Reviewerly/exHarmony huggingface dataset:
Understanding Merge Conflicts and Resolutions in Git Rebases

Software merging is an important activity during software development. Merge conflicts may arise and degrade the software quality. Empirical studies on software merging are helpful to understand developers' needs and the challenges of detecting and resolving conflicts. Existing studies collect merges by identifying commits that have more than one parent commit. Different from these explicit merges, rebasing branches is used to merge other changes but rewrites the evolutionary history. Hence, existing studies fail to identify implicit merges performed by rebasing branches. Consequently, the results of these studies may fail to provide comprehensive insights on software merging. In our study, we leverage the recently updated APIs of GitHub to study rebase activities in the pull requests. Our study shows that rebasing is widely used in pull requests. And our results indicate that, to resolve textual conflicts, developers adopt similar strategies shown in existing studies on explicit merges. However, in 34.2% of non-conflict rebase scenarios, developers add new changes during the rebase process. And this indicates that there are some new challenges of validating rebases. Our results provide useful insights for improving the state-of-the-art techniques on resolving conflicts and validating rebases.
Minimal model of permutation symmetry in unsupervised learning

Permutation of any two hidden units yields invariant properties in typical deep generative neural networks.This permutation symmetry plays an important role in understanding the computation performance of a broad class of neural networks with two or more hidden units.However, a theoretical study of the permutation symmetry is still lacking.Here, we propose a minimal model with only two hidden units in a restricted Boltzmann machine, which aims to address how the permutation symmetry affects the critical learning data size at which the concept-formation (or spontaneous symmetry breaking in physics language) starts, and moreover semi-rigorously prove a conjecture that the critical data size is independent of the number of hidden units once this number is finite.Remarkably, we find that the embedded correlation between two receptive fields of hidden units reduces the critical data size.In particular, the weaklycorrelated receptive fields have the benefit of significantly reducing the minimal data size that triggers the transition, given less noisy data.Inspired by the theory, we also propose an efficient fully-distributed algorithm to infer the receptive fields of hidden units.Furthermore, our minimal model reveals that the permutation symmetry can also be spontaneously broken following the spontaneous symmetry breaking.Overall, our results demonstrate that the unsupervised learning is a progressive combination of spontaneous symmetry breaking and permutation symmetry breaking which are both spontaneous processes driven by data streams (observations).All these effects can be analytically probed based on the minimal model, providing theoretical insights towards understanding unsupervised learning in a more general context.
Quantum Reservoir Computing: A Reservoir Approach Toward Quantum Machine Learning on Near-Term Quantum Devices

Quantum systems have an exponentially large degree of freedom in the number of particles and hence provide a rich dynamics that could not be simulated on conventional computers. Quantum reservoir computing is an approach to use such a complex and rich dynamics on the quantum systems as it is for temporal machine learning. In this chapter, we explain quantum reservoir computing and related approaches, quantum extreme learning machine and quantum circuit learning, starting from a pedagogical introduction to quantum mechanics and machine learning. All these quantum machine learning approaches are experimentally feasible and effective on the state-of-the-art quantum devices.
Quantum Algorithms for the Most Frequently String Search, Intersection of Two String Sequences and Sorting of Strings Problems

We study algorithms for solving three problems on strings. The first one is the Most Frequently String Search Problem. The problem is the following. Assume that we have a sequence of $n$ strings of length $k$. The problem is finding the string that occurs in the sequence most often. We propose a quantum algorithm that has a query complexity $\tilde{O}(n \sqrt{k})$. This algorithm shows speed-up comparing with the deterministic algorithm that requires $\Omega(nk)$ queries. The second one is searching intersection of two sequences of strings. All strings have the same length $k$. The size of the first set is $n$ and the size of the second set is $m$. We propose a quantum algorithm that has a query complexity $\tilde{O}((n+m) \sqrt{k})$. This algorithm shows speed-up comparing with the deterministic algorithm that requires $\Omega((n+m)k)$ queries. The third problem is sorting of $n$ strings of length $k$. On the one hand, it is known that quantum algorithms cannot sort objects asymptotically faster than classical ones. On the other hand, we focus on sorting strings that are not arbitrary objects. We propose a quantum algorithm that has a query complexity $O(n (\log n)^2 \sqrt{k})$. This algorithm shows speed-up comparing with the deterministic algorithm (radix sort) that requires $\Omega((n+d)k)$ queries, where $d$ is a size of the alphabet.
Multi-view clustering toward aerial images by combining spectral analysis and local refinement

Aerial images' clustering is of great significance in many domains of the last-generation computer systems, such as intelligent navigation, geological analysis, and disaster prediction. Conventional methods exploit only the global visual information and unimodal visual feature to characterize each aerial image, which is simple and self-descriptive in some cases. However, it cannot adequately simulate tiny and fine-grained visual patterns within each aerial image, such as automobiles of the "intersection" category and houses of the "residence" category. Moreover, the unimodal feature cannot effectively capture each aerial image. Other visual channels, such as the light spectrum, should also be encoded into the aerial image modeling system. We propose a novel aerial image clustering system, with a seamless fusion of spectrum and local features in this work. The multispectral visual clue is characterized by color plus texture channels from aerial images of each spectral channel. Simultaneously, locally distributed and very small objects combined with their spatial configurations are described by a set of graphlets. That is, each edge connects a pair of small objects, which are spatially neighboring. Subsequently, a multi-view learning algorithm is formulated to fuse the above channels to characterize each aerial image optimally. The importance of multiple spectral channels is flexibly tuned. A graph-based clustering algorithm is adopted to categorize the massive-scale aerial images into multiple types based on each aerial image's fused feature description. Extensive comparative results on a million-level real-world aerial photograph set prove our method's advantage over available state-of-the-art ones. Besides, visualization results imply that our clustering algorithm can optimally discriminate aerial images from different categories.
Attention based multilayer feature fusion convolutional neural network for unsupervised monocular depth estimation

Predicting depth from an image is an essential problem in the area of scene understanding and deep learning shows great potential in this area. Unsupervised methods regard image reconstruction loss as the supervised information which shows a great potential of application. Most methods face the problem that their depth estimation results are not accurate enough for detailed autopilot scenarios, which will limit its application. These methods are often based on the fully convolutional neural network, which is the most commonly used network in the image-to-image field. In this paper, aiming at optimizing the depth estimation network architecture, we propose two networks that fuse the features of different encoding layers for monocular depth estimation, a multilayer information fusion U-Net (FU-Net) and a more lightweight one (LFU-Net). In order to improve the efficiency of feature fusion, we propose a hybrid attention mechanism to optimize our network and named it as AgFU-Net. We compare our networks with other improvements of U-Net, and the result shows that our networks are more efficient. Also, the loss function is fine-tuned for the unsupervised depth estimation algorithm. Our improvements achieve results that are comparable with state-of-the-art unsupervised monocular depth prediction methods on the KITTI benchmarks.
Generalization from correlated sets of patterns in the perceptron

Generalization is a central aspect of learning theory. Here, we propose a framework that explores an auxiliary task-dependent notion of generalization, and attempts to quantitatively answer the following question: given two sets of patterns with a given degree of dissimilarity, how easily will a network be able to "unify" their interpretation? This is quantified by the volume of the configurations of synaptic weights that classify the two sets in a similar manner. To show the applicability of our idea in a concrete setting, we compute this quantity for the perceptron, a simple binary classifier, using the classical statistical physics approach in the replica-symmetric ansatz. In this case, we show how an analytical expression measures the "distance-based capacity", the maximum load of patterns sustainable by the network, at fixed dissimilarity between patterns and fixed allowed number of errors. This curve indicates that generalization is possible at any distance, but with decreasing capacity. We propose that a distance-based definition of generalization may be useful in numerical experiments with real-world neural networks, and to explore computationally sub-dominant sets of synaptic solutions.
Attribute-Based Encryption Scheme for Secure Multi-Group Data Sharing in Cloud

Most of the organizations using the cloud-based data sharing platforms are multi-group in nature. The existing directly revocable attribute-based encryption (ABE) schemes though seem to be a good fit, but they fail to provide any effective solution for secure multi-group data sharing scenarios. To bridge this gap, we first propose <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Re</b> vocable <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ABE</b> with <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V</b> erifiable <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</b> utsourced decryption (ReVO-ABE)- a directly revocable collusion-resistant ABE scheme that allows any number of user revocation and joining without affecting the secret membership keys of the nonrevoked users. Based on ReVO-ABE, we build a <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</b> ynamic <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</b> ulti- <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</b> roup <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</b> ecure <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</b> ata <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</b> haring scheme called DMG-SDS. For operations that are exclusive to multi-groups like group merge and split can be performed without affecting the attribute secret keys or membership keys of the nonrevoked users, which is not possible with any of the existing schemes. Our proposed scheme meets the necessary security requirements, and the performance assessment shows that it has much better performance benefits when compared with most the recent competitive schemes.
Automatic selection of clustering algorithms using supervised graph embedding

The widespread adoption of machine learning (ML) techniques and the extensive expertise required to apply them have led to increased interest in automated ML solutions that reduce the need for human intervention. One of the main challenges in applying ML to previously unseen problems is algorithm selection - the identification of high-performing algorithm(s) for a given dataset, task, and evaluation measure. This study addresses the algorithm selection challenge for data clustering, a fundamental task in data mining that is aimed at grouping similar objects. We present MARCO-GE, a novel meta-learning approach for the automated recommendation of clustering algorithms. MARCO-GE first transforms datasets into graphs and then utilizes a graph convolutional neural network technique to extract their latent representation. Using the embedding representations obtained, MARCO-GE trains a ranking meta-model capable of accurately recommending top-performing algorithms for a new dataset and clustering evaluation measure. Extensive evaluation on 210 datasets, 13 clustering algorithms, and 10 clustering measures demonstrates the effectiveness of our approach and its superiority in terms of predictive and generalization performance over state-of-the-art clustering meta-learning approaches.
Characterization of a Photon-Number Resolving SNSPD Using Poissonian and Sub-Poissonian Light

Photon-number resolving (PNR) single-photon detectors are of interest for a wide range of applications in the emerging field of photon based quantum technologies. Especially photonic integrated circuits will pave the way for a high complexity and ease of use of quantum photonics. Superconducting nanowire single-photon detectors (SNSPDs) are of special interest since they combine a high detection efficiency and a high timing accuracy with a high count rate and they can be configured as PNR-SNSPDs. Here, we present a PNR-SNSPD with a four photon resolution suitable for waveguide integration operating at a temperature of 4 K. A high statistical accuracy for the photon number is achieved for a Poissonian light source at a photon flux below 5 photons/pulse with a detection efficiency of 22.7 +- 3.0% at 900 nm and a pulse rate frequency of 76 MHz. We demonstrate the ability of such a detector to discriminate a sub-Poissonian from a Poissonian light source.
Dynamic keystroke pattern analysis and classifiers with competence for user recognition

The classification of biometric data is always a difficult task because these data are often unstable and depends on behavioural condition of a person. Personal keystroke activity is difficult to imitate and can be used for identity authentication. In this article, we suggest that the analysis of keystroke dynamics can dramatically increase the level of security of a computer system without disturbing the user’s comfort. First, the user’s established behavioural profile is loaded and then this profile is continuously compared with the current use of the keyboard. If a user’s temporary behavioural profile differs from their established profile, then the access to the computer is blocked. If a current profile of the user is recognized as legitimate, then profile can be dynamically changed, which allows to adjust profiles according to users abilities. Security based on keystroke dynamics analysis depends primarily on the quality of recognizing changes in a user’s profile. Recognition is performed on the dynamic analysis of the feature vector recorded while using the keyboard. In the presented approach, we propose using classifiers with their competence selection, which significantly improves the recognition of users. To select the most appropriate classifiers, the competences of the classifiers in a pool are calculated and then, the most powerful classifiers are selected. Conducted experiments, supported by the proper statistical analysis, confirm the usefulness of the proposed strategy in authorized user and intruder recognition.
Evaluation codes and their basic parameters

The aim of this work is to give degree formulas for the generalized Hamming weights of evaluation codes and to show lower bounds for these weights. In particular, we give degree formulas for the generalized Hamming weights of Reed–Muller-type codes, and we determine the minimum distance of toric codes over hypersimplices, and the 1st and 2nd generalized Hamming weights of squarefree evaluation codes.
Heavy metal index and geographical information system (GIS) approach to study heavy metal contamination: a case study of north Chennai groundwater

Abstract Fifty-four groundwater samples were collected from the highly industrialized area of north Chennai. These groundwater samples were tested for Fe, Mn, Cu, Ni, Pb, Zn and Cr in pre-monsoon and post-monsoon periods of 2015–2016. Most of the samples in the area were found to have high concentration of heavy metals. Geographical information system was used to develop contour maps for the analysis of heavy metals, and it has been found that most of the Ambattur area was affected by the heavy metals in both the seasons. ANOVA tests were carried out on the hydro-chemical data for both the monsoon periods, and it was found that there was a common source of origin for most of the heavy metals, which was also confirmed by the correlation and principal component analysis. T -test indicates that there was a common source of origin of heavy metals in the study area, viz. industrial and domestic pollutants, that were found to be the main source of heavy metals in both the monsoon periods. Principal component analysis gave three important factors (principal components) for both the seasons. Pre-monsoon groundwater samples showed a common cause of origin of heavy metals than the post-monsoon samples. Heavy metal pollution index indicates that almost all the samples were not fit for drinking purpose in both the monsoon periods and metal index also indicates the non-usability of the water for drinking purpose.
Multi-Party Quantum Summation Based on Quantum Teleportation

We present a secure multi-party quantum summation protocol based on quantum teleportation, in which a malicious, but non-collusive, third party (TP) helps compute the summation. In our protocol, TP is in charge of entanglement distribution and Bell states are shared between participants. Users encode the qubits in their hand according to their private bits and perform Bell-state measurements. After obtaining participants' measurement results, TP can figure out the summation. The participants do not need to send their encoded states to others, and the protocol is therefore congenitally free from Trojan horse attacks. In addition, our protocol can be made secure against loss errors, because the entanglement distribution occurs only once at the beginning of our protocol. We show that our protocol is secure against attacks by the participants as well as the outsiders.
Multipopulation Ensemble Particle Swarm Optimizer for Engineering Design Problems

Particle swarm optimization (PSO) is an efficient optimization algorithm and has been applied to solve various real-world problems. However, the performance of PSO on a specific problem highly depends on the velocity updating strategy. For a real-world engineering problem, the function landscapes are usually very complex and problem-specific knowledge is sometimes unavailable. To respond to this challenge, we propose a multipopulation ensemble particle swarm optimizer (MPEPSO). The proposed algorithm consists of three existing efficient and simple PSO searching strategies. The particles are divided into four subpopulations including three indicator subpopulations and one reward subpopulation. Particles in the three indicator subpopulations update their velocities by different strategies. During every learning period, the improved function values of the three strategies are recorded. At the end of a learning period, the reward subpopulation is allocated to the best-performed strategy. Therefore, the appropriate PSO searching strategy can have more computational expense. The performance of MPEPSO is evaluated by the CEC 2014 test suite and compared with six other efficient PSO variants. These results suggest that MPEPSO ranks the first among these algorithms. Moreover, MPEPSO is applied to solve four engineering design problems. The results show the advantages of MPEPSO. The MATLAB source codes of MPEPSO are available at https://github.com/zi-ang-liu/MPEPSO.
Automated Student Attendance System Using Face Recognition

In this study, an automated attendance taking system is developed and implemented. Two different face detection algorithms, namely Histogram of Oriented Gradients and Haar-Cascade algorithms, are applied and their performances are compared. Deep learning based on convolutional neural networks (CNNs) is employed for the identification of the students in the classroom. Furthermore, a mask checking feature is also included as a measure against the Covid-19 pandemic. A graphical user interface (GUI) system is designed using Python.
Quantum Correlation Dynamics in Controlled Two-Coupled-Qubit Systems

We study and compare the time evolutions of concurrence and quantum discord in a driven system of two interacting qubits prepared in a generic Werner state. The corresponding quantum dynamics is exactly treated and manifests the appearance and disappearance of entanglement. Our analytical treatment transparently unveils the physical reasons for the occurrence of such a phenomenon, relating it to the dynamical invariance of the X structure of the initial state. The quantum correlations which asymptotically emerge in the system are investigated in detail in terms of the time evolution of the fidelity of the initial Werner state.
Fuzzy Type-1 Triangular Membership Function Approximation Using Fuzzy C-Means

Fuzzy logic is a way of many-valued computing logic that deals with the truth values of the variables between 0 and 1, unlike the conventional Boolean logic. Membership functions are used to depict the fuzzy values of given variable. Though membership functions are determined through expert's opinion, however, the one estimated through heuristic algorithms is the preferable methods. Membership functions determined through statistical and knowledge engineering methods are usually application dependent and cannot be applied on different datasets. This research focuses on generating the parametric values of the triangular membership function using a novel method. Initially, the Fuzzy C-means algorithm is utilized to generate the parameters values of the Gaussian membership function. Using a set of equations, these values then estimate the parameters of the triangular membership function. The proposed method is applied to the quality of web services data. From the results it is verified that the new approach of generating triangular membership functions can be adopted.