Reviewerly/exHarmony huggingface dataset:
CMLM-CSE: Based on Conditional MLM Contrastive Learning for Sentence Embeddings

Traditional comparative learning sentence embedding directly uses the encoder to extract sentence features, and then passes in the comparative loss function for learning. However, this method pays too much attention to the sentence body and ignores the influence of some words in the sentence on the sentence semantics. To this end, we propose CMLM-CSE, an unsupervised contrastive learning framework based on conditional MLM. On the basis of traditional contrastive learning, an additional auxiliary network is added to integrate sentence embedding to perform MLM tasks, forcing sentence embedding to learn more masked word information. Finally, when Bertbase was used as the pretraining language model, we exceeded SimCSE by 0.55 percentage points on average in textual similarity tasks, and when Robertabase was used as the pretraining language model, we exceeded SimCSE by 0.3 percentage points on average in textual similarity tasks.
Pros and Cons of Web Mining in E- Commerce

This paper addresses an unconventional way of merging web mining techniques in order to discover business intelligence in electronic commerce scenarios.With the development of Internet, e-commerce, with its advantages of high efficiency and low cost, has become important business mode. Electronic commerce can be elaborate in the general sense that is, the use of Internet and Web to conduct business transactions. A moreover technically, e- commerce is the use of telecommunication and data processing technology to improve the quality of transaction between business partners and individual. The rapid growth of e-commerce has been fueled by its compatibility and speed with business transactions.Web mining is the process of analyzing data from different perspectives and summarizing it into useful information. It is the application of data mining techniques to extract information from Web data. Useful information to individual user with different backgrounds and interests, as well as to increase user satisfaction. The search engine log data can be used to analyze users search behavior. This ability to track user searching behavior has brought compatibility and new wings to e-commerce than ever before. Wherever data is concerned, privacy is critical problems for e-commerce customer and web alike.
Improving the Resiliency of Decentralized Crowdsourced Blockchain Oracles

The emergence of blockchain technologies has created the possibility of transforming business processes in the form of immutable agreements called smart contracts. Smart contracts suffer from a major limitation; they cannot authenticate the trustworthiness of real-world data sources, creating the need for intermediaries called oracles. Oracles are trusted entities that connect on-chain systems with off-chain data, allowing smart contracts to operate on real-world inputs in a trustworthy manner. A popular oracle protocol is a crowdsourced oracle, where unrelated individuals attest to facts through voting mechanisms in smart contracts. Crowdsourced oracles have unique challenges: the trustworthiness and correctness of outcomes cannot be explicitly verified. These problems are aggravated by inherent vulnerabilities to attacks, such as Sybil attacks. To address this weakness, this paper proposes a reputation-based mechanism, where oracles are given a reputation value depending on the implied correctness of their actions over time. This reputation score is used to eliminate malicious agents from the participant pool. Additionally, two reputation-based voting mechanisms are proposed. The effectiveness of the proposed mechanism is evaluated using an agent-based simulation of a crowdsourced oracle platform, where a pool of oracles performs evaluate Boolean queries.
RAFEN – Regularized Alignment Framework for Embeddings of Nodes

Learning representations of nodes has been a crucial area of the graph machine learning research area. A well-defined node embedding model should reflect both node features and the graph structure in the final embedding. In the case of dynamic graphs, this problem becomes even more complex as both features and structure may change over time. The embeddings of particular nodes should remain comparable during the evolution of the graph, what can be achieved by applying an alignment procedure. This step was often applied in existing works after the node embedding was already computed. In this paper, we introduce a framework – RAFEN – that allows to enrich any existing node embedding method using the aforementioned alignment term and learning aligned node embedding during training time. We propose several variants of our framework and demonstrate its performance on six real-world datasets. RAFEN achieves on-par or better performance than existing approaches without requiring additional processing steps.
Self-supervised Deep Heterogeneous Graph Neural Networks with Contrastive Learning

Heterogeneous graph neural networks have shown superior capabilities on graphs that contain multiple types of entities with rich semantic information. However, they are usually (semi-)supervised learning methods which rely on costly task-specific labeled data. Due to the problem of label sparsity on heterogeneous graphs, the performance of these methods is limited, prompting the emergence of some self-supervised learning methods. However, most of self-supervised methods aggregate meta-path based neighbors without considering implicit neighbors that also contain rich information, and the mining of implicit neighbors is accompanied by the problem of introducing irrelevant nodes. Therefore, in this paper we propose a self-supervised deep heterogeneous graph neural networks with contrastive learning (DHG-CL) which not only preserves the information of implicitly valuable neighbors but also further enhances the distinguishability of node representations. Specifically, (1) we design a cross-layer semantic encoder to incorporate information from different high-order neighbors through message passing across layers; and then (2) we design a graph-based contrastive learning task to distinguish semantically dissimilar nodes, further obtaining discriminative node representations. Extensive experiments conducted on a variety of real-world heterogeneous graphs show that our proposed DHG-CL outperforms the state-of-the-arts.
Vestigial Research for Postdigital Pataphysics

While digital research secures data in the service of generating stable bodies of knowledge, it cannot accommodate that which is uncomputable. Epistemically volatile and elusive experiences, phenomena, and situations are necessarily discounted. These include the irrational, the uncanny, the absurd, the revolutionary, the sublime, the orgiastic, and other states that defy quantification. Through his concept of expenditure, French theorist George Bataille seeks to engage with the reality of the incalculable, providing an alternate understanding of social construction based on surplus energy. For Bataille, such surplus cannot be measured, as it overwhelms the conditions required for the digital to take hold. It is unproductive, inutilious. In this chapter, we place Bataille in the tradition of Alfred Jarry’s pataphysics—which produces nonknowledge and impossible solutions—and argue that Bataille’s notion of expenditure can be used to delineate the postdigital. We also illustrate how postdigital research places singular demands on the researcher, resulting in the creation of what we term vestigial organs, instead of the utilitarian organs of digital research. To speak to this point, we investigate David Cronenberg’s early film Crimes of the Future (1970), which provides examples of pataphysical, postdigital research and is itself a vestigial organ.
Graph-Level Representations Using Ensemble-Based Readout Functions

Graph machine learning models have been successfully deployed in various application areas. One of the most prominent types of models – Graph Neural Networks (GNNs) – provides an elegant way of extracting expressive node-level representation vectors, which can be used to solve node-related problems, such as classifying users in a social network. However, many tasks require representations at the level of the whole graph, e.g., molecular applications. In order to convert node-level representations into a graph-level vector, a so-called readout function must be applied. In this work, we study existing readout methods, including simple non-trainable ones, as well as complex, parametrized models. We introduce a concept of ensemble-based readout functions that combine either representations or predictions. Our experiments show that such ensembles allow for better performance than simple single readouts or similar performance as the complex, parametrized ones, but at a fraction of the model complexity.
User Popularity Preference Aware Sequential Recommendation

In recommender systems, users’ preferences for item popularity are diverse and dynamic, which reveals the different items that users prefer. Therefore, identifying user popularity preferences are significant for personalized recommendations. Although many methods have analyzed user popularity preferences, most of them only consider particular types of popularity preferences, leading to inappropriate recommendations for users who have other popularity preferences. To comprehensively study user popularity preferences, we propose a User Popularity preference aware Sequential Recommendation (UPSR) method. By sequentially perceiving user behaviors, UPSR captures the type and the evolution of user popularity preferences. Furthermore, UPSR employs contrastive learning to gather similar users and enhance user interest encoding. Then, we can match items and user popularity preferences more accurately and make more proper recommendations. Extensive experiments validate that UPSR not only outperforms the state-of-the-art methods but also reduces popularity bias.
Measure Theory

The concept of a measure is a generalization and formalization of length, area, volume, and other common notions such as mass and probability of events. These seemingly different concepts have many similarities and can often be unified by the concept of measure. Measures are fundamental to probability theory and integration theory.
Reflexivity of Partitions Induced by Weighted Poset Metric and Combinatorial Metric

Let <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbf {H}$ </tex-math></inline-formula> be the Cartesian product of a family of finite abelian groups. Via a polynomial approach, we give sufficient conditions for a partition of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbf {H}$ </tex-math></inline-formula> induced by weighted poset metric to be reflexive, which also become necessary for some special scenarios. Moreover, by examining the roots of the Krawtchouk polynomials, we give sufficient conditions for a partition of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbf {H}$ </tex-math></inline-formula> induced by combinatorial metric to be non-reflexive, and then give several examples of non-reflexive partitions. When <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbf {H}$ </tex-math></inline-formula> is a vector space over a finite field <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbb {F}$ </tex-math></inline-formula> , we consider the property of admitting MacWilliams identity (PAMI) and the MacWilliams extension property (MEP) for partitions of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbf {H}$ </tex-math></inline-formula> . More specifically, under some invariance assumptions, we show that two partitions of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbf {H}$ </tex-math></inline-formula> admit MacWilliams identity if and only if they are mutually dual and reflexive, and any partition of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbf {H}$ </tex-math></inline-formula> satisfying MEP is in fact an orbit partition induced by some subgroup of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathrm {Aut}\,_{\mathbb {F}}(\mathbf {H})$ </tex-math></inline-formula> , which is necessarily reflexive. Furthermore, we show that the aforementioned non-reflexive partitions induced by combinatorial metric do not satisfy MEP, which further enables us to disprove a conjecture proposed by Pinheiro et al., (2019).
Hardware Accelerated Reusable Merkle Tree Generation for Bitcoin Blockchain Headers

As the value of Bitcoin increases, the difficulty level of mining keeps increasing. This is generally addressed with application-specific integrated circuits (ASIC), but block candidates are still created by the software. The overhead of block candidate generation is relatively growing because the hash computation is boosted by ASIC. Additionally, it is getting harder to find the target nonce; If it is not found for a block candidate, a new block candidate must be generated. A new candidate can be generated to reduce the overhead of block candidate generation by modifying the coinbase without selecting and verifying transactions again. To this end, we propose a hardware accelerator for generating Merkle trees efficiently. The hash computation for Merkle tree generation is conducted with ASIC to reduce the overhead of block candidate generation, and the tree with only the modified coinbase is rapidly regenerated by reusing the intermediate results of the previously generated tree. Our simulation results demonstrate that the execution time can be reduced by up to 98.92% and power consumption by up to 99.73% when the number of transactions in a tree is 2048.
Attacks on Acceleration-Based Secure Device Pairing With Automatic Visual Tracking

In an acceleration-based Secure Device Pairing (SDP) scheme, two unauthenticated devices continuously measure their own acceleration. If the similarity of their measurements are sufficiently high, the devices will build a secure communication channel assume that it is hard for any attacker to estimate their measurements in real-time. This paper demonstrates that the assumption does not hold and further proposes an effective Man-in-the-Middle (MitM) attack on acceleration-based SDP schemes. That is to say, an MitM adversary is able to quickly estimate the acceleration measurements of the target devices with automatic visual tracking technologies, and then compromise the device’s communication channel by impersonating the target devices with the estimated measurements. The present attack is extensively evaluated on acceleration-based SDP schemes in indoor and outdoor environments. The evaluation results show that the device’s acceleration can be estimated with high accuracy in real time. Thus, the present MitM attack is practical to defeat the acceleration-based SDP schemes.
VPiP: Values Packing in Paillier for Communication Efficient Oblivious Linear Computations

The technique of packing multiple values into one message without losing homomorphic computation properties is the main workhorse that drives many exciting advances in applying lattice-based homomorphic encryption schemes to privacy-preserving Machine-Learning-as-a-Service (MLaaS). However, this technique does not directly work for the classic Paillier homomorphic encryption scheme, limiting the use of the Paillier scheme in the privacy-preserving MLaaS. To enrich the applications of Paillier in privacy-preserving MLaaS, we present a set of new methods for efficient linear computations over packed values under the Paillier scheme, such as vector multiplication, matrix multiplication, and convolutional calculation between ciphertexts and plaintexts. Different from the packing methods of lattice-based schemes, the Paillier packing method naturally allows higher packing capability for values in lower bit-length. This property can significantly benefit privacy-preserving MLaaS, as the values of user inputs and parameters of machine learning models are often quantized into low bits (e.g., 1-8 bits). We conduct comparisons based on different linear computation tasks, the proposed methods under the Paillier scheme clearly outperform the state-of-the-art in terms of communication and computational efficiency, especially in realistic scenarios. For example, compared to one of the recent arts CrypTFlow2 [1], the communication cost of our solution can be 21.7× smaller at best. Thanks to the reduction of communication cost, the runtime can be 2.46× faster than CrypTFlow2 at the median-country-speed of current global mobile broadband.
Label-Consistent Generalizable Hash Codes

We present a supervised semantic hashing framework, named Label-Consistent Generalized Hashing (LCGH). The main novelty of LCGH is the explicit retention of information which may be irrelevant in training, but possibly useful for generalizing to unseen test classes. This is in stark contrast to typical semantic hashing methods which seek to remove redundant feature information from their hash codes in order to maximize the margin between hash codes of dissimilar data. This typical strategy leaves hash codes narrowly viable for discerning between training classes, and inadequate in discriminating between unseen test classes. Instead of limiting the information content of hash codes to those provided by the training labels, LCGH enhances its codes with information content from both supervised and unsupervised sources, improving their ability to discriminate across a wider range of data. To do so, LCGH builds upon the foundation of first agreeing with the provided training labels (label-consistency) and then incorporating possibly useful information using a reconstruction loss. In this way, LCGH respects the reliably given label information before exploring the addition of possibly useful ones. The outcome is a hashing scheme with slightly weaker within-domain (training and test classes are the same) retrieval performance, but much stronger cross-domain (training and test classes are disjoint) performance.
Code Semantics Learning with Deep Neural Networks: An AI-Based Approach for Programming Education

Modern programming languages are very complex, diverse, and non-uniform in their structure, code composition, and syntax. Therefore, it is a difficult task for computer science students to retrieve relevant code snippets from large code repositories, according to their programming course requirements. To solve this problem, an AI-based approach is proposed, for students to better understand and learn code semantics, with solutions for real-world coding exercises. First, a large number of solutions are collected from a course titled “Algorithms and Data Structures” and preprocessed, by removing unnecessary elements. Second, the solution code is converted into a sequence of words and tokenized. Third, the sequence of tokens is used to train and validate the model, through a word embedding layer. Finally, the model is used for the relevant code retrieval and classification task, for the students. In this study, a bidirectional long short-term memory neural network (BiLSTM) is used as the core deep neural network model. For the experiment, approximately 120,000 real-world solutions from three datasets are used. The trained model achieved an average precision, recall, F1 score, and accuracy of 94.35%, 94.71%, 94.45%, and 95.97% for the code classification task, respectively. These results show that the proposed approach has potential for use in programming education.
A Bayesian Optimization Through Sequential Monte Carlo and Statistical Physics-Inspired Techniques

In this paper, we propose an approach for an application of Bayesian optimization using Sequential Monte Carlo (SMC) and concepts from the statistical physics of classical systems. Our method leverages the power of modern machine learning libraries such as NumPyro and JAX, allowing us to perform Bayesian optimization on multiple platforms, including CPUs, GPUs, TPUs, and in parallel. Our approach enables a low entry level for exploration of the methods while maintaining high performance. We present a promising direction for developing more efficient and effective techniques for a wide range of optimization problems in diverse fields.
Ontological Modelling and Social Networks: From Expert Validation to Consolidated Domains

Data from Social Networks is a valuable asset within both a scientific and a business world. In the context of this work, ontological modelling from Social Networks is understood as a knowledge building process to generate a shared domain model. Such a technique relies on a balanced co-existence of human intuition/creativity and technological support, referred to as Hybrid Intelligence. Additionally, it assumes collaborative modelling and collective/social intelligence. The method implies a certain degree of uncertainty that is, in principle, inversely proportional to the achieved consensus. There are two clear different convergence points between the proposed process and collective/social intelligence: (i) at a data level, because of the nature of the input which is generated by different individuals, communities, stakeholders and actors; and (ii) at a modelling level, where human and automatically generated inputs, design decisions and validations are expected to involve several contributors, experts, modellers or analysts. Although looking holistically at the modelling process, this paper concisely focuses mostly on the ontological structure and the associated uncertainty, while resulting systems and studies are object of future work.
Semantic Hashing to Remedy Uncertainties in Ontology-Driven Edge Computing

This paper discusses the specific kind of uncertainties, which appear in ontology-driven software development. We focus on the development of IoT applications whose source code is generated automatically by an ontology-driven framework. So-called “compatibility uncertainties” pop up when the ontology is being changed while the corresponding generated application is in operation. This specific kind of uncertainties can be treated as a variant of implementation uncertainties. The algorithm of its automated handling is presented. The proposed algorithm is implemented within the SciVi platform and tested in the real-world project devoted to the development of custom IoT-based hardware user interfaces for virtual reality. We use the SciVi platform as a toolset for the automatic generation of IoT devices firmware for ontology-driven Edge Computing but the problem discussed is common for any tools which are used for the generation of ontology-driven software.