Reviewerly/exHarmony huggingface dataset:
An Ultra-Scalable Blockchain Platform for Universal Asset Tokenization: Design and Implementation

Since its introduction with Bitcoin in 2009, blockchain technology has received tremendous attention by academia, industry, politics and media alike, in particular, through extended blockchain-based visions such as smart contracts, decentralized finance, and, most recently, Web3. The critical prerequisite for any such blockchain-based vision to be turned into reality is uncapped scalability. Furthermore, and equally important, blockchain technology needs to transcend the stage of specialized tokens into an adaptive, heterogeneous tokenization platform. In this paper, we explain the Alphabill family of technologies that addresses both unlimited scalability and unrestricted adaptivity.We deliver a sharded blockchain technology with unlimited scalability and performance, called KSI Cash, which is based on a new form of electronic money scheme, the bill scheme. We present performance tests of KSI Cash that we have conducted with the European Central Bank and a group of eight national central banks from the Eurosystem in order to assess the technological feasibility of a digital euro.We show the system operating with 100 million wallets and 15 thousand transactions per second (under simulation of realistic usage); having an estimated carbon footprint of 0.0001g CO2 per transaction (Bitcoin = 100 kg and more). Furthermore, we show the system operating with up to 2 million payment orders per second, an equivalent of more than 300,000 transactions per second (in a laboratory setting with the central components of KSI Cash), scaling linearly in terms of the number of deployed shards. We explain, in detail, the key concepts that unlock this performance (i.e., the concepts of the bill money scheme). The results provide evidence that the scalability of our technology is unlimited in both permissioned and permissionless scenarios, resulting into the Alphabill Money technology. Next, we contribute the architecture of a universal tokenization platform that allows for universal asset tokenization, transfer and exchange as a global medium of exchange, called Alphabill platform. We reveal the crucial conceptual and technical contributions of the platform&#x2019;s architecture and their interplay, including the data structures of KSI Cash and Alphabill Money, the dust collection solution of Alphabill Money, and the atomic swap solution of the Alphabill platform.
Dual-Branch Interactive Networks on Multichannel Time Series for Human Activity Recognition

The popularity of convolutional architecture has made sensor-based human activity recognition (HAR) become one primary beneficiary. By simply superimposing multiple convolution layers, the local features can be effectively captured from multi-channel time series sensor data, which could output high-performance activity prediction results. On the other hand, recent years have witnessed great success of Transformer model, which uses powerful self-attention mechanism to handle long-range sequence modeling tasks, hence avoiding the shortcoming of local feature representations caused by convolutional neural networks (CNNs). In this paper, we seek to combine the merits of CNN and Transformer to model multi-channel time series sensor data, which might provide compelling recognition performance with fewer parameters and FLOPs based on lightweight wearable devices. To this end, we propose a new Dual-branch Interactive Network (DIN) that inherits the advantages from both CNN and Transformer to handle multi-channel time series for HAR. Specifically, the proposed framework utilizes two-stream architecture to disentangle local and global features by performing conv-embedding and patch-embedding, where a co-attention mechanism is used to adaptively fuse global-to-local and local-to-global feature representations. We perform extensive experiments on three mainstream HAR benchmark datasets including PAMAP2, WISDM, and OPPORTUNITY, which verify that our method consistently outperforms several state-of-the-art baselines, reaching an F1-score of 92.05%, 98.17%, and 91.55% respectively with fewer parameters and FLOPs. In addition, the practical execution time is validated on an embedded Raspberry Pi P3 system, which demonstrates that our approach is adequately efficient for real-time HAR implementations and deserves as a better alternative in ubiquitous HAR computing scenario. Our model code will be released soon.
Hierarchical Graph Convolutional Skeleton Transformer for Action Recognition

Graph convolutional networks (GCNs) have emerged as dom-inant methods for skeleton-based action recognition. How-ever, they still suffer from two problems, namely, neighbor-hood constraints and entangled spatiotemporal feature repre-sentations. Most studies have focused on improving the de-sign of graph topology to solve the first problem but they have yet to fully explore the latter. In this work, we design a dis-entangled spatiotemporal transformer (DSTT) block to over-come the above limitations of GCNs in three steps: (i) feature disentanglement for spatiotemporal decomposition; (ii) global spatiotemporal attention for capturing correlations in the global context; and (iii) local information enhancement for utilizing more local information. Thereon, we propose a novel architecture, named Hierarchical Graph Convolutional skeleton Transformer (HGCT), to employ the complementary advantages of GCN (i.e., local topology, temporal dynamics and hierarchy) and Transformer (i.e., global context and dy-namic attention). HGCT is lightweight and computationally efficient. Quantitative analysis demonstrates the superiority and good interpretability of HGCT.
Sensor Data Augmentation by Resampling in Contrastive Learning for Human Activity Recognition

While deep learning models have contributed to the advancement of sensor-based human activity recognition (HAR), it usually requires large amounts of annotated sensor data to extract robust features. To alleviate the limitations of data annotation, contrastive learning has been applied to sensor-based HAR. One of the essential factors of contrastive learning is data augmentation, significantly impacting the performance of pretraining. However, current popular augmentation methods do not achieve competitive performance in contrastive learning for sensor-based HAR. Motivated by this issue, we propose a new sensor data augmentation method by resampling, which introduces variable domain information and simulates realistic activity data by varying the sampling frequency to maximize the coverage of the sampling space. The resampling augmentation method was evaluated in supervised learning and contrastive learning [SimCLR for HAR (SimCLRHAR) and MoCo for HAR (MoCoHAR)]. In the experiment, we use four datasets, UCI-HAR, MotionSense, USC-HAD, and MobiAct, using the mean F1-score as the evaluation metric for downstream tasks. The experimental results show that the resampling data augmentation outperforms all state-of-the-art augmentation methods in supervised learning and contrastive learning with a small amount of labeled data. The results also demonstrate that not all data augmentation methods have positive effects in contrastive learning frameworks.
Learning to Affiliate: Mutual Centralized Learning for Few-shot Classification

Few-shot learning (FSL) aims to learn a classifier that can be easily adapted to accommodate new tasks, given only a few examples. To handle the limited-data in few-shot regimes, recent methods tend to collectively use a set of local features to densely represent an image instead of using a mixed global feature. They generally explore a unidirectional paradigm, e.g., finding the nearest support feature for every query feature and aggregating local matches for a joint classification. In this paper, we propose a novel Mutual Centralized Learning (MCL) to fully affiliate these two disjoint dense features sets in a bidirectional paradigm. We first associate each local feature with a particle that can bidirectionally random walk in discrete feature space. To estimate the class probability, we propose the dense features' accessibility that measures the expected number of visits to the dense features of that class in a Markov process. We relate our method to learning a centrality on an affiliation network and demonstrate its capability to be plugged in existing methods by highlighting centralized local features. Experiments show that our method achieves the new state-of-the-art.
Sentiment analysis using machine learning: Progress in the machine intelligence for data science

Sentiments and emotions of a person on social media is classified by the effective data science approaches. Data science is an inter-disciplinary domain that utilizes the scientific techniques, processes and algorithms to retrieve the sentiments from the twitter tweets. The classification of sentiments plays significant role in many application domain and with the assistance of the people emotions business industry can be developed accordingly. The sentiment extraction and the classification is attained by several approaches namely neuro-fuzzy and optimization algorithms. The technical contribution of this article is double feed forward neural network. These approaches face ineffective in classification when the real-time data contains numerous characters and stream of information. To attain proficient classification, double feed forward neural network is utilized and the output layer information is transmitted to the double layer of the network. Hence, the information’s are optimized and processed effectively, whereby the classification of sentiment is achieved. The entire process of the algorithm is carried and the acquired results are compared with the neuro-fuzzy and optimization algorithm. The DFFNN outperforms the existing algorithm in terms of classification parameters.
Benchmarking Intersectional Biases in NLP

There has been a recent wave of work assessing the fairness of machine learning models in general, and more specifically, on natural language processing (NLP) models built using machine learning techniques.While much work has highlighted biases embedded in stateof-the-art language models, and more recent efforts have focused on how to debias, research assessing the fairness and performance of biased/debiased models on downstream prediction tasks has been limited.Moreover, most prior work has emphasized bias along a single dimension such as gender or race.In this work, we benchmark multiple NLP models with regards to their fairness and predictive performance across a variety of NLP tasks.In particular, we assess intersectional bias -fairness across multiple demographic dimensions.The results show that while current debiasing strategies fare well in terms of the fairnessaccuracy trade-off (generally preserving predictive power in debiased models), they are unable to effectively alleviate bias in downstream tasks.Furthermore, this bias is often amplified across demographic dimensions.We conclude with implications for future NLP debiasing research.
Flexible and efficient simulation-based inference for models of decision-making

Inferring parameters of computational models that capture experimental data are a central task in cognitive neuroscience. Bayesian statistical inference methods usually require the ability to evaluate the likelihood of the model-however, for many models of interest in cognitive neuroscience, the associated likelihoods cannot be computed efficiently. Simulation-based inference (SBI) offers a solution to this problem by only requiring access to simulations produced by the model. Previously, Fengler et al. introduced likelihood approximation networks (LANs, Fengler et al., 2021) which make it possible to apply SBI to models of decision-making, but require billions of simulations for training. Here, we provide a new SBI method that is substantially more simulation efficient. Our approach, mixed neural likelihood estimation (MNLE), trains neural density estimators on model simulations to emulate the simulator, and is designed to capture both the continuous (e.g., reaction times) and discrete (choices) data of decision-making models. The likelihoods of the emulator can then be used to perform Bayesian parameter inference on experimental data using standard approximate inference methods like Markov Chain Monte Carlo sampling. We demonstrate MNLE on two variants of the drift-diffusion model and show that it is substantially more efficient than LANs: MNLE achieves similar likelihood accuracy with six orders of magnitude fewer training simulations, and is significantly more accurate than LANs when both are trained with the same budget. Our approach enables researchers to perform SBI on custom-tailored models of decision-making, leading to fast iteration of model design for scientific discovery.
ZeeStar: Private Smart Contracts by Homomorphic Encryption and Zero-knowledge Proofs

Data privacy is a key concern for smart contracts handling sensitive data. The existing work zkay addresses this concern by allowing developers without cryptographic expertise to enforce data privacy. However, while zkay avoids fundamental limitations of other private smart contract systems, it cannot express key applications that involve operations on foreign data.We present ZeeStar, a language and compiler allowing non-experts to instantiate private smart contracts and supporting operations on foreign data. The ZeeStar language allows developers to ergonomically specify privacy constraints using zkay’s privacy annotations. The ZeeStar compiler then provably realizes these constraints by combining non-interactive zero-knowledge proofs and additively homomorphic encryption.We implemented ZeeStar for the public blockchain Ethereum. We demonstrated its expressiveness by encoding 12 example contracts, including oblivious transfer and a private payment system like Zether. ZeeStar is practical: it prepares transactions for our contracts in at most 54.7s, at an average cost of 339k gas.
Waldo: A Private Time-Series Database from Function Secret Sharing

Applications today rely on cloud databases for storing and querying time-series data. While outsourcing storage is convenient, this data is often sensitive, making data breaches a serious concern. We present Waldo, a time-series database with rich functionality and strong security guarantees: Waldo supports multi-predicate filtering, protects data contents as well as query filter values and search access patterns, and provides malicious security in the 3-party honest-majority setting. In contrast, prior systems such as Timecrypt and Zeph have limited functionality and security: (1) these systems can only filter on time, and (2) they reveal the queried time interval to the server. Oblivious RAM (ORAM) and generic multiparty computation (MPC) are natural choices for eliminating leakage from prior work, but both of these are prohibitively expensive in our setting due to the number of roundtrips and bandwidth overhead, respectively. To minimize both, Waldo builds on top of function secret sharing, enabling Waldo to evaluate predicates non-interactively. We develop new techniques for applying function secret sharing to the encrypted database setting where there are malicious servers, secret inputs, and chained predicates. With 32-core machines, Waldo runs a query with 8 range predicates over 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">18</sup> records in 3.03s, compared to 12.88s or an MPC baseline and 16.56s for an ORAM baseline. Compared to Waldo, the MPC baseline uses $9-82 \times$ more bandwidth between servers (for different numbers of records), while the ORAM baseline uses $20-152 \times$ more bandwidth between the client and server(s) (for different numbers of predicates).
Robust Semantic Segmentation in Adverse Weather Conditions by means of Sensor Data Fusion

A robust and reliable semantic segmentation in adverse weather conditions is very important for autonomous cars, but most state-of-the-art approaches only achieve high accuracy rates in optimal weather conditions. The reason is that they are only optimized for good weather conditions and given noise models. However, most of them fail, if data with unknown disturbances occur, and their performance decrease enormously. One possibility to still obtain reliable results is to observe the environment with different sensor types, such as camera and lidar, and to fuse the sensor data by means of neural networks, since different sensors behave differently in diverse weather conditions. Hence, the sensors can complement each other by means of an appropriate sensor data fusion. Nevertheless, the fusion-based approaches are still susceptible to disturbances and fail to classify disturbed image areas correctly. This problem can be solved by means of a special training method, the so called Robust Learning Method (RLM), a method by which the neural network learns to handle unknown noise. In this work, two different sensor fusion architectures for semantic segmentation are compared and evaluated on several datasets. Furthermore, it is shown that the RLM increases the robustness in adverse weather conditions enormously, and achieve good results although no disturbance model has been learned by the neural network.
Channel pruning based on convolutional neural network sensitivity

Pruning is a useful technique for decreasing the memory consumption and floating point operations (FLOPs) of deep convolutional neural network (CNN) models. Nevertheless, at modest pruning levels, current structured pruning approaches often lead to considerable declines in accuracy. Furthermore, existing approaches often treat pruning rates as super parameters, neglecting the sensitivity of different convolution layers. In this study, we propose a novel sensitivity-based method for channel pruning that utilizes second-order sensitivity as a criterion. The essential concept is to prune insensitive filters while retaining sensitive ones. We quantify the sensitivity of the filter using the sum of the sensitivities of all weights in the filter, rather than the magnitude-based metric frequently applied in the literature. Furthermore, a layer sensitivity approach based on the Hessian eigenvalues of each layer is introduced into the process of automatically choosing the most appropriate pruning rate for each layer. Experiments on a variety of modern CNN architectures demonstrate that we can considerably enhance the pruning rate while sacrificing a small amount of accuracy, resulting in a reduction of more than 60% in FLOPs on CIFAR-10. Notably, on ImageNet, pruning based on ResNet50 decreased the FLOPs by 56.3% while losing only 0.92% of accuracy.
Discovering meaningful engagement through interaction between customers and service robots

Although service robots are causing a paradigm shift in the service industry by creating new interactions with customers, there is limited knowledge regarding which elements of service robots create desirable interactions with customers. To address this gap, this study invited situated action theory to investigate elements of service robots shaping customers' meaningful engagement. 252 responses from the survey were analyzed. These revealed that some socio-functional elements are primary for meaningful engagement by affecting both experiential and instrumental outcomes, whereas others are salient for either one of the outcomes. Our analysis also examined personal innovativeness and viability of human–robot team pertaining to meaningful engagement. This study contributes to the literature by identifying socio-functional elements of service robots and exploring how they create meaningful engagement. It also provides practical implications to service companies, demonstrating critical elements of service robots for enhancing their service quality and for developing robot-driven service environments.
Blockchain-Based Traceability for the Fishery Supply Chain

Efficient traceability management is necessary for managing products in the fishery supply chain. Any negligence in products&#x2019; traceability can result in food fraud that may adversely affect the consumer&#x2019;s health. Monitoring and tracking of the fishery supply chain operations can assist system stakeholders in identifying the origins and causes of product fraud and malpractice. Most of the existing systems and technologies used to manage the fishery supply chain processes fall short of providing traceability, accountability, transparency, reliability, trust, privacy, and security features. In this paper, we propose a private Ethereum blockchain-based solution to efficiently manage the fishery supply chain operations in a manner that is decentralized, transparent, traceable, secure, private, and trustworthy. We present the solution architecture and propose five smart contracts to automate the processes in fishery supply chain. We present ten algorithms along with their full implementation, testing, and validation details. We conduct a security analysis to show that the proposed solution is both secure and trustworthy. We compare our solution with the existing blockchain and non-blockchain-based solutions to show its effectiveness and novelty. We make the smart contract code publicly available on GitHub.
Metaverse in Education

This chapter details the usage and benefits in the implementation of metaverse in the education industry. Metaverse is an enormous term that contains every digital aspect of the future. As a result, spheres like education must have the systems readdressed to adopt this technology to retain their accessibility and prolonged existence. There are numerous benefits of digital resources, comprising interactive features and portability. Still, a number of barriers exist. Additionally, there are certain technical, infrastructural, and managerial limitations to it as well. This chapter gives an overview of several aspects of metaverse in education.
Imaginary-time evolution using forward and backward real-time evolution with a single ancilla: First-quantized eigensolver algorithm for quantum chemistry

Imaginary-time evolution (ITE) on a quantum computer is a promising formalism for obtaining the ground state of a quantum system. The probabilistic ITE (PITE) exploits measurements to implement nonunitary operations, and it can avoid the restriction of dynamics to a low-dimensional subspace imposed by variational parameters unlike other types of ITE. In this paper, we propose a PITE approach that uses only one ancillary qubit. Unlike the existing PITE approaches, the one proposed here constructs, under a practical approximation, the circuit from forward and backward real-time evolution (RTE) gates as black boxes for the original Hamiltonian. Thus all efficient unitary algorithms for RTE can be transferred to the ITE without any modifications. Our approach can be used to obtain the Gibbs state at a finite temperature and partition function. We validate the approach via several illustrative systems where the trial states are found to converge rapidly to the ground states. In addition, we discuss its applicability to quantum chemistry by focusing on the scaling of computational cost; this leads to the development of a framework referred to as a first-quantized eigensolver. The nonvariational generic approach will expand the scope of practical quantum computation for versatile objectives.