Reviewerly/exHarmony huggingface dataset:
Genetic-Algorithm-Inspired Difficulty Adjustment for Proof-of-Work Blockchains

In blockchains, the principle of proof-of-work (PoW) is used to compute a complex mathematical problem. The computation complexity is governed by the difficulty, adjusted periodically to control the rate at which new blocks are created. The network hash rate determines this, a phenomenon of symmetry, as the difficulty also increases when the hash rate increases. If the hash rate grows or declines exponentially, the block creation interval cannot be maintained. A genetic algorithm (GA) is proposed as an additional mechanism to the existing difficulty adjustment algorithm for optimizing the blockchain parameters. The study was conducted with four scenarios in mind, including a default scenario that simulates a regular blockchain. All the scenarios with the GA were able to achieve a lower standard deviation of the average block time and difficulty compared to the default blockchain network without GA. The scenario of a fixed difficulty adjustment interval with GA was able to reduce the standard deviation of the average block time by 80.1%, from 497.1 to 98.9, and achieved a moderate median block propagation time of 6.81 s and a stale block rate of 6.67%.
Nonlinear unknown input observability and unknown input reconstruction: The general analytical solution

Observability is a fundamental structural property of any dynamic system and describes the possibility of reconstructing the state that characterizes the system from observing its inputs and outputs. Despite the huge effort made to study this property and to introduce analytical criteria able to check whether a dynamic system satisfies this property or not, there is no general analytical criterion to automatically check the state observability when the dynamics are also driven by unknown inputs. Here, we introduce the general analytical solution of this fundamental problem, often called the unknown input observability problem. This paper provides the general analytical solution of this problem, namely, it provides the systematic procedure, based on automatic computation (differentiation and matrix rank determination), that allows us to automatically check the state observability even in the presence of unknown inputs (Algorithm 6.1). A first solution of this problem was presented in the second part of the book: "Observability: A New Theory Based on the Group of Invariance" [45]. The solution presented by this paper completes the previous solution in [45]. In particular, the new solution exhaustively accounts for the systems that do not belong to the category of the systems that are "canonic with respect to their unknown inputs". The analytical derivations largely exploit several new concepts and analytical results introduced in [45]. Finally, as a simple consequence of the results here obtained, we also provide the answer to the problem of unknown input reconstruction which is intimately related to the problem of state observability. We illustrate the implementation of the new algorithm by studying the observability properties of a nonlinear system in the framework of visual-inertial sensor fusion, whose dynamics are driven by two unknown inputs and one known input.
HYBRID THEATRE: PERFORMING TECHNIQUES IN THE EFFORTS TO PRESERVE THE ART OF THEATRE PERFORMANCE POST COVID19

The pandemic of COVID19 and the Movement Control Order (MCO) has opened up the minds of local theatre players to be more creative in applying the suitable medium in showcasing their creative performances. Thus, emerged various forms of virtual theatre known as `online theatre’. No one ever imagined that theatre performances would be performed online but now, it is a practical approach in ensuring the sustainability of the theatre art itself. However, various questions arose about online theatre in Malaysia, such as confusion with the understanding of the terms, forms, and concepts of the performance. This study and writing are based on the online theatre performance ~Sel Akhir’ by the Seni Ceritera Association and King Lear by the KL Shakespeare Players (KLSP). Based on Aristotle's Dramatic Elements approach, the authors used observation and comparison methods to explain the approach used by these two presentations and propose to our Malaysian theatre players a method of online performance that suits the current capabilities and requirements, known as hybrid theatre.
Interactive Music Instructional Mode Based on Cloud Computing

The interactive music education in cloud computing environment is designed to address the disadvantages of traditional music instructional mode. The content of interactive music education is investigated using cloud computing technology. The database of interactive teaching automation grouping platform is generated based on the basic learning information of learners in the data, and it is used to provide instructional resources and basic technical support. On this foundation, the automatic grouping algorithm is introduced to enable interactive teaching to be automatically grouped. The new music instructional mode, which incorporates IT into interactive music education, not only aids students in better understanding instructional content and mastering the inherent laws of the knowledge they have acquired but also significantly improves upon the traditional music instructional mode’s flaws. The results of the tests show that this method is extremely precise. And the designed platform has a better learning effect on students, indicating that the designed interactive music education platform has some practical application value.
APPLICATIONS OF DIGITAL MODELS IN INTEGRATED MANAGEMENT IN SMART TOURIST CITIES: AQABA CITY OF JORDAN AS A CASE STUDY

This study focused on highlighting the most important virtual technological applications at the global level used in the growth and development of tourist cities, by reviewing the most important technological means used in managing smart tourist cities and the conditions and procedures that must be provided in the urban transformation in tourist cities from their traditional framework to the digital framework that keeps pace with global developments in the digital tourism industry and meeting the requirements of sustainable tourism development by focusing on the city of Aqaba in Jordan. The study found the importance of adopting digital models with their multiple applications in providing sustainable management in tourist cities and transforming them into competitive digital tourist destinations and giving them comparative advantages in light of the rapid development at the international level in the growth and development of a high-end tourism industry.
Accelerating Code Search with Deep Hashing and Code Classification

Code search is to search reusable code snippets from source code corpus based on natural languages queries.Deep learning-based methods on code search have shown promising results.However, previous methods focus on retrieval accuracy, but lacked attention to the efficiency of the retrieval process.We propose a novel method CoSHC to accelerate code search with deep hashing and code classification, aiming to perform efficient code search without sacrificing too much accuracy.To evaluate the effectiveness of CoSHC, we apply our method on five code search models.Extensive experimental results indicate that compared with previous code search baselines, CoSHC can save more than 90% of retrieval time meanwhile preserving at least 99% of retrieval accuracy.
Analysis of optical quantum state preparation using photon detectors in the finite-temporal-resolution regime

Quantum state preparation is important for quantum information processing. In particular, in optical quantum computing with continuous variables, non-Gaussian states are needed for universal operation and error correction. Optical non-Gaussian states are usually generated by heralding schemes using photon detectors. In previous experiments, the temporal resolution of the photon detectors was sufficiently high relative to the time width of the quantum state, so that the conventional theory of non-Gaussian state preparation treated the detector's temporal resolution as negligible. However, when using various photon detectors including photon-number-resolving detectors, the temporal resolution is non-negligible. In this paper, we extend the conventional theory of quantum state preparation using photon detectors to the finite temporal resolution regime, analyze the cases of single-photon and two-photon preparation as examples, and find that the generated states are characterized by the dimensionless parameter $B$, defined as the product of the temporal resolution of the detectors $\Delta t$ and the bandwidth of the light source $\Delta f$. Based on the results, $B\sim0.1$ is required to keep the purity and fidelity of the generated quantum states high.
Depth Estimation by Combining Binocular Stereo and Monocular Structured-Light

It is well known that the passive stereo system cannot adapt well to weak texture objects, e.g., white walls. However, these weak texture targets are very common in indoor environments. In this paper, we present a novel stereo system, which consists of two cameras (an RGB camera and an IR camera) and an IR speckle projector. The RGB camera is used both for depth estimation and texture acquisition. The IR camera and the speckle projector can form a monocular structured-light (MSL) subsystem, while the two cameras can form a binocular stereo subsystem. The depth map generated by the MSL subsystem can provide external guidance for the stereo matching networks, which can improve the matching accuracy significantly. In order to verify the effectiveness of the proposed system, we build a prototype and collect a test dataset in indoor scenes. The evaluation results show that the Bad 2.0 error of the proposed system is 28.2% of the passive stereo system when the network RAFT is used. The dataset and trained models are available at https://github.com/YuhuaXu/MonoStereoFusion.
Disentangled Representation Learning and Generation With Manifold Optimization

Disentanglement is a useful property in representation learning, which increases the interpretability of generative models such as variational autoencoders (VAE), generative adversarial models, and their many variants. Typically in such models, an increase in disentanglement performance is traded off with generation quality. In the context of latent space models, this work presents a representation learning framework that explicitly promotes disentanglement by encouraging orthogonal directions of variations. The proposed objective is the sum of an autoencoder error term along with a principal component analysis reconstruction error in the feature space. This has an interpretation of a restricted kernel machine with the eigenvector matrix valued on the Stiefel manifold. Our analysis shows that such a construction promotes disentanglement by matching the principal directions in the latent space with the directions of orthogonal variation in data space. In an alternating minimization scheme, we use the Cayley ADAM algorithm, a stochastic optimization method on the Stiefel manifold along with the Adam optimizer. Our theoretical discussion and various experiments show that the proposed model is an improvement over many VAE variants in terms of both generation quality and disentangled representation learning.
Distinguishing Non-natural from Natural Adversarial Samples for More Robust Pre-trained Language Model

Recently, the problem of robustness of pretrained language models (PrLMs) has received increasing research interest.Latest studies on adversarial attacks achieve high attack success rates against PrLMs, claiming that PrLMs are not robust.However, we find that the adversarial samples that PrLMs fail are mostly non-natural and do not appear in reality.We question the validity of current evaluation of robustness of PrLMs based on these nonnatural adversarial samples and propose an anomaly detector to evaluate the robustness of PrLMs with more natural adversarial samples.We also investigate two applications of the anomaly detector: (1) In data augmentation, we employ the anomaly detector to force generating augmented data that are distinguished as non-natural, which brings larger gains to the accuracy of PrLMs.(2) We apply the anomaly detector to a defense framework to enhance the robustness of PrLMs.It can be used to defend all types of attacks and achieves higher accuracy on both adversarial samples and compliant samples than other defense frameworks.The code is available at https:
EGCN: An Ensemble-based Learning Framework for Exploring Effective Skeleton-based Rehabilitation Exercise Assessment

Recently, some skeleton-based physical therapy systems have been attempted to automatically evaluate the correctness or quality of an exercise performed by rehabilitation subjects. However, in terms of algorithms and evaluation criteria, the task remains not fully explored regarding making full use of different skeleton features. To advance the prior work, we propose a learning framework called Ensemble-based Graph Convolutional Network (EGCN) for skeleton-based rehabilitation exercise assessment. As far as we know, this is the first attempt that utilizes both two skeleton feature groups and investigates different ensemble strategies for the task. We also examine the properness of existing evaluation criteria and focus on evaluating the prediction ability of our proposed method. We then conduct extensive cross-validation experiments on two latest public datasets: UI-PRMD and KIMORE. Results indicate that the model-level ensemble scheme of our EGCN achieves better performance than existing methods. Code is available: https://github.com/bruceyo/EGCN.
Learning Temporally and Semantically Consistent Unpaired Video-to-Video Translation through Pseudo-Supervision from Synthetic Optical Flow

Unpaired video-to-video translation aims to translate videos between a source and a target domain without the need of paired training data, making it more feasible for real applications. Unfortunately, the translated videos generally suffer from temporal and semantic inconsistency. To address this, many existing works adopt spatiotemporal consistency constraints incorporating temporal information based on motion estimation. However, the inaccuracies in the estimation of motion deteriorate the quality of the guidance towards spatiotemporal consistency, which leads to unstable translation. In this work, we propose a novel paradigm that regularizes the spatiotemporal consistency by synthesizing motions in input videos with the generated optical flow instead of estimating them. Therefore, the synthetic motion can be applied in the regularization paradigm to keep motions consistent across domains without the risk of errors in motion estimation. Thereafter, we utilize our unsupervised recycle and unsupervised spatial loss, guided by the pseudo-supervision provided by the synthetic optical flow, to accurately enforce spatiotemporal consistency in both domains. Experiments show that our method is versatile in various scenarios and achieves state-of-the-art performance in generating temporally and semantically consistent videos. Code is available at: https://github.com/wangkaihong/Unsup_Recycle_GAN/.
Learning to Mediate Disparities Towards Pragmatic Communication

Human communication is a collaborative process.Speakers, on top of conveying their own intent, adjust the content and language expressions by taking the listeners into account, including their knowledge background, personalities, and physical capabilities.Towards building AI agents with similar abilities in language communication, we propose Pragmatic Rational Speaker (PRS), a framework extending Rational Speech Act (RSA).The PRS attempts to learn the speaker-listener disparity and adjust the speech accordingly, by adding a light-weighted disparity adjustment layer into working memory on top of speaker's long-term memory system.By fixing the long-term memory, the PRS only needs to update its working memory to learn and adapt to different types of listeners.To validate our framework, we create a dataset that simulates different types of speaker-listener disparities in the context of referential games.Our empirical results demonstrate that the PRS is able to shift its output towards the language that listeners are able to understand, significantly improve the collaborative task outcome.
On the Impossibility of Non-trivial Accuracy in Presence of Fairness Constraints

One of the main concerns about fairness in machine learning (ML) is that, in order to achieve it, one may have to trade off some accuracy. To overcome this issue, Hardt et al. proposed the notion of equality of opportunity (EO), which is compatible with maximal accuracy when the target label is deterministic with respect to the input features. In the probabilistic case, however, the issue is more complicated: It has been shown that under differential privacy constraints, there are data sources for which EO can only be achieved at the total detriment of accuracy, in the sense that a classifier that satisfies EO cannot be more accurate than a trivial (random guessing) classifier. In our paper we strengthen this result by removing the privacy constraint. Namely, we show that for certain data sources, the most accurate classifier that satisfies EO is a trivial classifier. Furthermore, we study the trade-off between accuracy and EO loss (opportunity difference), and provide a sufficient condition on the data source under which EO and non-trivial accuracy are compatible.
Quantum sampling for the Euclidean path integral of lattice gauge theory

Although the Hamiltonian formalism is, so far, favored for quantum computation of lattice gauge theory, the path integral formalism would never be useless. The advantages of the path integral formalism are the knowledge and experience accumulated by classical lattice simulation and manifest Lorentz invariance. In this article, we will discuss quantum computation of lattice gauge theory in the path integral formalism, utilize a quantum sampling algorithm to generate gauge configurations, and demonstrate a benchmark test of ${Z}_{2}$ lattice gauge theory on a four-dimensional hypercube.
TEE-based decentralized recommender systems: The raw data sharing redemption

Recommenders are central in many applications today. The most effective recommendation schemes, such as those based on collaborative filtering (CF), exploit similarities between user profiles to make recommendations, but potentially expose private data. Federated learning and decentralized learning systems address this by letting the data stay on user's machines to preserve privacy: each user performs the training on local data and only the model parameters are shared. However, sharing the model parameters across the network may still yield privacy breaches. In this paper, we present Rex, the first enclave-based decentralized CF recommender. Rex exploits Trusted execution environments (TEE), such as Intel software guard extensions (SGX), that provide shielded environments within the processor to improve convergence while preserving privacy. Firstly, Rex enables raw data sharing, which ultimately speeds up convergence and reduces the network load. Secondly, Rex fully preserves privacy. We analyze the impact of raw data sharing in both deep neural network (DNN) and matrix factorization (MF) recommenders and showcase the benefits of trusted environments in a full-fledged implementation of Rex. Our experimental results demonstrate that through raw data sharing, Rex significantly decreases the training time by 18.3 x and the network load by 2 orders of magnitude over standard decentralized approaches that share only parameters, while fully protecting privacy by leveraging trustworthy hardware enclaves with very little overhead.
TextPruner: A Model Pruning Toolkit for Pre-Trained Language Models

Pre-trained language models have been prevailed in natural language processing and become the backbones of many NLP tasks, but the demands for computational resources have limited their applications.In this paper, we introduce TextPruner, an open-source model pruning toolkit designed for pre-trained language models, targeting fast and easy model compression.TextPruner offers structured posttraining pruning methods, including vocabulary pruning and transformer pruning, and can be applied to various models and tasks.We also propose a self-supervised pruning method that can be applied without the labeled data.Our experiments with several NLP tasks demonstrate the ability of TextPruner to reduce the model size without re-training the model. 1
RESEARCH PAPER ON CYBERSECURITY

We live in the digital era. We use the internet and electronic media for paying bills, purchasing any items, watching a movie, doctor’s prescription, ordering food, and everything else. Due to this widespread use, there are innumerable incidents of security breach, fraud, malicious attack, etc. reported. To keep the internet age well-ordered and safe for users we need cybersecurity. It protects you from cyber-criminals, hackers, criminal syndicates, insiders, script kiddies, hacktivists and anybody who wants to damage you or your asset financially, mentally or engage in data theft online.
Inferring from References with Differences for Semi-Supervised Node Classification on Graphs

Following the application of Deep Learning to graphic data, Graph Neural Networks (GNNs) have become the dominant method for Node Classification on graphs in recent years. To assign nodes with preset labels, most GNNs inherit the end-to-end way of Deep Learning in which node features are input to models while labels of pre-classified nodes are used for supervised learning. However, while these methods can make full use of node features and their associations, they treat labels separately and ignore the structural information of those labels. To utilize information on label structures, this paper proposes a method called 3ference that infers from references with differences. Specifically, 3ference predicts what label a node has according to the features of that node in concatenation with both features and labels of its relevant nodes. With the additional information on labels of relevant nodes, 3ference captures the transition pattern of labels between nodes, as subsequent analysis and visualization revealed. Experiments on a synthetic graph and seven real-world graphs proved that this knowledge about label associations helps 3ference to predict accurately with fewer parameters, fewer pre-classified nodes, and varying label patterns compared with GNNs.
Modeling Multi-Granularity Hierarchical Features for Relation Extraction

Relation extraction is a key task in Natural Language Processing (NLP), which aims to extract relations between entity pairs from given texts.Recently, relation extraction (RE) has achieved remarkable progress with the development of deep neural networks.Most existing research focuses on constructing explicit structured features using external knowledge such as knowledge graph and dependency tree.In this paper, we propose a novel method to extract multi-granularity features based solely on the original input sentences.We show that effective structured features can be attained even without external knowledge.Three kinds of features based on the input sentences are fully exploited, which are in entity mention level, segment level, and sentence level.All the three are jointly and hierarchically modeled.We evaluate our method on three public benchmarks: SemEval 2010 Task 8, Tacred, and Tacred Revisited.To verify the effectiveness, we apply our method to different encoders such as LSTM and BERT.Experimental results show that our method significantly outperforms existing state-of-the-art models that even use external knowledge.Extensive analyses demonstrate that the performance of our model is contributed by the capture of multi-granularity features and the model of their hierarchical structure.Code and