Reviewerly/exHarmony huggingface dataset:
An Investigation of Smart Contract for Collaborative Machine Learning Model Training

Machine learning (ML) has penetrated various fields in the era of big data. The advantage of collaborative machine learning (CML) over most conventional ML lies in the joint effort of decentralized nodes or agents that results in better model performance and generalization. As the training of ML models requires a massive amount of good quality data, it is necessary to eliminate concerns about data privacy and ensure high-quality data. To solve this problem, we cast our eyes on the integration of CML and smart contracts. Based on blockchain, smart contracts enable automatic execution of data preserving and validation, as well as the continuity of CML model training. In our simulation experiments, we define incentive mechanisms on the smart contract, investigate the important factors such as the number of features in the dataset (num_words), the size of the training data, the cost for the data holders to submit data, etc., and conclude how these factors impact the performance metrics of the model: the accuracy of the trained model, the gap between the accuracies of the model before and after simulation, and the time to use up the balance of bad agent. For instance, the increase of the value of num_words leads to higher model accuracy and eliminates the negative influence of malicious agents in a shorter time from our observation of the experiment results. Statistical analyses show that with the help of smart contracts, the influence of invalid data is efficiently diminished and model robustness is maintained. We also discuss the gap in existing research and put forward possible future directions for further works.
Using Quantum computers to speed up dynamic testing of software

Software under test can be analyzed dynamically, while it is being executed, to find defects. However, as the number and possible values of input parameters increase, the cost of dynamic testing rises. This paper examines whether quantum computers (QCs) can help speed up the dynamic testing of programs written for classical computers (CCs). To accomplish this, an approach is devised involving the following three steps: (1) converting a classical program to a quantum program; (2) computing the number of inputs causing errors, denoted by $K$, using a quantum counting algorithm; and (3) obtaining the actual values of these inputs using Grover's search algorithm. This approach can accelerate exhaustive and non-exhaustive dynamic testing techniques. On the CC, the computational complexity of these techniques is $O(N)$, where $N$ represents the count of combinations of input parameter values passed to the software under test. In contrast, on the QC, the complexity is $O(\varepsilon^{-1} \sqrt{N/K})$, where $\varepsilon$ is a relative error of measuring $K$. The paper illustrates how the approach can be applied and discusses its limitations. Moreover, it provides a toy example executed on a simulator and an actual QC. This paper may be of interest to academics and practitioners as the approach presented in the paper may serve as a starting point for exploring the use of QC for dynamic testing of CC code.
On The Computational Complexity of Self-Attention

Transformer architectures have led to remarkable progress in many state-of-art applications. However, despite their successes, modern transformers rely on the self-attention mechanism, whose time- and space-complexity is quadratic in the length of the input. Several approaches have been proposed to speed up self-attention mechanisms to achieve sub-quadratic running time; however, the large majority of these works are not accompanied by rigorous error guarantees. In this work, we establish lower bounds on the computational complexity of self-attention in a number of scenarios. We prove that the time complexity of self-attention is necessarily quadratic in the input length, unless the Strong Exponential Time Hypothesis (SETH) is false. This argument holds even if the attention computation is performed only approximately, and for a variety of attention mechanisms. As a complement to our lower bounds, we show that it is indeed possible to approximate dot-product self-attention using finite Taylor series in linear-time, at the cost of having an exponential dependence on the polynomial order.
Rethink Decision Tree Traversal

We will show how to implement binary decision tree traversal in the language of matrix computation. Our main contribution is to propose some equivalent algorithms of binary tree traversal based on a novel matrix representation of the hierarchical structure of the decision tree. Our key idea is to travel the binary decision tree by maximum inner product search. We not only implement decision tree methods without the recursive traverse but also delve into the partitioning nature of tree-based methods.
Local-Aware Global Attention Network for Person Re-Identification

Learning representative, robust and discriminative information from images is essential for effective person re-identification (Re-Id). In this paper, we propose a compound approach for end-to-end discriminative deep feature learning for person Re-Id based on both body and hand images. We carefully design the Local-Aware Global Attention Network (LAGA-Net), a multi-branch deep network architecture consisting of one branch for spatial attention, one branch for channel attention, one branch for global feature representations and another branch for local feature representations. The attention branches focus on the relevant features of the image while suppressing the irrelevant backgrounds. In order to overcome the weakness of the attention mechanisms, equivariant to pixel shuffling, we integrate relative positional encodings into the spatial attention module to capture the spatial positions of pixels. The global branch intends to preserve the global context or structural information. For the the local branch, which intends to capture the fine-grained information, we perform uniform partitioning to generate stripes on the conv-layer horizontally. We retrieve the parts by conducting a soft partition without explicitly partitioning the images or requiring external cues such as pose estimation. A set of ablation study shows that each component contributes to the increased performance of the LAGA-Net. Extensive evaluations on four popular body-based person Re-Id benchmarks and two publicly available hand datasets demonstrate that our proposed method consistently outperforms existing state-of-the-art methods.
Geometric phase and its applications: topological phases, quantum walks and non-inertial quantum systems

Geometric phase plays a fundamental role in quantum theory and accounts for wide phenomena ranging from the Aharanov-Bohm effect, the integer and fractional quantum hall effects, and topological phases of matter, including topological insulators, to name a few. In this thesis, we have proposed a fresh perspective of geodesics and null phase curves, which are key ingredients in understanding the geometric phase. We have also looked at a number of applications of geometric phases in topological phases, quantum walks, and non-inertial quantum systems. The shortest curve between any two points on a given surface is a (minimal) geodesic. They are also the curves along which a system does not acquire any geometric phase. In the same context, we can generalize geodesics to define a larger class of curves, known as null phase curves (NPCs), along which also the acquired geometric phase is zero; however, they need not be the shortest curves between the two points. We have proposed a geometrical decomposition of geodesics and null phase curves on the Bloch sphere, which is crucial in improving our understanding of the geometry of the state space and the intrinsic symmetries of geodesics and NPCs. We have also investigated the persistence of topological phases in quantum walks in the presence of an external (lossy) environment. We show that the topological order in one and two-dimensional quantum walks persist against moderate losses. Further, we use the geometric phase to detect the non-inertial modifications to the field correlators perceived by a circularly rotating two-level atom placed inside a cavity.
Temporal Pattern Mining for Analysis of Longitudinal Clinical Data: Identifying Risk Factors for Alzheimer's Disease

A novel framework is proposed for handling the complex task of modelling and analysis of longitudinal, multivariate, heterogeneous clinical data. This method uses temporal abstraction to convert the data into a more appropriate form for modelling, temporal pattern mining, to discover patterns in the complex, longitudinal data and machine learning models of survival analysis to select the discovered patterns. The method is applied to a real-world study of Alzheimer's disease (AD), a progressive neurodegenerative disease that has no cure. The patterns discovered were predictive of AD in survival analysis models with a Concordance index of up to 0.8. This is the first work that performs survival analysis of AD data using temporal data collections for AD. A visualisation module also provides a clear picture of the discovered patterns for ease of interpretability.
SymphonyDB: A Polyglot Model for Knowledge Graph Query Processing

Unlocking the full potential of Knowledge Graphs (KGs) to enable or enhance various semantic and other applications requires Data Management Systems (DMSs) to efficiently store and process the content of KGs. However, the increases in the size and variety of KG datasets as well as the growing diversity of KG queries pose efficiency challenges for the current generation of DMSs to the extent that the performance of representative DMSs tends to vary significantly across diverse query types and no single platform dominates performance. We present our extensible prototype, SymphonyDB, as an approach to addressing this problem based on a polyglot model of query processing as part of a multi-database system supported by a unified access layer that can analyze/translate individual queries just-in-time and match each to the likely best-performing DMS among Virtuoso, Blazegraph, RDF-3X, and MongoDB as representative DMSs that are included in our prototype at this time. The results of our experiments with the prototype over well-known KG benchmark datasets and queries point to the efficiency and consistency of its performance across different query types and datasets.
Anticipating the Unseen Discrepancy for Vision and Language Navigation

Vision-Language Navigation requires the agent to follow natural language instructions to reach a specific target. The large discrepancy between seen and unseen environments makes it challenging for the agent to generalize well. Previous studies propose data augmentation methods to mitigate the data bias explicitly or implicitly and provide improvements in generalization. However, they try to memorize augmented trajectories and ignore the distribution shifts under unseen environments at test time. In this paper, we propose an Unseen Discrepancy Anticipating Vision and Language Navigation (DAVIS) that learns to generalize to unseen environments via encouraging test-time visual consistency. Specifically, we devise: 1) a semi-supervised framework DAVIS that leverages visual consistency signals across similar semantic observations. 2) a two-stage learning procedure that encourages adaptation to test-time distribution. The framework enhances the basic mixture of imitation and reinforcement learning with Momentum Contrast to encourage stable decision-making on similar observations under a joint training stage and a test-time adaptation stage. Extensive experiments show that DAVIS achieves model-agnostic improvement over previous state-of-the-art VLN baselines on R2R and RxR benchmarks. Our source code and data are in supplemental materials.
Covariance-based rational approximations of fractional SPDEs for computationally efficient Bayesian inference

The stochastic partial differential equation (SPDE) approach is widely used for modeling large spatial datasets. It is based on representing a Gaussian random field $u$ on $\mathbb{R}^d$ as the solution of an elliptic SPDE $L^βu = \mathcal{W}$ where $L$ is a second-order differential operator, $2β$ (belongs to natural number starting from 1) is a positive parameter that controls the smoothness of $u$ and $\mathcal{W}$ is Gaussian white noise. A few approaches have been suggested in the literature to extend the approach to allow for any smoothness parameter satisfying $β&gt;d/4$. Even though those approaches work well for simulating SPDEs with general smoothness, they are less suitable for Bayesian inference since they do not provide approximations which are Gaussian Markov random fields (GMRFs) as in the original SPDE approach. We address this issue by proposing a new method based on approximating the covariance operator $L^{-2β}$ of the Gaussian field $u$ by a finite element method combined with a rational approximation of the fractional power. This results in a numerically stable GMRF approximation which can be combined with the integrated nested Laplace approximation (INLA) method for fast Bayesian inference. A rigorous convergence analysis of the method is performed and the accuracy of the method is investigated with simulated data. Finally, we illustrate the approach and corresponding implementation in the R package rSPDE via an application to precipitation data which is analyzed by combining the rSPDE package with the R-INLA software for full Bayesian inference.
Gradient Descent Temporal Difference-difference Learning

Off-policy algorithms, in which a behavior policy differs from the target policy and is used to gain experience for learning, have proven to be of great practical value in reinforcement learning. However, even for simple convex problems such as linear value function approximation, these algorithms are not guaranteed to be stable. To address this, alternative algorithms that are provably convergent in such cases have been introduced, the most well known being gradient descent temporal difference (GTD) learning. This algorithm and others like it, however, tend to converge much more slowly than conventional temporal difference learning. In this paper we propose gradient descent temporal difference-difference (Gradient-DD) learning in order to improve GTD2, a GTD algorithm, by introducing second-order differences in successive parameter updates. We investigate this algorithm in the framework of linear value function approximation, theoretically proving its convergence by applying the theory of stochastic approximation. %analytically showing its improvement over GTD2. Studying the model empirically on the random walk task, the Boyan-chain task, and the Baird's off-policy counterexample, we find substantial improvement over GTD2 and, in several cases, better performance even than conventional TD learning.
INTERNATIONAL MONETARY ORGANIZATIONS: THE MAIN SOURCE OF MONETARY SUPPORT FOR CIVIL SOCIETY INSTITUTIONS

The present study analyzes the main trends and aspects of interaction between international financial organizations and civil society organizations. A central issue is financial support civil society organizations (CSOs) by international financial organizations (IFO). Financial sustainability is critically important for CSOs in case if they are going to become self-sustaining and effective organizations. Recently, there has been an increase of interest in studying of CSOs' excessive dependence on international funding as it makes civil society organizations vulnerable to changing donor priorities. In addition, CSOs cannot plan their own activities for the long term perspective. Furthermore, civil society organizations are less involved in the project development process. Over the last decade, CSOs face a number of external and internal problems that threaten their financial sustainability. However, international donors' approaches to partnering with civil society organizations can also indirectly affect the financial sustainability of CSOs. Unfortunately, this trend is due to the fact that financial support from international donors is often limited to capacity building, rather than considering structural changes or partnership approaches. The author, on the basis of statistical information and studies of international organizations, analyzed the financing mechanisms of civil society organizations by the World Bank Group. The article also examines the sectoral structure of projects and the regional structure of partners within the framework of the Global Partnership for Social Accountability (GPSA) as a fundamental financial mechanism of the World Bank. However, few investigations have focused on study the relationship between civil society organizations and the European Bank for Reconstruction and Development. In this paper, we summarized the practical experience of financing civil society organizations by the United States Agency for International Development (USAID). The author identified the main trends and regularities of the Facilitating Financial Sustainability program.
Digitizing Arudh and Qowafi Classics as Android-Based Student Learning Media Using Flutter

The book Arudh &amp; Qawafi is the work of the Caretaker of the At-Tarbiyah Guluk-Guluk Sumenep Islamic Boarding School, namely K.H. Wakid Yusuf, S.E. The book discusses Arabic literature in the form of Syi'ir and Nadham. The 2nd son of K.H. Nurul Huda Basyir has several religious texts, one of which is the book Arudh &amp; Qawafi which is regularly reviewed by students majoring in Language (Arabic Literature). The religious texts or the works of the caregivers are still in the form of printed media, namely books, therefore in this digitalization era, the role of the santri to develop the field of information technology is very necessary. This study aims to build an Android-based application in order to make it easier for students, alumni, and sympathizers to access the Arudh &amp; Qawafi Book practically. This application development applies the Extreme Programming (XP) software development methodology. Extreme Programming has several advantages, namely that it can perform updates without impacting the overall system design. This method is one of the methodologies that supports the acceleration of the development of a system with a minimum number of teams and produces software that meets the needs, including the planning phase, design phase, coding phase, and testing phase. By using the open source Flutter Framework, the Arudh &amp; Qawafi book application can be accessed via mobile more effectively and efficiently.
A Novel Integrated Freight Forwarding Information System Based on Block-Chain Technology

As an emerging foreign trade model, crossborder E-commerce is booming. This foreign trade model shortens the distance between products from manufacturers to foreign consumers and reshapes the value chain of foreign trade. However, because buyers and sellers use virtualization. Negotiations and consultations on the platform may also involve the participation of freight forwarders and the services of shipping companies, which increases the uncertainty of the transaction process and results. How to efficiently and confidentially transfer information to each other is the main idea and goal of this paper. Foreign buyers, domestic sellers, shipping companies, and freight forwarding companies are independent entities. Due to common transaction events, the interaction between the four parties has been promoted. However, independent information systems cannot exchange information (or content) with other entities. Based on this point, our goal has formulated an integrated freight forwarding program.
Mobile-Based COVID-19 Vaccination Registration Application Prototype

Information technology-based applications have entered the era of mobile phones or smartphones such as those using the Android or iOS operating system. Mobile-based application development has become a trend for today's society. Especially during the global COVID-19 pandemic, almost all activities are carried out remotely through mobile-based applications. To prevent the spread of COVID-19, mass vaccines are given to the public. So that the process of administering the vaccine does not cause crowds, it is necessary to create a mobile-based application. So that the application can be further developed properly, it is necessary to make a prototype. The prototype consists of 5 (steps): 1) Quick plan, 2) Modeling Quick Design, 3) Construction of prototype, 4) Deployment Delivery &amp; feedback, and 5) Communication. In this research, the InVision design tool is used which can help design prototypes for both mobile and web versions. InVision has been widely used in making prototypes and is used by many digital companies in the world. The results obtained are in the form of a prototype application for the registration of vaccine participants via mobile phones and also the web. The programmers will easily translate the prototype results into a mobile-based application for the benefit of mobile phone-based online vaccine registration.
Fighting Sybils in Airdrops

Airdrop is a crucial concept in tokenomics. Startups of decentralized applications (DApps) reward early supporters by airdropping newly issued tokens up to a certain amount as a free giveaway. This naturally induces greedy hackers, called Sybils, to create multiple accounts for more shares. Most airdrops have prerequisites for qualification, in which utilizing these DApps is unsurprisingly the principal. One particular characteristic of DApps is to implement users' interactions with them in the form of token transfer transactions or smart contract calling transactions on public blockchains. We argue that these individual transactions could reveal underlying signatures of their sending accounts. Specifically, accounts controlled by the same Sybil may exhibit some common behaviors. A careful analysis of Sybil's behaviors shows that accounts controlled by the same Sybil may produce similar DApp activities and regular token transfer patterns. We model the transactions as graphs by representing accounts as vertices and transactions as edges. When multiple accounts receive tokens from the same Sybil to conduct interactions with DApps, we inspect the graphs for these activities and patterns to detect suspicious accounts. We demonstrate the effectiveness of the proposed method in a recent airdrop by presenting the suspicious accounts controlled by Sybils. All the detected accounts exhibit similar interaction activities and regular transfer patterns.
Unsupervised Domain Adaptation for Extra Features in the Target Domain Using Optimal Transport

Domain adaptation aims to transfer knowledge of labeled instances obtained from a source domain to a target domain to fill the gap between the domains. Most domain adaptation methods assume that the source and target domains have the same dimensionality. Methods that are applicable when the number of features is different in each domain have rarely been studied, especially when no label information is given for the test data obtained from the target domain. In this paper, it is assumed that common features exist in both domains and that extra (new additional) features are observed in the target domain; hence, the dimensionality of the target domain is higher than that of the source domain. To leverage the homogeneity of the common features, the adaptation between these source and target domains is formulated as an optimal transport (OT) problem. In addition, a learning bound in the target domain for the proposed OT-based method is derived. The proposed algorithm is validated using both simulated and real-world data.