Reviewerly/exHarmony huggingface dataset:
Mitigation of Seller and Buyer's Dilemma with Transaction History and Escrow

In this paper, to estimate the risk of economic loss incurred by both parties in production order transactions, we propose a scheme that enables escrow and confirmation of the results without relying on a third party. In such transactions, both parties risk incurring economic losses if the other party behaves dishonestly. Generally, the risk can be reduced with an escrow service provided by a trusted third party. However, there is a risk of fraud by the third party; in some cases, the third party may not be available for the buyer or seller. Several existing schemes utilize fair exchange and blockchain to disburse the deposited payment upon the delivery of specific data. However, in production order transactions, some cases cannot be handled only by completion of delivery, such as disputes that arise when the data does not meet the quality expected by the buyer. In such cases, before the transaction starts, a party would confirm the counterparty's behavior in past transactions to estimate the risk of a dispute occurring. In this paper, we propose a scheme that records the history of past transaction processes while utilizing blockchain-based escrow and allows future counterparties to confirm the history as a reference for estimating risk. By the opportunity loss that a history of dishonest behavior causes and applying blockchain-based escrow, the scheme motivates sellers and buyers to behave in good faith. We implemented a prototype system on top of Ethereum and verified its feasibility. By expanding the scope of transactions, we expect that it will be possible to determine whether transactions between individuals over the Internet are feasible without relying on a specific escrow service.
AR Game Traffic Analysis: A Case of Pokemon Go in Flash Crowd Event

Latency is a major issue towards practical use of augmented reality (AR) in mobile apps such as navigation and gaming. A string of work has appeared recently, proposing to offload a part of the AR-related processing pipeline to the edge [8]. One pitfall in these studies is the (simplified) assumption about the traffic demand and its implication to the network delay. As a reality check and to gather insights to deliver and render AR content in real time, we seek in this work a better understanding of how a popular AR game, Pokémon Go, delivers its data in situ.
A practical approach to determine minimal quantum gate durations using amplitude-bounded quantum controls

We present an iterative scheme to estimate the minimal duration in which a quantum gate can be realized while satisfying hardware constraints on the control pulse amplitudes. The scheme performs a sequence of unconstrained numerical optimal control cycles that each minimize the gate fidelity for a given gate duration alongside an additional penalty term for the control pulse amplitudes. After each cycle, the gate duration is adjusted based on the inverse of the resulting maximum control pulse amplitudes by re-scaling the dynamics to a new duration where control pulses satisfy the amplitude constraints. Those scaled controls then serve as an initial guess for the next unconstrained optimal control cycle, using the adjusted gate duration. We provide multiple numerical examples that each demonstrate fast convergence of the scheme toward a gate duration that is close to the quantum speed limit, given the control pulse amplitude bound. The proposed technique is agnostic to the underlying system and control Hamiltonian models, as well as the target unitary gate operation, making the time-scaling iteration an easy to implement and practically useful scheme for reducing the durations of quantum gate operations.
Supply chain management with secured data transmission via improved DNA cryptosystem

Supply chain management (SCM) is most significant place of concentration in various corporate circumstances. SCM has both designed and monitored numerous tasks with the following phases such as allocation, creation, product sourcing, and warehousing. Based on this perspective, the privacy of data flow is more important among producers, suppliers, and customers to ensure the responsibility of the market. This work aims to develop a novel Improved Digital Navigator Assessment (DNA)-based Self Improved Pelican Optimization Algorithm (IDNA-based SIPOA model) for secured data transmission in SCM via blockchain. An improved DNA cryptosystem is done for the process of preservation for data. The original message is encrypted by Improved Advanced Encryption Standard (IAES). The optimal key generation is done by the proposed SIPOA algorithm. The efficiency of the adopted model has been analyzed with conventional methods with regard to security for secured data exchange in SCM. The proposed IDNA-based SIPOA obtained the lowest value for the 40% cypher text is 0.71, while the BWO is 0.79, DOA is 0.77, TWOA is 0.84, BOA is 0.83, POA is 0.86, SDSM is 0.88, DNASF is 0.82 and FSA-SLnO is 0.78, respectively.
A coarse-to-fine unsupervised domain adaptation method based on metric learning

Domain adaptation solves the challenge of inadequate labeled samples in the target domain by leveraging the knowledge learned from the labeled source domain. Most existing approaches aim to reduce the domain shift by performing some coarse alignments such as domain-wise alignment and class-wise alignment. To circumvent the limitation, we propose a coarse-to-fine unsupervised domain adaptation method based on metric learning, which can fully utilize more geometric structure and sample-wise information to obtain a finer alignment. The main advantages of our approach lie in four aspects: (1) it employs a structure-preserving algorithm to automatically select the optimal subspace dimension on the Grassmannian manifold; (2) based on coarse distribution alignment using maximum mean discrepancy, it utilizes the smooth triplet loss to leverage the supervision information of samples to improve the discrimination of data; (3) it introduces structure regularization to preserve the geometry of samples; (4) it designs a graph-based sample reweighting method to adjust the weight of each source domain sample in the cross-domain task. Extensive experiments on several public datasets demonstrate that our method achieves remarkable superiority over several competitive methods (more than 1.5% improvement of the average classification accuracy over the best baseline).
“Somewhere along your pedigree, a bitch got over the wall!” A proposal of implicitly offensive language typology

Abstract The automatic detection of implicitly offensive language is a challenge for NLP, as such language is subtle, contextual, and plausibly deniable, but it is becoming increasingly important with the wider use of large language models to generate human-quality texts. This study argues that current difficulties in detecting implicit offence are exacerbated by multiple factors: (a) inadequate definitions of implicit and explicit offense; (b) an insufficient typology of implicit offence; and (c) a dearth of detailed analysis of implicitly offensive linguistic data. In this study, based on a qualitative analysis of an implicitly offensive dataset, a new typology of implicitly offensive language is proposed along with a detailed, example-led account of the new typology, an operational definition of implicitly offensive language, and a thorough analysis of the role of figurative language and humour in each type. Our analyses identify three main issues with previous datasets and typologies used in NLP approaches: (a) conflating content and form in the annotation; (b) treating figurativeness, particularly metaphor, as the main device of implicitness, while ignoring its equally important role in the explicit offence; and (c) an over-focus on form-specific datasets (e.g. focusing only on offensive comparisons), which fails to reflect the full complexity of offensive language use.
A tiny library with a million volumes: Open-source resource sharing with fulfillment interlibrary loan (ILL)

his paper is based upon a lightning talk given at the NISO Plus conference on February 14, 2023 and it describes interlibrary Loan syste4m, Fulfillment, developed by the Equinox Open Library Initiative. A thriving resource-sharing consortium allows libraries to offer broader and deeper opportunities for individual learning and greater community equity. It unlocks access to additional copies beyond what an owning library has available, along with a broad range of content owned within the network, which provides a diverse collection for library users. Providing access to such a rich network of resources is the goal of Fulfillment, an open-source resource sharing system which breaks down barriers to sharing collections.
The Enhanced Wagner–Hagras OLS–BP Hybrid Algorithm for Training IT3 NSFLS-1 for Temperature Prediction in HSM Processes

This paper presents (a) a novel hybrid learning method to train interval type-1 non-singleton type-3 fuzzy logic systems (IT3 NSFLS-1), (b) a novel method, named enhanced Wagner–Hagras (EWH) applied to IT3 NSFLS-1 fuzzy systems, which includes the level alpha 0 output to calculate the output y alpha using the average of the outputs y alpha k instead of their weighted average, and (c) the novel application of the proposed methodology to solve the problem of transfer bar surface temperature prediction in a hot strip mill. The development of the proposed methodology uses the orthogonal least square (OLS) method to train the consequent parameters and the backpropagation (BP) method to train the antecedent parameters. This methodology dynamically changes the parameters of only the level alpha 0, minimizing some criterion functions as new information becomes available to each level alpha k. The precursor sets are type-2 fuzzy sets, the consequent sets are fuzzy centroids, the inputs are type-1 non-singleton fuzzy numbers with uncertain standard deviations, and the secondary membership functions are modeled as two Gaussians with uncertain standard deviation and the same mean. Based on the firing set of the level alpha 0, the proposed methodology calculates each firing set of each level alpha k to dynamically construct and update the proposed EWH IT3 NSFLS-1 (OLS–BP) system. The proposed enhanced fuzzy system and the proposed hybrid learning algorithm were applied in a hot strip mill facility to predict the transfer bar surface temperature at the finishing mill entry zone using, as inputs, (1) the surface temperature measured by the pyrometer located at the roughing mill exit and (2) the time taken to translate the transfer bar from the exit of the roughing mill to the entry of the descale breaker of the finishing mill. Several fuzzy tools were used to make the benchmarking compositions: type-1 singleton fuzzy logic systems (T1 SFLS), type-1 adaptive network fuzzy inference systems (T1 ANFIS), type-1 radial basis function neural networks (T1 RBFNN), interval singleton type-2 fuzzy logic systems (IT2 SFLS), interval type-1 non-singleton type-2 fuzzy logic systems (IT2 NSFLS-1), type-2 ANFIS (IT2 ANFIS), IT2 RBFNN, general singleton type-2 fuzzy logic systems (GT2 SFLS), general type-1 non-singleton type-2 fuzzy logic systems (GT2 NSFLS-1), interval singleton type-3 fuzzy logic systems (IT3 SFLS), and interval type-1 non-singleton type-3 fuzzy systems (IT3 NSFLS-1). The experiments show that the proposed EWH IT3 NSFLS-1 (OLS–BP) system presented superior capability to learn the knowledge and to predict the surface temperature with the lower prediction error.
Automatic Evaluation of Functional Movement Screening Based on Attention Mechanism and Score Distribution Prediction

Functional movement screening (FMS) is a crucial testing method that evaluates fundamental movement patterns in the human body and identifies functional limitations. However, due to the inherent complexity of human movements, the automated assessment of FMS poses significant challenges. Prior methodologies have struggled to effectively capture and model critical human features in video data. To address this challenge, this paper introduces an automatic assessment approach for FMS by leveraging deep learning techniques. The proposed method harnesses an I3D network to extract spatiotemporal video features across various scales and levels. Additionally, an attention mechanism (AM) module is incorporated to enable the network to focus more on human movement characteristics, enhancing its sensitivity to diverse location features. Furthermore, the multilayer perceptron (MLP) module is employed to effectively discern intricate patterns and features within the input data, facilitating its classification into multiple categories. Experimental evaluations conducted on publicly available datasets demonstrate that the proposed approach achieves state-of-the-art performance levels. Notably, in comparison to existing state-of-the-art (SOTA) methods, this approach exhibits a marked improvement in accuracy. These results corroborate the efficacy of the I3D-AM-MLP framework, indicating its significance in extracting advanced human movement feature expressions and automating the assessment of functional movement screening.
Radar Signal Recognition Based on Bagging SVM

Radar signal recognition under low signal-to-noise ratio (SNR) conditions is a critical issue in modern electronic reconnaissance systems, which face significant challenges in recognition accuracy due to signal diversity. A novel method for radar signal detection based on the bagging support vector machine (SVM) is proposed in this paper.This method firstly utilizes the Choi–Williams distribution (CWD) and the smooth pseudo Wigner-Ville distribution (SPWVD) to obtain different time–frequency images of radar signals, which effectively leverages CWD’s strong time–frequency aggregation and SPWVD’s robust cross-interference resistance. Moreover, histograms of oriented gradient (HOG) features are extracted from time–frequency images to train multiple SVM classifiers by bootstrap sampling. Finally, the performance of each SVM classifier is aggregated using plurality voting to reduce the risk of model overfitting and improve recognition accuracy. We evaluate the effectiveness of the proposed method using 12 different types of radar signals. The experimental results demonstrate that its overall identification rate reaches around 79% at an SNR of −10 dB, and it improves the recognition rate by 5% compared with a single classifier.
The development of syntactic complexity of Chinese JFL learners based on Mean Dependency Distance and Mean Hierarchical Distance

Abstract Mean dependency distance (MDD) and mean hierarchical distance (MHD) are two linguistic measures used in dependency syntax studies to investigate the syntactic features of compositions written by English as a foreign language (EFL) learners. However, their applicability and validity in differentiating proficiency levels and genre effects among Japanese as a foreign language (JFL) learners remain unknown. This study uses a longitudinal dataset that tracks 110 Chinese JFL learners over 12 months and examines their syntactic development as well as the effects of genres. The results indicate that both MDD and MHD effectively capture developmental and genre effects; moreover, both measures show significantly higher values in argumentative writing than narrative writing. However, the extent of genre effects over time is not the same in MDD and MHD. The findings provide new insights into the developmental characteristics of JFL learners’ interlanguage and may contribute to evaluating syntactic complexity and developing automatic evaluation systems.
SDAT-Former++: A Foggy Scene Semantic Segmentation Method with Stronger Domain Adaption Teacher for Remote Sensing Images

Semantic segmentation based on optical images can provide comprehensive scene information for intelligent vehicle systems, thus aiding in scene perception and decision making. However, under adverse weather conditions (such as fog), the performance of methods can be compromised due to incomplete observations. Considering the success of domain adaptation in recent years, we believe it is reasonable to transfer knowledge from clear and existing annotated datasets to images with fog. Technically, we follow the main workflow of the previous SDAT-Former method, which incorporates fog and style-factor knowledge into the teacher segmentor to generate better pseudo-labels for guiding the student segmentor, but we identify and address some issues, achieving significant improvements. Firstly, we introduce a consistency loss for learning from multiple source data to better converge the performance of each component. Secondly, we apply positional encoding to the features of fog-invariant adversarial learning, strengthening the model’s ability to handle the details of foggy entities. Furthermore, to address the complexity and noise in the original version, we integrate a simple but effective masked learning technique into a unified, end-to-end training process. Finally, we regularize the knowledge transfer in the original method through re-weighting. We tested our SDAT-Former++ on mainstream benchmarks for semantic segmentation in foggy scenes, demonstrating improvements of 3.3%, 4.8%, and 1.1% (as measured by the mIoU) on the ACDC, Foggy Zurich, and Foggy Driving datasets, respectively, compared to the original version.
The Capabilities of Boltzmann Machines to Detect and Reconstruct Ising System’s Configurations from a Given Temperature

The restricted Boltzmann machine (RBM) is a generative neural network that can learn in an unsupervised way. This machine has been proven to help understand complex systems, using its ability to generate samples of the system with the same observed distribution. In this work, an Ising system is simulated, creating configurations via Monte Carlo sampling and then using them to train RBMs at different temperatures. Then, 1. the ability of the machine to reconstruct system configurations and 2. its ability to be used as a detector of configurations at specific temperatures are evaluated. The results indicate that the RBM reconstructs configurations following a distribution similar to the original one, but only when the system is in a disordered phase. In an ordered phase, the RBM faces levels of irreproducibility of the configurations in the presence of bimodality, even when the physical observables agree with the theoretical ones. On the other hand, independent of the phase of the system, the information embodied in the neural network weights is sufficient to discriminate whether the configurations come from a given temperature well. The learned representations of the RBM can discriminate system configurations at different temperatures, promising interesting applications in real systems that could help recognize crossover phenomena.
Cloud Key-Value Storage

The paper represents all the stages of designing, architecting, and developing cloud-based key-value storage. This work aims to bring new approaches to distributed data systems. The authors focus on the security and productivity of the project as well as security and maintainability. The authors have studied the use of hash tables in a multi-threaded environment. Architectural approaches and tools have been described. The general structure of the key-value storage server has been presented. The server algorithm has been presented. Our research delves into the intricate nuances of utilizing hash tables in a multi-threaded environment, shedding light on the intricacies and challenges of managing concurrent access to key-value data structures. The authors have explored the trade-offs between lock-free designs and traditional locking approaches.
How Do QR Codes Enhance Customer Experience? Omnichannel Customer Experiences in a Brick-and-Mortar Fashion Store

In this qualitative study, we investigate how customers perceive QR codes enhancing their customer experience (CX) in the brick-and-mortar (B&amp;M) store environment. Introducing QR codes to B&amp;M stores is a part of the larger phenomenon of retailers creating omnichannel CXs by utilizing digital and cyber-physical elements in B&amp;M stores. To collect data from real customers’ CXs, we displayed QR codes in a Finnish fashion brand’s B&amp;M store and interviewed customers who had authentically visited the store. The QR codes displayed in the store were linked to a product information page on the brand’s online store, a brand ambassador’s Instagram posts, and an online survey. The data consist of 15 individual semi-structured thematic interviews of customers aged under 50 years. By conducting qualitative content analysis, we found six main advantages (i.e., interestingness, informativeness, usability, attractiveness, interaction, and innovativeness) and 14 subgroups of QR codes’ advantages in enhancing the CX.
Ethical Considerations of Augmented Reality in High-Tech Manufacturing

The use of Augmented Reality (AR) in industry is growing rapidly, driven by benefits such as efficiency gains and ability to overcome physical boundaries. Existing studies stress the need to take stakeholder values into account in the design process. In this study the impact of AR on stakeholders' values is investigated by conducting focus groups and interviews, using value sensitive design as a framework. Significant impacts were found on the values of safety, accuracy, privacy, helpfulness and autonomy. Twenty practical design choices to mitigate potential negative impact emerged from the study.
Modifying twist algorithms for determining the key length of a Vigenère cipher

In this article, we analyze and improve upon the twist-based algorithms introduced by Barr–Simoson and Park–Kim–Cho–Yum for determining the key length of a Vigenère cipher. We provide an in-depth discussion on how the domain of the twist index affects the accuracy of these algorithms along with supporting experimental evidence. We also introduce a new twist-based algorithm, the twist++ algorithm, and show this algorithm is more accurate than the twist+ algorithm for a wide range of key lengths and text lengths.
Customers’ QR Code Usage Barriers in a Brick-and-Mortar Store: A Qualitative Study

The purpose of this study is to explore the factors that hinder customers from utilizing QR codes when they visit brick-and-mortar (B&amp;M) stores. The research was conducted at a Finnish fashion retailer, where three types of QR codes were displayed for customers to use. In order to identify the barriers that customers face, two different sets of data were collected and analyzed: open-ended survey data (n = 101) and interview data (n = 16). The findings showed two main categories of barriers, customer and company related, with seven sub-barriers. The customer-related barriers included lack of interest in QR codes, user-related usage issues, desire for a device-free B&amp;M store, and dislike toward QR codes. The company-related barriers included unnoticeable QR codes in the B&amp;M store, service personnel in the B&amp;M store, and QR code-related technical problems.