy to improve the scalability of the distributed system. Experiments show that PSRA-HGADMM has better convergence performance and better scalability than ADMMLib and AD-ADMM. Compared with ADMMLib, the overall communication cost of PSRA-HGADMM is reduced by 32%.
Purity and dangers: Market making, structural uncertainty, and circuits of exchange in the cryptoeconomy

Market making is a crucial activity in financial markets, expected to reduce uncertainties related to price and liquidity information, and to enable strategic interactions among traders. Sociologists of finance have recently emphasized the role of automation and calculative devices such as algorithms in market making, highlighting the transition from informal human interactions on the trading floor to multilayered, formalized interactions involving both algorithms and human actors. In this process, social relationships become infrastructural to calculative devices. Less understood is what happens when uncertainties cannot be mitigated by the use of calculative devices and by the relationships infrastructural to them. We argue that in such situations market participants resort to circuits of exchange as arrangements that allow them to continue their activities in a legitimate manner. We ground the argument in an ethnographic investigation of market making in crypto markets, where traders are confronted with uncertainties that cannot be solved only by using algorithms. We show that market makers resort to horizontal and vertical circuits in order to address uncertainties and legitimize their activities. We make a double contribution: theoretical, by arguing that circuits and market transactions are not mutually exclusive; empirical, by showing that market makers harness circuits in order to endow their activities with legitimacy.
Complex Scenario Image Retrieval via Deep Similarity-aware Hashing

When performing hashing-based image retrieval, it is difficult to learn discriminative hash codes especially for the multi-label, zero-shot and fine-grained settings. This is due to the fact that the similarities vary, even within the same category, under the conditions of complex scenario settings. To address this problem, this study develops a deep similarity-aware hashing method for complex scenario image retrieval (DEPISH). DEPISH more focuses on the samples that are difficult to distinguish from other images (i.e., “difficult samples”), such as images that contain multiple semantics. It dynamically divides attention among samples according to their difficulty levels with a margin weighting strategy. Furthermore, by adding special terms in the model, DEPISH is capable of avoiding the inconsistency between the hash code representation and true similarity among negative samples. In addition, unlike the existing methods that use a pre-defined similarity matrix with fixed values, the DEPISH adopts an adaptive similarity matrix, which accurately captures the various similarities among all samples. The results of our experiment on multiple benchmark datasets containing complex scenarios (i.e., multi-label, zero-shot, and fine-grained datasets) verify the effectiveness of this method.
Hindi Text Summarization Using Sequence to Sequence Neural Network

Text summarizing reduces a large block of text data to a precise, short, and intelligible text that conveys the whole meaning of the actual text in a few words while maintaining the original context. Due to a lack of relevant summaries, it is hard to understand the main idea of the document. Text summarization using the abstractive technique is well-studied in English, although it is still in its infancy in Indian regional languages. In this study, we investigate the effectiveness of using a sequence-to-sequence (Seq2Seq) neural network based on attention and its optimization for text summarization for the Hindi language (HiATS), explicitly comparing the Adam and RMSprop optimizers. Our method allows the model to take the Hindi language dataset and, as output, provides a concise summary that accurately reflects the gist of the original text. The performance of the models will be evaluated using Rouge-1 and Rouge-2 metrics.
Detecting Deepfake Videos Using Spatiotemporal Trident Network

The widespread dissemination of Deepfake in social networks has posed serious security risks, thus necessitating the development of an effective Deepfake detection technique. Currently, video-based detectors have not been explored as extensively as image-based detectors. Most existing video-based methods only consider temporal features without combining spatial features, and do not mine deeper-level subtle forgeries, resulting in limited detection performance. In this paper, a novel spatiotemporal trident network (STN) is proposed to detect both spatial and temporal inconsistencies of Deepfake videos. Since the large amount of redundant information in Deepfake video frames, we introduce convolutional block attention module (CBAM) on the basis of the I3D network and optimize the structure to make the network better focus on the meaningful information of the input video. Aiming at the defects in the deeper-level subtle forgeries, we designed three feature extraction modules (FEMs) of RGB, optical flow, and noise to further extract deeper video frame information. Extensive experiments on several well-known datasets demonstrate that our method has promising performance, surpassing several state-of-the-art Deepfake video detection methods.
Meaning-Sensitive Text Data Augmentation with Intelligent Masking

With the recent popularity of applying large-scale deep neural network-based models for natural language processing (NLP), attention to develop methods for text data augmentation is at its peak, since the limited size of training data tends to significantly affect the accuracy of these models. To this end, we propose a novel text data augmentation technique called Intelligent Masking with Optimal Substitutions Text Data Augmentation (IMOSA). IMOSA, developed for labelled sentences, can identify the most favourable sentences and locate the appropriate word combinations in a particular sentence to replace and generate synthetic sentences with a meaning closer to the original sentence, while also significantly increasing the diversity of the dataset. We demonstrate that the proposed technique notably improves the performance of classifiers based on attention-based transformer models through the extensive experiments for five different text classification tasks which are performed under the low data regime in a context-aware NLP setting. The analysis clearly shows that IMOSA effectively generates more sentences using favourable original examples and completely ignores undesirable examples. Furthermore, the experiments carried out confirm IMOSA’s ability to add diversity to the augmented dataset using multiple distinct masking patterns against the same original sentence, which remarkably adds variety to the training dataset. IMOSA consistently outperforms the two key masked language model-based text data augmentation techniques, and demonstrates a robust performance against the critical challenging NLP tasks.
Transfer Learning-Based Neural Machine Translation for Low-Resource Languages

College of Liberal Arts, Ludong University, Yantai 264025, China Chinese Lexicography Research Center, Yantai 264025, China Neural Machine Translation (NMT) improves readability by augmenting sentence suggestions based on the precise likelihood of the words. The word suggestions are trained using learning paradigms through repeated translations, word searches, and user inputs. However, the challenging process is the implication of NMT for low-resource language wherein the chances of false suggestions/ word substitutions are high. So, this article proposes a Likelihood-based Machine Translation Model (LMTM) for low-resource languages. The model uses word frequency and potential substitutions from less-known sentences to identify sentences with high precision. This is achieved through a combination of recurrence and substitutions using transfer learning. The identified high-likelihood words are used for sentence augmentation, and the entire set of words from the generated sentence updates the learning paradigm. The model suggests the highest likelihood words for NMT, preventing sentence falsification and ensuring accurate translations. The proposed model increases likelihood by 9.15%, correctness by 7.46%, and substitutions by 7.7%, respectively. It reduces falsification and time complexity by 9.33% and 8.52%, respectively. Overall, the LMTM improves translation quality for low-resource languages.
A New Calculus for Intuitionistic Strong Löb Logic: Strong Termination and Cut-Elimination, Formalised

Abstract We provide a new sequent calculus that enjoys syntactic cut-elimination and strongly terminating backward proof search for the intuitionistic Strong Löb logic $$\textsf{iSL}$$ , an intuitionistic modal logic with a provability interpretation. A novel measure on sequents is used to prove both the termination of the naive backward proof search strategy, and the admissibility of cut in a syntactic and direct way, leading to a straightforward cut-elimination procedure. All proofs have been formalised in the interactive theorem prover Coq.
ICT and Renewable Energy

In the last few decades, we have seen remarkable growth in the technology sector. But this growth has cost us in the form of global warming and climate change. Information and communication technology (ICT) is the quickest grown area in the technology sector and impacted a lot on every part of our lives and is somehow responsible for global warming and climate change in the form of energy emissions. The increasing level of emission of carbon dioxide (CO2) and other gases from various sources has has great affect. ICT, RES, and strict governance can help to mitigate the problem and contribute to energy efficiency by becoming smart. It can contribute to developing smart homes, smart grids, smart logistics, and other smart devices. Using renewable energy sources (RES), small and medium scale systems, computers and related peripherals, and electric and solar vehicles may contribute to conserving the environment and mitigate climate change and global warming. If we can work honestly, we can secure our future generations by making technologies smarter by integrating RES.
Capsule Neural Network Guided by Compact Convolutional Transformer for Discriminating Earthquakes from Quarry Blasts

Abstract Misclassified nonearthquake seismic events like quarry blasts can contaminate the earthquake catalog. The local earthquakes sometimes have similar features as the quarry blasts, which makes manual discrimination difficult and unreliable. Thus, we propose to use the compact convolutional transformer (CCT) and capsule neural network to discriminate between earthquakes and quarry blasts. First, we extract 60 s three-channel seismograms, that is, 10 and 50 s before and after the P-wave arrival time. Then, we transform the time-series data into a time–frequency domain (scalogram) using the continuous wavelet transform. Afterward, we utilize the CCT network to extract the most significant features from the input scalograms. The capsule neural network is utilized to extract the spatial relation between the extracted features using the routing-by-agreement approach (dynamic routing). The capsule neural network extracts different digit vectors for the earthquake and the quarry blast classes, allowing a robust classification accuracy. The proposed algorithm is evaluated using the seismic dataset recorded by the Egyptian Seismic Network. The dataset is divided into 80% for training and 20% for testing. Although the dataset is unbalanced, the proposed algorithm shows promising results. The testing accuracy of the proposed algorithm is 97.31%. The precision, recall, and F1-score are 97.23%, 98.83%, and 98.02%, respectively. In addition, the proposed algorithm outperforms the traditional deep learning models, for example, convolutional neural network, ResNet, Visual Geometry Group (VGG), and AlexNet networks. Finally, the proposed method is demonstrated to enjoy a high-generalization ability through a real-time monitoring experiment.