Reviewerly/exHarmony huggingface dataset:
Topic Modeling and Sentiment Analysis with LDA and NMF on Moroccan Tweets

Twitter is one of the most popular social media platforms. Due to its simplicity of use and the services provided by twitter API, it is extensively used around the world, including Morocco. It provides huge volume of information and is considered as a large source of data for opinion mining. The aim of this paper is to analyze Moroccan tweets, in order to generate some useful statistics, identify different sentiments, and extract then visualize predominant topics. In our research work, we collected 25 146 tweets using Twitter API and python language, and stored them into MongoDB database. Stored tweets were preprocessed by applying natural language processing techniques (NLP) using NLTK library. Then, we performed sentiment analysis which classifies the polarity of twitter comments into negative, positive, and neutral categories. Finally, we applied topic modeling over the tweets to obtain meaningful data from Twitter, comparing and analyzing topics detected by two popular topic modeling algorithms; Non-negative Matrix Factorization (NMF) and Latent Dirichlet Allocation (LDA). The observed results show that LDA outperforms NMF in terms of their topic coherence.
Relevant Region Exploration On General Cost-maps For Sampling-Based Motion Planning

Asymptotically optimal sampling-based planners require an intelligent exploration strategy to accelerate convergence. After an initial solution is found, a necessary condition for improvement is to generate new samples in the so-called "Informed Set". However, Informed Sampling can be ineffective in focusing search if the chosen heuristic fails to provide a good estimate of the solution cost. This work proposes an algorithm to sample the "Relevant Region" instead, which is a subset of the Informed Set. The Relevant Region utilizes cost-to-come information from the planner's tree structure, reduces dependence on the heuristic, and further focuses the search. Benchmarking tests in uniform and general cost-space settings demonstrate the efficacy of Relevant Region sampling.
CYBERSECURITY AS AN ELEMENT OF FINANCIAL SECURITY IN THE CONDITIONS OF GLOBALIZATION

The methodological foundations of the formation of analytical support for public administration of cybersecurity have been improved.Based on a combination of hierarchical and non-hierarchical clustering methods using the IBM SPSS Statistics package, the country's regions have been grouped into four clusters, which is the basis for adjusting the priorities of the state cybersecurity policy, a well-grounded approach when choosing means and instruments of influence at the regional level.In modern doctrine and practice of international law, the issues of qualification of cyber warfare remain controversial.There are approaches to justify the application of international humanitarian and criminal law.The most justified, in our opinion, is the qualification of cyber warfare as a violation of the UNO Charter and the use of force, and in some cases -the crime of aggression.The substantive rules of the institution of international cooperation in the fight against cybercrime determine the special principles of this kind of cooperation, the criminalization of certain types of illegal acts, as well as institutional mechanisms and capacity building.The system of international combating cybercrime is based on the principles of technical neutrality, multi-stakeholderism (public-private partnerships), as well as the equivalence of human rights online and offline.In the future, cybercrime will be associated with the use of innovative technologies.As it has been established by the example of the Internet of things, the latest technology, as a general rule, is included in the scope of existing international agreements on cybercrime, but there is no special regulation for them.We propose formal consolidation of the provision on "emergent technologies" in the texts of international legal acts in the field of combating cybercrime.First of all, this concerns the future UNO Convention on the fight against cybercrime, which should also provide for an additional body, such as the T-CY Committee under the Council of Europe Convention, which will provide clarification on the application of the agreement in specific changed circumstances.
Customized Quantum Annealing Schedules

In a typical quantum annealing protocol, the system starts with a transverse field Hamiltonian that is gradually turned off and replaced by a longitudinal Ising Hamiltonian. The ground state of the Ising Hamiltonian encodes the solution to the computational problem of interest, and the state overlap with this ground state gives the success probability of the annealing protocol. The form of the annealing schedule can have a significant impact on the ground-state overlap at the end of the anneal, so precise control over these annealing schedules can be a powerful tool for increasing success probabilities of annealing protocols. Here we show how superconducting circuits, in particular capacitively shunted flux qubits, can be used to construct quantum annealing systems by providing tools for mapping circuit flux biases to Pauli coefficients. We use this mapping to find customized annealing schedules: appropriate circuit control biases that yield a desired annealing schedule, while accounting for the physical limitations of the circuitry. We then provide examples and proposals that utilize this capability to improve quantum annealing performance.
EDS-MEMBED: Multi-sense embeddings based on enhanced distributional semantic structures via a graph walk over word senses

Several language applications often require word semantics as a core part of their processing pipeline either as precise meaning inference or semantic similarity. Multi-sense embeddings (m-se) can be exploited for this important requirement. m-se seeks to represent each word by their distinct senses in order to resolve the conflation of meanings of words as used in different contexts. Previous works usually approach this task by training a model on a large corpus and often ignore the effect and usefulness of the semantic relations offered by lexical resources. However, even with large training data, coverage of all possible word senses is still an issue. In addition, a considerable percentage of contextual semantic knowledge are never learned because a huge amount of possible distributional semantic structures are never explored. In this paper, we leverage the rich semantic structures in WordNet using a graph-theoretic walk technique over word senses to enhance the quality of multi-sense embeddings. This algorithm composes enriched texts from the original texts. Furthermore, we derive new distributional semantic similarity measures for m-se from prior ones. We adapt these measures to word sense disambiguation (wsd) aspect of our experiment. We report evaluation results on 11 benchmark datasets involving wsd and Word Similarity tasks and show that our method for enhancing distributional semantic structures improves embeddings quality on the baselines. Despite the small training data, it achieves state-of-the-art performance on some of the datasets.
Extracting Arguments Based on User Decisions in App Reviews

Review mining from app marketplaces has gained immense popularity from researchers in recent years.Most studies in this area, however, tend to focus on improving the performance of classification prediction.In this study, we consider review mining from a different perspective, that is, mining user actions/decisions along with their respective arguments/reasons.Our motivation is to obtain a deeper understanding of users' decisions regarding applications and their underlying justifications, e.g., why users give ratings or recommendations.These information abstractions can benefit app developers, especially in planning app updates, by providing data-driven requirements from users' points of view.We utilized a supervised learning approach and built a machine-based annotator to set the ground truth.Seven classifiers and different feature configurations were trained and evaluated on two app review datasets.We then extracted relations between user decisions and arguments based on functional and nonfunctional requirement attributes.The results show an improved performance over the results of the baselines and favorably acceptable performance compared to the results from a human assessment.
Gaussian Process-Aided Function Comparison Using Noisy Scattered Data

This work proposes a nonparametric method to compare the underlying mean functions given two noisy datasets. The motivation for the work stems from an application of comparing wind turbine power curves. Comparing wind turbine data presents new problems, namely the need to identify the regions of difference in the input space and to quantify the extent of difference that is statistically significant. Our proposed method, referred to as funGP, estimates the underlying functions for different data samples using Gaussian process models. We build a confidence band using the probability law of the estimated function differences under the null hypothesis. Then, the confidence band is used for the hypothesis test as well as for identifying the regions of difference. This identification of difference regions is a distinct feature, as existing methods tend to conduct an overall hypothesis test stating whether two functions are different. Understanding the difference regions can lead to further practical insights and help devise better control and maintenance strategies for wind turbines. The merit of funGP is demonstrated by using three simulation studies and four real wind turbine datasets.
Learning Features for Writer Identification from Handwriting on Papyri

Computerized analysis of historical documents has remained an interesting research area for the pattern classification community for many decades. From the perspective of computerized analysis, key challenges in the historical manuscripts include automatic transcription, dating, retrieval, classification of writing styles and identification of scribes etc. Among these, the focus of our current study lies on identification of writers from the digitized manuscripts. We exploit convolutional neural networks for extraction of features and characterization of writer. The ConvNets are first trained on contemporary handwriting samples and then fine-tuned to the limited set of historical manuscripts considered in our study. Dense sampling is carried out over a given manuscript producing a set of small writing patches for each document. Decisions on patches are combined using a majority vote to conclude the authorship of a query document. Preliminary experiments on a set of challenging and degraded manuscripts report promising performance.
Feeless Micropayments as Drivers for New Business Models: Two Exemplary Application Cases

Upcoming cryptocurrency approaches claim to enable feeless micropayments. Assuming that this promise will be kept, this is likely to give rise to new business models that entail transactions worth fractions of a cent. In this mini review, we describe two exemplary application cases and outline the associated research activities for each. The first case considers subscription models in consumer markets for which feeless micropayments might be used to expand and, thus, enrich the range of plans by offering shorter subscription times. The second case is concerned with prosumers who might be willing to sell self-generated data (e.g., data generated through their smart meters).
Computational Modeling of Structural Synaptic Plasticity in Echo State Networks

Most existing studies on computational modeling of neural plasticity have focused on synaptic plasticity. However, regulation of the internal weights in the reservoir based on synaptic plasticity often results in unstable learning dynamics. In this article, a structural synaptic plasticity learning rule is proposed to train the weights and add or remove neurons within the reservoir, which is shown to be able to alleviate the instability of the synaptic plasticity, and to contribute to increase the memory capacity of the network as well. Our experimental results also reveal that a few stronger connections may last for a longer period of time in a constantly changing network structure, and are relatively resistant to decay or disruptions in the learning process. These results are consistent with the evidence observed in biological systems. Finally, we show that an echo state network (ESN) using the proposed structural plasticity rule outperforms an ESN using synaptic plasticity and three state-of-the-art ESNs on four benchmark tasks.
Lifting Transformer for 3D Human Pose Estimation in Video.

Despite great progress in video-based 3D human pose estimation, it is still challenging to learn a discriminative single-pose representation from redundant sequences. To this end, we propose a novel Transformer-based architecture, called Lifting Transformer, for 3D human pose estimation to lift a sequence of 2D joint locations to a 3D pose. Specifically, a vanilla Transformer encoder (VTE) is adopted to model long-range dependencies of 2D pose sequences. To reduce redundancy of the sequence and aggregate information from local context, fully-connected layers in the feed-forward network of VTE are replaced with strided convolutions to progressively reduce the sequence length. The modified VTE is termed as strided Transformer encoder (STE) and it is built upon the outputs of VTE. STE not only significantly reduces the computation cost but also effectively aggregates information to a single-vector representation in a global and local fashion. Moreover, a full-to-single supervision scheme is employed at both the full sequence scale and single target frame scale, applying to the outputs of VTE and STE, respectively. This scheme imposes extra temporal smoothness constraints in conjunction with the single target frame supervision. The proposed architecture is evaluated on two challenging benchmark datasets, namely, Human3.6M and HumanEva-I, and achieves state-of-the-art results with much fewer parameters.
An Efficient Approach to Software Fault Prediction

The use of machine learning concepts in the software engineering field is now ubiquitous to predict software defects. Most of the Software Defect Prediction (SDP) datasets are highly imbalanced and filled with multiple irrelevant features that cause negative effects on the results. The goal of this paper is to create an approach to predict the software faults efficiently from imbalanced and multi-featured SDP datasets. Two highly imbalanced and multi-featured NASA MDP datasets have been used in this experiment. Initially, data cleaning has been performed with the help of Z-score technique to eliminate noisy instances. To balance the datasets, Synthetic Minority Oversampling Technique (SMOTE) oversampling technique has been used. Furthermore, to select the relevant features, three well-known feature selection techniques, as well as their ensembles, have been applied. Finally, to measure the performance, four well-known classification algorithms are implemented and evaluated the results by their accuracy, TPR and TNR.
An architecture for non-linear discovery of aggregated multimedia document web search results

The recent proliferation of multimedia information on the web enhances user information need from simple textual lookup to multi-modal exploration activities. The current search engines act as major gateways to access the immense amount of multimedia data. However, access to the multimedia content is provided by aggregating disjoint multimedia search verticals. The aggregation of the multimedia search results cannot consider relationships in them and are partially blended. Additionally, the search results’ presentation is via linear lists, which cannot support the users’ non-linear navigation patterns to explore the multimedia search results. Contrarily, users’ are demanding more services from search engines. It includes adequate access to navigate, explore, and discover multimedia information. Our discovery approach allow users to explore and discover multimedia information by semantically aggregating disjoint verticals using sentence embeddings and transforming snippets into conceptually similar multimedia document groups. The proposed aggregation approach retains the relationship in the retrieved multimedia search results. A non-linear graph is instantiated to augment the users’ non-linear information navigation and exploration patterns, which leads to discovering new and interesting search results at various aggregated granularity levels. Our method’s empirical evaluation results achieve 99% accuracy in the aggregation of disjoint search results at different aggregated search granularity levels. Our approach provides a standard baseline for the exploration of multimedia aggregation search results.
Predicting Goal-directed Attention Control Using Inverse-Reinforcement Learning

Understanding how goals control behavior is a question ripe for interrogation by new methods from machine learning. These methods require large and labeled datasets to train models. To annotate a large-scale image dataset with observed search fixations, we collected 16,184 fixations from people searching for either microwaves or clocks in a dataset of 4,366 images (MS-COCO). We then used this behaviorally-annotated dataset and the machine learning method of inverse-reinforcement learning (IRL) to learn target-specific reward functions and policies for these two target goals. Finally, we used these learned policies to predict the fixations of 60 new behavioral searchers (clock = 30, microwave = 30) in a disjoint test dataset of kitchen scenes depicting both a microwave and a clock (thus controlling for differences in low-level image contrast). We found that the IRL model predicted behavioral search efficiency and fixation-density maps using multiple metrics. Moreover, reward maps from the IRL model revealed target-specific patterns that suggest, not just attention guidance by target features, but also guidance by scene context (e.g., fixations along walls in the search of clocks). Using machine learning and the psychologically meaningful principle of reward, it is possible to learn the visual features used in goal-directed attention control.
Consolidated Dataset and Metrics for High-Dynamic-Range Image Quality

Increasing popularity of high-dynamic-range (HDR) image and video content brings the need for metrics that could predict the severity of image impairments as seen on displays of different brightness levels and dynamic range. Such metrics should be trained and validated on a sufficiently large subjective image quality dataset to ensure robust performance. As the existing HDR quality datasets are limited in size, we created a Unified Photometric Image Quality dataset (UPIQ) with over 4000 images by realigning and merging existing HDR and standard-dynamic-range (SDR) datasets. The realigned quality scores share the same unified quality scale across all datasets. Such realignment was achieved by collecting additional cross-dataset quality comparisons and re-scaling data with a psychometric scaling method. Images in the proposed dataset are represented in absolute photometric and colorimetric units, corresponding to light emitted from a display. We use the new dataset to retrain existing HDR metrics and show that the dataset is sufficiently large for training deep architectures. We show the utility of the dataset on brightness aware image compression.
AdS/Deep-Learning made easy: simple examples *

Abstract Deep learning has been widely and actively used in various research areas. Recently, in gauge/gravity duality, a new deep learning technique called AdS/DL (Deep Learning) has been proposed. The goal of this paper is to explain the essence of AdS/DL in the simplest possible setups, without resorting to knowledge of gauge/gravity duality. This perspective will be useful for various physics problems: from the emergent spacetime as a neural network to classical mechanics problems. For prototypical examples, we choose simple classical mechanics problems. This method is slightly different from standard deep learning techniques in the sense that we not only have the right final answers but also obtain physical understanding of learning parameters.
Metric studies of the compliance questionnaire on rheumatology (CQR): A case of validity induction?

By leveraging Industrial Cyber–Physical Systems (ICPSs), deep learning-based methods are applied to address various industrial issues. Due to privacy policy reasons, conventional centralized learning (CL) may be improper for some industrial scenarios with sensitive data, such as smart medicine. Recently, federated learning (FL) as a novel collaboration learning approach has received extensive attention, which can break data barriers between different institutions to improve the model performance. However, the privacy information of the industrial agents may be inferred from their shared parameters. In this paper, we propose a Privacy-Enhanced Momentum Federated Learning framework, named PEMFL, that amalgamates differential privacy (DP), Momentum FL (MFL) and chaos-based encryption method. During the training, differentially privacy is used to disturb the industrial agents’ gradient parameters in order to preserve their privacy information. Meanwhile, each industrial agent uses the chaos system-based encryption method to encrypt the weight parameters of their local models, which has two advantages: (1) the encryption method can enhance privacy protection; (2) the cloud server cannot access the truth value of the global model parameters which is a vital asset to the industrial agents. In addition, Momentum Gradient Descent (MGD) and an adjusting learning rate schedule are adopted to improve training efficiency for the PEMFL. The performance of the PEMFL is evaluated based on two non-i.i.d datasets. Theoretical analysis and experimental results demonstrate the excellent performance of the PEMFL in terms of accuracy and privacy security.
Equivalence classes of Niho bent functions

Equivalence classes of Niho bent functions are described for all known types of hyperovals.
Hyperentanglement-enhanced quantum illumination

In quantum illumination, the signal mode of light, entangled with an idler mode, is dispatched towards a suspected object bathed in thermal noise, and the returning mode, along with the stored idler mode, is measured to determine the presence or absence of the object. In this process, entanglement is destroyed but its benefits in the form of classical correlations and enlarged Hilbert space survive. Here, we propose the use of a probe state hyperentangled in two degrees of freedom, polarization and frequency, to achieve a significant 12-dB performance improvement in the error probability exponent over the best-known quantum illumination procedure in the low-noise regime. We present a simple receiver model using four optical parametric amplifiers (OPAs) that exploits hyperentanglement in the probe state to match the performance of the feed-forward sum-frequency generator (FF-SFG) receiver in the high-noise regime. By replacing each OPA in the proposed model with a FF-SFG receiver, a further 3-dB improvement in the performance of a lone FF-SFG receiver can be seen.
Uncertainty Relation between Detection Probability and Energy Fluctuations

A classical random walker starting on a node of a finite graph will always reach any other node since the search is ergodic, namely it is fully exploring space, hence the arrival probability is unity. For quantum walks, destructive interference may induce effectively non-ergodic features in such search processes. Under repeated projective local measurements, made on a target state, the final detection of the system is not guaranteed since the Hilbert space is split into a bright subspace and an orthogonal dark one. Using this we find an uncertainty relation for the deviations of the detection probability from its classical counterpart, in terms of the energy fluctuations.