Reviewerly/exHarmony huggingface dataset:
Out of Sight, Out of Place: Detecting and Assessing Swapped Arguments

Programmers often add meaningful information about program semantics when naming program entities such as variables, functions, and macros. However, static analysis tools typically discount this information when they look for bugs in a program. In this work, we describe the design and implementation of a static analysis checker called SWAPD, which uses the natural language information in programs to warn about mistakenly-swapped arguments at call sites. SWAPD combines two independent detection strategies to improve the effectiveness of the overall checker. We present the results of a comprehensive evaluation of SWAPD over a large corpus of C and C++ programs totaling 417 million lines of code. In this evaluation, SWAPD found 154 manually-vetted real-world cases of mistakenly-swapped arguments, suggesting that such errors- while not pervasive in released code-are a real problem and a worthwhile target for static analysis.
Entanglement Control of Two-Level Atoms in Dissipative Cavities

An open quantum bipartite system consisting of two independent two-level atoms interacting non-linearly with a two-mode electromagnetic cavity field is investigated by proposing a suitable non-Hermitian generalization of Hamiltonian. The mathematical procedure of obtaining the corresponding wave function of the system is clearly given. Panchartnam phase is studied to give a precise information about the required initial system state, which is related to artificial phase jumps, to control the Degree of Entanglement (DEM) and get the highest Concurrence. We discuss the effect of time-variation coupling, and dissipation of both atoms and cavity. The effect of the time-variation function appears as frequency modulation (FM) effect in the radio waves. Concurrence rapidly reaches the disentangled state (death of entanglement) by increasing the effect of field decay. On the contrary, the atomic decay has no effect.
The effects of postural threat induced by a virtual environment on performance of a walking balance task

Rapid motor learning may occur in situations where individuals perceive a threat if they do not perform a task well. This rapid motor learning may be facilitated by improved motor performance and, consequently, more errorless practice. As a first step towards understanding the role of perceived threat on rapid motor learning, the purpose of this study was to determine how performance of a motor task is affected in situations where perceived threat is high. We hypothesized that perceived threat in a virtual environment would result in improved performance of a walking task (i.e., walking on a narrow beam). Results demonstrated that increased perceived threat did not yield statistically significantly greater balance performance in the high-threat virtual environment (median percentage of successful steps: 78.8%, 48.3%, and 55.2% in the real low-threat, virtual low-threat, and virtual high-threat environments, respectively). These results may be partially attributed to habituation to threat over time and practice. If implemented carefully, virtual reality technology can be an effective tool for investigating walking balance in environments that are perceived as threatening.
Individual Eye Gaze Prediction with the Effect of Image Enhancement Using Deep Neural Networks

The prediction of individual eye gaze is a research topic that has gained the interest of researchers with its wide range of applications because neural networks majorly increase the rate of accuracy of individual gaze. In this research work, MPIIGaze dataset has been employed for the prediction of individual gaze and the direction of individual gaze was grouped into down view, left view, right view and lastly centre view. A CNN model was used to train and validate a random selection of images. Firstly, the ordinary images were trained and validated, after which image enhancement processing technique was applied. With the image brightness enhancement technique, a higher rate of gaze prediction accuracy was achieved. Hence, it can be deduced that image enhancement has proved its purpose by providing image interpretation with better quality.
Causal Graphical Models for Systems-Level Engineering Assessment

Systems-level analysis of an engineered structure demands robust scientific and statistical protocols to assess model-driven conclusions that are often nontraditional and causal in their content. The formal mathematical, statistical, and philosophical foundations of causal inference on which such protocols are based are, nevertheless, not widely understood. The aims of this article are to (1) communicate the essentials of graph-based causal inference to the civil engineering community, (2) demonstrate how rigorous causal conclusions—and formal quantification of uncertainty regarding those conclusions—may be obtained in a typical engineered system application, and (3) discuss the value of this approach in the context of engineered system assessment. The concepts are illustrated via a river-weir ecosystem case study as an example of decision making for engineered systems in the built environment. In this setting, it is demonstrated how rigorous predictions can be made about the outcome of decisions that take a lack of prior knowledge about the system into account. The findings highlight to end users the value in applying this approach in providing quantitative probabilistic outputs that counter decision uncertainty at system level.
Stable super-resolution of images: theoretical study

Abstract We study the ubiquitous super-resolution problem, in which one aims at localizing positive point sources in an image, blurred by the point spread function of the imaging device. To recover the point sources, we propose to solve a convex feasibility program, which simply finds a non-negative Borel measure that agrees with the observations collected by the imaging device. In the absence of imaging noise, we show that solving this convex program uniquely retrieves the point sources, provided that the imaging device collects enough observations. This result holds true if the point spread function of the imaging device can be decomposed into horizontal and vertical components and if the translations of these components form a Chebyshev system, i.e., a system of continuous functions that loosely behave like algebraic polynomials. Building upon the recent results for one-dimensional signals, we prove that this super-resolution algorithm is stable, in the generalized Wasserstein metric, to model mismatch (i.e., when the image is not sparse) and to additive imaging noise. In particular, the recovery error depends on the noise level and how well the image can be approximated with well-separated point sources. As an example, we verify these claims for the important case of a Gaussian point spread function. The proofs rely on the construction of novel interpolating polynomials—which are the main technical contribution of this paper—and partially resolve the question raised in Schiebinger et al. (2017, Inf. Inference, 7, 1–30) about the extension of the standard machinery to higher dimensions.
A Lightweight Encoder–Decoder Path for Deep Residual Networks

In this article, we present a novel lightweight path for deep residual neural networks. The proposed method integrates a simple plug-and-play module, i.e., a convolutional encoder-decoder (ED), as an augmented path to the original residual building block. Due to the abstract design and ability of the encoding stage, the decoder part tends to generate feature maps where highly semantically relevant responses are activated, while irrelevant responses are restrained. By a simple elementwise addition operation, the learned representations derived from the identity shortcut and original transformation branch are enhanced by our ED path. Furthermore, we exploit lightweight counterparts by removing a portion of channels in the original transformation branch. Fortunately, our lightweight processing does not cause an obvious performance drop but brings a computational economy. By conducting comprehensive experiments on ImageNet, MS-COCO, CUB200-2011, and CIFAR, we demonstrate the consistent accuracy gain obtained by our ED path for various residual architectures, with comparable or even lower model complexity. Concretely, it decreases the top-1 error of ResNet-50 and ResNet-101 by 1.22% and 0.91% on the task of ImageNet classification and increases the mmAP of Faster R-CNN with ResNet-101 by 2.5% on the MS-COCO object detection task. The code is available at https://github.com/Megvii-Nanjing/ED-Net.
Prediction of Job Openings in IT Sector using Long Short -Term Memory Model

Addressing the unemployment problem is a bit challenging task. The non-engineering graduates can work in all the sectors, whereas engineering graduates can work in their designated job domain. So, the engineering graduate needs to be guided in getting the employment opportunity in their job domain. The unemployment rate in India is increasing drastically every year. The unemployment of engineering graduates is mainly due to the lack of knowledge on various job categories and all the graduates are enriching their skills in the attractive domain or the upcoming technologies. So, the graduates are falling only in a specified job category where the competition is more. This problem must be resolved by guiding the graduates. There is a need for a balanced approach in guiding the graduates to avoid the problem of searching for the job in the attractive domain. This paper presents a new method to predict the number of job openings based on location and job category using the Long Short-Term Memory model (LS TM). After the series of experiments conducted, the results show that the proposed method is 96% effective. The performance of the proposed system is found to be superior to the Simple Recurrent Neural Network (SRNN). By using the proposed model, the graduates are benefited in getting knowledge about the current job opportunities.
A Webpage Segmentation Method Based on Node Information Entropy of DOM Tree

Abstract Aiming at the problems of DOM tree-based webpage segmentation methods in rationality of segmentation results, efficiency, and relying on auxiliary means, in this paper, we propose a new method based on node information entropy of the DOM tree. First, it classified webpage tags and only processed tags that affected the structure of pages. Second, comprehensively considering the content features and the structure features of DOM tree nodes, it calculated information entropy of nodes and maximum text density of subnodes, and based on them, determined whether a node was an independent page block. Finally, this method did node fusion to get the segmentation results. Segmentation tests of multiple web pages with different structures show that the proposed method can accurately and quickly segment the content of web pages.
Dimsum @LaySumm 20

Lay summarization aims to generate lay summaries of scientific papers automatically.It is an essential task that can increase the relevance of science for all of society.In this paper, we build a lay summary generation system based on the BART model.We leverage sentence labels as extra supervision signals to improve the performance of lay summarization.In the CL-LaySumm 2020 shared task, our model achieves 46.00% Rouge1-F1 score.
Chronological networks in archaeology: A formalised scheme

This paper proposes a new methodology for modelling chronological data in archaeology. We introduce the concept of “chronological network”, a flexible model for representing chronological entities, synchronisms between them, and other chronological constraints such as termini post/ante quem and duration bounds. We propose a procedure for checking the consistency of a chronological network and for refining dating estimates from the available synchronisms and constraints. We introduce ChronoLog, a chronology software application that allows users to build a chronological network interactively. The software automatically checks the consistency of the network and computes the tightest possible chronological range for each entity, within seconds. ChronoLog is freely available online at http://chrono.ulb.be.
Unnatural Human Motion Detection using Weakly Supervised Deep Neural Network

Analyzing human behavior from surveillance videos can help improve public safety. Anomaly detection from time-series data is effective in facilitating this technique, and various methods based on statistical, machine learning, and deep learning techniques have been proposed. In particular, deep neural network (DNN) based methods are attracting attention because of their expressive power and ease of implementation. However, in order to apply a DNN model to time-series data analysis, it is necessary to train the DNN with a large amount of data. This not only requires time-series data obtained from an actual system but also requires data labels that describe data abnormalities. This implies a large amount of data preparation and manual annotation by humans. It is not only time consuming but also difficult to annotate data with unclear definitions, such as unnatural human behavior. In this paper, we propose a DNN model in order to automatically extract abnormal features with unclear definitions that are hidden in time-series data. We adopt a weakly supervised training method by devising a loss function to optimize the DNN model. Through experiments, we confirm that the proposed approach can be used to detect and quantify the degree of outliers in time-series data. Furthermore, unnatural human motion can be detected by applying the proposed method.
An Efficient Approach for Sequential Pattern Mining on GPU Using CUDA Platform

In modern days to handle gargantuan amount of data, high computation system is needed for processing them in real time. High Performance Computing (HPC) system offers high computation power for processing big data efficiently. Data mining algorithms that deal with different types of data to discover hidden relationships and find complex patterns among them is a demanding field for the data scientists. As the size of the data grows, traditional algorithms, tools and techniques need some special computing environment so that they can work in real time. Sequential pattern mining is a crucial field for data mining that helps to analyze different essential data fields for finding sequence patterns. Prefixspan is one of the most efficient algorithms to find sequential patterns. However, in the sequential program of Prefixspan, high execution time is required to compute a big sequence database because of executing over a single processor. In the HPC system, multiple processors connected through a high-speed connection and they can compute the same task concurrently. Our goal is to minimize the execution time of Prefixspan using a Heterogeneous computing system. We proposed a method where two main tasks of Prefixspan can be implemented in a parallel way in GPU by using the NVIDIA CUDA platform. These main two tasks are to find frequent items and construct projected databases. This research shows a new way to build projected databases, only start index and ending index can be stored for a sequence in a single array. So, memory consumption can reduce in the HPC system.
Valid Model-Free Prediction of Future Insurance Claims

Bias resulting from model misspecification is a concern when predicting insurance claims. Indeed, this bias puts the insurer at risk of making invalid or unreliable predictions. A method that could provide provably valid predictions uniformly across a large class of possible distributions would effectively eliminate the risk of model misspecification bias. Conformal prediction is one such method that can meet this need, and here we tailor that approach to the typical insurance application and show that the predictions are not only valid but also efficient across a wide range of settings.
A useful technique for piecewise deterministic Markov decision processes

This note presents a technique that is useful for the study of piecewise deterministic Markov decision processes (PDMDPs) with general policies and unbounded transition intensities. This technique produces an auxiliary PDMDP from the original one. The auxiliary PDMDP possesses certain desired properties, which may not be possessed by the original PDMDP. We apply this technique to risk-sensitive PDMDPs with total cost criteria, and comment on its connection with the uniformization technique.
Multi-Modality Global Fusion Attention Network for Visual Question Answering

Visual question answering (VQA) requires a high-level understanding of both questions and images, along with visual reasoning to predict the correct answer. Therefore, it is important to design an effective attention model to associate key regions in an image with key words in a question. Up to now, most attention-based approaches only model the relationships between individual regions in an image and words in a question. It is not enough to predict the correct answer for VQA, as human beings always think in terms of global information, not only local information. In this paper, we propose a novel multi-modality global fusion attention network (MGFAN) consisting of stacked global fusion attention (GFA) blocks, which can capture information from global perspectives. Our proposed method computes co-attention and self-attention at the same time, rather than computing them individually. We validate our proposed method on the two most commonly used benchmarks, the VQA-v2 datasets. Experimental results show that the proposed method outperforms the previous state-of-the-art. Our best single model achieves 70.67% accuracy on the test-dev set of VQA-v2.
The Short Path Algorithm Applied to a Toy Model

We numerically investigate the performance of the short path optimization algorithm on a toy problem, with the potential chosen to depend only on the total Hamming weight to allow simulation of larger systems. We consider classes of potentials with multiple minima which cause the adiabatic algorithm to experience difficulties with small gaps. The numerical investigation allows us to consider a broader range of parameters than was studied in previous rigorous work on the short path algorithm, and to show that the algorithm can continue to lead to speedups for more general objective functions than those considered before. We find in many cases a polynomial speedup over Grover search. We present a heuristic analytic treatment of choices of these parameters and of scaling of phase transitions in this model.
Navigation-aware Path Planning for Multiple UAVs in Urban Environment

This paper tackles the problem of multi-VAV routing in urban environment. Due to the heterogenous GNSS coverage characterizing these environments, path selection and waypoint assignment should account for the navigation performance of the UAVs, so as to guarantee flyable trajectories within an autonomous flight framework. Therefore, this paper accounts for navigation capability and onboard sensor performance while assigning waypoints to the VAVs. A centralized strategic flight planner is developed, which adapts to navigation performance levels and can also handle fleets of heterogeneous UAVs. The proposed path planning algorithm is tested in simulations using a 3D real-world scenario. The impact on path planning efficiency of different onboard sensors performance and of eventual ground infrastructure support is discussed.
Multi-channel optical neuromorphic processor for frequency-multiplexed signals

Abstract Here we develop the first optical neuromorphic processor for frequency-multiplexed and multi-channel signals and demonstrate its application for orthogonal frequency-division multiplexing and wavelength-division multiplexing. The presented architecture supports multichannel operation through incorporation of the optical Fourier transform and dispersion-managed fiber echo state network analogue enabling processing of dual-quadrature high bandwidth signals. We demonstrate applicability of the design for prediction and equalization tasks. The proposed technology paves the way to multi-channel neuromorphic signal processing.
Heterogeneous Social Recommendation Model With Network Embedding

Due to the number of users and items increasing sharply, data sparsity has become an extremely serious problem for recommendation systems. Social relations consist of complex and rich information, which have a good alleviation effect on sparsity problems. Heterogeneous Information Network (HIN) is excellent in modeling the complex and structural information. Hence, we integrate HIN into the social recommendation. In this paper, we propose a model named Heterogeneous Social Recommendation model with Network Embedding (HSR). The social relations are divided into direct social relations and indirect social relations. We design a novel social influence calculation method to evaluate the influence of direct social relations. Based on the heterogeneous information network embedding method, we represent indirect social relations as feature embeddings and transform the learned embeddings into user-item feature interaction matrix by outer product. The final item list for a user is generated by the method of the convolutional neural network combined with the list of items generated by direct social relations. Extensive experiments on three real-world datasets show significant improvements of our proposed method over state-of-the-art methods. Additionally, experiments show that using heterogeneous network embedding can obtain better recommendation performance.
Are YouTube™ Videos a Reliable Source of Information About Root Resorption?

An endodontist and an orthodontist sought to assess the content and completeness of YouTube™ videos as an information source for orthodontic treatment and other issues among patients with root resorption.Aim.The aim of this study is to investigate the information about root resorption available on the Internet using YouTube™ as a key source and to evaluate this data comparatively from the perspectives of different specialties in dentistry.In addition, it is aimed to evaluate whether YouTube ™ videos can be used as a source of information against the possible root resorption risk of orthodontic patients.Material and methods.A search was made using four different terms related to root resorption in the YouTube™.After exclusion criteria were
Quantum generalizations of the polynomial hierarchy with applications to QMA(2)

The polynomial-time hierarchy ($\mathrm{PH}$) has proven to be a powerful tool for providing separations in computational complexity theory (modulo standard conjectures such as $\mathrm{PH}$ does not collapse). Here, we study whether two quantum generalizations of $\mathrm{PH}$ can similarly prove separations in the quantum setting. The first generalization, $\mathrm{QCPH}$, uses classical proofs, and the second, $\mathrm{QPH}$, uses quantum proofs. For the former, we show quantum variants of the Karp-Lipton theorem and Toda's theorem. For the latter, we place its third level, $\mathrm{Q} \Sigma_3$, into $\mathrm{NEXP}$ {using the Ellipsoid Method for efficiently solving semidefinite programs}. These results yield two implications for $\mathrm{QMA}(2)$, the variant of Quantum Merlin-Arthur ($\mathrm{QMA}$) with two unentangled proofs, a complexity class whose characterization has proven difficult. First, if $\mathrm{QCPH} = \mathrm{QPH}$ (i.e., alternating quantifiers are sufficiently powerful so as to make classical and quantum proofs "equivalent"), then $\mathrm{QMA}(2)$ is in the Counting Hierarchy (specifically, in $\mathrm{P}^{\mathrm{PP}^{\mathrm{PP}}}$). Second, unless $\mathrm{QMA}(2)={\mathrm{Q} \Sigma_3}$ (i.e., alternating quantifiers do not help in the presence of "unentanglement"), $\mathrm{QMA}(2)$ is strictly contained in $\mathrm{NEXP}$.