Reviewerly/exHarmony huggingface dataset:
Fabricating QR codes on 3D objects using self-shadows

QR codes are essentially 2D matrix images that can be easily displayed physically on printed media or digitally on a screen. Their popularity is due to their fast readability and storage capacity. Applications vary widely from product tracking and identification to entertainment, advertising, encryption, and payment. In this work, we explore a novel representation of QR codes as 3D structures that are embedded on arbitrary shapes. Specifically, our method physically prints the 3D QR-code representation on a given region on the shape. Thus, in comparison to paper printed QR-codes, our 3D QR-codes are more resilient and reliable (e.g. harder to break and reproduce). 3D QR-codes are computed from their 2D counterpart as a carefully designed set of carved geometry modules on the surface which encode the black-and-white QR pattern. At the core of our method, we utilize self-shadows which are cast by 3D geometry modules on the surface to generate a correct black–white pattern with adequate contrast which can be easily decoded by standard QR readers. Our technique allows embedding 3D QR-codes on arbitrary objects with minimal modification of the shape and even on thin shell surfaces or highly curved ones. Our technique is robust to lighting environments and scanning angles and allows to easily decode 3D QR-codes with similarity to the 2D version. Finally, we demonstrate our technique feasibility by fabricating a wide range of examples using consumer-level 3D printers and common homogeneous materials. The feasibility and robust readability make 3D QR-codes a good candidate for embedding information on physical objects and open interesting directions in 3D manufacturing such as 3D printed objects ownership and 3D watermarking.
LGM-Net: Learning to Generate Matching Networks for Few-Shot Learning

In this work, we propose a novel meta-learning approach for few-shot classification, which learns transferable prior knowledge across tasks and directly produces network parameters for similar unseen tasks with training samples. Our approach, called LGM-Net, includes two key modules, namely, TargetNet and MetaNet. The TargetNet module is a neural network for solving a specific task and the MetaNet module aims at learning to generate functional weights for TargetNet by observing training samples. We also present an intertask normalization strategy for the training process to leverage common information shared across different tasks. The experimental results on Omniglot and miniImageNet datasets demonstrate that LGM-Net can effectively adapt to similar unseen tasks and achieve competitive performance, and the results on synthetic datasets show that transferable prior knowledge is learned by the MetaNet module via mapping training data to functional weights. LGM-Net enables fast learning and adaptation since no further tuning steps are required compared to other meta-learning approaches.
<i>Face off:</i> Travel Habits, Road Conditions and Traffic City Characteristics Bared Using Twitter

The adequacy of traditional transport related issues detection is often limited by physical sparse sensor coverage and reporting incident/issues to the emergency response system is labor intensive. The social media tweet text have been mined so as to identify the complaints regarding various road transportation issues of traffic, accident, and potholes. In order to identify and segregate tweets related to different issues, keyword-based approaches have been used previously, but these methods are solely dependent on seed keywords which are manually given and these set of keywords are not sufficient to cover all tweets posts. So, to overcome this issue, a novel approach has been proposed that captures the semantic context through dense word embedding by employing word2vec model. However, the process of tweet segregation on the basis of semantic similar keywords may suffer from the problem of pragmatic ambiguity. To handle this, Word2Vec model has been applied to match the semantically similar tweets with respect to each category. Furthermore, the hotspots have been identified corresponding to each category. However, due to the scarcity of geo-tagged tweets, we have proposed a hybrid method which amalgamates Named Entity Recognition (NER), Part of speech (POS), and Regular Expression (RE) to extract the location information from the tweet textual content. Due to the lack of availability of the ground truth dataset, model feasibility has been validated from the existing data records ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e., published by government official accounts and reported on news media</i> ) and the evaluation results signify that the stated approach identifies few additional hotspots as compared to the existing reports while analyzing the tweets.
ART4SQLi: The ART of SQL Injection Vulnerability Discovery

SQL injection (SQLi) is one of the chief threats to the security of database-driven Web applications. It can cause serious security issues such as authentication bypassing, privacy leakage, and arbitrary code execution. Dynamic testing techniques are used in SQLi vulnerability discovery, which de-facto approach is to maintain a collection of elaborately designed user inputs (aka. attack payloads) and based on it to compose malicious SQL queries to Web applications. Such techniques are effective to reveal SQLi threats before an application is released, thus reducing the cost of manual analysis, monitoring or postdeployment of other defensive mechanisms. However, because of the diversity of SQLi attacks and the difficulty of SQLi discovery, the process to execute payloads can be costly, time-consuming, and even risky. In this paper, we approach from a test case prioritization perspective to give a more effective SQLi discovery proposal, which is based on adaptive random testing with the aim to successfully trigger an SQLi within limited attempts. To evaluate our method, we conduct an experiment using three extensively adopted open source vulnerable benchmarks. The experiment results indicate that our method ART4SQLi can effectively improve the conventional random testing approach on three common benchmarks by more than 26% in reducing the number of SQLi attempts before accomplishing a successful injection.
Nonrigid registration of medical image based on adaptive local structure tensor and normalized mutual information

Nonrigid registration of medical images is especially critical in clinical treatment. Mutual information is a popular similarity measure for medical image registration; however, only the intensity statistical characteristics of the global consistency of image are considered in MI, and the spatial information is ignored. In this paper, a novel intensity-based similarity measure combining normalized mutual information with spatial information for nonrigid medical image registration is proposed. The different parameters of Gaussian filtering are defined according to the regional variance, the adaptive Gaussian filtering is introduced into the local structure tensor. Then, the obtained adaptive local structure tensor is used to extract the spatial information and define the weighting function. Finally, normalized mutual information is distributed to each pixel, and the discrete normalized mutual information is multiplied with a weighting term to obtain a new measure. The novel measure fully considers the spatial information of the image neighborhood, gives the location of the strong spatial information a larger weight, and the registration of the strong gradient regions has a priority over the small gradient regions. The simulated brain image with single-modality and multimodality are used for registration validation experiments. The results show that the new similarity measure improves the registration accuracy and robustness compared with the classical registration algorithm, reduces the risk of falling into local extremes during the registration process.
The effects of perceived chronic pressure and time constraint on information search behaviors and experience

In this paper, we explore the effects of individual pressure level and time constraint on searchers' behaviors and their assessment of search experience within the framework of interactive information retrieval. A user experiment was conducted in which 40 participants individually searched for information in a laboratory setting under two conditions: with time constraint (TC) and with no time constraint (NTC). Participants filled in a Perceived Stress Scale questionnaire to measure their chronic pressure value (subjective stress), and their pressure value was recorded as their individual characteristic. The results showed that the more chronic pressure the searcher has, the more search efforts they devote, including more time in searching and more time to complete the search tasks, especially when there was no time constraint. Time constraint and searchers’ pressure value had a significant effect on users’ numbers of scrolling actions per minute. The results indicate that when given a time constraint, searchers with higher-pressure values tend to lower their reading or scanning speed, while searchers with lower-pressure values tend to accelerate their reading or scanning speed. The results suggested different people would react to the time condition change in different ways, especially people with higher pressure. Therefore, it is necessary to examine users’ search behaviors in person-in-situation frameworks to analyze the effects of contextual factors on users. This study contributes to our knowledge of how contextual factors and individual characteristics affect searchers’ behaviors and have implications for the design of IIR systems.
ConvNet and LSH-Based Visual Localization Using Localized Sequence Matching

Convolutional Network (ConvNet), with its strong image representation ability, has achieved significant progress in the computer vision and robotic fields. In this paper, we propose a visual localization approach based on place recognition that combines the powerful ConvNet features and localized image sequence matching. The image distance matrix is constructed based on the cosine distance of extracted ConvNet features, and then a sequence search technique is applied on this distance matrix for the final visual recognition. To speed up the computational efficiency, the locality sensitive hashing (LSH) method is applied to achieve real-time performances with minimal accuracy degradation. We present extensive experiments on four real world data sets to evaluate each of the specific challenges in visual recognition. A comprehensive performance comparison of different ConvNet layers (each defining a level of features) considering both appearance and illumination changes is conducted. Compared with the traditional approaches based on hand-crafted features and single image matching, the proposed method shows good performances even in the presence of appearance and illumination changes.
Measures of Harm from Heavy Metal Pollution in Battery Technicians’ Workshop within Ilorin Metropolis, Kwara State, Nigeria

Soil pollution with Heavy Metals (HMs) has been of much interests lately and is one of the major issues to be faced globally and requires attention because heavy metals above their normal ranges are extremely threatened to both biotic and abiotic life. It was therefore of interest to conduct study to assess the extent of heavy-metal contamination of soils within battery technicians’ workshops within Ilorin metropolis, Kwara State, Nigeria. A total of twenty-five composite soil samples were collected from six selected battery charger workshop within Ilorin metropolis and analyzed for the presence of heavy metals using atomic absorption spectrophotometer. Result reveals significant positive relationship between Mn and Fe (r=0.511**, p&lt;0.001), Mn and Cu (r=0.565**, p&lt;0.001), Fe and Cr (r=0.895**, p&lt;0.001), Fe and Cu (r=0.823**, p&lt;0.001) and between Cr and Cu (r=830**, p&lt;0.001). Result also shows significant negative relationship between Mn and Cr (r=-0.679**, p&lt;0.001), Pb and Cu (r=-0.468*, p&lt;0.05) respectively. The pollution status of heavy metals in soils was evaluated using quantitative indices (pollution index–PI). The result shows that Zn was moderately contaminated while other heavy metals (Pb, Cd, Cr and Cu) had very slight contamination (pollution index&lt;0.1). The Ilorin metropolis soils of Kwara State were found to have a moderate to very slight contamination respectively. Large variations in PI values of Zn revealed that soil in those areas of the city, which are influenced by anthropogenic activities, have moderate concentrations of Zn resulting in “considerable risk”. The findings of this study recommend comprehensive continuous annual monitoring and auditing and further studies on the level of these heavy metals in the near future to ascertain long-term effects of anthropogenic impact is forestalled to protect the men and the environment. This should also involve larger coverage with studies on ground water around such locations. Furthermore, continuous metals speciation should be carried out so that the form and extent of metal bioavailability can be evaluated further.
Quantum sensing for dynamical tracking of chemical processes

Quantum photonics has demonstrated its potential for enhanced sensing. Current sources of quantum light states tailored to measuring allow one to monitor phenomena evolving on timescales of second order. These are characteristic of product accumulation in chemical reactions of technological interest, in particular those involving chiral compounds. Here we adopt a quantum multiparameter approach to investigate the dynamic process of sucrose acid hydrolysis as a test bed for such applications. The estimation is made robust by monitoring different parameters at once.
Multi-party quantum key agreement with four-qubit cluster states

Based on four-qubit cluster states, we present a new multi-party quantum key agreement (QKA) protocol. The proposed protocol makes full use of four-qubit cluster states as quantum resources and performs the X operation to generate a shared key. In addition, our multi-party QKA protocol ensures each participant to contribute equally to the final key. Compared with other existed multi-party QKA protocols, our protocol has high efficiency. Furthermore, the security analysis shows that our protocol can resist the outsider and participant attacks.
A Lightweight Assisted Vulnerability Discovery Method Using Deep Neural Networks

Thousands of vulnerabilities are discovered in programs every day, which is extremely harmful to software security.Thus, discovering vulnerabilities in projects has become a central issue.Facing a sustained growth of software complexity and large code size, manual code auditing becomes time-consuming and labor-intensive.With more open source programs available and a high degree of code formalization, it is possible to study features from source code to guide vulnerability discovery work.In this paper, we present a lightweight-assisted vulnerability discovery method using a deep neural network (LAVDNN) to detect weakness and to provide guidance for manual auditing.The method proposed in this paper leverages function names as semantics features to uncover weak functions in large-scale open source programs.First, we extract function names and classify into weak and benign datasets.Then, we construct deep neural networks and compare the performances of different models.According to the experimental results, our method performs well for both C/C++ and Python programs, with the F 2 -score reaching 0.91 and 0.915, respectively.Ultimately, we evaluate the method by comparing with other approaches using the libraries FFmpeg 0.6 and LibTIFF 4.0.6.The results show that the LAVDNN could narrow the range of functions to be analyzed and report more weak functions without any prior vulnerability information.As a lightweight-assisted tool, the LAVDNN significantly reduces the false positive rate and hardly misses weak functions.
Characterizing the Usage, Evolution and Impact of Java Annotations in Practice

Annotations have been formally introduced into Java since Java 5. Since then, annotations have been widely used by the Java community for different purposes, such as compiler guidance and runtime processing.Despite the ever-growing use, there is still limited empirical knowledge about the actual usage of annotations in practice, the changes made to annotations during software evolution, and the potential impact of annotations on code quality.To fill this gap, we perform the first large-scale empirical study about Java annotations on 1,094 notable open-source projects hosted on GitHub.Our study systematically investigates annotation usage, annotation evolution, and annotation impact, and generates 10 novel and important findings.We also present the implications of our findings, which shed light for developers, researchers, tool builders, and language or library designers in order to improve all facets of Java annotation engineering.
Dynamic Visualization and Fast Computation for Convex Clustering via Algorithmic Regularization

Convex clustering is a promising new approach to the classical problem of clustering, combining strong performance in empirical studies with rigorous theoretical foundations. Despite these advantages, convex clustering has not been widely adopted, due to its computationally intensive nature and its lack of compelling visualizations. To address these impediments, we introduce Algorithmic Regularization, an innovative technique for obtaining high-quality estimates of regularization paths using an iterative one-step approximation scheme. We justify our approach with a novel theoretical result, guaranteeing global convergence of the approximate path to the exact solution under easily-checked non-data-dependent assumptions. The application of algorithmic regularization to convex clustering yields the Convex Clustering via Algorithmic Regularization Paths (CARP) algorithm for computing the clustering solution path. On example data sets from genomics and text analysis, CARP delivers over a 100-fold speed-up over existing methods, while attaining a finer approximation grid than standard methods. Furthermore, CARP enables improved visualization of clustering solutions: the fine solution grid returned by CARP can be used to construct a convex clustering-based dendrogram, as well as forming the basis of a dynamic path-wise visualization based on modern web technologies. Our methods are implemented in the open-source R package clustRviz, available at https://github.com/DataSlingers/clustRviz.
SCAN: Spatial Color Attention Networks for Real Single Image Super-Resolution

Conceptually similar to adaptation in model-based approaches, attention has received increasing more attention in deep learning recently. As a tool to reallocate limited computational resources based on the importance of informative components, attention mechanism has found successful applications in both high-level and low-level vision tasks which includes channel attention, spatial attention, non-local attention and etc. However, to the best of our knowledge, attention mechanism has not been studied for the R, G, B channels of color images in the open literature. In this paper, we propose a spatial color attention networks (SCAN) designed to jointly exploit the spatial and spectral dependency within color images. More specifically, we present a spatial color attention module that calibrates important color information for individual color components from output feature maps of residual groups. When compared against previous state-of-the-art method Residual Channel Attention Networks (RCAN), SCAN has achieved superior performance in terms of both subjective and objective qualities on the dataset provided by NTIRE2019 real single image super-resolution challenge.
Embeddia at SemEval-2019 Task 6: Detecting Hate with Neural Network and Transfer Learning Approaches

SemEval-2019 Task 6 was OffensEval: Identifying and Categorizing Offensive Language in Social Media.The task was further divided into three sub-tasks: offensive language identification, automatic categorization of offense types, and offense target identification.In this paper, we present the approaches used by the Embeddia team, who qualified as fourth, eighteenth and fifth on the three sub-tasks.A different model was trained for each sub-task.For the first sub-task, we used a BERT model fine-tuned on the provided dataset, while for the second and third tasks we developed a custom neural network architecture which combines bag-of-words features and automatically generated sequence-based features.Our results show that combining automatically and manually crafted features fed into a neural architecture outperform transfer learning approach on more unbalanced datasets.
An Ensemble Learning Approach for Fault Diagnosis in Self-Organizing Heterogeneous Networks

Self-organizing networks (SON) aim to offer high quality services while reducing both capital and operational expenditures by enabling three main functions: self-configuration, self-optimization, and self-healing.Though there exits only few studies on self-healing, it plays an important role in intelligent network maintaining.In this paper, we firstly propose an ensemble learning based fault diagnosis system for self-organizing heterogeneous networks.Specifically, the base learner is strengthened in each iteration and the final diagnosis result is obtained from the combination of all base classifications for performance improvement.Moreover, traditional classification algorithms are designed with the premise of balanced data set.However, when they are applied to fault diagnosis in cellular network which has imbalanced data, the classification accuracy for minority classes is not satisfying.To address this issue, synthetic minority over-sampling technique (SMOTE) is applied to handle the data-imbalance.Furthermore, considering the fact that misclassification is unavoidable, and most existing schemes aim to achieve a low detection error rate, while ignoring the fact that different type of misclassification errors can cause different economic losses to the operators.We consider the cost sensitivity and use rescaling method to help the classifier differentiate the importance of different samples, so that a minimal total loss can be achieved.Also, for handling the sparse data and dense deployment issues in small cells of a heterogeneous network, we provide a distributed diagnose system for lowering the communications cost.Extensive simulations are performed and the results show the effectiveness of the proposed system.
Web Service Selection Using Modified Artificial Bee Colony Algorithm

Web services are a type of application software, which can be remotely accessed through the Internet. Due to the proliferating growth of web services of the same functionality, the user goes into a dilemma to select suitable service for him. In this paper, we study the web service selection (WSS) problem in a sequential composition model. We formulated the WSS as a constrained optimization problem. To solve the problem, we suggest a modified artificial bee colony (mABC) algorithm, which uses a chaotic-based opposition learning method to generate a better initial population. To improve the exploration capability of the mABC, a new search equation for employed bee phase is suggested. On the other hand, to improve the exploitation capacity of the mABC, a new search strategy, inspired by differential evolution (DE), is adopted in the onlooker bee phase. We test the mABC on synthetic web service selection problem taken from QWS dataset. To assess the relative performance of the mABC, we compare it against five other state-of-the-art algorithms. The experimental results show that the mABC is better than other existing approaches in terms of response time, latency, availability, and reliability.