Reviewerly/exHarmony huggingface dataset:
Bioinspired Divide-and-Conquer Design Methodology for a Multifunctional Contour of a Curved Lever

In this study, we propose a bioinspired design methodology for a multifunctional lever based on the morphological principle of the lever mechanism in the Salvia pratensis flower. The proposed divide-and-conquer contour design methodology does not treat a lever contour as a single curve that satisfies multiple functions. Rather, the lever contour combines partial contours to achieve its assigned subfunction. This approach can simplify the complex multifunctional problem in lever design. We include a case study of a lever utilized in a compact variable gravity compensator (CVGC) to explain the methodology in more detail. In the case study, four partial contours were designed to satisfy three types of functional requirements. The final design for the lever contour was manufactured and verified with visual measurement experiments. The experimental result shows that each partial contour successfully achieved its subfunctions.
Towards Efficient Discovery of Periodic-Frequent Patterns in Columnar Temporal Databases

Finding periodic-frequent patterns in temporal databases is a challenging problem of great importance in many real-world applications. Most previous studies focused on finding these patterns in row temporal databases. To the best of our knowledge, there exists no study that aims to find periodic-frequent patterns in columnar temporal databases. One cannot ignore the importance of the knowledge that exists in very large columnar temporal databases. It is because the real-world big data is widely stored in columnar temporal databases. With this motivation, this paper proposes an efficient algorithm, Periodic Frequent-Equivalence CLass Transformation (PF-ECLAT), to find periodic-frequent patterns in a columnar temporal database. Experimental results on sparse and dense real-world databases demonstrate that PF-ECLAT is not only memory and runtime efficient but also highly scalable. Finally, we present the usefulness of PF-ECLAT with a case study on air pollution analytics.
Online Encrypted Mobile Application Traffic Classification at the Early Stage: Challenges, Evaluation Criteria, Comparison Methods

Encrypted mobile application traffic classification at the early stage refers to the identification of the application to which the mobile network traffic belongs in a limited time or within limited network packets and feeding back the results to the policy routing control or QoS module. Early traffic classification is the basis for mobile ISPs to implement tasks such as QoS, network billing, and network security management. Current studies generally improve the performance of classification in encrypted traffic by exploring more complex model or constructing more representative features of off-line traffic. Few studies are focusing on the classification of mobile application encrypted traffic in the early stage. What's more, encrypted traffic classification still faces the constraints of limited computing and memory resources when applied to the high-speed network. Therefore, in addition to meeting real-time requirements, early encrypted traffic identification methods must also make the constraints of limited resources into consideration. In this paper, we analyze the challenges of early classification of encrypted mobile application traffic in online settings and proposes evaluation criteria for early traffic classification based on different scenarios. Several typical encrypted traffic classification methods in recent years were selected to make comparisons over our mobile application traffic datasets under the standards of this paper.
PoetryLab as Infrastructure for the Analysis of Spanish Poetry

The development of the network of ontologies of the ERC POSTDATA Project brought to light some deficiencies in terms of completeness in the currently available European poetry corpora.To tackle the issue in the realm of the Spanish poetic tradition, our approach consisted in designing a set of tools that any scholar could use to automatically enrich the analysis of Spanish poetry.The effort crystallized in the PoetryLab, an extensible open source toolkit for syllabification, scansion, enjambment detection, rhyme detection, stanza identification, and historical named entity recognition for Spanish poetry.We designed the system to be interoperable, compliant with the project ontologies, easy to use by tech-savvy and non-expert researchers, and requiring minimal maintenance and setup.Furthermore, we propose the integration of the PoetryLab as a core functionality in the tool catalog of CLARIN for Spanish poetry.
A Dataset of Multidimensional and Multilingual Social Opinions for Malta’s Annual Government Budget

This paper presents three high quality social opinion datasets in the socio-economic domain, specifically Malta's annual Government Budgets of 2018, 2019 and 2020. They contain over 6,000 online posts of user-generated content in English and/or Maltese, gathered from newswires and social networking services. These have been annotated for multiple opinion dimensions, namely subjectivity, sentiment polarity, emotion, sarcasm and irony, and in terms of negation, topic and language. These datasets are a valuable resource for developing Opinion Mining tools and Language Technologies, and can be used as a baseline for assessing the state-of-the-art and for developing new advanced analytical methods for Opinion Mining. Moreover, they can be used for policy formulation, policy-making, decision-making and decision-taking. This research can also support similar initiatives in other countries, studies in the socio-economic domain and applied in other areas, such as Politics, Finance, Marketing, Advertising, Sales and Education.
Signposts for CLARIN

An implementation of CMDI-based signposts and its use is presented in this paper.Arnold, Fisseni et al. (2020) present signposts as a solution to challenges in long-term preservation of corpora.Though applicable to digital resources in general, we focus on corpora, especially those that are continuously extended or subject to modification, e.g., due to legal injunctions, but also may overlap with respect to constituents, and may be subject to migrations to new data formats.We describe the contribution signposts can make to the CLARIN infrastructure, notably virtual collections, and document the design for the CMDI profile.
REM: Efficient Semi-Automated Real-Time Moderation of Online Forums

This paper presents REM, a novel tool for the semi-automated real-time moderation of large scale online forums.The growing demand for online participation and the increasing number of user comments raise challenges in filtering out harmful and undesirable content from public debates in online forums.Since a manual moderation does not scale well and pure automated approaches often lack the required level of accuracy, we suggest a semi-automated moderation approach.Our approach maximizes the efficiency of manual efforts by targeting only those comments for which human intervention is needed, e.g.due to high classification uncertainty.Our tool offers a rich visual interactive environment enabling the exploration of online debates.We conduct a preliminary evaluation experiment to demonstrate the suitability of our approach and publicly release the source code of REM.
A Fast Exact Algorithm for the Resource Constrained Shortest Path Problem

Resource constrained path finding is a well studied topic in AI, with real-world applications in different areas such as transportation and robotics. This paper introduces several heuristics in the resource constrained path finding context that significantly improve the algorithmic performance of the initialisation phase and the core search. We implement our heuristics on top of a bidirectional A* algorithm and evaluate them on a set of large instances. The experimental results show that, for the first time in the context of constrained path finding, our fast and enhanced algorithm can solve all of the benchmark instances to optimality, and compared to the state of the art algorithms, it can improve existing runtimes by up to four orders of magnitude on large-size network graphs.
Shape Matters: Understanding the Implicit Bias of the Noise Covariance

The noise in stochastic gradient descent (SGD) provides a crucial implicit regularization effect for training overparameterized models. Prior theoretical work largely focuses on spherical Gaussian noise, whereas empirical studies demonstrate the phenomenon that parameter-dependent noise -- induced by mini-batches or label perturbation -- is far more effective than Gaussian noise. This paper theoretically characterizes this phenomenon on a quadratically-parameterized model introduced by Vaskevicius et el. and Woodworth et el. We show that in an over-parameterized setting, SGD with label noise recovers the sparse ground-truth with an arbitrary initialization, whereas SGD with Gaussian noise or gradient descent overfits to dense solutions with large norms. Our analysis reveals that parameter-dependent noise introduces a bias towards local minima with smaller noise variance, whereas spherical Gaussian noise does not. Code for our project is publicly available.
Analyzing the Energy-Latency-Area-Accuracy Trade-off Across Contemporary Neural Networks

Deep learning provides a wide range of neural networks (NNs) with varying accuracy and complexity, which can be mapped to a wide variety of specialized hardware accelerators. This results in a huge optimization space across energy, latency, area and task accuracy, complicating the assessment of which NNs, resp. which hardware architectures are most efficient as it strongly depends on the hardware platform, resp. suite of algorithms to support. The ultimate goal is to find hardware supporting a broad suite of NNs and to construct NNs which can be efficiently deployed on broad set of hardware platforms. This paper, therefore, brings 3 contributions: 1) an assessment methodology across a suite of networks and architectures; 2) deployment of this analysis for a set of architecture on popular networks for the ImageNet task; 3) derivation of insights from this study on characteristic of optimal architectures for modern NN models, and characteristic of optimal network topologies for modern processor architectures.
Parameterized Complexity of Small Decision Tree Learning

We study the NP-hard problem of learning a decision tree (DT) of smallest depth or size from data. We provide the first parameterized complexity analysis of the problem and draw a detailed parameterized complexity map for the natural parameters: size or depth of the DT, maximum domain size of all features, and the maximum Hamming distance between any two examples. Our main result shows that learning DTs of smallest depth or size is fixed-parameter tractable (FPT) parameterized by the combination of all three of these parameters. We contrast this FPT-result by various hardness results that underline the algorithmic significance of the considered parameters.
Information technology for career guidance of creatively gifted university students. Region aspect

The article continues the description of the functioning of the typical regional information and communication system “Student and Labor” (SAL), begun by the authors in No. 8-2020 of the “Informatics and Education” journal. This system is aimed at identifying and developing university students who are creatively gifted in the field of science, techniques and technology in the process of their gradual involvement in real labor activity in cooperation with the leading enterprises in the region. The algorithm of functioning and the organizational structure of the “Student and Labor” system, informational, scientific, methodological and motivational aspects of interaction between students and enterprises on the basis of continuous measurement of the index of the prospects of interaction between a student and an enterprise are considered in detail. This index includes 10 components, the main of which is the creative index of research works / projects carried out by the student, taking into account the interests of the enterprise. In turn, the creative index is calculated on the basis of a multi-criteria assessment of a work / project according to 15 particular criteria. “Convolution” into scalar values of individual components and criteria of the index of the prospects of the student’s interaction with the enterprise and the creative index of the work / project is carried out in the most objective way using the so-called universal coefficients of importance.
Boosting in the Presence of Massart Noise

We study the problem of boosting the accuracy of a weak learner in the (distribution-independent) PAC model with Massart noise. In the Massart noise model, the label of each example $x$ is independently misclassified with probability $\eta(x) \leq \eta$, where $\eta<1/2$. The Massart model lies between the random classification noise model and the agnostic model. Our main positive result is the first computationally efficient boosting algorithm in the presence of Massart noise that achieves misclassification error arbitrarily close to $\eta$. Prior to our work, no non-trivial booster was known in this setting. Moreover, we show that this error upper bound is best possible for polynomial-time black-box boosters, under standard cryptographic assumptions. Our upper and lower bounds characterize the complexity of boosting in the distribution-independent PAC model with Massart noise. As a simple application of our positive result, we give the first efficient Massart learner for unions of high-dimensional rectangles.
The Legal Characterization of Crypto-Exchange Platforms

Abstract In a thriving crypto-assets market, crypto exchange platforms play a pivotal role. Indeed, such trading systems are instrumental to the matching of demand and supply (trading) as well as the transfer of tokens (settlement and post-trading). Yet, there is currently no bespoke regulatory framework applicable to crypto exchange, raising pressing questions on whether and how those platforms are regulated. This article examines the Italian regulatory framework applicable to crypto-exchange platforms. It shows that the present regulatory engagement with crypto exchanges relates to the qualification of the crypto assets traded. Depending on such qualification, different financial regimes could be triggered.
Understanding Generalization through Visualizations

The power of neural networks lies in their ability to generalize to unseen data, yet the underlying reasons for this phenomenon remain elusive. Numerous rigorous attempts have been made to explain generalization, but available bounds are still quite loose, and analysis does not always lead to true understanding. The goal of this work is to make generalization more intuitive. Using visualization methods, we discuss the mystery of generalization, the geometry of loss landscapes, and how the curse (or, rather, the blessing) of dimensionality causes optimizers to settle into minima that generalize well.
Multitask learning over shared subspaces

This paper uses constructs from machine learning to define pairs of learning tasks that either shared or did not share a common subspace. Human subjects then learnt these tasks using a feedback-based approach and we hypothesised that learning would be boosted for shared subspaces. Our findings broadly supported this hypothesis with either better performance on the second task if it shared the same subspace as the first, or positive correlations over task performance for shared subspaces. These empirical findings were compared to the behaviour of a Neural Network model trained using sequential Bayesian learning and human performance was found to be consistent with a minimal capacity variant of this model. Networks with an increased representational capacity, and networks without Bayesian learning, did not show these transfer effects. We propose that the concept of shared subspaces provides a useful framework for the experimental study of human multitask and transfer learning.
Usage of Blockchain to Ensure Audit Data Integrity

Usage of technology to help finance audit process is not a new instance. But since the rise of 4th industrial revolution and emergence of smart technology relatively in a short period of time, adopting technology have its challenges and drawbacks. Data Integrity has been an issue for finance audit process because digital data is easy to tamper. This condition makes audit process become difficult and potential of audit fraud is high. In this study we would like to explore usage blockchain technology as future database engine for AIS. Blockchain as a technology relatively unheard before cryptocurrency albeit its advanced technology in data storage to ensure data integrity. We will explore the advantages and risk in adopting blockchain as well as current state of academics and technology regarding blockchain adoption.
Fast Policy Interpretation and Dynamic Conflict Resolution for Blockchain-Based IoT System

Although the blockchain-based Internet of Things (BC-IoT) has been applied in many fields, it still faces many security attacks due to lacking policy-based security management (PbSM). Previous PbSM is usually time-consuming, which is difficult to integrate into BC-IoT directly. The high-latency policy conflict resolving in traditional PbSM cannot meet the BC-IoT’s low-latency requirement. Moreover, the conflict resolution rate is low as the PbSM usually neglects the runtime information. Therefore, it is challenging that achieving an efficient PbSM for BC-IoT and overcomes both time and resource consumption. To address the problem, we propose a novel PbSM for BC-IoT named FPICR to realize fast policy interpretation and dynamic conflict resolution efficiently. We first present policy templates based on system log to interpret policy in high speed in BC-IoT. Benefiting from matching the characteristics of the system processing, FPICR supports interpreting a policy into the smart contract directly without complex content parsing. We then propose a weighted directed policy graph (WDPG) to evaluate the importance of the deployed policies more accurately. To improve the policy conflict resolution rate, we implement the resolution algorithm through reconstructing the WDPG. Taking the traits of these properties, FPICR thus can also remove the redundant data to compress storage space by the WDPG. Experiment results highlight that FPICR outperforms the baseline in all measure metrics. Especially, compared with the state-of-the-art method, the speedup of interpretation in FPICR is about up to <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mn>2.1</mn> <mo>×</mo> </math> . The conflict resolution rate in FPICR can be improved by 6.2% on average and achieve up to 96.1%.
Accelerated Quantum Computation based on Quantum Coherent Dynamics of Evanescent Photons in Liquid Water

It has been shown how evanescent photons, produced in highly-coherent excited quantum states of liquid water, could be considered in order to perform quantum computations in a completely novel and still unexplored fashion by considering the formation of excited coherent quantum domains in liquid water, associated to cold vortex of quasi-free electrons, and their interaction through the mutual exchange of virtual evanescent photons, by quantum tunnel effect. Furthermore, the use of metamaterials to enclose water molecules, in order to form suitable waveguide for the evanescent photons generated inside water coherent domains, could allow for the implementation of a superfast network of interacting coherent domains able to represent a basic architecture for a novel kind of quantum hyper-computer based on the coherent dynamics of liquid water. This introduces a new frontier in the field of quantum computation, whose applications to both theoretical and advanced-technology fields (from the simulation of complex quantum systems to biotechnology, artificial intelligence, data encryption and decryption, etc.) would be very deep and nowadays unimaginable.
Temporal Index Scheme of Hyperledger Fabric System in IoT

As a large number of mobile terminals are connected to the IoT, the security problem of IoT is a challenge to the IoT technology. Blockchain technology has the characteristics of decentralization, data encryption, smart contract, and so on, especially suitable in the complex heterogeneous network. However, sequential access based on block files in the blockchain hinders efficient query processing. The problem is due to current blockchain solutions do not support temporal data processing. In this paper, we propose two index building methods (TISD and TIF) to address this issue in Hyperledger Fabric System. TISD (temporal index based on state databases) segments the historical data by time interval in the time dimension and indexes events at the same time interval. TIF (temporal index based on files) builds the index of files by the block transaction data, which is arranged in chronological order and is stored at a certain time interval. In the experimental part, we compare the query time on two datasets and analyse the query performance. Experiments demonstrated that our two methods are relatively stable in overall time performance on different datasets in the Hyperledger Fabric System.
Explainability and performance of anticipatory learning classifier systems in non-deterministic environments

In the field of Reinforcement Learning, models based on neural networks are highly performing, but explaining their decisions is very challenging. Instead of seeking to open these "black boxes" to meet the increasing demand for explainability, another approach is to used rule-based machine learning models that are explainable by design, such as the Anticipatory Learning Classifier Systems (ALCS). ALCS are able to develop simultaneously a complete representation of their environment and a decision policy based on this representation to solve their learning tasks. This paper focuses on the ability of ALCS to deal with non-deterministic environments used in reinforcement learning problems, while discussing their explainability. Directions for future research are thus highlighted to improve both the performance and the explainability of the ALCS to meet the needs of critical real-world applications.
U-til: lightweight approach for traceability management between user stories and test suites

Tracing the numerous artifacts produced during a software project is challenging. It is not uncommon to overlook this aspect due to the additional workload to keep organized and effective records of what changes between iterations. In this work, we propose a lightweight approach called U-til for traceability management focusing on the impact of changes in both user stories and test cases. We have conducted a case study to assess the proposal's impact concerning the efficiency to manage the requirements traceability from the point of view of testers and requirements analysts in the context of an industry software project. We performed quantitative and qualitative analysis before and after applying our approach and observed that it helped the team to manage requirements dependencies and improved the traceability between user stories and test cases.