Reviewerly/exHarmony huggingface dataset:
Is It Worth It? Comparing Six Deep and Classical Methods for Unsupervised Anomaly Detection in Time Series

Detecting anomalies in time series data is important in a variety of fields, including system monitoring, healthcare and cybersecurity. While the abundance of available methods makes it difficult to choose the most appropriate method for a given application, each method has its strengths in detecting certain types of anomalies. In this study, we compare six unsupervised anomaly detection methods of varying complexity to determine whether more complex methods generally perform better and if certain methods are better suited to certain types of anomalies. We evaluated the methods using the UCR anomaly archive, a recent benchmark dataset for anomaly detection. We analyzed the results on a dataset and anomaly-type level after adjusting the necessary hyperparameters for each method. Additionally, we assessed the ability of each method to incorporate prior knowledge about anomalies and examined the differences between point-wise and sequence-wise features. Our experiments show that classical machine learning methods generally outperform deep learning methods across a range of anomaly types.
Worldwide Overview and Country Differences in Metaverse Research: A Bibliometric Analysis

As a research topic integrating various subjects and technologies, Metaverse research has been a global concern in recent years. This study explores the hotspots and frontiers of academic research on the Metaverse based on a bibliometric analysis from 2012 to 2021. A keyword retrieval dataset related to Metaverse research was constructed based on expert consultation and manual reading of the literature, retrieving articles and conference papers from the Scopus database. Critical points in Metaverse research are represented in terms of research scale, keyword co-occurrence networks, keyword citation bursts, and international collaborative networks with the application of VOSviewer and CiteSpace’s bibliometric visualization software. The results indicate that Metaverse research is experiencing rapid growth, with countries/regions increasing their production at varying speeds. The results also indicate the three most prolific countries, the United States, China and Germany, for comparison, showing the leading topic as virtual reality in Metaverse research, and we find that there are differences in topic clustering and hotspot evolution among the three countries over the past decade. By determining the current research status and the overall development path of the Metaverse field, the paper intends to provide a reference for the future research development and technical application of the Metaverse.
Research on Image Encryption Based on Fractional Seed Chaos Generator and Fractal Theory

In this paper, a new fractional-order seed chaotic generator is designed to solve the problem of the complex operations of single low-dimensional systems and simple high-dimensional systems. The fractional-order chaotic system generated is proven to have better chaotic performance using Lyapunov exponential differential calculus, approximate entropy, 0–1 test and other indicators. On this basis, the “multiple squares nested body scrambling (MSNBS)” model is extended from fractal theory to complete the image scrambling process, and a new algorithm is proposed to be applied to the encryption field in combination with the fractional-order coupled chaotic system. Experimental simulations show that the algorithm can resist common differential attacks and noise attacks and improve the security of the algorithm.
A Systematic Review of Consensus Mechanisms in Blockchain

Since the birth of Bitcoin, blockchain has shifted from a critical cryptocurrency technology to an enabling technology. Due to its immutability and trustworthiness, blockchain has revolutionized many fields requiring credibility and high-quality data for decision making. Particularly in business intelligence and business process management, users can use blockchain to build their blockchain-enabled collaboration and data-sharing ecosystem with their partners. In this paper, we present the development process of blockchain and consensus mechanisms, where important blockchain consensus mechanisms are introduced. The consensus mechanism is the kernel among various blockchain components to ensure security and performance. Again, we present a comparison of these consensus mechanisms from different perspectives. We take the blockchain-enabling business as an example and analyze the relationship between blockchain and business process characteristics and the ideas and principles for selecting consensus mechanisms. Finally, we describe the differences and connections among many consensus mechanisms while laying a foundation for selecting appropriate consensus mechanisms for different scenarios and fields of application.
Impact of Innovation on Economic Growth of G8 Countries- Analysis Over 1996-2020

Purpose: The study aimed to analyze the relationship between innovation and economic growth in the G8 countries over the period 1996-2020. Theoretical framework: The theoretical framework id built upon the neo-classical growth theory, endogenous growth theory, and the innovation systems approach. Design/methodology/approach: The research used the Vector Auto Regression (VAR) model and penal regression, and found evidence of a positive and significant relationship between innovation and economic growth through the results of the Johansen co-integration test. The Granger causality Wald test also indicated that lgdp (GDP) Granger causes irrd (research and development), ipan (patent application), lpar (participation rate), lhte (high-tech exports), lede (educational expenditure), and istj (scientists and engineers), with strong evidence against the null hypothesis. Results: The test results presented in the table provide information on the rank of the co-integrating relationships. The maximum and minimum values of the trace statistics and eigenvalues are reported at each rank, along with the critical values at 5% and 1% significance levels. Granger causes the other time series results suggests that lgdp Granger causes irrd (p-value = 0.034), ipan (p-value = 0.005), lpar (p-value = 0.001), lhte (p-value = 0.029), lede (p-value = 0.000), and istj (p-value = 0.000). The p-value for the overall test (lgdp vs. all) is 0.000, indicating that lgdp Granger causes all the other time series. Findings: The fixed effects regression model showed a significant relationship with an F-statistic of 2.54 and a corresponding p-value of 0.0218. The study provided policy recommendations to support innovation-led economic growth in the G8 countries.
An Empirical Comparison of Pre-Trained Models of Source Code

While a large number of pre-trained models of source code have been successfully developed and applied to a variety of software engineering (SE) tasks in recent years, our understanding of these pre-trained models is arguably fairly limited. With the goal of advancing our understanding of these models, we perform the first systematic empirical comparison of 19 recently-developed pre-trained models of source code on 13 SE tasks. To gain additional insights into these models, we adopt a recently -developed 4-dimensional categorization of pre-trained models, and subsequently investigate whether there are correlations between different categories of pre-trained models and their performances on different SE tasks.
PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

It is widely acknowledged that large models have the potential to deliver superior performance across a broad range of domains. Despite the remarkable progress made in the field of machine learning systems research, which has enabled the development and exploration of large models, such abilities remain confined to a small group of advanced users and industry leaders, resulting in an implicit technical barrier for the wider community to access and leverage these technologies. In this paper, we introduce PyTorch Fully Sharded Data Parallel (FSDP) as an industry-grade solution for large model training. FSDP has been closely co-designed with several key PyTorch core components including Tensor implementation, dispatcher system, and CUDA memory caching allocator, to provide non-intrusive user experiences and high training efficiency. Additionally, FSDP natively incorporates a range of techniques and settings to optimize resource utilization across a variety of hardware configurations. The experimental results demonstrate that FSDP is capable of achieving comparable performance to Distributed Data Parallel while providing support for significantly larger models with near-linear scalability in terms of TFLOPS.
Rethinking Mobile Block for Efficient Attention-based Models

This paper focuses on developing modern, efficient, lightweight models for dense predictions while trading off parameters, FLOPs, and performance. Inverted Residual Block (IRB) serves as the infrastructure for lightweight CNNs, but no counterpart has been recognized by attention-based studies. This work rethinks lightweight infrastructure from efficient IRB and effective components of Transformer from a unified perspective, extending CNN-based IRB to attention-based models and abstracting a one-residual Meta Mobile Block (MMB) for lightweight model design. Following simple but effective design criterion, we deduce a modern Inverted Residual Mobile Block (iRMB) and build a ResNetlike Efficient MOdel (EMO) with only iRMB for down-stream tasks. Extensive experiments on ImageNet-1K, COCO2017, and ADE20K benchmarks demonstrate the superiority of our EMO over state-of-the-art methods, e.g., EMO-1M/2M/5M achieve 71.5, 75.1, and 78.4 Top-1 that surpass equal-order CNN-/Attention-based models, while trading-off the parameter, efficiency, and accuracy well: running 2.8-4.0× ↑ faster than EdgeNeXt on iPhone14.
SPARQL Query Containment Under SHI Axioms

SPARQL query containment under schema axioms is the problem of determining whether, for any RDF graph satisfying a given set of schema axioms, the answers to a query are contained in the answers of another query. This problem has major applications for verification and optimization of queries. In order to solve it, we rely on the mu-calculus. Firstly, we provide a mapping from RDF graphs into transition systems. Secondly, SPARQL queries and RDFS and SHI axioms are encoded into mu-calculus formulas. This allows us to reduce query containment and equivalence to satisfiability in the mu-calculus. Finally, we prove a double exponential upper bound for containment under SHI schema axioms.
Search-Based Planning with Provable Suboptimality Bounds for Continuous State Spaces

Search-based planning is widely used for mobile robot motion planning because of its guarantees of optimality and completeness. In continuous state-spaces, however, most existing approaches have significant limitations in terms of optimality and completeness because of the underlying grid used. We propose an approach that eliminates the dependency on grids by using more general equivalence classes to quickly find an initial solution and instead of pruning states that fall within an equivalence class and have higher cost, we use an inflated heuristic to lower the priority of these states in the search. In further iterations, we reduce the inflated heuristic in a principled way, thus providing fast solutions with provable suboptimality bounds that can be improved as time allows. The proposed approach produces smooth paths with the resolution dictated by the action set. Finer action sets produce higher resolution paths that are more computationally intensive to calculate and coarser action sets produce lower resolution paths that are faster to compute. To the best of our knowledge, this is the first algorithm that is able to plan in continuous state-spaces with provable guarantees on completeness and bounds on suboptimality for a given action set. Experimental results on 3D (x,y,theta) path planning show that, on average, this approach is able to find paths in less than two seconds that are within 2% of the optimal path cost in worlds of up to 1000x1000 m with a minimum step size of one meter.
Sensing Real-World Events Using Arabic Twitter Posts

In recent years, there has been increased interest in eventdetection using data posted to social media sites. Automaticallytransforming user-generated content into informationrelating to events is a challenging task due to the short informallanguage used within the content and the variety oftopics discussed on social media. Recent advances in detectingreal-world events in English and other languages havebeen published. However, the detection of events in the Arabiclanguage has been limited to date. To address this task, wepresent an end-to-end event detection framework which comprisessix main components: data collection, pre-processing,classification, feature selection, topic clustering and summarization.Large-scale experiments over millions of ArabicTwitter messages show the effectiveness of our approach fordetecting real-world event content from Twitter posts.
Student Software Engineering Learning via Participation in Humanitarian FOSS Projects

Ralph Morelli has been teaching computer science at Trinity College since 1985.His areas of expertise include artificial intelligence, free and open source software, and computing education.He is one of the directors of the Humanitarian Free and Open Source Software (HFOSS) project, which seeks to get undergraduates engaged in computing through building free software that serves the public good.Most recently, he has become involved with mobile computing and is using App Inventor for Android to teach a course on "Computing with Mobile Phones" as one of the pilot courses of the CS Principles project, an NSF-funded effort by the College Board to develop a new AP exam in computer science.
Simulating, Off-Chain and On-Chain: Agent-Based Simulations in Cross-Organizational Business Processes

Information systems execute increasingly complex business processes, often across organizations. Blockchain technology has emerged as a potential facilitator of (semi)-autonomous cross-organizational business process execution; in particular, so-called consortium blockchains can be considered as promising enablers in this context, as they do not require the use of cryptocurrency-based blockchain technology, as long as the trusted (authenticated) members of the network are willing to provide computing resources for consensus-finding. However, increased autonomy in the execution of business processes also requires the delegation of business decisions to machines. To support complex decision-making processes by assessing potential future outcomes, agent-based simulations can be considered a useful tool for the autonomous enterprise. In this paper, we explore the intersection of multi-agent simulations and consortium blockchain technology in the context of enterprise applications by devising architectures and technology stacks for both off-chain and on-chain agent-based simulation in the context of blockchain-based business process execution.
Face recognition using total loss function on face database with ID photos

With the development of deep neural networks, researchers have developed lots of algorithms related to face and achieved comparable results to human-level performance on several databases. However, few feature extraction models work well in the real world when the subject which is to be recognized has limited samples, for example, only one ID photo can be obtained before the face recognition task. To our best knowledge, there is no face database which contains ID photos and pictures from the real world for a subject simultaneously. To fill this gap, we collected 100 celebrities’ ID photos and their about 1000 stills or life pictures and formed a face database called FDID. Besides, we proposed a novel face recognition algorithm and evaluated it with this new database on the real-life videos.
Efficient and secure pairing-free certificateless directed signature scheme

In an ordinary signature scheme any one can verify the validity of a signature produced by the signer. But public verifiability of signatures is not desirable in some applications where the signed message is sensitive to the signature receiver, for example signatures on medical records, tax information. To meet this requirement, the concept of directed signature was introduced. A directed signature scheme is a kind of signature scheme in which the verification ability is controlled by the signer. Many directed signature schemes have been proposed in different cryptographic settings and most of the schemes are using bilinear pairings over elliptic curves. But the computation of a bilinear pairing is very expensive. Hence the schemes which use pairings are less efficient and are not much applicable in practice. In order to improve the computational and communicational efficiency, in this paper, we propose a pairing- free certificateless directed signature scheme. The proposed scheme is proven secure in the random oracle model under the assumption that the elliptic curve discrete logarithm problem is hard. We compare our scheme with well known existing schemes and efficiency analysis shows that the proposed scheme is more efficient.
Learning to Match via Inverse Optimal Transport

We propose a unified data-driven framework based on inverse optimal transport that can learn adaptive, nonlinear interaction cost function from noisy and incomplete empirical matching matrix and predict new matching in various matching contexts. We emphasize that the discrete optimal transport plays the role of a variational principle which gives rise to an optimization-based framework for modeling the observed empirical matching data. Our formulation leads to a non-convex optimization problem which can be solved efficiently by an alternating optimization method. A key novel aspect of our formulation is the incorporation of marginal relaxation via regularized Wasserstein distance, significantly improving the robustness of the method in the face of noisy or missing empirical matching data. Our model falls into the category of prescriptive models, which not only predict potential future matching, but is also able to explain what leads to empirical matching and quantifies the impact of changes in matching factors. The proposed approach has wide applicability including predicting matching in online dating, labor market, college application and crowdsourcing. We back up our claims with numerical experiments on both synthetic data and real world data sets.
Combo-Action: Training Agent For FPS Game with Auxiliary Tasks

Deep reinforcement learning (DRL) has achieved surpassing human performance on Atari games, using raw pixels and rewards to learn everything. However, first-person-shooter (FPS) games in 3D environments contain higher levels of human concepts (enemy, weapon, spatial structure, etc.) and a large action space. In this paper, we explore a novel method which can plan on temporally-extended action sequences, which we refer as Combo-Action to compress the action space. We further train a deep recurrent Q-learning network model as a high-level controller, called supervisory network, to manage the Combo-Actions. Our method can be boosted with auxiliary tasks (enemy detection and depth prediction), which enable the agent to extract high-level concepts in the FPS games. Extensive experiments show that our method is efficient in training process and outperforms previous stateof-the-art approaches by a large margin. Ablation study experiments also indicate that our method can boost the performance of the FPS agent in a reasonable way.
Predicting and Analyzing Language Specificity in Social Media Posts

In computational linguistics, specificity quantifies how much detail is engaged in text. It is an important characteristic of speaker intention and language style, and is useful in NLP applications such as summarization and argumentation mining. Yet to date, expert-annotated data for sentence-level specificity are scarce and confined to the news genre. In addition, systems that predict sentence specificity are classifiers trained to produce binary labels (general or specific).We collect a dataset of over 7,000 tweets annotated with specificity on a fine-grained scale. Using this dataset, we train a supervised regression model that accurately estimates specificity in social media posts, reaching a mean absolute error of 0.3578 (for ratings on a scale of 1-5) and 0.73 Pearson correlation, significantly improving over baselines and previous sentence specificity prediction systems. We also present the first large-scale study revealing the social, temporal and mental health factors underlying language specificity on social media.
Secure Cloud Storage Based on RLWE Problem

With the rapid development of cloud storage and quantum computing, ensuring the integrity of the outsourced data for data owners becomes a serious concern.To address this problem, the existing protocols for auditing cloud storage are usually based on the post-quantum cryptographies to check data integrity.Nevertheless, these protocols employ heavy cryptographic operations to construct the data tags, making their efficiency low and extensibility poor.In this paper, we take a new perspective and explore the possibility of designing secure cloud storage (SCS) protocols based on the ring learning with errors problem.Instead of matrix variables, our protocol only utilizes vector variables to generate data block tags so that it has a much lower computational complexity.We then give a strict security proof of cheating resistance against the malicious cloud and privacy guarantee against the curious third-party auditor.We also extend the proposed protocol to support data dynamics and batch auditing for more application scenarios.As a further contribution, we summarize a systematic framework for designing the lattice-based SCS protocols.Finally, exhaustive performance analysis and comparison are provided to verify that the proposed protocol outperforms the existing lattice-based SCS protocols in terms of both operational efficiency and functional extensibility.
Modeling Opinion of IPTV Viewers Based on Implicit Feedback and Content Metadata

In today's hyper-connected world, rich social data feeds can be obtained from various sources, including the data exhaust of many commonly used systems.In this paper, we analyze the social pulse, obtained from viewer activity in an IPTV network-we attempt to validate a framework for determining public opinion and public interest through implicit feedback of IPTV viewers.First, we address the hypothesis that implicit viewer feedback in the form of channel change events, paired with the content metadata, can be used to model viewers' opinion and interest.For this, we design a controlled experiment to collect explicit feedback by rating a set of general-interest news clips.In addition to collecting demographic information, we also survey viewers' opinion, interest, and the probability of channel change during each clip.Furthermore, we extract weighted feature vectors from the closed captions of the video; this data, combined with the reported probability of channel change, is used to build a model that classifies opinion in five categories based on the probability of channel change and content.Next, we build a simplified model that classifies opinion in five categories based on the interest, which shows a linear relationship, but further consideration of content, in this case, provides better accuracy and possibility to analyze anomalous cases.Finally, we discuss and analyze the applications of such models in large systems and the necessary modifications to scale the system and to ensure the adequate performance on massive IPTV event data streams.
Speech Processing and Recognition System

In the initial decade of the twentieth century, scientists in the Bell System realized that the idea of universal services like telephony services is becoming feasible due to large-scale technological revolution [1].