Reviewerly/exHarmony huggingface dataset:
Video salient region detection model based on wavelet transform and feature comparison

With the advent of the era of big data, the Internet industry has produced massive amounts of multimedia video data. In order to process these video sequence data quickly and effectively, a visual information extraction method based on wavelet transform and feature comparison is proposed to perceive the target of interest by simulating the multi-channel spatial frequency decomposition function of human visual system which can quickly extract the significant distribution from the image and obtain region of interest (ROI). Firstly, the principle of visual attention mechanism and the visual salience detection were analyzed. Then, the DOG (Difference of Gaussian) function is taken as the wavelet basis function, and the wavelet data is used to decompose the image data in the spatial domain and frequency domain, thus applying to the multi-channel of the human visual system. Finally, the significant distribution in the entire image is obtained by global color comparison; thus, the region of interest is extracted. The extraction model of visual information proposed is simulated in MATLAB environment. The simulation results show that the proposed algorithm can extract ROI more accurately and efficiently, compared with the existing algorithms.
Information Model of Evaluation and Output Rating of Start-up Projects Development Teams

The problem of constructing an informational model of evaluation and output of the start-up team rating is considered.This model is based on neuro-fuzzy network when there are expert fuzzy data on the teams of developers.As the success of a start-up implementation depends on the quality of the team of developers, then the development of such a model will increase the degree of validity of the decision to finance the start-up projects.
Can the development of a patient’s condition be predicted through intelligent inquiry under the e-health business mode? Sequential feature map-based disease risk prediction upon features selected from cognitive diagnosis big data

The data-driven mode has promoted the researches of preventive medicine. In prediction of disease risks, physicians' clinical cognitive diagnosis data can be used for early prevention of diseases and, therefore, to reduce medical cost, to improve accessibility of medical services and to lower medical risk. However, researches involved no physicians' cognition of patients' conditions in intelligent inquiry under e-health business mode, offered no diagnosis big data, neglected the values of the fused text information generated by joint activities of online and offline medical data, and failed to thoroughly analyze the phenomenon of redundancy-complementarity dispersion caused by high-order information shortage from the online inquiry data-driven perspective. Besides, the risk prediction simply based on offline clinical cognitive diagnosis data undoubtedly reduces prediction precision. Importantly, relevant researches rarely considered temporal relationships of different medical events, did not conduct detailed analysis on practical problems of pattern explosion, did not offer a thought of intelligent portrayal map, and did not conduct relevant risk prediction based on the sub-maps obtained from the map. In consequence, the paper presents a disease risk prediction method with the model for redundancy-complementarity dispersion-based feature selection from physicians' online cognitive diagnosis big data to realize features selection from the cognitive diagnosis big data of online intelligent inquiry; the obtained features were ranked intelligently for subsequent high-dimensional information shortage compensation; the compensated key feature information of the cognitive diagnosis big data was fused with offline electronic medical record (EMR) to form the virtual electronic medical record (VEMR). The formed VEMR was combined with the method of the sequential feature map for modelling, and a sequential feature map-based model for disease risk prediction was presented to obtain online users' medical conditions. A neighborhood-based collaborative prediction model was presented for prediction of an online intelligent medical inquiry user's possible diseases in the future and to intelligently rank the risk probabilities of the diseases. In the experiments, the online intelligent medical inquiry users' VEMRs were used as the foundation of the simulation experiments to predict disease risks in chronic obstructive pulmonary disease (OCPD) population and rheumatic heart disease (RHD) population. The experiments demonstrated that the presented method showed relatively good metric performances in the VEMR and improved disease risk prediction.
A Session and Dialogue-Based Social Engineering Framework

Social engineering has been increasingly used during the past few years. Social engineering attacks have resulted in great financial losses. Research on social engineering models and frameworks is still in its elementary stage. An appropriate social engineering framework can interpret all the attack components and their relationships clearly, which will contribute to the defense of social engineering attacks. In this tutorial paper, existing social engineering models and frameworks are summarized and a new social engineering framework is proposed involving the concept of the session and dialogue. An entire social engineering attack is defined as a social engineering session (SES). A social engineering dialogue (SED) refers to a specific attack phase, which is included in a SES. A SES contains several well-organized SEDs. Then, the attack graph is used to formalize the proposed social engineering framework. The SED is treated as an atomic attack during the whole SES. The human weaknesses that an attacker can exploit are described as vulnerabilities, the information, and trust that an attacker owns as permissions. Finally, three real-world social engineering cases are analyzed using the proposed framework and attack graph. The analyses illustrate the usability of the proposed framework and provide a better understanding of various social engineering attacks.
Sistem Informasi Pemasaran Busana Syar’i dengan Penerapan Customer Relationship Management (CRM) Berbasis Web

Toko Cahaya Sunnah is a business that produces and sells syar'i clothing. In its business development, Toko Cahaya Sunnah keep trying to expand its market target to out of town even all of Indonesia. To achieve its goal, Toko Cahaya Sunnah requires website. Currently in Toko Cahaya Sunnah in making sales report is still manual using excel application so that data processing of sales report still running slow and not yet detailed. Currently Toko Cahaya Sunnah does not have any special activities or strategies that handle marketing, product marketing is done in a conventional manner by providing customers with information about places, features and products to other potential customers. Ineffective information dissemination prevents customers from knowing the latest information in the Sunnah Light Store thus affecting customers to switch to other stores. With the website using the method of Customer Relationship Management (CRM) then the data storage process can be more accurate and stored neatly and making sales reports will be fast and timely and delivery of promotional information will be faster and response to sales will be faster.&#x0D; Keywords: Information System, Customer Relationship Management (CRM), Web
Experimental Evaluation of Subgraph Isomorphism Solvers

Subgraph Isomorphism (SI) is an NP-complete problem which is at the heart of many structural pattern recognition tasks as it involves finding a copy of a pattern graph into a target graph. In the pattern recognition community, the most well-known SI solvers are VF2, VF3, and RI. SI is also widely studied in the constraint programming community, and many constraint-based SI solvers have been proposed since Ullman, such as LAD and Glasgow, for example. All these SI solvers can solve very quickly some large SI instances, that involve graphs with thousands of nodes. However, McCreesh et al. have recently shown how to randomly generate SI instances the hardness of which can be controlled and predicted, and they have built small instances which are computationally challenging for all solvers. They have also shown that some small instances, which are predicted to be easy and are easily solved by constraint-based solvers, appear to be challenging for VF2 and VF3. In this paper, we widen this study by considering a large test suite coming from eight benchmarks. We show that, as expected for an NP-complete problem, the solving time of an instance does not depend on its size, and that some small instances coming from real applications are not solved by any of the considered solvers. We also show that, if RI and VF3 can solve very quickly a large number of easy instances, for which Glasgow or LAD need more time, they fail at solving some other instances that are quickly solved by Glasgow or LAD, and they are clearly outperformed by Glasgow on hard instances. Finally, we show that we can easily combine solvers to take benefit of their complementarity.
GENERATION OF A BENCHMARK DATASET USING HISTORICAL PHOTOGRAPHS FOR AN AUTOMATED EVALUATION OF DIFFERENT FEATURE MATCHING METHODS

Abstract. This contribution shows the generation of a benchmark dataset using historical images. The difficulties when working with historical images are pointed out and structured in three categories. Especially large viewpoint differences, image artifacts and radiometric differences lead to weak matching results with classical feature matching approaches. The necessity of publishing an own benchmark dataset is emphasized when comparing to existing datasets which are partly using synthetic data, well-known orientation or strictly categorized image differences. The presented image dataset consists at the moment of 24 images which are oriented in image triples using the properties of the Trifocal Tensor as a more stable image geometry. In the following, three different feature detectors and descriptors that have already been proven well on historical images (MSER, ORB, RIFT) are evaluated using the new benchmark dataset. Then, several outlier removal methods were applied on the detected features. The tests show that for the entirety of image pairs RIFT performs slightly better than the other two methods. Nonetheless, for some image pairs MSER significantly improves the matching score but even so, historical image pairs are difficult to be matched with the presented methods due to challenging outlier removal. Still, the estimated projective relative orientation could be used in an autocalibration approach to place the images in a metric scene.
Simulating Shadow Interactions for Outdoor Augmented Reality With RGBD Data

In augmented reality, interactive shadow casting between virtual and real objects is crucial for the consistency of the appearance of integrated scenes. Since real shadow-casting objects may not be fully visible in live videos, simulating the shadows that they cast onto virtual objects based on 3D reconstruction will fail. To address this issue, we propose a framework for simulating shadow interactions in live outdoor videos based on shadow edges. The simulation framework consists of two components: real-time casting shadow detection from live videos with moving viewpoints and generation of shadow volumes. First, we propose a novel split-based shadow detection framework that accounts for moving viewpoints. Then, a visual-perception-guided adaptive sampling strategy is developed to generate high-quality shadow volumes. The experimental results demonstrate that our algorithm achieves visually consistent shadow interactions for live outdoor videos, which significantly enhances the photorealism of the generated scenes for augmented reality (AR) applications.
Towards a distributed reality

In this article we define the concept of Distributed Reality, a new form of immersive media based on the combination of captured realities (e.g a remote and a local one), hence with a strong focus on video. While Virtual Reality fully substitutes the real surrounding with a simulation and all existing variations of Mixed Reality just provide additional synthetic elements into our visual field, our proposal is to capture and segment different scenes (one of them being the local one) in real time and merge the resulting elements dynamically, to create a completely new reality in front of the user senses. We compare this concept with prior art in the field and describe a taxonomy in which it fits.
Z-Channel: Scalable and efficient scheme in Zerocash

Decentralized ledger-based cryptocurrencies like Bitcoin present a way to construct payment systems without trusted banks. However, the anonymity of Bitcoin is fragile. Many altcoins and protocols are designed to improve Bitcoin on this issue, among which Zerocash is the first full-fledged anonymous ledger-based currency, using zero-knowledge proof, specifically zk-SNARK, to protect privacy. However, Zerocash suffers two problems: Poor scalability and low efficiency. In this paper, we address the above issues by constructing a micropayment system in Zerocash called Z-Channel. First, we improve Zerocash to support multisignature and time lock functionalities, and prove that the reconstructed scheme is secure. Then we construct Z-Channel based on the improved Zerocash scheme. Our experiments demonstrate that Z-Channel significantly improves the scalability and reduces the confirmation time for Zerocash payments.
Parallel Region-Based Deep Residual Networks for Face Hallucination

Face hallucination is a super-resolution algorithm specially designed to improve the resolution and quality of low-resolution (LR) input face images.Although a deep neural network offers an end-toend mapping from LR to high-resolution (HR) images, most of the deep learning-based face hallucinations neglect the structure prior for face images.To utilize the highly structured facial prior, a parallel regionbased deep residual network (PRDRN) was developed to predict the missing detailed information for accurate image reconstruction.Initially, the image is divided into multiple regions with the symmetry of face structures.Then, the sub-networks corresponding to multiple regions are trained in parallel.Finally, all reconstructed regions are combined to form the HR image.The experimental results on FEI, CASIA-Webface and CMU-MIT public face databases show that the proposed network outperforms other state-ofthe-art approaches.
A Novel Focal Length Measurement Method for Center-Obstructed Omni-Directional Reflective Optical Systems

An omni-directional optical system can be used as a surveillance camera owing to its wide field angle. In cases in which a system is designed with a central screen obscuring structure to increase the resolution of the off-axis field, however, the conventional methods cannot be used to measure the effective focal length (EFL). We assumed the actual and theoretical distortion values of the fabricated optical system to be the same and determined the system’s EFL by finding the minimum deviation point of the measured and theoretical distortions. The feasibility of the determined EFL was verified through a tolerance analysis of the system. For these precise measurements we also analyzed the sources of error. To verify our proposed measurement method, we measured the focal length of a center-obstructed omni-directional reflective optical system with an 80–135° field of view (FOV). The EFL from the measurement was 0.3739 mm and was only approximately 11 µm different from the EFL calculated using the design software. Thus, the reliability of focal length measurements in omni-directional optical systems was improved.
A Novel Expert Evaluation Methodology Based on Fuzzy Logic

In order to maintain the training quality and ensure efficient learning, the introduction of a scalable and well-adapted evaluation system is essential. An adequate evaluation system will positively involve students in the evaluation of their own learning, as well as providing teachers with indicators on the student's strengths, the specific encountered difficulties and the false or misunderstood studied parts. In this context, we present, in this article, a novel intelligent evaluation methodology based on fuzzy logic and knowledge based expert systems. The principle of this methodology is to reify abstract concepts of a human expertise in a numerical inference engine applied to evaluation. It reproduces, therefore, the cognitive mechanisms of evaluation experts. An im-plementation example is presented to compare this method with the classical one and draw conclusions about its efficiency. Furthermore, thanks to its flexibility, different kinds of extensions are possible by updating the basic rules and adjusting to possible new architectures and new types of evaluation.
Horizontal or Vertical?

Data parallelism and model parallelism are two typical parallel modes for distributed machine learning (DML). Traditionally, DML mainly leverages data parallelism, which maintains one model instance for each node and synchronizes the model parameters at the end of every iteration. However, as the model grows larger, communication cost and GPU memory consumption become significant. Data parallelism thus fails to work efficiently in large scale, and model-parallel solutions are proposed in recent years. In this paper, we comprehensively discuss the benefits and drawbacks on both sides. Based on the comparative analysis, we propose Hove, a hybrid approach incorporating data parallelism and model parallelism to balance the overheads and achieve high performance for large-scale DML.
Translating Translationese: A Two-Step Approach to Unsupervised Machine Translation

Given a rough, word-by-word gloss of a source language sentence, target language natives can uncover the latent, fully-fluent rendering of the translation.In this work we explore this intuition by breaking translation into a two step process: generating a rough gloss by means of a dictionary and then 'translating' the resulting pseudo-translation, or 'Translationese' into a fully fluent translation.We build our Translationese decoder once from a mish-mash of parallel data that has the target language in common and then can build dictionaries on demand using unsupervised techniques, resulting in rapidly generated unsupervised neural MT systems for many source languages.We apply this process to 14 test languages, obtaining better or comparable translation results on high-resource languages than previously published unsupervised MT studies, and obtaining good quality results for low-resource languages that have never been used in an unsupervised MT scenario.
Basalt Tectonic Discrimination Using Combined Machine Learning Approach

Geochemical discrimination of basaltic magmatism from different tectonic settings remains an essential part of recognizing the magma generation process within the Earth’s mantle. Discriminating among mid-ocean ridge basalt (MORB), ocean island basalt (OIB) and island arc basalt (IAB) is that matters to geologists because they are the three most concerned basalts. Being a supplement to conventional discrimination diagrams, we attempt to utilize the machine learning algorithm (MLA) for basalt tectonic discrimination. A combined MLA termed swarm optimized neural fuzzy inference system (SONFIS) was presented based on neural fuzzy inference system and particle swarm optimization. Two geochemical datasets of basalts from GEOROC and PetDB served as to test the classification performance of SONFIS. Several typical discrimination diagrams and well-established MLAs were also used for performance comparisons with SONFIS. Results indicated that the classification accuracy of SONFIS for MORB, OIB and IAB in both datasets could reach over 90%, superior to other methods. It also turns out that MLAs had certain advantages in making full use of geochemical characteristics and dealing with datasets containing missing data. Therefore, MLAs provide new research tools other than discrimination diagrams for geologists, and the MLA-based technique is worth extending to tectonic discrimination of other volcanic rocks.
Many-body effects in quantum metrology

Abstract We study the impact of many-body effects on the fundamental precision limits in quantum metrology. On the one hand such effects may lead to nonlinear Hamiltonians, studied in the field of nonlinear quantum metrology, while on the other hand they may result in decoherence processes that cannot be described using single-body noise models. We provide a general reasoning that allows to predict the fundamental scaling of precision in such models as a function of the number of atoms present in the system. Moreover, we describe a computationally efficient approach that allows for a simple derivation of quantitative bounds. We illustrate these general considerations by a detailed analysis of fundamental precision bounds in a paradigmatic atomic interferometry experiment with standard linear Hamiltonian but with both single and two-body losses taken into account—a model which is motivated by the most recent Bose–Einstein condensate magnetometry experiments. Using this example we also highlight the impact of the atom number super-selection rule on the possibility of protecting interferometric protocols against decoherence.
Gestures In-The-Wild: Detecting Conversational Hand Gestures in Crowded Scenes Using a Multimodal Fusion of Bags of Video Trajectories and Body Worn Acceleration

This paper addresses the detection of hand gestures during free-standing conversations in crowded mingle scenarios. Unlike the scenarios of the previous works in gesture detection and recognition, crowded mingle scenes have additional challenges such as cross-contamination between subjects, strong occlusions, and nonstationary backgrounds. This makes them more complex to analyze using computer vision techniques alone. We propose a multimodal approach using video and wearable acceleration data recorded via smart badges hung around the neck. In the video modality, we propose to treat noisy dense trajectories as bags-of-trajectories. For a given bag, we can have good trajectories corresponding to the subject, and bad trajectories due for instance to cross-contamination. However, we hypothesize that for a given class, it should be possible to learn trajectories that are discriminative while ignoring noisy trajectories. We do this by exploiting multiple instance learning via embedded instance selection as our multiple instance learning approach. This technique also allows us to identify which instances contribute more to the classification. By fusing the decisions of the classifiers from the video and wearable acceleration modalities, we show improvements over the unimodal approaches with an AUC of 0.69. We also present a static analysis and a dynamic analysis to assess the impact of noisy data on the fused detection results, showing that the moments of high occlusion in the video are compensated by the information from the wearables. Finally, we applied our method to detect speaking status, leveraging the close relationship found in the literature between hand gestures and speech.