Reviewerly/exHarmony huggingface dataset:
Evaluating Performance of Clone Detection Tools in Detecting Cloned Cochange Candidates

Code reuse by copying and pasting from one place to another place in a codebase is a very common scenario in software development which is also one of the most typical reasons for introducing code clones. There is a huge availability of tools to detect such cloned fragments and a lot of studies have already been done for efficient clone detection. There are also several studies for evaluating those tools considering their clone detection effectiveness. Unfortunately, we find no study which compares different clone detection tools in the perspective of detecting cloned co-change candidates during software evolution. Detecting cloned co-change candidates is essential for clone tracking. In this study, we wanted to explore this dimension of code clone research. We used six promising clone detection tools to identify cloned and non-cloned co-change candidates from six C and Java-based subject systems and evaluated the performance of those clone detection tools in detecting the cloned co-change fragments. Our findings show that a good clone detector may not perform well in detecting cloned co-change candidates. The amount of unique lines covered by a clone detector and the number of detected clone fragments plays an important role in its performance. The findings of this study can enrich a new dimension of code clone research.
CSF: An Efficient Parallel Deduplication Algorithm by Clustering Scattered Fingerprints

Deduplication is one of the most effective and efficient techniques to save memory space. It is widely used in data centers and cloud storage systems. Multi-stream concurrency is expected to increase the throughput of deduplication. However, multiple data streams hurt the locality of accessed data and weaken the benefit of data concurrency, which forms a challenge for data deduplication. Usually, the ordered index can reshape the locality of data streams, which can improve the cache hit rate during deduplication. In this paper, we first propose an efficient parallel deduplication algorithm by clustering scattered fingerprints, called CSF, to exploit the data locality as much as possible. It tries to improve the utilization rate of the fingerprint page by the clustered fingerprints. Moreover, it retains the scattered fingerprint to next round fingerprint comparison by re-using the fingerprints on the same page. Thus the number of the fingerprint pages to read is reduced. We further optimize the proposed algorithm by a scheduling strategy, which effectively schedules the task of part streams ahead while ensuring the overall performance. Finally, we evaluated the performance of our algorithm with various data sets in experiments. The experimental results show that our proposed algorithm achieves better performance than the state-of-the-art method.
Enhancing Privacy Preservation in Speech Data Publishing

In speech data publishing, users' data privacy is disclosed and thereby more privacy of users is breached since speech data contains a large amount of information about speakers. Existing work focused on sanitization in speech content, speakers' voice, and data descriptions, without considering the correlation of speech content and speaker's voice. Therefore, these existing work cannot protect speakers' data privacy when attackers utilize such correlation to identify speakers' speech data. To tackle this problem, in this article, we propose a protocol to decrease such potential risks in speech data publishing while keeping the balance of privacy preservation and data utility. Specifically, we define both the risks of privacy disclosure and the data utility loss in speech content, speaker's voice, and data set description. Moreover, we do the first attempt to formalize the correlation between speech content and speaker's voice and regard it as a new kind of privacy leakage risk. Thereafter, we utilize the classifier in machine learning and optimize speech data sanitization considering the defined risks of privacy disclosure and data utility loss. Finally, simulation results validate the effectiveness of the proposed protocol.
Leaming Structured Signals Using GAN s with Applications in Denoising and Demixing

Recently, Generative Adversarial Networks (GANs) have emerged as a popular alternative for modeling complex high dimensional distributions. Most of the existing works implicitly assume that the clean samples from the target distribution are easily available, which is not true in real-world applications. In this paper, we consider the observation setting when the samples from target distribution are given by the superposition of two structured components and leverage GANs for learning the structure of the components. We propose two novel frameworks: denoising-GAN and demixing-GAN. The denoising-GAN assumes access to clean samples from the second component and try to learn the other distribution, whereas demixing-GAN learns the distribution of the components at the same time. Through extensive numerical experiments, we demonstrate that proposed frameworks can generate clean samples from unknown distributions, and provide competitive performance in tasks such as denoising and demixing.
A System for Text Extraction in Complex-Background Document Images

Due to the demand of information transportation, identification, archive, the digitization of document images is increasingly concerned. Detecting text regions is the first and crucial step in End-to-End text recognition system. With the complex background document images, they are still a challenging problem due to the variety of fonts, sizes, colors of the text, and background complexity. This paper presents a system based on a Connectionist Text Proposal Network (CTPN) for extracting text regions in the document image with a complex background. This method consists of two fundamental stages: detect fine-scale text and text line extraction based on the obtained text components. We tried many-core of the feature extracting method such as VGG19, Resnet50 as well as evaluate the system's performance on many different datasets such as ICDAR2011, ICDAR2013, and a private real book cover. Besides, we also built an online visualize evaluation system to compare the results.
Service Identification Based on SNI Analysis

It is expected that heavy congestion occurs at a time of a severe disaster. In such a situation, traffic priority control in network elements for preferentially transferring information for rescue is important. For achieving this, service identification from IP flows in network elements is required. A method based on deep packet inspection (DPI) of multiple TLS sessions has been proposed for identifying services from IP flows. However, its analysis is extremely CPU time-consuming. In this paper, we propose a new service identification method based on the analysis of Server Name Indication (SNI). SNI is a not-encrypted field in the ClientHello message, which is transferred in the TLS session establishment. We then evaluate the proposed method and show that the proposed methods significantly reduce time to identify without a large decline in the accuracy of identification.
Blanker: A Refactor-Oriented Cloned Source Code Normalizer

Refactoring is widely practiced by developers and has become a key factor in order to increase the maintainability of software. However, code clones pose a threat in any refactor process due to the fact that a developer should edit identical portions of code more than once. Despite the numerous researches in this topic, most of the results are focused on discovering type-3 and type-4 clones, that require an higher effort to be refactored and removed. In this paper we present our tool, Blanker, that searches and unifies equivalent statements available in the language before feeding the source to an existing code clone detector limited to type-2 clones. This step acts as a normalization step and produces refactorable results without the error introduced by potentially unrelated added statements (like in type-3 clones), that would be unsuitable for refactoring purposes, and with added flexibility compared to checking for identical code portions (like in type-2 clones). We used NiCad to detect clones before and after our normalization step and found up to 10% more type-2 clones after our normalization, all of them being refactor candidates.
Prediction of MEMS-based INS Error Using Interval Type-2 Fuzzy Logic System in INS/GPS Integration

In recent years, for reliable, accurate, and robust navigation, Global Positioning System (GPS) and Inertial Navigation System (INS) has been integrated to use their complementary advantages and overcome their drawbacks. Kalman Filtering methods such as Extended Kalman Filter (EKF) have been used for INS/GPS integration widely. The EKF-based navigation systems are complex, and they might not have effective real-time performance, especially with the MicroElectro Mechanical System (MEMS)-based INS when GPS is blocked. To overcome these problems, Artificial Intelligence (AI) based integration was proposed over the Kalman filtering models. Due to the stochastic noise, bias, and drift of the low-cost MEMS-based inertial sensor outputs over time, in this study, we propose an Interval Type-2 Fuzzy Logic System (IT2FLS) to predict the MEMS-based sensor errors in GPS blockage. The IT2FLS can model uncertainty and stochastic noise of both input and training data in complex, noisy environments such as our application. Therefore, we use the IT2FLS to forecast the cumulative INS error during GPS outages to improve the accuracy of the navigation system. The experimental tests show that the IT2FLS has acceptable realtime performance and accuracy in predicting the INS error during the long-term GPS outages.
Efficient electro-magnetic analysis of a GPU bitsliced AES implementation

Abstract The advent of CUDA-enabled GPU makes it possible to provide cloud applications with high-performance data security services. Unfortunately, recent studies have shown that GPU-based applications are also susceptible to side-channel attacks. These published work studied the side-channel vulnerabilities of GPU-based AES implementations by taking the advantage of the cache sharing among multiple threads or high parallelism of GPUs. Therefore, for GPU-based bitsliced cryptographic implementations, which are immune to the cache-based attacks referred to above, only a power analysis method based on the high-parallelism of GPUs may be effective. However, the leakage model used in the power analysis is not efficient at all in practice. In light of this, we investigate electro-magnetic (EM) side-channel vulnerabilities of a GPU-based bitsliced AES implementation from the perspective of bit-level parallelism and thread-level parallelism in order to make the best of the localization effect of EM leakage with parallelism. Specifically, we propose efficient multi-bit and multi-thread combinational analysis techniques based on the intrinsic properties of bitsliced ciphers and the effect of multi-thread parallelism of GPUs, respectively. The experimental result shows that the proposed combinational analysis methods perform better than non-combinational and intuitive ones. Our research suggests that multi-thread leakages can be used to improve attacks if the multi-thread leakages are not synchronous in the time domain.
Estimation of the Homography Matrix to Image Stitching

In many problems of computer vision, there are some problems for the estimation of homography matrix between images, the homography matrix is necessary to solve different problems in this area of science, one of them is Stitching, which in simple words is the process by several images are combined to produce a panoramic image or a high resolution image, usually through a computer program. The homography matrix is a fundamental basis to perform the Stitching on the images, there are many methods to calculate the homography, the most common to find this estimation is the random sampling consensus (RANSAC). But there are some works that consider the estimation process in a different way, the way in which these works deal with the problem is taking the problem as a multi-objective estimation process, with this approach it is possible to facilitate the calculation of multidimensional problems. In order to solve the multi-objective formulation, many different evolutionary algorithms have been explored, obtaining good results in their tests. In this chapter the problem of the estimation of the homography matrix is considered as a problem of multi-objective optimization and will be faced with the evolutionary algorithm ABC.
Predicting the Types of Striking and Thrusting Motions by using Deep Learning

The final goal is to make a "tankendo machine" that is easy to use, safe, fun, and can provide a mechanical competitor for humans. First, deep learning technology was used to install an image processing system that could detect the start (initial motion) of striking and thrusting motions by humans and quickly predict the type of technique used.
Deep Learning for the Degraded Broadcast Channel

Machine learning has shown promising results for communications system problems. We present results for using deep auto-encoders for the multiuser degraded broadcast channel. The auto encoder is able to learn to communicate on this channel using superposition coding. Additionally, the deep neural net is also able to determine a bit labeling and optimize the per user power allocation that depends on the per user SNR.
Image Steganography using YCbCr Color Space and Matrix Pattern

In this paper, a data hiding method to transfer secret data over unsecure networks is proposed. The method is based on a matrix pattern approach to hide the secret data into color images. In the proposed method, the image is first transformed into the YCbCr color space, and then each image component is divided into blocks. The Y component is used to generate matrix patterns using the texture of each Y block and assign these patterns to ASCII characters. The Cb and Cr components are then used to hide the secret data using the generated matrix patterns. The results show that the proposed scheme has achieved higher embedding capacity compared to other methods while maintaining the stego-image quality.
Deep Modulation Recognition in an Unknown Environment

Deep modulation recognition has demonstrated high classification accuracy when a neural network is trained on large-scale datasets. However, when applied in an unknown environment where there are not any ground-truth labels in collected data, its performance can be significantly degraded. In this paper, we propose incorporating an adversarial discriminative neural network to adapt the deep modulation recognition to an unknown environment. Results show that, when the neural network is trained under an AWGN channel but applied under a frequency-selective Rayleigh fading channel, the adversarial network based domain adaptation can achieve comparable performance with that of the network trained with sufficiently large labeled data.
An Efficient Algorithm for Graph Laplacian Optimization Based on Effective Resistances

In graph signal processing, data samples are associated to vertices on a graph, while edge weights represent similarities between those samples. We propose a convex optimization problem to learn sparse well connected graphs from data. We show that the proposed solution has edge weights that are upper bounded by the inverse of the distance between data features at different nodes. Moreover, the optimal graph is also well connected, since the effective resistance distance between nodes is upper bounded by the distance between nodal data features. These properties indicate that distance based graph topology selection algorithms such as K nearest neighbors can be used for graph topology inference. We also propose a coordinate minimization algorithm that at each iteration, updates an edge weight using exact minimization. The algorithm has a simple and low complexity implementation based on closed form expressions.
Made-Up Rubbish: Design Fiction as a Tool for Participatory Internet of Things Research

As Internet of Things (IoT) technologies become embedded in public infrastructure, it is important that we consider how they may introduce new challenges in areas such as privacy and governance. Public technology implementations can be more democratically developed by facilitating citizen participation during the design process, but this can be challenging. This work demonstrates a novel method for participatory research considering the privacy implications of IoT deployments in public spaces, through the use of worldbuilding design fictions. Using three fictional contexts and their associated tangible design fiction objects, we report on findings to inform transparency and governance in public space IoT deployments.
FUsing Global and Semantic-Part Features with Multiple Granularities for Person Re-Identification

A multiple granularities method for person re-identification (re-ID) is proposed in this paper, which fuses global and semantic-part representations. A prior guided human parsing method is employed to parse a human body into precise basic semantic parts from low-resolution images, and multiple granularities are generated by recombining the adjacent basic semantic parts. Then, convolutional neural networks that seam-lessly unify the Softmax and TriHard losses are proposed to learn and fuse the global-level and the part-level features in different granularities. The proposed method not only extracts precise part-level features, but also incorporates gradual cues between part-level and global-level features to boost a high performance of person re-ID. Extensive experimental results show our proposed SPMG model achieves state-of-the-art performance on three common datasets .
Towards a controllability analysis of multiscale systems: Application of the set-theoretic approach to a semi-batch emulsion polymerization process

This work presents a framework to evaluating the controllability of multiscale systems based on a set-theoretic approach. Calculation of the Controllable Trajectories Set is the core of the set-theoretic approach. From a multi-scale perspective, such calculation is a computationally- intractable problem. Therefore, to overcome the intrinsic curse of dimensionality of the problem, Variance Algebra concepts and a statistical modeling approach are combined to obtain a closed-form allowing to perform an output-controllability analysis for a multiscale system. A semi-batch emulsion polymerization process is adopted as a case study. Results have shown that both, the final Particle Size Distribution (mesoscopic variable) and the secondary particle nucleation rate (microscopic variable) are output-controllable. Evaluating the controllability of lower-scale variables (i.e. below macroscopic) could mitigate the dependency of the current control strategies of on-line measurements at the lower scales as well as help to better understand the process capabilities of achieving the desired product quality specifications.
Machine Learning Techniques for Handwritten Digit Recognition

The following study examines how various classification algorithms perform on the problem of handwritten digit recognition. The classifiers discussed are k-Nearest Neighbour (k-NN), Single Classification Decision Trees and Bagged Decision Trees. These algorithms were evaluated with the use of information from the United States Postal Service (USPS). This study’s results show that the k-NN classifier had the fastest performance while the bagged decision trees were the slowest. In terms of classification performance, the bagged decision tree method was found to have the fewest misclassifications and outperformed k-NN and single classification trees in all of the considered metrics.
A Deep Convolutional Neural Network Based Approach to Extract and Apply Photographic Transformations

Sophisticated image editing techniques like colour and tone adjustments are used to enhance the perceived visual quality of images and are used in a broad variety of applications from professional grade image post-processing to sharing in social media platforms. Given a visually appealing reference image that has some photographic filter or effects applied, it is often desired to apply the same effects on a different target image to provide it the same look and feel. Interpreting the effects applied on such images is not a trivial task and requires knowledge and expertise on advanced image editing techniques, which is not easy. Existing deep learning based techniques fail to directly address this problem and offer partial solutions in the form of Neural Style Transfer, which can be used for texture transfer between images. In this paper, a novel method using a convolutional neural network (CNN) is introduced that can transfer the photographic filter and effects from a given reference image to a desired target image via adaptively predicting the parameters of the transformations that were applied on the reference image. These predicted parameters are then applied to the target image to get the same transformations as that of the reference image. In contrast to the existing stylization methods, the predicted parameters are independent of the semantics of the reference image and is well generalized to transfer complex filters from the reference image to any target image.
Battery-less shoe-type wearable location sensor system for monitoring people with dementia

Wearable monitoring sensors are a promising method for monitoring the daily activities of elderly people, but they have the problem of needing periodic battery replacement when used for long-term monitoring. To solve this drawback of conventional wearable devices, we developed a shoe-type wearable location transmitter that generates its own electricity and transmits location information without a battery. The developed transmitter generates electric power using changes in pressure at the bottom of the foot during walking, and the system estimates the location by evaluating the radio signal strength. Using the new location system, we successfully monitored the movement of one 80-year-old woman with dementia in an actual nursing home for over 130 days.
Localized Assistive Scene Understanding using Deep Learning and the IoT

In this paper, we propose a system for localized scene understanding to assist sufferers of visual disabilities. Our system determines the user's indoor location using WiFi fingerprinting and synthesizes a real-time description of the surrounding environment. The description is synthesized from prior information about the environment, real-time information obtained from object detection and localization using deep learning, and sensory information collected from IoT sensors. Our system can be activated automatically or on-demand as configured. On-demand activation happens by issuing a voice command to the environment's smart speaker or the user's mobile phone. Alternatively, our system can be set to activate automatically when detecting a change in the user's environment. When triggered, our system initiates a capture of an image using an environment-attached stationary camera and offloads the image to our server for identifying objects and their approximate locations in the environment. The server uses deep learning to localize persons, pets, or furniture. We then use a prior mapping of the environment to change the detected image-domain pixel coordinates to real-world relative locations. We also collect using IoT sensors information about the environment's temperature, humidity level, and light intensity. With the information available, we fuse a fixed description of the environment's permanent features with a dynamic description from the localized objects and sensors data. We finally use text-to-speech to change the textual description into an audio signal played on the user's Bluetooth headset or the environment's smart speaker. Our results show that our system can be an effective tool in helping the visually-challenged navigate unknown environments using increasingly available smart home technologies.