Reviewerly/exHarmony huggingface dataset:
Commitment Capacity under Cost Constraints

We study the problem of commitment over channels under cost constraints. Commitment is a widely studied cryptographic primitive, where two mutually distrustful parties, say Alice and Bob, interact over two phases of a protocol, viz., commit phase followed by reveal phase, to achieve commitment on a bit string available to Alice. Commitment (over the string) is said to occur if (i) Alice commits to the string which remains securely hidden from Bob at the end of the commit phase involving Alice's transmission to Bob, and (ii) Alice reveals a string to Bob and Bob is able to successfully detect whether the string is the committed one or not. When Alice and Bob are computationally unbounded, i.e., under the information-theoretic setting, it is well known that even a single bit commitment is impossible when the channel available to Alice and Bob is noiseless. Noisy channels, however, offer the potential of non-zero commitment rate, and thus, are a valuable resource. We study information-theoretically secure commitment over noisy discrete memoryless channels (DMCs). The largest commitment throughput over noisy channels is called the commitment capacity or simply capacity. In this work, we completely characterize via a single-letter expression, the commitment capacity of DMCs under general cost constraints; this generalizes the previously known result in the absence of such cost constraints. We show that cost constrained commitment capacity of any given DMC can significantly differ from its unconstrained value. We also present a dual capacity characterization in terms of output distributions. Interestingly, we show that every input distribution achieving the capacity results in the same output distribution; the latter is the unique optimizer of our dual capacity expression.
Transfer Learning for Scene Text Recognition in Indian Languages

Scene text recognition in low-resource Indian languages is challenging because of complexities like multiple scripts, fonts, text size, and orientations. In this work, we investigate the power of transfer learning for all the layers of deep scene text recognition networks from English to two common Indian languages. We perform experiments on the conventional CRNN model and STAR-Net to ensure generalisability. To study the effect of change in different scripts, we initially run our experiments on synthetic word images rendered using Unicode fonts. We show that the transfer of English models to simple synthetic datasets of Indian languages is not practical. Instead, we propose to apply transfer learning techniques among Indian languages due to similarity in their n-gram distributions and visual features like the vowels and conjunct characters. We then study the transfer learning among six Indian languages with varying complexities in fonts and word length statistics. We also demonstrate that the learned features of the models transferred from other Indian languages are visually closer (and sometimes even better) to the individual model features than those transferred from English. We finally set new benchmarks for scene-text recognition on Hindi, Telugu, and Malayalam datasets from IIIT-ILST and Bangla dataset from MLT-17 by achieving 6%, 5%, 2%, and 23% gains in Word Recognition Rates (WRRs) compared to previous works. We further improve the MLT-17 Bangla results by plugging in a novel correction BiLSTM into our model. We additionally release a dataset of around 440 scene images containing 500 Gujarati and 2535 Tamil words. WRRs improve over the baselines by 8%, 4%, 5%, and 3% on the MLT-19 Hindi and Bangla datasets and the Gujarati and Tamil datasets.
Transfer learning for multiobjective optimization algorithms supporting dynamic software product lines

Dynamic Software Product Lines (DSPLs) are a well-accepted approach for self-adapting Cyber-Physical Systems (CPSs) at run-time. The DSPL approaches make decisions supported by performance models, which capture system features' contribution to one or more optimization goals. Combining performance models with Multi-Objectives Evolutionary Algorithms (MOEAs) as decision-making mechanisms is common in DSPLs. However, MOEAs algorithms start solving the optimization problem from a randomly selected population, not finding good configurations fast enough after a context change, requiring too many resources so scarce in CPSs. Also, the DSPL engineer must deal with the hardware and software particularities of the target platform in each CPS deployment. And although each system instantiation has to solve a similar optimization problem of the DSPL, it does not take advantage of experiences gained in similar CPS. Transfer learning aims at improving the efficiency of systems by sharing the previously acquired knowledge and applying it to similar systems. In this work, we analyze the benefits of transfer learning in the context of DSPL and MOEAs testing on 8 feature models with synthetic performance models. Results are good enough, showing that transfer learning solutions dominate up to 71% of the non-transfer learning ones for similar DSPL.
Strategic capabilities and firm performance in Omani manufacturing and service SMEs

Purpose This paper aims to examine small- and medium-sized enterprises’ (SMEs) strategic capabilities in terms of their marketing and management capabilities, their sources of environmental uncertainty and their organizational capabilities. Additionally, to what extent the effect differs across two sectors (manufacturing and service). Design/methodology/approach Partial least squares structural equation modeling was used to conduct multigroup analysis for the two sectors. Data was collected from a sample of 315 Omani SMEs, 166 from manufacturing and 149 from services. Findings The results show that strategic capabilities have a significant positive effect on customer satisfaction. However, the effect differs between manufacturing and service SMEs; the effect is greater in service than in manufacturing SMEs. Furthermore, the effect of organizational capabilities on customer satisfaction was found to be positive. However, the effect is higher in manufacturing as the difference is statistically significant. Originality/value Due to the growing importance of the service and manufacturing SMEs in developing countries and their considerable involvement in economic development, it is important to understand the characteristics of the strategic capabilities in both sectors. Thus, according to the authors’ knowledge, this paper is one of the first to propose a comprehensive framework that measures collectively the direct impact of strategic capabilities, organizational capabilities and environmental uncertainties on SMEs customer satisfaction and effectiveness.
Smart Train Accident Detection And Prevention System Using Iot Technology

The point of this paper is to plan and foster a finger impression based train motor getting to framework with a perspective on lessening manual working and to avert unauthorised users. As of late, train commandeer has been on the increment as equipped burglars center around taking vehicles, particularly the pristine ones. This is accomplished with the utilization of a finger impression module, ATMega328P microcontroller and Liquid Crystal Display (LCD) module. Correspondence based train control (CBTC) [8] is a control mechanism which use bidirectional train–ground to operate the trains in a safer manner. Since there is problem in controlling the train via remote operation and its transportability, wireless networks can be incorporated to manage the execution of train control system.For that we used ESP2866 wifi module which is used for uploading the train engine drivers database into the server. However we can avoid this collision and prevent the train from accident by utilizing the impact identification innovation which can be made by ultrasonic sound. This technology is used to detect the obstacle and it gradually slow down the speed of the train by applying the air brake to stop the train before the mishap occurs.
Protecting Gender and Identity with Disentangled Speech Representations

Besides its linguistic content, our speech is rich in biometric information that can be inferred by classifiers. Learning privacy-preserving representations for speech signals enables downstream tasks without sharing unnecessary, private information about an individual. In this paper, we show that protecting gender information in speech is more effective than modelling speaker-identity information only when generating a non-sensitive representation of speech. Our method relies on reconstructing speech by decoding linguistic content along with gender information using a variational autoencoder. Specifically, we exploit disentangled representation learning to encode information about different attributes into separate subspaces that can be factorised independently. We present a novel way to encode gender information and disentangle two sensitive biometric identifiers, namely gender and identity, in a privacy-protecting setting. Experiments on the LibriSpeech dataset show that gender recognition and speaker verification can be reduced to a random guess, protecting against classification-based attacks.
Detecting failure modes in image reconstructions with interval neural network uncertainty

The quantitative detection of failure modes is important for making deep neural networks reliable and usable at scale. We consider three examples for common failure modes in image reconstruction and demonstrate the potential of uncertainty quantification as a fine-grained alarm system.We propose a deterministic, modular and lightweight approach called Interval Neural Network (INN) that produces fast and easy to interpret uncertainty scores for deep neural networks. Importantly, INNs can be constructed post hoc for already trained prediction networks. We compare it against state-of-the-art baseline methods (MCDROP, PROBOUT).We demonstrate on controlled, synthetic inverse problems the capacity of INNs to capture uncertainty due to noise as well as directional error information. On a real-world inverse problem with human CT scans, we can show that INNs produce uncertainty scores which improve the detection of all considered failure modes compared to the baseline methods.Interval Neural Networks offer a promising tool to expose weaknesses of deep image reconstruction models and ultimately make them more reliable. The fact that they can be applied post hoc to equip already trained deep neural network models with uncertainty scores makes them particularly interesting for deployment.
Blockchain-based energy-efficient smart green city in IoT environments

The blockchain technologies in smart cities can act as a platform for managing the utilization of energy and for enhancing the security for data transmission. Smart Green cities are the future technology that emphasizes the impending of digital arrangement to enhance green environments and simplifies the authorization in green space planning. As the devices which are used in smart city applications are entirely equipped with the Internet of Things (IoT), there comes the need for ensuring the sustainability of such devices. Blockchain-based energy management is the most sensational issue that drives the implementation of smart green cities. For real-time IoT applications, there exists a demand for sensitivity and latency. Edge computing can provide support to deal with these kinds of issues, by providing a most well-organized structure in reducing the delays and thus improves the performance of the system. This chapter deals with the applications of blockchain and its technologies with the development of the energy-efficient smart green city in IoT environments. A novel model for the management of energy, waste, soil, and air is proposed by using the integrated blockchain technologies. The blockchain method will deliver a safety base for data regulation, and also the transaction of information is in between the unconventionally working devices in the edge-based smart green city environments. This will create a sustainable environment with more security features. The proper handoff mechanism with appropriate authentication routing strategy is proposed in this chapter. This will provide security against all kinds of suspicious attacks and thus improves the performance of the network and reduces the response time. Thus, the proposed sensor-based resource management with edge computing through SDN and blockchain paves the way for security and reliability in a smart city, making human life more comfortable and innocuous.
OctCNN: A High Throughput FPGA Accelerator for CNNs using Octave Convolution Algorithm

With the rapid development of convolutional neural networks (CNNs), FPGAs have become one of the most attractive candidates for deploying CNNs. However, previous FPGA solutions based on the traditional convolution are still limited by computational power. In this article, we introduce the octave convolution (OctConv) into the CNN accelerator design for the first time to improve the hardware acceleration efficiency and design a dedicated OctPU for mapping OctConv to FPGAs, which employs a parallel dataflow pattern to exploit the parallelism of OctConv. Then, we present a novel and scalable architecture that dynamically combines the inter-layer pipelined structure and multi-layer reuse structure. Meanwhile, to obtain the optimized solution, we build a multidimensional performance and resource analysis model and a two-stage search algorithm based on greedy and heuristic algorithms. We evaluate our proposal by implementing VGG16 and ResNet50 on the Xilinx VU9P FPGA. Experimental results show that our prototypes can achieve an average of 3321 GOP/s for the convolutional layers for VGG16 and 2873 GOP/s for the overall ResNet50 using OctConv. Compared to previous works based on the traditional convolution, our prototypes own a 1.72 to 2.33 speedup in throughput and a 2.01 to 5.18 improvement in computational density. Our design also presents an excellent compromise performance and generalization
A New Deep Wavefront Based Model for Text Localization in 3D Video

The rapid advancements in digital technologies have prompted organizations to embrace digital transformations (DTs) in order to enhance efficiency, gain a competitive advantage, and achieve long-term sustainability objectives.However, the successful adoption of innovative digital technologies necessitates the careful consideration of various factors, such as stakeholder engagement, resource allocation, risk mitigation, and the availability of resources and implementation support.This study examines the sustainable adoption of innovative digital technologies (DTs) within digital transformations.The data for this study were collected from 760 stakeholders through a questionnaire survey and analyzed using SPSS software (Version 27).This study's results underscore the significance of considering the efficiency of the transformation process and the long-term sustainability outcomes for organizations.The findings of the analysis clarify that integrating sustainability principles and DT has a positive impact on the effectiveness of the transformation, as indicated by environmental, social, and economic performance indicators.This study's novelty lies in its focus on incorporating sustainability principles into the digital transformation process.The results of this study demonstrate that organizations' long-term sustainability outcomes are enhanced when their digital transformation goals align with the Sustainable Development Goals (SDGs).The purpose of this study emphasizes the importance of arranging digital transformations with sustainable objectives to ensure the overall success and longevity of transformation efforts.
Sound of databases

Sonifications map data to auditory dimensions and offer a new audible experience to their listeners. We propose a sonification of query processing paired with a corresponding visualization both integrated in a web application. In this demonstration we show that the sonification of different types of relational operators generates different sound patterns, which can be recognized and identified by listeners increasing their understanding of the operators' functionality and supports easy remembering of requirements like merge joins work on sorted input. Furthermore, new ways of analyzing query processing are possible with the sonification approach.
A Literature Review of the Concept of Entitlement and the Theoretical Informants of Excessive Teacher Entitlement

This chapter identifies the broad interdisciplinary ideologies of entitlement in order to situate and understand the potential theoretical informants of excessiveness in teacher entitlement. Although the authors' perspectives and experiences on the theme of entitlement are located in the US educational system, this is accompanied by an awareness of the need to examine the topic internationally since the topic needs to “be reconsidered in terms of contextual variables.”Psychological and organizational entitlement were the prevalent strains of entitlement that emerged in the reviewed literature and “academic entitlement” specific to the field of education. Therefore, three strands, psychological, organizational and academic, form the thematic categories for this scoping literature review.Most literature on “academic entitlement” deals with excessive entitlement amongst students. No reference to excessive teacher entitlement was found. However, specific gaps were found in: (1) what qualifies as excessive teacher entitlement, (2) research scholarship on teacher entitlement, and (3) entitlement studies specifically aimed at global reach and applicable to teachers.The theoretical informants of teacher entitlement identified in this study indicate that the phenomenon goes beyond individual mindset to encompass the mediation of sociocultural and political factors in its construction, thus rendering a simple theory of excessiveness in association with teacher entitlement improbable at this time.
Breaking KASLR Using Memory Deduplication in Virtualized Environments

Recent operating systems (OSs) have adopted a defense mechanism called kernel page table isolation (KPTI) for protecting the kernel from all attacks that break the kernel address space layout randomization (KASLR) using various side-channel analysis techniques. In this paper, we demonstrate that KASLR can still be broken, even with the latest OSs where KPTI is applied. In particular, we present a novel memory-sharing-based side-channel attack that breaks the KASLR on KPTI-enabled Linux virtual machines. The proposed attack leverages the memory deduplication feature on a hypervisor, which provides a timing channel for inferring secret information regarding the victim. By conducting experiments on KVM and VMware ESXi, we show that the proposed attack can obtain the kernel address within a short amount of time. We also present several countermeasures that can prevent such an attack.
Bosonic Dirty Paper Coding

The bosonic channel is addressed with modulation interference and side information at the transmitter. The model can be viewed as the quantum counterpart of the classical random-parameter Gaussian channel. Based on Costa's writing-on-dirty-paper result, the effect of the interference can be canceled. For both homodyne and heterodyne detection, we observe the same phenomenon, as the model reduces to a classical Gaussian channel. Then, we consider the bosonic channel with joint detection, for which the classical results do not apply, and derive a dirty-paper coding lower bound. We demonstrate that the optimal coefficient for dirty paper coding is not necessarily the MMSE estimator coefficient as in the classical setting.
Road Distance Computation Using Homomorphic Encryption in Road Networks

Road networks have been used in a wide range of applications to reduces the cost of transportation and improve the quality of related services. The shortest road distance computation has been considered as one of the most fundamental operations of road networks computation. To alleviate privacy concerns about location privacy leaks during road distance computation, it is desirable to have a secure and efficient road distance computation approach. In this paper, we propose two secure road distance computation approaches, which can compute road distance over encrypted data efficiently. An approximate road distance computation approach is designed by using Partially Homomorphic Encryption and road network set embedding. An exact road distance computation is built by using Somewhat Homomorphic Encryption and road network hypercube embedding. We implement our two road distance computation approaches, and evaluate them on the real city-scale road network. Evaluation results show that our approaches are accurate and efficient.
Optimal Mapping for Near-Term Quantum Architectures based on Rydberg Atoms

Quantum algorithms promise quadratic or exponential speedups for applications in cryptography, chemistry and material sciences. The topologies of today's quantum computers offer limited connectivity, leading to significant overheads for implementing such quantum algorithms. One-dimensional topology displacements that remedy these limits have been recently demonstrated for architectures based on Rydberg atoms, and they are possible in principle in photonic and ion trap architectures. We present the first optimal quantum circuit-to-architecture mapping algorithm that exploits such one-dimensional topology displacements. We benchmark our method on quantum circuits with up to 15 qubits and investigate the improvements compared with conventional mapping based on inserting swap gates into the quantum circuits. Depending on underlying technology parameters, our approach can decrease the quantum circuit depth by up to 58% and increase the fidelity by up to 29%. We also study runtime and fidelity requirements on one-dimensional displacements and swap gates to derive conditions under which one-dimensional topology displacements provide benefits.
Research on Chinese Event Extraction Method Based on RoBERTa-WWM-CRF

To extract the event information contained in the Chinese text effectively, this paper takes Chinese event extraction as a sequential labeling task, and proposes a method to extract events based on the combination of RoBERTa-WWM (A Robustly Optimized BERT Pre-training Approach-Whole Word Masking) and Conditional Random Fields (CRF). This method uses RoBERTa-WWM to generate semantic representation with prior knowledge, and then inputs them into the Conditional Random Fields (CRF) model. The argument is predicted by the output label sequence. The experimental results show that this method can effectively improve accuracy, recall, and F1-score on the Chinese event extraction dataset DUEE1.0, which Baidu recently released, and improve the performance of event extraction in Chinese text.
Two Ways Gas Price Oracles Miss The Mark

Blockchain transactions compete for limited space in blockchain blocks. Miners prefer to include transactions with higher fees into new blocks. In the context of Ethereum, gas price price oracles predict fees such that transactions submitted at those fees make it into a block within a target delay. In practice, however, oracles are not accurate, which makes it difficult for distributed applications to operate predictable services in terms of price and performance.To understand oracle performance we define a new gas prediction accuracy metric. We demonstrate that oracles underprice transactions, causing them to miss the delay target, as well as overprice transactions, causing them to meet the delay target, but at a higher than necessary cost. We provide comparative analysis of five gas price oracles showing their relative accuracy, transaction accept rates, price stability, and discuss factors that influence oracle accuracy. We observe that the ETHGasStation oracle produces the most accurate and stable price predictions. For users that prefer to run their own oracle Web3.py provides comparable performance.