Reviewerly/exHarmony huggingface dataset:
HyperX: A Scalable Hypergraph Framework

Hypergraphs are generalizations of graphs where the (hyper)edges can connect any number of vertices. They are powerful tools for representing complex and non-pairwise relationships. However, existing graph computation frameworks cannot accommodate hypergraphs without converting them into graphs, because they do not offer APIs that support (hyper)edges directly. This graph conversion may create excessive replicas and result in very large graphs, causing difficulties in workload balancing. A few tools have been developed for hypergraph partitioning, but they are not general-purpose frameworks for hypergraph processing. In this paper, we propose HyperX, a general-purpose distributed hypergraph processing framework built on top of Spark. HyperX is based on the computation paradigm “Pregel”, which is user-friendly and has been widely adopted by popular graph computation frameworks. To help create balanced workloads for distributed hypergraph processing, we further investigate the hypergraph partitioning problem and propose a novel label propagation partitioning (LPP) algorithm. We conduct extensive experiments using both real and synthetic data. The result shows that HyperX achieves an order of magnitude improvement for running hypergraph learning algorithms compared with graph conversion based approaches in terms of running time, network communication costs, and memory consumption. For hypergraph partitioning, LPP outperforms the baseline algorithms significantly in these measures as well.
Fast quantum control in dissipative systems using dissipationless solutions

Abstract We report on a systematic geometric procedure, built up on solutions designed in the absence of dissipation, to mitigate the effects of dissipation in the control of open quantum systems. Our method addresses a standard class of open quantum systems that encompasses non-Hermitian Hamiltonians. It provides the analytical expression of the extra magnetic field to be superimposed to the driving field in order to compensate the geometric distortion induced by dissipation for spin systems, and produces an exact geometric optimization of fast population transfer. Interestingly, it also preserves the robustness properties of protocols originally optimized against noise. Its extension to two interacting spins restores a fidelity close to unity for the fast generation of Bell state in the presence of dissipation.
Long-term transmission of entangled photons from a single quantum dot over deployed fiber

Non-classical light sources based on a single quantum emitter are considered as core technology for multiple quantum network architectures. A large variety of sources has been developed, but the generated photons remained far from being utilized in established standard fiber networks. Here, we report a week-long transmission of polarization-entangled photons from a single InAs/GaAs quantum dot over a metropolitan network fiber. The emitted photons are in the telecommunication O-band, favored for fiber optical communication. We employ a polarization stabilization system overcoming changes of birefringence introduced by 18.23km of installed fiber. Stable transmission of polarization-encoded entanglement with a high fidelity of 91% is achieved, facilitating the operation of sub-Poissonian quantum light sources over existing fiber networks.
Full-field phase error analysis and compensation for nonsinusoidal waveforms in phase shifting profilometry with projector defocusing

Phase shifting profilometry (PSP) using binary fringe patterns with projector defocusing is widely used for high-speed three-dimensional (3D) shape measurement. In this technique, the projector projects binary fringe patterns instead of the sinusoidal fringe patterns to perform 3D shape measurement. However, inappropriate defocusing degree of the projector causes the captured fringe patterns to be nonsinusoidal waveforms or remaining binary structure, which results in phase error and therefore measurement error. In this paper, a novel method was proposed not only to measure the phase error directly but also to extract the full-field phase error distribution in a large depth range of projector defocusing. Then a mathematical phase error function in terms of the depth z was established. Finally, with help of the detected phase error distribution, the phase error function was calibrated and used to compensate the phase error at arbitrary depth ranges within the calibration volume. The experimental results demonstrated that the proposed method can detect full-field phase error distribution successfully, and improve the phase accuracy significantly.
A comparative study of laravel and symfony PHP frameworks

&lt;p&gt;With the current explosion of Information Systems, the market offers a wide range of interesting technological solutions. Yet, this does not mean adopting a technology without considering its impact on the existing information system and user expectations. It is recommended to identify and implement the technological solutions most suited to the Information Systems strategy. Therefore, new methods are emerging and design tools are still evolving; the PHP Frameworks are part of it, which open up new perspectives in terms of information system enrichment. In this context, this paper focuses on the elaboration of a comparative study between Laravel, symfony framworks, which are the most popular PHP frameworks. Thus, it provides an effective comparison model that merges seven dimensions: Features, Multilingual, System requirements, Technical architecture, Code Organization, Continuous Integration (CI) and finally Documentation and learning curve dimension. Results show that our model can be beneficial for IT project developers to select the suitable PHP Framework.&lt;/p&gt;&lt;p&gt; &lt;/p&gt;
Retrieving Hidden Friends: A Collusion Privacy Attack Against Online Friend Search Engine

Online social networks (OSNs) are providing a variety of applications for human users to interact with families, friends, and even strangers. One such application, the friend search engine, allows the general public to query individual users' friend lists and has been gaining popularity recently. However, without proper design, this application may mistakenly disclose users' private relationship information. Our previous work has proposed a privacy preservation solution that can effectively boost OSNs' sociability while protecting users' friendship privacy against attacks launched by individual malicious requestors. In this paper, we propose an advanced collusion attack, where a victim user's friendship privacy can be compromised through a series of carefully designed queries coordinately launched by multiple malicious requestors. The effect of the proposed collusion attack is validated through synthetic and real-world social network data sets. The in-depth research on the advanced collusion attacks will help us design a more robust and secure friend search engine on OSNs in the near future.
Stochastic Analysis of DASH-Based Video Service in High-Speed Railway Networks

The latest increasing popularity of high-speed railways (HSR) has stimulated growing demands for wireless Internet services in HSR networks, especially for video streaming. However, due to the high variability and unpredictability of wireless communications in HSR networks, it is still difficult for the existing solutions to provide high-quality video streaming services to HSR passengers. This paper addresses this crucial problem first by reporting on field experiments performed to investigate the characteristics of HSR networks. Then the paper formulates an intractable optimization problem for dynamic adaptive streaming over HTTP (DASH)-enabling service in HSR networks considering various factors, including packet loss, energy consumption, video service quality, etc. By leveraging Lyapunov optimization approaches, the formulated optimization problem is transformed into a queue stability problem which is of high scalability and generality. Moreover, in order to overcome the intractability of the initial optimization problem, the queue stability problem is further decomposed into three subproblems which can be easily solved individually. Finally, a novel joint stochastic DASH optimization (JSDO) mechanism consisting of three algorithms for the derived subproblems is proposed. Rigorous theoretical analyses and realistic dataset-based simulations demonstrate the effectiveness of the proposed JSDO mechanism.
Saliency-Driven Oil Tank Detection Based on Multidimensional Feature Vector Clustering for SAR Images

A novel saliency-driven oil tank detection method based on multidimensional feature vector clustering (MFVC) is proposed in this letter for synthetic aperture radar (SAR) images. There are three major contributions: 1) a specially designed MFVC method, which is suitable for SAR images without true colors, is employed to detect oil tanks roughly by saliency analysis. Five important complementary features, including intensity, texture, structure, and 2-D coordinates, form a 5-D vector, and then an unsupervised strategy is employed for clustering the 5-D feature vectors to acquire the saliency map; 2) For accurate location, the shape limit coefficient is added to the original active contour model to extract contours of top surfaces; and 3) according to the relations of top, bottom, and the brightest arch, bottoms of oil tanks are computed precisely. Experiments are conducted in two aspects: evaluation for saliency analysis, and for bottom location. Results show that the MFVC method outperforms competing methods in maintaining complete oil tanks and accurate boundaries, and removing the background clutter as much as possible.
Switch-LSTMs for Multi-Criteria Chinese Word Segmentation

Multi-criteria Chinese word segmentation is a promising but challenging task, which exploits several different segmentation criteria and mines their common underlying knowledge. In this paper, we propose a flexible multi-criteria learning for Chinese word segmentation. Usually, a segmentation criterion could be decomposed into multiple sub-criteria, which are shareable with other segmentation criteria. The process of word segmentation is a routing among these sub-criteria. From this perspective, we present Switch-LSTMs to segment words, which consist of several long short-term memory neural networks (LSTM), and a switcher to automatically switch the routing among these LSTMs. With these auto-switched LSTMs, our model provides a more flexible solution for multi-criteria CWS, which is also easy to transfer the learned knowledge to new criteria. Experiments show that our model obtains significant improvements on eight corpora with heterogeneous segmentation criteria, compared to the previous method and single-criterion learning.
Unsupervised Cross-Spectral Stereo Matching by Learning to Synthesize

Unsupervised cross-spectral stereo matching aims at recovering disparity given cross-spectral image pairs without any depth or disparity supervision. The estimated depth provides additional information complementary to original images, which can be helpful for other vision tasks such as tracking, recognition and detection. However, there are large appearance variations between images from different spectral bands, which is a challenge for cross-spectral stereo matching. Existing deep unsupervised stereo matching methods are sensitive to the appearance variations and do not perform well on cross-spectral data. We propose a novel unsupervised crossspectral stereo matching framework based on image-to-image translation. First, a style adaptation network transforms images across different spectral bands by cycle consistency and adversarial learning, during which appearance variations are minimized. Then, a stereo matching network is trained with image pairs from the same spectra using view reconstruction loss. At last, the estimated disparity is utilized to supervise the spectral translation network in an end-to-end way. Moreover, a novel style adaptation network F-cycleGAN is proposed to improve the robustness of spectral translation. Our method can tackle appearance variations and enhance the robustness of unsupervised cross-spectral stereo matching. Experimental results show that our method achieves good performance without using depth supervision or explicit semantic information.
Loss Guided Activation for Action Recognition in Still Images

One significant problem of deep-learning based human action recognition is that it can be easily misled by the presence of irrelevant objects or backgrounds. Existing methods commonly address this problem by employing bounding boxes on the target humans as part of the input, in both training and testing stages. This requirement of bounding boxes as part of the input is needed to enable the methods to ignore irrelevant contexts and extract only human features. However, we consider this solution is inefficient, since the bounding boxes might not be available. Hence, instead of using a person bounding box as an input, we introduce a human-mask loss to automatically guide the activations of the feature maps to the target human who is performing the action, and hence suppress the activations of misleading contexts. We propose a multi-task deep learning method that jointly predicts the human action class and human location heatmap. Extensive experiments demonstrate our approach is more robust compared to the baseline methods under the presence of irrelevant misleading contexts. Our method achieves 94.06% and 40.65% (in terms of mAP) on Stanford40 and MPII dataset respectively, which are 3.14% and 12.6% relative improvements over the best results reported in the literature, and thus set new state-of-the-art results. Additionally, unlike some existing methods, we eliminate the requirement of using a person bounding box as an input during testing.
Few-Shot Image and Sentence Matching via Gated Visual-Semantic Embedding

Although image and sentence matching has been widely studied, its intrinsic few-shot problem is commonly ignored, which has become a bottleneck for further performance improvement. In this work, we focus on this challenging problem of few-shot image and sentence matching, and propose a Gated Visual-Semantic Embedding (GVSE) model to deal with it. The model consists of three corporative modules in terms of uncommon VSE, common VSE, and gated metric fusion. The uncommon VSE exploits external auxiliary resources to extract generic features for representing uncommon instances and words in images and sentences, and then integrates them by modeling their semantic relation to obtain global representations for association analysis. To better model other common instances and words in rest content of images and sentences, the common VSE learns their discriminative representations directly from scratch. After obtaining two similarity metrics from the two VSE modules with different advantages, the gated metric fusion module adaptively fuses them by automatically balancing their relative importance. Based on the fused metric, we perform extensive experiments in terms of few-shot and conventional image and sentence matching, and demonstrate the effectiveness of the proposed model by achieving the state-of-the-art results on two public benchmark datasets.
Message-Passing Neural Networks Learn Little’s Law

This letter presents a solution to the problem of universal representation of graphs exemplifying communication network topologies with the help of neural networks. The proposed approach is based on message-passing neural networks. The approach enables us to represent topologies and operational aspects of networks. The usefulness of the solution is illustrated with a case study of delay prediction in queuing networks. This shows that performance evaluation can be provided without having to apply complex modeling. In consequence, the proposed solution makes it possible to effectively apply the methods elaborated in the field of machine learning in communications.
Use of Sentiment Analysis Techniques in Healthcare Domain

Every day a large amount of subjective information is generated through social networks such as Facebook® and Twitter®. The subjective information implies the opinions, beliefs, feelings and attitudes that people express towards different topics of interest. Moreover, this type of information is of great importance for companies, organizations or individuals, because it allows them to carry out actions that benefit them. Besides, sentiment analysis is the field that studies subjective information through natural language processing, computational linguistics, information retrieval and data mining techniques. Sentiment analysis is very useful in various domains, such as politics, marketing, tourism, among others. Actually, healthcare domain implies a large area of opportunity to obtain benefits using sentiment analysis, such as obtaining information about the patients' mood, diseases, adverse drug reactions, epidemics, among others. However, healthcare domain has been very little explored. Therefore, in this chapter we propose a module based on sentiment analysis to obtain sentiments and emotions at the comment and entity levels from texts related to the healthcare domain. Also, different case studies are presented to validate the proposed module.
Graph Convolutional Network with Sequential Attention for Goal-Oriented Dialogue Systems

Domain-specific goal-oriented dialogue systems typically require modeling three types of inputs, namely, (i) the knowledge-base associated with the domain, (ii) the history of the conversation, which is a sequence of utterances, and (iii) the current utterance for which the response needs to be generated. While modeling these inputs, current state-of-the-art models such as Mem2Seq typically ignore the rich structure inherent in the knowledge graph and the sentences in the conversation context. Inspired by the recent success of structure-aware Graph Convolutional Networks (GCNs) for various NLP tasks such as machine translation, semantic role labeling, and document dating, we propose a memory-augmented GCN for goal-oriented dialogues. Our model exploits (i) the entity relation graph in a knowledge-base and (ii) the dependency graph associated with an utterance to compute richer representations for words and entities. Further, we take cognizance of the fact that in certain situations, such as when the conversation is in a code-mixed language, dependency parsers may not be available. We show that in such situations we could use the global word co-occurrence graph to enrich the representations of utterances. We experiment with four datasets: (i) the modified DSTC2 dataset, (ii) recently released code-mixed versions of DSTC2 dataset in four languages, (iii) Wizard-of-Oz style CAM676 dataset, and (iv) Wizard-of-Oz style MultiWOZ dataset. On all four datasets our method outperforms existing methods, on a wide range of evaluation metrics.
Versification and authorship attribution. A pilot study on Czech, German, Spanish, and English poetry

This article describes pilot experiments performed as one part of a longterm project examining the possibilities for using versification analysis to determine the authorships of poetic texts. Since we are addressing this article to both stylometry experts and experts in the study of verse, we first introduce in detail the common classifiers used in contemporary stylometry (Burrows’ Delta, Argamon’s Quadratic Delta, Smith-Aldridge’s Cosine Delta, and the Support Vector Machine) and explain how they work via graphic examples. We then provide an evaluation of these classifiers’ performance when used with the versification features found in Czech, German, Spanish, and English poetry. We conclude that versification is a reasonable stylometric marker, the strength of which is comparable to the other markers traditionally used in stylometry (such as the frequencies of the most frequent words and the frequencies of the most frequent character n-grams).
Intelligence artificielle : quel avenir en anatomie pathologique ?

Artificial Intelligence, in particular deep neural networks are the most used machine learning technics in the biomedical field. Artificial neural networks are inspired by the biological neurons; they are interconnected and follow mathematical models. Two phases are required: a learning and a using phase. The two main applications are classification and regression Computer tools such as GPU computational accelerators or some development tools such as MATLAB libraries are used. Their application field is vast and allows the management of big data in genomics and molecular biology as well as the automated analysis of histological slides. The Whole Slide Image scanner can acquire and store slides in the form of digital images. This scanning associated with deep learning algorithms allows automatic recognition of lesions through the automatic recognition of regions of interest previously validated by the pathologist. These computer aided diagnosis techniques are tested in particular in mammary pathology and dermatopathology. They will allow an efficient and a more comprehensive vision, and will provide diagnosis assistance in pathology by correlating several biomedical data such as clinical, radiological and molecular biology data.
Sarcasm Detection For Sentiment Analysis in Indonesian Tweets

Twitter is one of the social medias that are widely used at the moment. Tweet conversations can be classified according to their sentiments. The existence of sarcasm contained in a tweet sometimes causes incorrect determination of the tweet’s sentiment because sarcasm is difficult to analyze automatically, even by humans. Hence, sarcasm detection needs to be conducted, which is expected to improve the results of sentiment analysis. The effect of sarcasm detection on sentiment analysis can be seen in terms of accuracy, precision and recall. In this paper, detection of sarcasm is applied to Indonesian tweets. The feature extraction of sarcasm detection uses unigram and 4 Boazizi feature sets which consist of sentiment-relate features, punctuation-relate features, lexical and syntactic features, and top word features. Detection of sarcasm uses the Random Forest algorithm. The feature extraction of sentiment analysis uses TF-IDF, while the classification uses Naïve Bayes algorithm. The evaluation shows that sentiment analysis with sarcasm detection improves the accuracy of sentiment analysis about 5.49%. The accuracy of the model is 80.4%, while the precision is 83.2%, and the recall is 91.3%.
Chameleon 2

Traditional clustering algorithms fail to produce human-like results when confronted with data of variable density, complex distributions, or in the presence of noise. We propose an improved graph-based clustering algorithm called Chameleon 2, which overcomes several drawbacks of state-of-the-art clustering approaches. We modified the internal cluster quality measure and added an extra step to ensure algorithm robustness. Our results reveal a significant positive impact on the clustering quality measured by Normalized Mutual Information on 32 artificial datasets used in the clustering literature. This significant improvement is also confirmed on real-world datasets. The performance of clustering algorithms such as DBSCAN is extremely parameter sensitive, and exhaustive manual parameter tuning is necessary to obtain a meaningful result. All hierarchical clustering methods are very sensitive to cutoff selection, and a human expert is often required to find the true cutoff for each clustering result. We present an automated cutoff selection method that enables the Chameleon 2 algorithm to generate high-quality clustering in autonomous mode.