Reviewerly/exHarmony huggingface dataset:
Generic Code Clone Detection Model for Java Applications

Abstract Code clone is a common term used for codes that are repeated multiple times in a program. There are Type 1, Type 2, Type 3 and Type 4 code clones. Various code clone detection approaches and models have been used to detect a code clone. However, a major challenge faced in detecting code clone using these models is the lack of generality in detecting all clone types. To address this problem, Generic Code Clone Detection (GCCD) model that consists of five processes which are Preprocessing, Transformation, Parameterization, Categorization and Match Detection process is proposed. Initially, a pre-processing process produces source units through the application of five combinatorial rules. This is followed by the transformation process to produce transformed source units based on the letter to number substitution concept. Next, a parameterization process produces parameters used in categorization and match detection process. Next, a categorization process groups the source units into pools. Finally, a match detection process uses a hybrid exact matching with Euclidean distance to detect the clones. Based on these processes, a prototype of the GCCD was developed using Netbeans 8.0. The model was compared with the Generic Pipeline Model (GPM). The comparisons showed that the GCCD was able to detect clone pairs of Type-1 until Type-4 while the GPM was able to detect clone pair for Type-1 only. Furthermore, the GCCD prototype was empirically tested with Bellons benchmark data and it was able to detect clones in Java applications with up to 203,000 line of codes. As a conclusion, the GCCD model is able to overcome the lack of generality in detecting all code clone types by detecting Type 1, Type 2, Type 3 and Type 4 clones.
Interactive shallow Clifford circuits: Quantum advantage against NC¹ and beyond

Recent work of Bravyi et al. and follow-up work by Bene Watts et al. demonstrates a quantum advantage for shallow circuits: constant-depth quantum circuits can perform a task which constant-depth classical (i.e., 0) circuits cannot. Their results have the advantage that the quantum circuit is fairly practical, and their proofs are free of hardness assumptions (e.g., factoring is classically hard, etc.). Unfortunately, constant-depth classical circuits are too weak to yield a convincing real-world demonstration of quantum advantage. We attempt to hold on to the advantages of the above results, while increasing the power of the classical model. Our main result is a two-round interactive task which is solved by a constant-depth quantum circuit (using only Clifford gates, between neighboring qubits of a 2D grid, with Pauli measurements), but such that any classical solution would necessarily solve -hard problems. This implies a more powerful class of constant-depth classical circuits (e.g., 0[p] for any prime p) unconditionally cannot perform the task. Furthermore, under standard complexity-theoretic conjectures, log-depth circuits and log-space Turing machines cannot perform the task either. Using the same techniques, we prove hardness results for weaker complexity classes under more restrictive circuit topologies. Specifically, we give 0 interactive tasks on 2 × n and 1 × n grids which require classical simulations of power 1 and 0[6], respectively. Moreover, these hardness results are robust to a small constant fraction of error in the classical simulation.
Indoor Navigation with Deep Reinforcement Learning

Indoor navigation solutions are essential for navigating within the building where GPS based solutions cannot be used. The applications of indoor navigations are robotics, drones, and few gaming apps. Deep reinforcement learning algorithms are used for building indoor navigation solutions, but such algorithms are computationally intensive and practically infeasible. But it is feasible to use reinforcement learning by reducing the scope and indoor scene representation information to propose an indoor navigation solution, which is the focus of the proposed work. The proposed work uses deep learning to detect objects in the given indoor scene and find a path using deep reinforcement learning. The proposed solution gives the navigation path in 2 minutes 10 seconds approximately, which is comparable with previous works. The authors also indicate the research work to be carried out in the future.
SDCOR: Scalable density-based clustering for local outlier detection in massive-scale datasets

This paper presents a batch-wise density-based clustering approach for local outlier detection in massive-scale datasets. Unlike the well-known traditional algorithms, which assume that all the data is memory-resident, our proposed method is scalable and processes the input data chunk-by-chunk within the confines of a limited memory buffer. A temporary clustering model is built at the first phase; then, it is gradually updated by analyzing consecutive memory loads of points. Subsequently, at the end of scalable clustering, the approximate structure of the original clusters is obtained. Finally, by another scan of the entire dataset and using a suitable criterion, an outlying score is assigned to each object called SDCOR (Scalable Density-based Clustering Outlierness Ratio). Evaluations on real-life and synthetic datasets demonstrate that the proposed method has a low linear time complexity and is more effective and efficient compared to best-known conventional density-based methods, which need to load all data into the memory; and also, to some fast distance-based methods, which can perform on data resident in the disk.
Learning to Ask Screening Questions for Job Postings

At LinkedIn, we want to create economic opportunity for everyone in the global workforce. A critical aspect of this goal is matching jobs with qualified applicants. To improve hiring efficiency and reduce the need to manually screening each applicant, we develop a new product where recruiters can ask screening questions online so that they can filter qualified candidates easily. To add screening questions to all 20M active jobs at Linked In, we propose a new task that aims to automatically generate screening questions for a given job posting. To solve the task of generating screening questions, we develop a two-stage deep learning model called Job2Questions, where we apply a deep learning model to detect intent from the text description, and then rank the detected intents by their importance based on other contextual features. Since this is a new product with no historical data, we employ deep transfer learning to train complex models with limited training data. We launched the screening question product and our AI models to LinkedIn users and observed significant impact in the job marketplace. During our online A/B test, we observed +53.10% screening question suggestion acceptance rate, +22.17% job coverage, +190% recruiter-applicant interaction, and +11 Net Promoter Score. In sum, the deployed Job2Questions model helps recruiters to find qualified applicants and job seekers to find jobs they are qualified for.
Improving the Performance of Association Rules Hiding using Hybrid Optimization Algorithm

In this digital world, information evolves from several sources like social media, e-commerce sites, and mobile networks, etc., in large volumes for processing. The sensitiveness in the form of rules extracted from different resources entails that privacy-preserving is a significant research issue to be cared for. In this context, it is imperative to impose confidentiality on sensitive rule data during its processing. Optimization algorithms play a vital role in the reduction of ghost rules and lost rules in association rule hiding. This paper proposes a novel Hybrid optimization algorithm that acquires the characteristics of the above-said algorithms for association rule hiding and it has been shown that it produces better results in less time. Further, the newly introduced concepts on the lost rule generation and recovery are seen to produce almost 99% of lost rules with a reduction in side effect factors from 24–5%.
Model Predictive Control for Vision-Based Quadrotor Guidance

A model predictive control algorithm for autonomous approaches of quadrotor helicopters to a window is presented in this paper. The target is selected by an operator in a reference image which is sent to the vehicle. A wide baseline image matching algorithm is used to obtain the target position in the current image. The vehicle guidance for the approach is realized by a nonlinear model predictive control algorithm which calculates the desired thrust and angular velocities while taking the goals of approaching the target orthogonally and keeping the target visible in the camera image into account. Moreover, two novel methods for the estimation of the orthogonal distance and orientation to the façade plane in which the target window is located are presented. The first method relies on laser rangefinder measurements while the second method is based on the decomposition of the estimated homography matrix between the façade plane in the reference and the live image. Monte Carlo simulations of approaches to a house show the accuracy and robustness of the vehicle guidance. The trajectories converge even from difficult start positions, for example approaches with acute angles between the façade and the quadrotor's orientation. As desired, the window is approached perpendicularly towards the end of the approaches.
An Improved Farmland Fertility Algorithm for Global Function Optimization

This paper proposes an improved farmland fertility (IFF) algorithm to increase the convergence rate and precision of the farmland fertility algorithm. A search mode that combines subspace and full space is proposed. The two modes are automatically converted in accordance with the current learning level of the population. Such hybrid search balances the algorithm exploration and exploitation capabilities. The global memory capacity with a fixed size is processed self-adaptively to make its size adaptively change with the iterative process. A mechanism of soil fusion based on neighbor memory learning is also proposed to expand the search range of the current population and ensure population diversity. On the basis of the cosine similarity between the population and the current optimal individual, the area to which the individual belongs is periodically redivided, and the computing resources are reasonably allocated. The results of experiments on the CEC2013 test suite indicate that IFF has evident advantages over seven excellent algorithms in terms of convergence rate and precision.
Voting-Based Ensemble of Unsupervised Outlier Detectors

Datasets may contain small sets of data objects whose characteristics are not in accordance with the mainstream characteristics of the data objects in a dataset. These data objects, which are not noise, may contain valuable information and are called outliers. Outlier detection is a topic of research in many fields like detecting malwares in cyber security, finding fake financial transactions, identifying defects in industrial products, detecting abnormality in health data, etc. Researchers have developed several application methods for detecting outliers and a few generic methods. These methods can be grouped into unsupervised methods, supervised methods and semi-supervised methods based on the readiness of class labels. We, in this paper, present the performance of three outlier detection algorithms using the realworld datasets. The algorithms used are one-class SVM, elliptic envelope and local outlier factor. In order to improve the performance, all these algorithms were selected and ensemble based on voting mechanism. The influence of dimensionality reduction on the proposed ensemble method has also been studied. Experiments using publicly available datasets show that the proposed technique outperforms individual outlier detectors.
Single Image Deraining Using Time-Lapse Data

Leveraging on recent advances in deep convolutional neural networks (CNNs), single image deraining has been studied as a learning task, achieving an outstanding performance over traditional hand-designed approaches. Current CNNs based deraining approaches adopt the supervised learning framework that uses a massive training data generated with synthetic rain streaks, having a limited generalization ability on real rainy images. To address this problem, we propose a novel learning framework for single image deraining that leverages time-lapse sequences instead of the synthetic image pairs. The deraining networks are trained using the time-lapse sequences in which both camera and scenes are static except for time-varying rain streaks. Specifically, we formulate a background consistency loss such that the deraining networks consistently generate the same derained images from the time-lapse sequences. We additionally introduce two loss functions, the structure similarity loss that encourages the derained image to be similar with an input rainy image and the directional gradient loss using the assumption that the estimated rain streaks are likely to be sparse and have dominant directions. To consider various rain conditions, we leverage a dynamic fusion module that effectively fuses multi-scale features. We also build a novel large-scale time-lapse dataset providing real world rainy images containing various rain conditions. Experiments demonstrate that the proposed method outperforms state-of-the-art techniques on synthetic and real rainy images both qualitatively and quantitatively. On the high-level vision tasks under severe rainy conditions, it has been shown that the proposed method can be utilized as a pre-preprocessing step for subsequent tasks.
EdgeFrame

We describe the design and implementation of EdgeFrame: a graph-specialized Spark DataFrame that caches the edges of a graph in compressed form on all worker nodes of a cluster, and provides a fast and scalable Worst-Case-Optimal Join (WCOJ) that is especially useful for matching of complex and cyclical patterns in large graphs. Our choice to forego shuffle- or communication-based WCOJ is motivated by our analysis of the Shares algorithm for distributed WCOJ, that was proven communication-optimal, but which we show to quickly deteriorate to a full broadcast of all data already with moderately complex graph patterns. Our work shows that specializing WCOJ to a multi-way self-join, and leveraging compressed storage, provides a significant opportunity for better WCOJ performance. Finally, we investigate WCOJ parallelization and load-balancing strategies and show that fine-grained dynamic load-balancing with work-stealing is to be preferred, creating interesting insights and challenges for the future evolution of the Spark scheduler.
Characteristics of particles in groundwater and their prospecting significance for the Shijiangshan Pb-Zn-Ag deposit, Inner Mongolia, China

The Shijiangshan concealed deposit in Inner Mongolia, China is located in a semi-arid region with a thick sediment layer on the surface. Samples of particles from deep and shallow groundwater in the deposit were analyzed by transmission electron microscopy (TEM). Corresponding fault gouge and oxide ore powder were collected and analyzed at the same time. These particles had sizes ranging from 5 to 400 nm and contained metallogenic elements, such as Pb, Zn, As, Cu, and Ag. Groundwater particles can reflect the properties of the deep concealed ores because the metal-bearing particles in the groundwater are similar in composition to the deep ores. In this study, particles with a carbon film covering their surface were discovered in groundwater. The carbon coating helped particles to remain in their primitive state, and thus, retain the more direct metallogenic information. Shallow groundwater particles can be used to predict the composition of deep concealed ore deposits. In thick overburdened areas, where local geochemical information on abnormal elemental levels is not obvious and geophysical prospecting methods may have multiple solutions, the accuracy of prospecting can be improved by using particles in groundwater that provide concealed orebody information, making such particles very important for prospecting.
A nature-inspired feature selection approach based on hypercomplex information

Feature selection for a given model can be transformed into an optimization task. The essential idea behind it is to find the most suitable subset of features according to some criterion. Nature-inspired optimization can mitigate this problem by producing compelling yet straightforward solutions when dealing with complicated fitness functions. Additionally, new mathematical representations, such as quaternions and octonions, are being used to handle higher-dimensional spaces. In this context, we are introducing a meta-heuristic optimization framework in a hypercomplex-based feature selection, where hypercomplex numbers are mapped to real-valued solutions and then transferred onto a boolean hypercube by a sigmoid function. The intended hypercomplex feature selection is tested for several meta-heuristic algorithms and hypercomplex representations, achieving results comparable to some state-of-the-art approaches. The good results achieved by the proposed approach make it a promising tool amongst feature selection research.
Sensor-realistic Synthetic Data Engine for Multi-frame High Dynamic Range Photography

Deep learning-based mobile imaging applications are often limited by the lack of training data. To this end, researchers have resorted to using synthetic training data. However, pure synthetic data does not accurately mimic the distribution of the real data. To improve the utility of synthetic data, we present a systematic pipeline that takes synthetic data coming purely from a game engine and then produces synthetic data with real sensor characteristics such as noise and color gamut. We validate the utility of our sensor-realistic synthetic data for multi-frame high dynamic range (HDR) photography using a Samsung Galaxy S10 Plus smartphone. The result of training two baseline neural networks using our sensor realistic synthetic data modeled for the S10 Plus show that our sensor realistic synthetic data improves the quality of HDR photography on the modeled device. The synthetic dataset is publicly available at https://github.com/nadir-zeeshan/sensor-realistic-synthetic-data.
HOW INNOVATIVENESS MEDIATES THE EFFECTS OF ORGANIZATIONAL CULTURE AND LEADERSHIP ON PERFORMANCE

This study explores how innovativeness mediates the effects of organizational culture and leadership on performance. Survey data are collected from 288 middle and top managers in the Jordanian airline industry. Validity and reliability analyses are performed, and direct and indirect effects are tested using structural equation modeling. Innovativeness positively and significantly mediates the effects of organizational culture and leadership on performance. Organizational culture, in turn, may contribute to innovativeness and performance, depending on the effectiveness of the organization’s leadership style. A bureaucratic culture does not statistically or significantly correlate with the perceived organizational performance, but it does positively and significantly affect the perceived market performance. This study is one of the first investigations that connects innovativeness to culture and leadership, and provides empirical evidence on the topic.
Large-scale Image Retrieval with Sparse Binary Projections

Inspired by the recent discoveries in neuroscience, the study of the sparse binary projection model started to attract people's attention, shedding new light on image retrieval. Different from the classical work that tries to reduce the dimension of the data for faster retrieval speed, the model projects dense input samples into a higher-dimensional space and outputs sparse binary data representations after winner-take-all competition. Following the work along this line, this paper designed a new algorithm which obtains a high-quality sparse binary projection matrix through unsupervised training. Simple as it is, the algorithm reported significantly improved results over the state-of-the-art methods in both search accuracy and retrieval speed in a series of empirical evaluations on large-scale image retrieval tasks, which exhibited its promising potential in industrial applications.
Dithered backprop: A sparse and quantized backpropagation algorithm for more efficient deep neural network training

Deep Neural Networks are successful but highly computationally expensive learning systems. One of the main sources of time and energy drains is the well known back-propagation (backprop) algorithm, which roughly accounts for 2/3 of the computational cost of training. In this work we propose a method for reducing the computational complexity of backprop, which we named dithered backprop. It consists on applying a stochastic quantization scheme to intermediate results of the method. The particular quantisation scheme, called non-subtractive dither (NSD), induces sparsity which can be exploited by computing efficient sparse matrix multiplications. Experiments on popular image classification tasks show that it induces 92% sparsity on average across a wide set of models at no or negligible accuracy drop in comparison to state-of-the-art approaches, thus significantly reducing the computational complexity of the backward pass. Moreover, we show that our method is fully compatible to state-of-the-art training methods that reduce the bit-precision of training down to 8-bits, as such being able to further reduce the computational requirements. Finally we discuss and show potential benefits of applying dithered backprop on a distributed training settings, in that communication as well as compute efficiency may increase simultaneously with the number of participant nodes.
Privacy‐preserving cooperative localization in vehicular edge computing infrastructure

Summary Advancement of computing and communication techniques transforms the traditional transport system into the intelligent transportation system (ITS). The development of distributed computing in a vehicular network platform also called Vehicular Edge Computing (VEC) promise to address most of the challenges faced by the ITS. Localization is important in these vehicular networks because of its key contribution in autonomous driving, smart traffic monitoring, and collision avoidance services. For localization, current GPS and hybrid methods are in‐efficient because of GPS outage in urban infrastructure and dynamic nature of the vehicular networks. The cooperative localization approaches, on the other hand, use dedicated short range communication to broadcast messages and estimate location. However, these messages are un‐encrypted and periodic which gives a privacy risk for vehicles. This article presents a privacy‐preserving cooperative localization in vehicular network based upon dynamic pseudonym changing strategy. First, the localization delay is addressed with the implementation of dynamic vehicular edge assignment for computational task management. In the next step, the localization is estimated from the neighbor and road side unit ranging measurement followed by a real‐time prediction of the vehicle. The performance of the proposed algorithms is analyzed in terms of localization accuracy and privacy preservation strength. Furthermore, the proposed method is simulated in a real city scenario followed by localization accuracy and privacy analysis. Finally, the localization accuracy and privacy strength of the proposed approach are compared with the state‐of‐the‐art methods.