Reviewerly/exHarmony huggingface dataset:
Fingerprinting Concepts in Data Streams with Supervised and Unsupervised Meta-Information

Streaming sources of data are becoming more common as the ability to collect data in real-time grows. A major concern in dealing with data streams is concept drift, a change in the distribution of data over time, for example, due to changes in environmental conditions. Representing concepts (stationary periods featuring similar behaviour) is a key idea in adapting to concept drift. By testing the similarity of a concept representation to a window of observations, we can detect concept drift to a new or previously seen recurring concept. Concept representations are constructed using meta-information features, values describing aspects of concept behaviour. We find that previously proposed concept representations rely on small numbers of meta-information features. These representations often cannot distinguish concepts, leaving systems vulnerable to concept drift. We propose FiCSUM, a general framework to represent both supervised and unsupervised behaviours of a concept in a fingerprint, a vector of many distinct meta-information features able to uniquely identify more concepts. Our dynamic weighting strategy learns which meta-information features describe concept drift in a given dataset, allowing a diverse set of meta-information features to be used at once. FiCSUM outperforms state-of-the-art methods over a range of 11 real world and synthetic datasets in both accuracy and modeling underlying concept drift.
Needs analysis-based design principles for constructing a context-aware English learning system

In response to the needs of an international workforce, English for Specific Purpose courses are being promoted in higher education, and instructors are turning to learning technology to help design the instruction. In this paper, a needs analysis was conducted to guide the design of the system, materials and activities for context-aware ubiquitous language learning (CAULL) and English for Fitness Purposes (EFP) in a fitness center as part of a Design-Based Research project. The data collection methods included on-site observations, semi-structured interviews, and a questionnaire. The participants included 82 undergraduate students, seven physical education (PE) instructors, and six non-PE faculty members who frequently used the fitness center. All data forms were then triangulated and analyzed. The results show that all three groups consistently agreed that they need receptive language skills as well as specific terminology for fitness. Furthermore, it was suggested that the oral communication skills for EFP learners should also be developed for sharing ideas, experience, and suggestions. They pointed out that vocabulary related to diverse topics such as warm-up and cool-down stretches, cardiovascular exercises, and weight training are needed. Furthermore, participants reported that the CAULL system should be hypermedia-based, and contain a portfolio and a user-friendly interface. The findings were used to develop a blue print for a CAULL system based on design principles. The findings are of interest to context-aware language learning researchers, instructional designers, and the field of English for Specific Purposes.
Handwriting features based detection of fake signatures

Detection of fake signatures is a hard task. In this paper, we present a novel method for detecting trained forgeries using features extracted from sliding windows with different overlaps on a public available dataset of static images of signatures. Using a linear machine learning model named Extreme Learning Machine (ELM), our methodology achieves, in average, an Equal Error Rates (EER) of 2.31% for an overlap of 90%. In line with the state-of-the-art results available in the scientific literature.
Improving the backpropagation algorithm with consequentialism weight updates over mini-batches

Normalized least mean squares (NLMS) and affine projection algorithm (APA) are two successful algorithms that improve the stability of least mean squares (LMS) by reducing the necessity to change the learning rate during the training process. In this paper, we extend them to multi-layer neural networks. We first prove that it is possible to consider a multi-layer neural network as a stack of adaptive filters. It opens the door to bring successful algorithms from adaptive filters to neural networks. We additionally introduce a more comprehensible interpretation than the complicated geometric interpretation in APA for a single fully-connected (FC) layer that can easily be generalized, for instance, to convolutional neural networks and mini-batch training. With this new viewpoint, we introduce a more robust algorithm by predicting and then amending the adverse consequences of some actions that take place in mini-batch backpropagation (BP), even before they happen. The proposed method is a modification to the BP that can be used alongside stochastic gradient descent (SGD) and its momentum variants like Adam and Nesterov. Our experiments show the usefulness of the proposed method in the training of deep neural networks. It is less sensitive to hyper-parameters and needs less intervention during the training process. Besides, it usually converges more smoothly and in fewer iterations. Such predictable behavior helps it to get tuned easier, be resilient during the training, and reduce or eliminate its reliance on other techniques such as momentum.
Avatar-User Bond as Meta-Cognitive Experience: Explicating Identification and Embodiment as Cognitive Fluency

Scholars have not reached an agreement on a theoretical foundation that underlies the psychological effects of avatar use on users. One group of scholars focuses on the perceptual nature of avatar use, proposing that perceiving the self-being represented by a virtual representation leads to the effects (i.e., Proteus effect). Another group suggests that social traits in avatars prime users causing them to behave in accordance with the social traits (i.e., priming effects). We combine these two theoretical explanations and present an alternative approach, hinging on a concept of meta-cognitive experience . The psychological mechanism of the avatar-user bond is explicated in terms of cognitive fluency , a type of meta-cognitive experience reflecting an awareness of how readily or easily information is processed. Under this explication, two concepts related to avatar-user bond, identification and embodiment, are understood as the meta-cognitive experience of cognitive fluency at the level of one’s identity and physical body, respectively. Existing empirical evidence on avatar effects is revisited to explore how this new theoretical framework can be applied.
Undecidability of Underfitting in Learning Algorithms

Using recent machine learning results that present an information-theoretic perspective on underfitting and overfitting, we prove that deciding whether an encodable learning algorithm will always underfit a dataset, even if given unlimited training time, is undecidable. We discuss the importance of this result and potential topics for further research, including information-theoretic and probabilistic strategies for bounding learning algorithm fit.
An overview of LCS research from 2020 to 2021

The International Workshop on Learning Classifier Systems (IWLCS) is an annual workshop at the GECCO conference where new concepts and results regarding learning classifier systems (LCSs) are presented and discussed. One recurring part of the workshop agenda is a presentation that reviews and summarizes the advances made in the field over the last year; this is intended to provide an easy entry point to the most recent progress and achievements. The 2020 presentation was accompanied by a survey workshop paper, a practice which we hereby continue. We give an overview of all the LCS-related publications from 11 March 2020 to 10 March 2021. The 46 publications we review are grouped into seven overall topics: Formal theoretic advances, contributions to LCS-based multi-agent reinforcement learning, approaches to setting and adapting LCS hyperparameters, new LCS architectures and adaptations, LCS implementations, improvements to existing LCSs and applications of LCSs.
A sampling approach to Debiasing the offline evaluation of recommender systems

Offline evaluation of recommender systems (RSs) mostly relies on historical data, which is often biased. The bias is a result of many confounders that affect the data collection process. In such biased data, user-item interactions are Missing Not At Random (MNAR). Measures of recommender system performance on MNAR test data are unlikely to be reliable indicators of real-world performance unless something is done to mitigate the bias. One widespread way that researchers try to obtain less biased offline evaluation is by designing new, supposedly unbiased performance metrics for use on MNAR test data. We investigate an alternative solution, a sampling approach. The general idea is to use a sampling strategy on MNAR data to generate an intervened test set with less bias - one in which interactions are Missing At Random (MAR) or, at least, one that is more MAR-like. An existing example of this approach is SKEW, a sampling strategy that aims to adjust for the confounding effect that an item's popularity has on its likelihood of being observed. In this paper, after extensively surveying the literature on the bias problem in the offline evaluation of RSs, we propose and formulate a novel sampling approach, which we call WTD; we also propose a more practical variant, which we call WTD_H. We compare our methods to SKEW and to two baselines which perform a random intervention on MNAR data. We empirically validate for the first time the effectiveness of SKEW and we show our approach to be a better estimator of the performance that one would obtain on (unbiased) MAR test data. Our strategy benefits from high generality (e.g. it can also be employed for training a recommender) and low overheads (e.g. it does not require any learning).
Label Noise in Segmentation Networks: Mitigation Must Deal with Bias

Imperfect labels limit the quality of predictions learned by deep neural networks. This is particularly relevant in medical image segmentation, where reference annotations are difficult to collect and vary significantly even across expert annotators. Prior work on mitigating label noise focused on simple models of mostly uniform noise. In this work, we explore biased and unbiased errors artificially introduced to brain tumour annotations on MRI data. We found that supervised and semi-supervised segmentation methods are robust or fairly robust to unbiased errors but sensitive to biased errors. It is therefore important to identify the sorts of errors expected in medical image labels and especially mitigate the biased errors.
Pareto-optimal progressive neural architecture search

Neural Architecture Search (NAS) is the process of automating architecture engineering, searching for the best deep learning configuration. One of the main NAS approaches proposed in the literature, Progressive Neural Architecture Search (PNAS), seeks for the architectures with a sequential model-based optimization strategy: it defines a common recursive structure to generate the networks, whose number of building blocks rises through iterations. However, NAS algorithms are generally designed for an ideal setting without considering the needs and the technical constraints imposed by practical applications. In this paper, we propose a new architecture search named Pareto-Optimal Progressive Neural Architecture Search (POPNAS) that combines the benefits of PNAS to a time-accuracy Pareto optimization problem. POPNAS adds a new time predictor to the existing approach to carry out a joint prediction of time and accuracy for each candidate neural network, searching through the Pareto front. This allows us to reach a trade-off between accuracy and training time, identifying neural network architectures with competitive accuracy in the face of a drastically reduced training time.
A Combination of Query Expansion Ranking and GA-SVM for Improving Indonesian Sentiment Classification Performance

The sentiment classification method is a research field that is proliferating in Indonesia since it is fast in extracting public opinion and provides essential and valuable information for stakeholders. Of the best-performing sentiment classification approaches, machine learning is one of them that has excellent performance. However, the method has several problems, such as noisy features and high dimensionality of features that significantly affect the sentiment classification performance. Therefore, to overcome the problems, this paper presents a novel feature selection using a combination of Query Expansion Ranking (QER) and Genetic Algorithm-Support Vector Machine (GA-SVM) for improving sentiment classification performance. Based on the experimental results, the proposed method could significantly improve sentiment classification performance, outperform all state-of-the-art algorithms, and decrease computational time. The method achieved the best performance in average precision, recall, and f-measure with the value of 96.78%, 96.76%, and 96.75%, respectively.
Enabling self-verifiable mutable content items in IPFS using Decentralized Identifiers

In IPFS content identifiers are constructed based on the item's data therefore the binding between an item's identifier and its data can be deterministically verified. Nevertheless, once an item is modified, its identifier also changes. Therefore when it comes to mutable content there is a need for keeping track of the “latest” IPFS identifier. This is achieved using naming protocols on top of IPFS, such as IPNS and DNSlink, that map a constant name to an IPFS identifier, allowing at the same time content owners to update these mappings. Nevertheless, IPNS relies on a cryptographic key pair that cannot be rotated, and DNSlink does not provide content authenticity protection. In this paper, we propose a naming protocol that combines DNSlink and decentralized identifiers to enable self-verifiable content items. Our protocol provides content authenticity without imposing any security requirement to DNSlink. Furthermore, our protocol prevent fake content even if attackers have access to the DNS server of the content owner or have access to the content owner secret keys. Our proof of concept implementation shows that our protocol is feasible and can be used with existing IPFS tools.
ME-MEOA/D<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si2.svg"><mml:msub><mml:mrow /><mml:mrow><mml:mi>C</mml:mi><mml:mi>C</mml:mi></mml:mrow></mml:msub></mml:math>: Multiobjective constrained clustering through decomposition-based memetic elitism

Traditionally unsupervised, clustering techniques have received renewed attention recently, as they have been shown to produce better results when provided with incomplete information about the dataset in the form of constraints. Combining classic clustering and constraints leads to constrained clustering, a semi-supervised learning problem still unexplored in many aspects. Based on the exploration-exploitation requirements of constrained clustering, a memetic elitist multiobjective evolutionary algorithm based on decomposition is proposed, which combines classic multiobjective optimization strategies with single-objective optimization procedures. The application scheme of our proposal for the constrained clustering problem is scrutinized and compared to several state-of-the-art methods for 20 datasets with incremental levels of constraint-based information. Experimental results, supported by Bayesian statistical testing, show a consistent improvement in clustering and multiobjective optimization related measures in favor of our proposal over the state-of-the-art.
LNMNT-New Mersenne Number based Lightweight crypto Hash function for IoT

In this era of Internet of things, it allows billions of devices and applications to communicate and exchange information. The major issue of IoT applications is in the field of security. The lower level of IoT consists of sensors and actuators network. The main issue related with security of these devices is that these are very resource constrained devices. In order to implement security mechanism in these types of devices we require lightweight techniques. Today's available lightweight hash functions are fixed parameter based and less flexible to hash small messages. We proposes a New Mersenne Number transform based lightweight hash function suitable for IoT. The New mersenne number transform provides good diffusion. The Proposed method is tested in Zolertia mote using Cooja simulator. The proposed system has less execution time, 1.3 seconds and less resource requirements as compared to other lightweight hash functions. The proposed LNMNT Lightweight hash provides the 128 bit hash digest.
LuxGeo: Efficient and Secure Enhanced Geometric Range Queries

As the location-based applications ourishing, we will witness soon the transferring of a prodigious amount of data from the local to a public cloud. The rising demand for outsourced data is moving toward a wider geographical area with arbitrary distribution (i.e., dense or sparse) and query scope (i.e., limited or vast). In terms of cloud risks, the outsourced individual data should be preserved when being queried, especially for location information. Geometric range queries are one of the most fundamental search functions. However, the existed works of secure geometric queries are far from practical usage on efciency and security simultaneously. In this paper, we propose a novel scheme, LuxGeo. Our scheme reaches a constant navigation and a linear sweep, which is tailored for secure and efcient location-lookup. Our experiments over three real-world spatial datasets have shown its practical efciency. For example, it only takes 10.01s with 728 tuples retrieved over 63, 369 ciphertext dataset for a single query. LuxGeo has better performance than the existed solutions for a GSE problem on efciency and security.
Employing Vertical Elasticity for Efficient Big Data Processing in Container-Based Cloud Environments

Recently, “Big Data” platform technologies have become crucial for distributed processing of diverse unstructured or semi-structured data as the amount of data generated increases rapidly. In order to effectively manage these Big Data, Cloud Computing has been playing an important role by providing scalable data storage and computing resources for competitive and economical Big Data processing. Accordingly, server virtualization technologies that are the cornerstone of Cloud Computing have attracted a lot of research interests. However, conventional hypervisor-based virtualization can cause performance degradation problems due to its heavily loaded guest operating systems and rigid resource allocations. On the other hand, container-based virtualization technology can provide the same level of service faster with a lightweight capacity by effectively eliminating the guest OS layers. In addition, container-based virtualization enables efficient cloud resource management by dynamically adjusting the allocated computing resources (e.g., CPU and memory) during the runtime through “Vertical Elasticity”. In this paper, we present our practice and experience of employing an adaptive resource utilization scheme for Big Data workloads in container-based cloud environments by leveraging the vertical elasticity of Docker, a representative container-based virtualization technique. We perform extensive experiments running several Big Data workloads on representative Big Data platforms: Apache Hadoop and Spark. During the workload executions, our adaptive resource utilization scheme periodically monitors the resource usage patterns of running containers and dynamically adjusts allocated computing resources that could result in substantial improvements in the overall system throughput.
VVC In-Loop Filtering Based on Deep Convolutional Neural Network

With the rapid advancement in many multimedia applications, such as video gaming, computer vision applications, and video streaming and surveillance, video quality remains an open challenge. Despite the existence of the standardized video quality as well as high definition (HD) and ultrahigh definition (UHD), enhancing the quality for the video compression standard will improve the video streaming resolution and satisfy end user’s quality of service (QoS). Versatile video coding (VVC) is the latest video coding standard that achieves significant coding efficiency. VVC will help spread high-quality video services and emerging applications, such as high dynamic range (HDR), high frame rate (HFR), and omnidirectional 360-degree multimedia compared to its predecessor high efficiency video coding (HEVC). Given its valuable results, the emerging field of deep learning is attracting the attention of scientists and prompts them to solve many contributions. In this study, we investigate the deep learning efficiency to the new VVC standard in order to improve video quality. However, in this work, we propose a wide-activated squeeze-and-excitation deep convolutional neural network (WSE-DCNN) technique-based video quality enhancement for VVC. Thus, the VVC conventional in-loop filtering will be replaced by the suggested WSE-DCNN technique that is expected to eliminate the compression artifacts in order to improve visual quality. Numerical results demonstrate the efficacy of the proposed model achieving approximately <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mo>−</mo> <mn>2.85</mn> <mo>%</mo> </math> , <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mo>−</mo> <mn>8.89</mn> <mo>%</mo> </math> , and <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <mo>−</mo> <mn>10.05</mn> <mo>%</mo> </math> BD-rate reduction of the luma (Y) and both chroma (U, V) components, respectively, under random access profile.