Reviewerly/exHarmony huggingface dataset:
Reinforcement Learning-Based Resource Partitioning for Improving Responsiveness in Cloud Gaming

Cloud gaming has been very popular in recent years, but issues relating to maintaining low interaction delay to guarantee satisfactory user experience are still prevalent. We observe that the server-side processing delay in cloud gaming system could be heavily influenced by how the resources are partitioned among processes. However, finding the optimal partitioning policy that minimizes the response delay faces several critical challenges. First, fine-grained resource partitioning is non-trivial due to the limitations of hardwre-based resource isolation techniques. Second, game wokload is highly dynamic and unpredictable, making the design of efficient resource partitioning policy more challenging. In this article, we propose an online resource partitioning framework for reducing response delay in cloud gaming, which has several promising properties. First, we divide the processes into disjoint groups and partition resources among process groups, which greatly simplifies the resource partitioning problem while ensuring high partitioning effectiveness. Second, to tackle dynamic workload changes, we classify game workloads into several clusters and maintain separate process grouping plan for each cluster. Third, we leverage reinforcement learning to adaptively choose the best actions for minimizing response delay in real time. We evaluate the proposed framework in a real cloud gaming environment using several real games. The experimental results show that our approach can reduce the response delay by 22 to 41 percent compared to a system without resource partitioning, and outperforms other resource partitioning policies significantly.
Techniques of Steganography and Cryptography in Digital Transformation

Digital enterprise transformation is the amalgamation of digital techniques into the scopes of a business enterprise, fundamentally altering how one can employ and furnish ethics to clients. An organization can be authorized to take digital transformation due to several reasons. But the most important reason is that it is the survival issuance for many people. Digital transformation considers dissimilarity in every organization. Generally, it is the amalgamation of digital technology into all quarters of business. That consolidation brings about major alterations in how the business functions and conveys usefulness to its clients. Here, steganography and cryptography are used to facilitate digital transformation in any business.
DRMaestro: orchestrating disaggregated resources on virtualized data-centers

Abstract Modern applications demand resources at an unprecedented level. In this sense, data-centers are required to scale efficiently to cope with such demand. Resource disaggregation has the potential to improve resource-efficiency by allowing the deployment of workloads in more flexible ways. Therefore, the industry is shifting towards disaggregated architectures, which enables new ways to structure hardware resources in data centers. However, determining the best performing resource provisioning is a complicated task. The optimality of resource allocation in a disaggregated data center depends on its topology and the workload collocation. This paper presents DRMaestro , a framework to orchestrate disaggregated resources transparently from the applications. DRMaestro uses a novel flow-network model to determine the optimal placement in multiple phases while employing best-efforts on preventing workload performance interference. We first evaluate the impact of disaggregation regarding the additional network requirements under higher network load. The results show that for some applications the impact is minimal, but other ones can suffer up to 80% slowdown in the data transfer part. After that, we evaluate DRMaestro via a real prototype on Kubernetes and a trace-driven simulation. The results show that DRMaestro can reduce the total job makespan with a speedup of up to ≈1.20x and decrease the QoS violation up to ≈2.64x comparing with another orchestrator that does not support resource disaggregation.
An Optimized Deep Residual Network with a Depth Concatenated Block for Handwritten Characters Classification

Even though much advancements have been achieved with regards to the recognition of handwritten characters, researchers still face difficulties with the handwritten character recognition problem, especially with the advent of... | Find, read and cite all the research you need on Tech Science Press
Overview of Touché 2021: Argument Retrieval

Technologies for argument mining and argumentation analysis are maturing rapidly, so that, as a result, the retrieval of arguments in search scenarios becomes a feasible objective. For the second time, we organize the Touché lab on argument retrieval with two shared tasks: (1) argument retrieval for controversial questions, where arguments are to be retrieved from a focused debate portal-based collection and, (2) argument retrieval for comparative questions, where argumentative documents are to be retrieved from a generic web crawl. In this paper, we briefly summarize the results of Touché 2020, the first edition of the lab, and describe the planned setup for the second edition at CLEF 2021.
Reliably fast adversarial training via latent adversarial perturbation

While multi-step adversarial training is widely popular as an effective defense method against strong adversarial attacks, its computational cost is notoriously expensive, compared to standard training. Several single-step adversarial training methods have been proposed to mitigate the above-mentioned overhead cost; however, their performance is not sufficiently reliable depending on the optimization setting. To overcome such limitations, we deviate from the existing input-space-based adversarial training regime and propose a single-step latent adversarial training method (SLAT), which leverages the gradients of latent representation as the latent adversarial perturbation. We demonstrate that the ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> norm of feature gradients is implicitly regularized through the adopted latent perturbation, thereby recovering local linearity and ensuring reliable performance, compared to the existing single-step adversarial training methods. Because latent perturbation is based on the gradients of the latent representations which can be obtained for free in the process of input gradients computation, the proposed method costs roughly the same time as the fast gradient sign method. Experiment results demonstrate that the proposed method, despite its structural simplicity, outperforms state-of-the-art accelerated adversarial training methods.
Different metal sources in the evolution of an epithermal ore system: Evidence from mercury isotopes associated with the Erdaokan epithermal Ag-Pb-Zn deposit, NE China

Epithermal metal deposits along convergent plate margins are of large economic value, however, the origin of the typical metal spectrum (Au, Ag, Sb, As, Hg) in these deposits remains debated. Here, we study the Hg isotope geochemistry of the vein-style Erdaokan Ag-Pb-Zn deposit of epithermal type in the eastern part of the Central Asian Orogenic Belt in NE China, to provide direct constraints on its metal source. Bulk ore samples and early-stage hydrothermal minerals (magnetite, pyrite, and quartz) show negative δ202Hg (−1.52 to −0.18‰) and positive Δ199Hg (−0.03 to 0.17‰) values similar to those of previously reported epithermal Au deposits in NE China, and also of marine systems in general. However, late-stage calcite shows weak negative to positive δ202Hg (-0.61 to 0.90‰) and significant negative Δ199Hg (−0.18 to −0.02‰), similar to the Late Paleozoic sandstone country rocks (δ202Hg, −1.09 to 0.76‰; Δ199Hg, −0.09 to 0.04‰). Hydrothermally altered gabbro and diorite show large variations in δ202Hg (−1.83 to 0.50‰) and Δ199Hg (−0.14 to 0.15‰) which overlap with those of the ore and country rocks, indicating a mixed source. The Δ199Hg signature in the bulk ore and early-stage hydrothermal minerals likely derives from dehydration of the subducted oceanic slab, whereas Hg in late-stage calcite is likely derived from the sedimentary country rocks. Hydrothermal deposits in different tectonic settings have distinct Δ199Hg signals, and our study suggests that Hg isotopes are a robust tool to distinguish metal sources of hydrothermal ore deposits.
Learning 3D face reconstruction from a single sketch

3D face reconstruction from a single image is a classic computer vision problem with many applications. However, most works achieve reconstruction from face photos, and little attention has been paid to reconstruction from other portrait forms. In this paper, we propose a learning-based approach to reconstruct a 3D face from a single face sketch. To overcome the problem of no paired sketch-3D data for supervised learning, we introduce a photo-to-sketch synthesis technique to obtain paired training data, and propose a dual-path architecture to achieve synergistic 3D reconstruction from both sketches and photos. We further propose a novel line loss function to refine the reconstruction with characteristic details depicted by lines in sketches well preserved. Our method outperforms the state-of-the-art 3D face reconstruction approaches in terms of reconstruction from face sketches. We also demonstrate the use of our method for easy editing of details on 3D face models.
Privacy‐preserving statistical computing protocols for private set intersection

With the rapid development of Internet and the widespread application of distributed computing, people enjoy various conveniences while at the same time their privacy has also been threatened. Secure multiparty computation (MPC) can solve the problem of how data owners who do not trust each other jointly compute in distributed scenarios. Using MPC technique, people can not only realize data joint computing, but also ensure data privacy. In most data application scenarios, private data held by different parties can often be represented by sets. To complete the relevant statistical computations of the intersection of two private sets, we propose a suite of protocols based on MPC. These protocols can compute the statistical functions of the associated data of the intersection, including cardinality, sum, average, variance, range, and so forth, without revealing any additional information other than the result. To achieve these functions, we design a private membership test protocol with the result as the arithmetic sharing value, called the arithmetic shared private membership test (ASPMT) protocol. On the basis of the ASPMT protocol, the size and other statistics of the intersection can be computed securely and efficiently. All fundamental computations are constructed based on secret sharing and oblivious transfer techniques. Thanks to the use of precomputation technique, all protocols are highly efficient.
An Experimental Study of Weight Initialization and Lamarckian Inheritance on Neuroevolution

Weight initialization is critical in being able to successfully train artificial neural networks (ANNs), and even more so for recurrent neural networks (RNNs) which can easily suffer from vanishing and exploding gradients. In neuroevolution, where evolutionary algorithms are applied to neural architecture search, weights typically need to be initialized at three different times: when the initial genomes (ANN architectures) are created, when offspring genomes are generated by crossover, and when new nodes or edges are created during mutation. This work explores the difference between the state-of-the-art Xavier and Kaiming methods, and novel Lamarckian weight inheritance for weight initialization during crossover and mutation operations. These are examined using the Evolutionary eXploration of Augmenting Memory Models (EXAMM) neuroevolution algorithm, which is capable of evolving RNNs with a variety of modern memory cells (e.g., LSTM, GRU, MGU, UGRNN and Delta-RNN cells) as well as recurrent connections with varying time skips through a high performance island based distributed evolutionary algorithm. Results show that with statistical significance, the Lamarckian strategy outperforms both Kaiming and Xavier weight initialization, can speed neuroevolution by requiring less backpropagation epochs to be evaluated per genome, and that the neuroevolutionary process provides further benefits to neural network weight optimization.
LOPDF: a framework for extracting and producing open data of scientific documents for smart digital libraries

Results of scientific experiments and research work, either conducted by individuals or organizations, are published and shared with scientific community in different types of scientific publications such as books, chapters, journals, articles, reference works and reference works entries. One aspect of these documents is their contents and the other is metadata. Metadata of scientific documents could be used to increase mutual cooperation, find people with common interest and research work, and to find scientific documents in the matching domains. The major issue in getting these benefits from metadata of scientific publications is availability of these data in unstructured (or semi-structured) format so that it can not be used to ask smart queries that can help in computing and performing different types of analysis on scientific publications data. Also, acquisition and smart processing of publications data is a complicated as well as time and resource consuming task.To address this problem we have developed a generic framework named as Linked Open Publications Data Framework (LOPDF). The LOPDF framework can be used to crawl, process, extract and produce machine understandable data (i.e., LOD) about scientific publications from different publisher specific sources such as portals, XML export and websites. In this paper we present the architecture, process and algorithm that we developed to process textual publications data and to produce semantically enriched data as RDF datasets (i.e., open data).The resulting datasets can be used to make smart queries by making use of SPARQL protocol. We also present the quantitative as well as qualitative analysis of our resulting datasets which ultimately can be used to compute the research behavior of organizations in rapidly growing knowledge society. Finally, we present the potential usage of producing and processing such open data of scientific publications and how results of performing smart queries on resulting open datasets can be used to compute the impact and perform different types of analysis on scientific publications data.
Imbalanced Learning with Oversampling based on Classification Contribution Degree

Abstract Imbalanced datasets exist commonly in the real world, which leads to poor performance of general machine learning models because of skewed class distribution. To address the data‐imbalance problem, a novel oversampling method based on classification contribution degree, called OS‐CCD is presented. First a new concept, classification contribution degree, is established based on micro and macro information extracted from raw datasets. With the classification contribution degree, OS‐CCD enables positive samples near the class boundary and located in an area with high density of positive samples to generate more synthetic samples than others. Furthermore, the neighbor selection for oversampling is no longer random but in the light of a selected probability. Experimental results on 12 benchmark datasets substantiate that four commonly used classifiers with the oversampling method outperform those with six popular oversampling methods in terms of accuracy, F1‐score and AUC.
Multimodal Joint Head Orientation Estimation in Interacting Groups via Proxemics and Interaction Dynamics

Human head orientation estimation has been of interest because head orientation serves as a cue to directed social attention. Most existing approaches rely on visual and high-fidelity sensor inputs and deep learning strategies that do not consider the social context of unstructured and crowded mingling scenarios. We show that alternative inputs, like speaking status, body location, orientation, and acceleration contribute towards head orientation estimation. These are especially useful in crowded and in-the-wild settings where visual features are either uninformative due to occlusions or prohibitive to acquire due to physical space limitations and concerns of ecological validity. We argue that head orientation estimation in such social settings needs to account for the physically evolving interaction space formed by all the individuals in the group. To this end, we propose an LSTM-based head orientation estimation method that combines the hidden representations of the group members. Our framework jointly predicts head orientations of all group members and is applicable to groups of different sizes. We explain the contribution of different modalities to model performance in head orientation estimation. The proposed model outperforms baseline methods that do not explicitly consider the group context, and generalizes to an unseen dataset from a different social event.
Strumming to the Beat: Audio-Conditioned Contrastive Video Textures

We introduce a non-parametric approach for infinite video texture synthesis using a representation learned via contrastive learning. We take inspiration from Video Textures [43], which showed that plausible new videos could be generated from a single one by stitching its frames together in a novel yet consistent order. This classic work, however, was constrained by its use of hand-designed distance metrics, limiting its use to simple, repetitive videos. We draw on recent techniques from self-supervised learning to learn this distance metric, allowing us to compare frames in a manner that scales to more challenging dynamics, and to condition on other data, such as audio. We learn representations for video frames and frame-to-frame transition probabilities by fitting a video-specific model trained using contrastive learning. To synthesize a texture, we randomly sample frames with high transition probabilities to generate diverse temporally smooth videos with novel sequences and transitions. The model naturally extends to an audio-conditioned setting without requiring any finetuning. Our model outperforms baselines on human perceptual scores, can handle a diverse range of input videos, and can combine semantic and audiovisual cues in order to synthesize videos that synchronize well with an audio signal.
STUDENT PERCEPTIONS OF ECUADORIAN VIRTUAL PLATFORMS DURING THE COVID-19 PANDEMIC

In February 2020 Ecuador declared a health emergency due to the Covid-19 pandemic. On-site classes were suspended. The Ecuadorian university population does not adapt to virtual educational platforms. The objective of the study was to analyse student perception of Ecuadorian educational platforms in higher education during the COVID-19 pandemic. The study was quantitative. The sample was non-probabilistic participatory, consisting of students from different universities in the Province of Chimborazo. For data collection, a questionnaire with four study variables was applied. The results were analysed with T student and MANOVA. There is a positive perception of students using the educational platforms. There were significant statistical differences according to gender variables, contrary to the levels of study and location, there is a greater sympathetic affection of students when receiving virtual classes by female teachers. It can be concluded that the Covid-19 pandemic accelerated the adoption of online learning by higher education institutions. Keywords: Covid-19, virtual education, higher education, Ecuador, education platform, virtual education, Ecuador
Adaptive Covariance Pattern Search

Pattern search is a family of single solution deterministic optimisation algorithms for numerical optimisation. Pattern search algorithms generate a new candidate solution by means of an archive of potential moves, named pattern. This pattern is generated by a basis of vectors that span the domain where the function to optimise is defined. The present article proposes an adaptive implementation of pattern search that performs, at run-time, a fitness landscape analysis of the problem to determine the pattern and adapt it to the geometry of the problem. The proposed algorithm, called Adaptive Covariance Pattern Search (ACPS) uses at the beginning the fundamental orthonormal basis (directions of the variables) to build the pattern. Subsequently, ACPS saves the successful visited solutions, calculates the covariance matrix associated with these samples, and then uses the eigenvectors of this covariance matrix to build the pattern. ACPS is a restarting algorithm that at each restart recalculates the pattern that progressively adapts to the problem to optimise. Numerical results show that the proposed ACPS appears to be a promising approach on various problems and dimensions.
A new feature extraction approach for script invariant handwritten numeral recognition

Abstract Handwritten numeral recognition is a challenging research problem because of the enormous varieties of styles in which human beings write the numerals. Several researchers have tried to find solutions to this problem with exceptional recognition accuracies. However, most of these solutions have been dedicated to single script numerals. Such methods are inappropriate for multi‐lingual nations such as India where a large number of scripts are used. Keeping this issue in mind, a new feature descriptor named symbolization of binary images ( SBI ) is introduced here for the recognition of handwritten numerals of different scripts. Effectiveness of SBI is supported with experiments showing its script‐invariant nature. Classification of numerals using a multiclass support vector machine (SVM) classifier yields the recognition accuracies of 98.18, 96.22, 96.52, and 95.53% on datasets of numerals written in four popular scripts of the world: Arabic , Bangla , Devanagari , and Latin , respectively. This scheme has also been extended to the situation when the script used is not known a priori or the numerals written in a document belong to pairs of mixed scripts of { Arabic , Devanagari , Bangla } with Latin producing recognition rates of 92.97, 91.25, and 91.67%, respectively. When all four scripts are mixed, the recognition rate is still 90.98% overall. Encouraging outcomes suggest that the proposed SBI feature descriptor can recognize numerals invariant of the script class.