Reviewerly/exHarmony huggingface dataset:
An abstract stack based approach to verified compositional compilation to machine code

A key ingredient contributing to the success of CompCert, the state-of-the-art verified compiler for C, is its block-based memory model, which is used uniformly for all of its languages and their verified compilation. However, CompCert's memory model lacks an explicit notion of stack. Its target assembly language represents the runtime stack as an unbounded list of memory blocks, making further compilation of CompCert assembly into more realistic machine code difficult since it is not possible to merge these blocks into a finite and continuous stack. Furthermore, various notions of verified compositional compilation rely on some kind of mechanism for protecting private stack data and enabling modification to the public stack-allocated data, which is lacking in the original CompCert. These problems have been investigated but not fully addressed before, in the sense that some advanced optimization passes that significantly change the ways stack blocks are (de-)allocated, such as tailcall recognition and inlining, are often omitted. We propose a lightweight and complete solution to the above problems. It is based on the enrichment of CompCert's memory model with an abstract stack that keeps track of the history of stack frames to bound the stack consumption and that enforces a uniform stack access policy by assigning fine-grained permissions to stack memory. Using this enriched memory model for all the languages of CompCert, we are able to reprove the correctness of the full compilation chain of CompCert, including all the optimization passes. In the end, we get Stack-Aware CompCert, a complete extension of CompCert that enforces the finiteness of the stack and fine-grained stack permissions. Based on Stack-Aware CompCert, we develop CompCertMC, the first extension of CompCert that compiles into a low-level language with flat memory spaces. Based on CompCertMC, we develop Stack-Aware CompCertX, a complete extension of CompCert that supports a notion of compositional compilation that we call contextual compilation by exploiting the uniform stack access policy provided by the abstract stack.
Domain Randomization for Scene-Specific Car Detection and Pose Estimation

We address the issue of domain gap when making use of synthetic data to train a scene-specific object detector and pose estimator. While previous works have shown that the constraints of learning a scene-specific model can be leveraged to create geometrically and photometrically consistent synthetic data, care must be taken to design synthetic content which is as close as possible to the real-world data distribution. In this work, we propose to solve domain gap through the use of appearance randomization to generate a wide range of synthetic objects to span the space of realistic images for training. An ablation study of our results is presented to delineate the individual contribution of different components in the randomization process. We evaluate our method on VIRAT, UA-DETRAC, EPFL-Car datasets, where we demonstrate that using scene specific domain randomized synthetic data is better than fine-tuning off-the-shelf models on limited real data.
Comparative Analysis of OptiTrack Motion Capture Systems

A comparative analysis of four different OptiTrack motion capture systems is provided in this paper. Tracking accuracy, workspace volume, marker size and camera range are the main criteria used for comparison. The tracking accuracy is tested using Quanser’s linear motion platform by comparing the measured position of the cart with the position obtained using the encoder. The workspace volume limits are obtained by flying a LiteHawk Neon drone in circles of increasing radius at different heights until tracking fails. The relationship between marker size and camera range is obtained through theoretical calculations. Experimental as well as theoretical results are presented, illustrating the performance of these four systems. These will serve as a baseline to select the right motion capture system for any particular application.
Hidden topic–emotion transition model for multi-level social emotion detection

With the fast development of online social platforms, social emotion detection, focusing on predicting readers' emotions evoked by news articles, has been intensively investigated. Considering emotions as latent variables, various probabilistic graphical models have been proposed for emotion detection. However, the bag-of-words assumption prohibits those models from capturing the inter-relations between sentences in a document. Moreover, existing models can only detect emotions at either the document-level or the sentence-level. In this paper, we propose an effective Bayesian model, called hidden Topic–Emotion Transition model, by assuming that words in the same sentence share the same emotion and topic and modeling the emotions and topics in successive sentences as a Markov chain. By doing so, not only the document-level emotion but also the sentence-level emotion can be detected simultaneously. Experimental results on the two public corpora show that the proposed model outperforms state-of-the-art approaches on both document-level and sentence-level emotion detection.
Partition-and-merge based fuzzy genetic clustering algorithm for categorical data

Categorical data clustering is a difficult and challenging task due to the special characteristic of categorical attributes: no natural order. Thus, this study aims to propose a two-stage method named partition-and-merge based fuzzy genetic clustering algorithm (PM-FGCA) for categorical data. The proposed PM-FGCA uses a fuzzy genetic clustering algorithm to partition the dataset into a maximum number of clusters in the first stage. Then, the merge stage is designed to select two clusters among the clusters that generated in the first stage based on its inter-cluster distances and merge two selected clusters to one cluster. This procedure is repeated until the number of clusters equals to the predetermined number of clusters. Thereafter, some particular instances in each cluster are considered to be re-assigned to other clusters based on the intra-cluster distances. The proposed PM-FGCA is implemented on ten categorical datasets from UCI machine learning repository. In order to evaluate the clustering performance, the proposed PM-FGCA is compared with some existing methods such as k-modes algorithm, fuzzy k-modes algorithm, genetic fuzzy k-modes algorithm, and non-dominated sorting genetic algorithm using fuzzy membership chromosomes. Adjusted Ranked Index (ARI), Normalized Mutual Information (NMI), and Davies–Bouldin (DB) index are selected as three clustering validation indices which are represented to both external index (i.e., ARI and NMI) and internal index (i.e., DB). Consequently, the experimental result shows that the proposed PM-FGCA outperforms the benchmark methods in terms of the tested indices.
A Virtual Reality Platform for Analyzing Remote Archaeological Sites

This paper describes a Virtual Reality (VR) prototype developed to help archaeologists and other stakeholders explore and analyse archaeological data in a more immersive context. We describe a VR reconstruction of Pleito Cave, a fragile world class rock-art site with accessibility limitation. Key stakeholders are identified and a prototype is described that provides a VR platform for visualizing and interacting with complex archaeological data (gathered from techniques such as decorrelation stretch and X-ray fluorescence) virtually 'in situ' , in a way that would not be possible at the real site. The prototype allows multiple remote users to interact with the cave together remotely providing opportunities for collaborative interpretation and analysis of archaeological data. We also present a survey-based evaluation in which both archaeologists and Native American stakeholders indicate positive responses for measures of both engagement and value.
Region-of-interest based image compression using the discrete Tchebichef transform in wireless visual sensor networks

Reducing the algorithmic complexity of image compression techniques is a major challenge in wireless image sensor networks (WISNs). Many image compression standards, such as JPEG and JPEG2000, are unsuitable for implementation in WISNs because of their high energy consumption. In this paper, a solution to this problem is proposed. It consists of a region-of-interest (ROI) based image compression using the discrete Tchebichef transform (DTT).The main idea is about compressing only the ROI instead of the whole image. The DTT is used as an alternative to the discrete cosine transform (DCT) due to its low complexity and good energy compaction. Simulation results have shown that the proposed method reduces the number of arithmetic operations, the processing/transmission energy consumption and the amount of transmitted data. The savings obtained generally exceed 50%. Furthermore, it has a competitive compression efficiency compared with the state-of-the-art image compression techniques.
Geo-statistical and multivariate analyses of potentially toxic elements' distribution in the soil of Hainan Island (China): A comparison between the topsoil and subsoil at a regional scale

Geo-statistical and multivariate analyses are effective methods to determine the source and influence factors of potentially toxic elements (PTEs) in the soil. Hainan Island, belonging to tropical marine monsoon climate, is a typically individual island with slight industrial activities. In order to study the impact factors on PTEs distribution in the soil at a regional scale, such as impact of lithology, pedogenesis and anthropogenic activities, Hainan Island is an optimal place. For comparison, the topsoil and the corresponding subsoil were sampled and eight PTEs were analyzed respectively. The concentrations of PTEs were counted, such as minimum, maximum, median and mean values. Based on it, the background values and concentration enrichment factors of PTEs were also calculated or illustrated. Exception to Cd and Hg, the concentration of other PTEs in the topsoil were lower than those in the subsoil for all lithological areas since the strong leaching under special climate. By using geo-statistical and multivariate analyses, such as Pearson correlation analysis, principal component analysis, variography and robust biplot analyses, the dominant impact factors are determined for PTEs both in the topsoil and subsoil. Thus, the spatial distribution of PTEs is well explained at a regional scale. Generally, PTEs in the soil represent the components of soil parent materials associated with lithological rocks. Nevertheless, Co, Cr, Cu and Sc elements were dominantly inherited from soil parent materials while Cd, Hg and Pb were greatly superposed by exogenous inputs. On the other hand, elements of As and Zn might be greatly influenced by pedogenesis and weathering process. So, geo-statistical and multivariate analyses are effective methods to discuss the sources or impact factors for PTEs in the soil, and also the comparison between the topsoil and the subsoil provides an opportunity to illuminate the main impact factors on PTEs at a regional scale, including parent materials, weathering process and anthropogenic inputs.
Robust Drift Characterization from Event Streams of Business Processes

Process workers may vary the normal execution of a business process to adjust to changes in their operational environment, e.g., changes in workload, season, or regulations. Changes may be simple, such as skipping an individual activity, or complex, such as replacing an entire procedure with another. Over time, these changes may negatively affect process performance; hence, it is important to identify and understand them early on. As such, a number of techniques have been developed to detect process drifts , i.e., statistically significant changes in process behavior, from process event logs (offline) or event streams (online). However, detecting a drift without characterizing it, i.e., without providing explanations on its nature, is not enough to help analysts understand and rectify root causes for process performance issues. Existing approaches for drift characterization are limited to simple changes that affect individual activities. This article contributes an efficient, accurate, and noise-tolerant automated method for characterizing complex drifts affecting entire process fragments. The method, which works both offline and online, relies on two cornerstone techniques, one to automatically discover process trees from event streams (logs) and the other to transform process trees using a minimum number of change operations. The operations identified are then translated into natural language statements to explain the change behind a drift. The method has been extensively evaluated on artificial and real-life datasets, and against a state-of-the-art baseline method. The results from one of the real-life datasets have also been validated with a process stakeholder.
Directly recognizing and removing the projector nonlinearity errors from a phase map in phase-shifting fringe projection profilometry

In phase-shifting fringe projection profilometry, the projector nonlinearity has been recognized as one of the main error-inducing factors, especially when only very few fringe patterns are available. For solving this issue, this paper presents a simple and effective method, that allows us to recognize and remove, directly from a calculated phase map, the nonlinearity-induced errors without calibrating the projector luminance or the phase errors in advance. In implementation, after filtering the phase map using a low-pass filter, we estimate the phase error coefficients by exploiting the dependence between the isolated artifacts and the smoothed phases under 3-sigma criterion. Furthermore, using the phase error function just estimated, the phase errors are corrected in a pointwise way through fixed-point iterations. Experimental results demonstrated that this method offers some advantages over others, such as being independent of a special nonlinearity model, being easy to implement and not requiring a prior calibration, protecting the edges and details of the measured shape from being blurred, and enabling dealing with time-variant projector nonlinearities.
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In this paper, we introduce a total variation l 1 - l 2 regularization scheme with adapting the parameter for image restoration involving blurry and noisy colour images. Numerically, an efficient augmented Lagrangian method associated with alternating minimization method is described to obtain the optimal solution recursively. We provide the convergence analysis for the resulting algorithm. Experimental results show that our proposed model and algorithm have good signal to noise ratio (SNR) and improvement in signal to noise ratio (ISNR) values for a motion blur with different kinds of noises. • Total variation l 1 - l 2 regularization scheme with adapting the parameter for image restoration involving blurry and noisy colour images. • Efficient augmented Lagrangian method associated with alternating minimization method for restoration. • Proposed model and algorithm have good signal to noise ratio (SNR) and improvement in signal to noise ratio (ISNR) values for a motion blur with different kinds of noises.
Enabling Encrypted Rich Queries in Distributed Key-Value Stores

To accommodate massive digital data, distributed data stores have become the main solution for cloud services. Among others, key-value stores are widely adopted due to their superior performance. But with the rapid growth of cloud storage, there are growing concerns about data privacy. In this paper, we design and build EncKV, an encrypted and distributed key-value store with rich query support. First, EncKV partitions data records with secondary attributes into a set of encrypted key-value pairs to hide relations between data values. Second, EncKV uses the latest cryptographic techniques for searching on encrypted data, i.e., searchable symmetric encryption (SSE) and order-revealing encryption (ORE) to support secure exact-match and range-match queries, respectively. It further employs a framework for encrypted and distributed indexes supporting query processing in parallel. To address inference attacks on ORE, EncKV is equipped with an enhanced ORE scheme with reduced leakage. For practical considerations, EncKV also enables secure system scaling in a minimally intrusive way. We complete the prototype implementation and deploy it on Amazon Cloud. Experimental results confirm that EncKV preserves the efficiency and scalability of distributed key-value stores.
Saliency-Aware Texture Smoothing

Texture smoothing aims to smooth out textures in images, while retaining the prominent structures. This paper presents a saliency-aware approach to the problem with two key contributions. First, we design a deep saliency network with guided non-local blocks (GNLBs) for learning long-range pixel dependencies by taking the predicted saliency map at former layer as the guidance image to help suppress the non-saliency regions in the shallow layer. The GNLB computes the saliency response at a position by a weighted sum of features at all positions, and enables us to produce results that outperform existing deep saliency models. Second, we formulate a joint optimization framework to take saliency information when iteratively separating textures from structures: on the texture layer, we smooth out structures with the help of the saliency information and migrate structures from the texture to structure layer, while on the structure layer, we adopt another deep model to detect edges and simultaneous sparse coding to push textures back to the texture layer. We tested our method on a rich variety of images and compared it with several state-of-the-art methods. Both visual and quantitative comparison results show that our method better preserves structures while removing the texture components.
Machine Learning Model for Adaptive Modulation of Multi-Stream in MIMO-OFDM System

In this paper, we propose the adaptive modulation (AM) model based on machine learning for a multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) system. Since 5G new radio (NR) system can be used in a large variety of Internet of Things fields than the conventional systems, the AM scheme that adjusts data rate and reliability according to channel condition can be effectively utilized. The conventional AM schemes are implemented by defining modulation schemes to be used according to each channel condition as table in advance. However, since the rule-based AM cannot analyze the communication performance according to the correlation of channels between antennas and the number of transmission modes is exponentially increased according to the number of available modulation schemes and antennas, it is not suitable for 5G NR system. The learning of the proposed AM model is based on the generated training signal by the extracted features from the received signal and assigned label through the performance analysis for signal detection. We focus on the application of deep neural network for AM and cover the precedence method of principal component analysis to improve the performance of the model. The simulations on the classification of optimal transmission mode for the MIMO-OFDM signal demonstrate that the proposed model supports the adaptability according to the condition of complex MIMO channel.
Evaluating combination models of solar irradiance on inclined surfaces and forecasting photovoltaic power generation

The traditional photovoltaic (PV) forecasting method depends on sufficient historical data (PV power station historical power generation data and numerical weather prediction meteorological data), which is not suitable for a newly built PV power plant. In order to calculate the PV array irradiance and to predict the PV power, a physical prediction approach based on solar irradiance on inclined surfaces is proposed. This method selects three decomposition models and four transposition models to be combined into 12 combination forecasting models. Furthermore, solar spectral response, incidence angle, and soiling factor are taken into account in the modified model. The results show that the methods combining the Liu–Jordan transposition model have higher forecasting accuracy under the different weather types. Among them, the Erbs + Liu–Jordan model predictions are the most accurate.
Dynamic replication and migration of data objects with hot-spot and cold-spot statuses across storage data centers

Cloud Storage Providers (CSPs) offer geographically dispersed data stores providing several storage classes with different prices. A vital problem faced by application providers is how to exploit price differences across data stores to minimize monetary cost of applications that include hot-spot objects that are accessed frequently and cold-spot objects that are often accessed far less. This monetary cost consists of replica creation, storage, Put, Get, and potential migration costs. To optimize such costs, we first propose the optimal solution that leverages dynamic and linear programming techniques with the assumption that the workload on objects is known in advance. We also propose a lightweight heuristic solution, inspired from an approximate algorithm for the Set Covering Problem, which does not make any assumption on the object workload. This solution jointly determines object replicas location, object replicas migration times, and redirection of Get (read) requests to object replicas so that the monetary cost of data storage management is optimized while the user-perceived latency is satisfied. We evaluate the effectiveness of the proposed lightweight algorithm in terms of cost savings via extensive simulations using CloudSim simulator and traces from Twitter. In addition, we have built a prototype system running over Amazon Web Service (AWS) and Microsoft Azure to evaluate the duration of objects migration within and across regions.
Edge-enhancing bi-histogram equalisation using guided image filter

Histogram equalisation (HE) is a simple and effective contrast enhancement method. However, it has certain drawbacks, namely, brightness inconsistency, over-enhancement, and noise amplification. In addition, there is structure information loss while processing HE. To overcome those drawbacks simultaneously, we propose a novel edge enhancing bi-histogram equalisation method using guided image filter. In the proposed algorithm, a new adaptive plateau limit and a new edge-enhancing transformation function are proposed. The adaptive plateau limit makes the method robust to various histogram distributions, and the edge-enhancing transformation enhances edges while suppressing noise amplification in the flat region. The performance of the various HE algorithms are evaluated both quantitatively and qualitatively. The qualitative assessment shows that the proposed algorithm avoids over-enhancement and noise amplification, effectively. In addition, the quantitative metrics show that the proposed algorithm outperforms the existing HE algorithms in terms of local contrast, discrete entropy, and perceptual sharpening index.