Reviewerly/exHarmony huggingface dataset:
Multi-Queue Request Scheduling for Profit Maximization in IaaS Clouds

In cloud computing, service providers rent heterogeneous servers from cloud providers, i.e., Infrastructure as a Service (IaaS), to meet requests of consumers. The heterogeneity of servers and impatience of consumers pose great challenges to service providers for profit maximization. In this article, we transform this problem into a multi-queue model where the optimal expected response time of each queue is theoretically analyzed. A multi-queue request scheduling algorithm framework is proposed to maximize the total profit of service providers, which consists of three components: request stream splitting, requests allocation, and server assignment. A request stream splitting algorithm is designed to split the arriving requests to minimize the response time in the multi-queue system. An allocation algorithm, which adopts a one-step improvement strategy, is developed to further optimize the response time of the requests. Furthermore, an algorithm is developed to determine the appropriate number of required servers of each queue. After statistically calibrating parameters and algorithm components over a comprehensive set of random instances, the proposed algorithms are compared with the state-of-the-art over both simulated and real-world instances. The results indicate that the proposed multi-queue request scheduling algorithm outperforms the other algorithms with acceptable computational time.
Fall Event Detection System Using Inception-Densenet Inspired Sparse Siamese Network

A novel few-shot Siamese architecture inspired by Inception and Densenet architectures is proposed as a fall event detection system to detect fall events in signals obtained from waist-worn inertial measurement unit sensors. The proposed system consists of an Inception module followed by a relatively sparse Densenet-based module on each arm of the Siamese network to effectively learn feature representations for the detection of fall events. The proposed system is tested using the SisFall dataset. The proposed system's performance in a few-shot scenario is compared with fall detection systems based on the regular Inception and Densenet121 architectures and the state-of-the-art Siamese convolutional autoencoders. The proposed system outperforms all three fall detection systems. The proposed fall detection system achieved F-scores of 97 ±4% and 68.5 ±10% in 15-shot and 1-shot learning scenarios, respectively.
End-to-End Learning-Based Framework for Amplify-and-Forward Relay Networks

We study end-to-end learning-based frameworks for amplify-and-forward (AF) relay networks, with and without the channel state information (CSI) knowledge. The designed framework resembles an autoencoder (AE) where all the components of the neural network (NN)-based source and destination nodes are optimized together in an end-to-end manner, and the signal transmission takes place with an AF relay node. Unlike the literature that employs an NN-based relay node with full CSI knowledge, we consider a conventional relay node that only amplifies the received signal using CSI gains. Without the CSI knowledge, we employ power normalization-based amplification that normalizes the transmission power of each block of symbols. We propose and compare symbol-wise and bit-wise AE frameworks by minimizing categorical and binary cross-entropy loss that maximizes the symbol-wise and bit-wise mutual information (MI), respectively. We determine the estimated MI and examine the convergence of both AE frameworks with signal-to-noise ratio (SNR). For both these AE frameworks, we design coded modulation and differential coded modulation, depending upon the availability of CSI at the destination node, that obtains symbols in 2n-dimensions, where n is the block length. To explain the properties of the 2n-dimensional designs, we utilize various metrics like minimum Euclidean distance, normalized second-order and fourth-order moments, and constellation figures of merit. We show that both these AE frameworks obtain similar spherical coded-modulation designs in 2n-dimensions, and bit-wise AE that inherently obtains the optimal bit-labeling outperforms symbol-wise AE (with faster convergence under low SNR) and the conventional AF relay network with a considerable SNR margin.
Unveiling Fake Accounts at the Time of Registration: An Unsupervised Approach

Online social networks (OSNs) are plagued by fake accounts. Existing fake account detection methods either require a manually labeled training set, which is time-consuming and costly, or rely on rich information of OSN accounts, e.g., content and behaviors, which incurs significant delay in detecting fake accounts. In this work, we propose UFA (Unveiling Fake Accounts) to detect fake accounts immediately after they are registered in an unsupervised fashion. First, through a measurement study on the registration patterns on a real-world registration dataset, we observe that fake accounts tend to cluster on outlier registration patterns, e.g., IP and phone numbers. Then, we design an unsupervised learning algorithm to learn weights for all registration accounts and their features that reveal outlier registration patterns. Next, we construct a registration graph to capture the correlation between registration accounts, and utilize a community detection method to detect fake accounts via analyzing the registration graph structure. We evaluate UFA using real-world WeChat datasets. Our results demonstrate that UFA achieves a precision 94% with a recall ~80%, while a supervised variant requires 600K manual labels to obtain the comparable performance. Moreover, UFA has been deployed by WeChat to detect fake accounts for more than one year. UFA detects 500K fake accounts per day with a precision ~93% on average, via manual verification by the WeChat security team.
Complexity and Entropy in Legal Language

We study the language of legal codes from different countries and legal traditions, using concepts from physics, algorithmic complexity theory and information theory. We show that vocabulary entropy, which measures the diversity of the author’s choice of words, in combination with the compression factor, which is derived from a lossless compression algorithm and measures the redundancy present in a text, is well suited for separating different writing styles in different languages, in particular also legal language. We show that different types of (legal) text, e.g. acts, regulations or literature, are located in distinct regions of the complexity-entropy plane, spanned by the information and complexity measure. This two-dimensional approach already gives new insights into the drafting style and structure of statutory texts and complements other methods.
Tourist Demand Prediction Model Based on Improved Fruit Fly Algorithm

To accurately predict the development and change trend of the future, tourism market can effectively improve the planning and purpose of tourism development. In order to improve the accuracy of tourist demand prediction, this paper studies the tourist demand prediction model based on improved fruit fly algorithm. Aiming at the optimization defects of the traditional fruit fly optimization algorithm (FOA), the model introduces two concepts of sensitivity and pheromone, improves the optimization strategy and position replacement of fruit fly, improves the diversity of fruit fly population, modifies the global optimization characteristics of the algorithm, and improves the local search ability and search efficiency of the algorithm. By combining the improved AFOA with echo state network (ESN), a two-stage combined prediction model (AAFOA-ESN) is constructed. The experimental results show that the minimum prediction error accuracy of the model is only 0.55%, which has more robust prediction effect, faster convergence speed, and higher prediction accuracy.
A SURVEY ON SIMILARITY MEASURES AND MACHINE LEARNING ALGORITHMS FOR CLASSIFICATION AND PREDICTION

An important observation which figures out when we look into several applications which are the result of applying data science, machine learning, and deep learning techniques is that most of these techniques are based on the concept of measuring similarity between any two vectors. These vectors may act as representatives for objects being considered. Similarity measurement thus gains a great importance in the design of machine learning or deep learning algorithms and techniques. In similar lines, when we are required to carry a supervised or unsupervised learning task, an algorithm is required to carry the task efficiently. Thus, in this paper, our objective is to outline various similarity measures that have been considered for carrying supervised or unsupervised learning tasks and also to throw light on different machine learning algorithms employed for supervised and unsupervised learning tasks from disease classification and prediction point of view and also interdisciplinary domains such as time series analysis, temporal data mining, medical data mining, and anomaly or intrusion detection.
Improved Document Categorization Through Feature-Rich Combinations

Several comparatives studies report new findings relevant to the Text Categorization (TC) task, and all provide valuable observations. However, many of them addressed western languages, especially English. By writing this paper, we take a step toward filling this gap and focus on less commonly investigated languages (i.e., Arabic) to provide a more balanced perspective. In that respect, this paper presents a deeper investigation regarding the performance of some well-known probabilistic methods successfully implemented for automatic TC, such as Naïve Bayesian, Support Vector Machines, and Decision Tree. Besides, the investigation covers pre-processing techniques and feature selection methods that deal with data’s high dimensionality. Expressly, stop words elimination, stemming, and lemmatization are the pre-processing techniques included along with the TF-IDF and Chi-square as the feature selection methods. Moreover, all possible combinations are considered. To make this study accurate and comprehensive, we trained and evaluated the selected classifiers and the pre-processing techniques on common ground. To this end, we used an in-house balanced and large corpus with 300,000 news articles which are equally distributed into six categories. The findings obtained prove the effectiveness of combining the pre-processing techniques, feature selection methods, and classifiers.
Improving Person Reidentification Using a Self-Focusing Network in Internet of Things

Person reidentification (re-ID), which is a significant and potential application in the Internet of Things (IoT), aims to retrieve pedestrians of interest given a labeled image in a camera network. Now, it is still existing many challenges that severely influence feature representation in practical scenarios. Many methods adopt the attention mechanism in convolutional neural network (CNN) to improve the ability of feature learning. Although they only apply 1-D attention block in the popular deep learning architecture, the learned features are not discriminative for the feature representation. In this work, we investigate a self-focusing network (SFNet) that considers both the channel-dimensional attention and spatial-dimensional attention to adaptively learn more discriminative features. Namely, we embed the new attention module into the common backbone network, which can focus on the salient region by inhibiting the redundant features. Specifically, we design eight variants of the channel-dimensional attention and spatial-dimensional attention throughout the entire network and explore the most powerful feature representation. The heatmaps of different layers are visualized to intuitively present the performance of SFNet. Furthermore, we compare SFNet with the prior work on three popular person re-ID benchmarks by abundant experiments.
Convolutional Siamese networks for one-shot malaria parasite recognition in microscopic images

Malaria is a life-threatening, mosquito-borne disease caused by a Plasmodium parasite that is commonly spread to humans from the bite of Anopheles mosquitoes. It is widespread across the world, particularly in tropical regions, and if not treated in time, it can be deadly. Thus, early and effective treatment and diagnosis of malaria will save lives. Modern machine learning and image analysis methods are practiced on microscopic images of blood smears by using hand-crafted features that need expertise in interpreting these features. In contrast, convolutional neural networks (CNNs) have proved to be adequate in feature extraction and classification of parasite detection toward malaria diagnosis. CNNs' techniques can extract highly influenced features, determine filters to serve as an effective prediction that provides an accurate detection to aid malaria diagnosis. The motivation of the research is to state the significance of deep learning techniques in the diagnosis of malaria. This research proposes the convolution Siamese network (CSN) for a one-shot recognition method for parasite recognition in microscopic images. In this study, microscopic images were used to examine the performance of the CSN model, and it will assist in diagnosing various infectious diseases rapidly. CSN is implemented in a two-phase criteria to detect similarity or dissimilarity. The first phase consists of extracting detailed features using a fully connected convolutional block and the second phase consists of a similarity check where inputs are discriminated with similarity measures (L1, L2). This discriminated feature is fed forward to the final layer with a sigmoid function that depicts whether it is similar to the given input. Further, the proposed model is evaluated to attain optimum generalization, and its performance is captured by splitting data into identical divisions (50–50). As the model is trained only on half the malaria blood samples, it gives precisive generalization and attains malaria test accuracy scores of 87.10% and 87.38% with L1 and L2, respectively. We also depicted the mean square error (MSE) rate and confusion matrix for each to incorporate the performance for similar and dissimilar identification individually. The evolution of these metrics described the stability of the proposed network. Experiments and results have proven that the proposed CSN can leverage performance and build comprehensive models with fewer samples. The obtained result shows the potential of the Siamese network in the field of medical diagnostic tools. This research is the first study to construct and assess the Siamese network diagnostic model toward malaria diagnosis.
Abduction as “Leading Away”

In this article I will take advantage of the logical and cognitive studies I have illustrated in my recent book The Abductive Structure of Scientific Creativity. An Essay on the Ecology of Cognition (2017), in which the process of building new hypotheses is clarified thanks to my eco-cognitive model (EC-Model) of abduction. Also resorting to a new interpretation of Aristotle’s seminal work on abduction, I will emphasize the crucial role played in abductive cognition by the so-called “optimization of eco-cognitive openness and situatedness”. Indeed, in the chapter B25 of Prior Analytics concerning Open image in new window (“leading away”), we can see some of the current well-known distinctive characters of abductive cognition already expressed, which are in tune with the EC-Model: Aristotle is still pointing to the fundamental inferential role in reasoning of those externalities that substantiate the process of “leading away” ( Open image in new window ). Hence, we can gain a new positive perspective about the “constitutive” eco-cognitive character of abduction, just thanks to Aristotle himself. Situatedness is related to eco-cognitive aspects: to favor the solution of the abductive problem input and output of the formula $$\begin{aligned} \Lambda _1,\ldots ,\Lambda _i,?_I\Vdash _L^X\Upsilon _1,\ldots ,.\Upsilon _j \end{aligned}$$have to be thought as optimally positioned: indeed I also contend than a disregarded issue concerning abduction is related to the current lack of knowledge about what I call “discoverability” and “diagnosticability”. In the formula above \(\Vdash _L^X\) indicates that inputs and outputs do not stand each other in an expected relation and that the modification of the inputs \(?_I\) can provide the abductive solution. In general, in this characterization the direction is not from evidence/premises to abductive result but the forward fashion is adopted, where the inferential parameter \(\Vdash \) sets some appropriate logical relationship between an input which consists in both the abductive guess to be found and a background theory (or just some premisses), and an output—for example an evidence, a novel phenomenon to be abductively “explained” through facts, rules, or even new theories. Further, in the case of scientific settings, this optimality is made possible by a maximization of changeability of both input and output: not only inputs have to be enriched with the possible solution but, to do that, other inputs have usually to be changed and/or modified. This changeability first of all refers to a wide epistemological openness.
Neighbor-Anchoring Adversarial Graph Neural Networks

Graph neural networks (GNNs) have witnessed widespread adoption due to their ability to learn superior representations for graph data. While GNNs exhibit strong discriminative power, they often fall short of learning the underlying node distribution for increased robustness. To deal with this, inspired by generative adversarial networks (GANs), we investigate the problem of adversarial learning on graph neural networks, and propose a novel framework named NAGNN (i.e., Neighbor-anchoring Adversarial Graph Neural Networks) for graph representation learning, which trains not only a discriminator but also a generator that compete with each other. In particular, we propose a novel neighbor-anchoring strategy, where the generator produces samples with explicit features and neighborhood structures anchored on a reference real node, so that the discriminator can perform neighborhood aggregation on the fake samples to learn superior representation. The advantage of our neighbor-anchoring strategy can be demonstrated both theoretically and empirically. Furthermore, as a by-product, our generator can synthesize realistic-looking features, enabling potential applications such as automatic content summarization. Finally, we conduct extensive experiments on four public benchmark datasets, and achieve promising results under both quantitative and qualitative evaluations.
Procedural Content Generation: Better Benchmarks for Transfer Reinforcement Learning

The idea of transfer in reinforcement learning (TRL) is intriguing: being able to transfer knowledge from one problem to another problem without learning everything from scratch. This promises quicker learning and learning more complex methods. To gain an insight into the field and to detect emerging trends, we performed a database search. We note a surprisingly late adoption of deep learning that starts in 2018. The introduction of deep learning has not yet solved the greatest challenge of TRL: generalization. Transfer between different domains works well when domains have strong similarities (e.g. MountainCar to Cartpole), and most TRL publications focus on different tasks within the same domain that have few differences. Most TRL applications we encountered compare their improvements against self-defined baselines, and the field is still missing unified benchmarks. We consider this to be a disappointing situation. For the future, we note that: (1) A clear measure of task similarity is needed. (2) Generalization needs to improve. Promising approaches merge deep learning with planning via MCTS or introduce memory through LSTMs. (3) The lack of benchmarking tools will be remedied to enable meaningful comparison and measure progress. Already Alchemy and Meta-World are emerging as interesting benchmark suites. We note that another development, the increase in procedural content generation (PCG), can improve both benchmarking and generalization in TRL.
Graph-state representation of the toric code

Given their potential for fault-tolerant operations, topological quantum states are currently the focus of intense activity. Of particular interest are topological quantum error correction codes, such as the surface and planar stabilizer codes that are equivalent to the celebrated toric code. While every stabilizer state maps to a graph state under local Clifford operations, the graphs associated with topological stabilizer codes remain unknown. We show that the toric code graph is composed of only two kinds of subgraphs: star graphs (which encode Greenberger-Horne-Zeilinger states) and half graphs. The topological order is identified with the existence of multiple star graphs, which reveals a connection between the repetition and toric codes. The graph structure readily yields a log-depth quantum circuit for state preparation, assuming geometrically non-local gates, which can be reduced to a constant depth including ancillae and measurements at the cost of increasing the circuit width. The results provide a new graph-theoretic framework for the investigation of topological order and the development of novel topological error correction codes.
Upper Palaeolithic sites and where to find them: A predictive modelling approach to assess site expectancy in the Southern Levant

Site preservation and associated terrain characteristics are among the major agents controlling the spatial distribution and discoverability of late Quaternary archaeological sites. However, site expectancy in extensive areas can be determined using a combination of geoarchaeological investigation and digital spatial analysis. In that sense a prediction model, respectively the result of a Multi Criteria Decision Analysis (MCDA), is presented to highlight areas with a high potential to contain Upper Palaeolithic sites in the Eastern Mediterranean and bordering arid margins. Based on detailed on-site analysis in the Wadi Sabra region, with its many sites and the location of other well-known sites in the southern Levant, eight individual parameters, aspect (cardinal point), elevation, geomorphology, hydrogeology, drainage network, slope inclination, vegetation, and a terrain ruggedness index (TRI) are evaluated and tested for significance. The statistical results confirm a significant divergence of the spatial distribution of Upper Palaeolithic sites from the distribution of natural terrain position factors. This allows for a definition of parameter classes with a high site expectancy and their use in a predictive model. For the results map, the site expectancy factors determined in this way were intersected with each other, thus enabling a distinction between areas with a high and low site occurrence probability. The accuracy of the presented result is assessed by the implementation of another MCDA pass based on an independent dataset of archaeological survey sites and compared to the original. Both datasets complement each other and can show the potential for further investigations with more numerous site datasets as well as more detailed spatial information. A local classification based on high-resolution elevation information and field data is evaluated for the Wadi Sabra, Southern Jordan, which can confirm yet further specify the result from the MCDA.