Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-Attribute Missing Data Reconstruction Based on Adaptive Weighted Nuclear Norm Minimization in IoT

In the Internet of Things, data sets gathered from sensor nodes are missing a significant fraction of data, owing to noise, collision, unreliable links, and unexpected damage. This phenomenon is more serious in some scenarios, which limits the applications of sensor data. It is therefore necessary to develop methods to reconstruct these lost data with high accuracy. In this paper, a new multi-attribute missing data reconstruction method based on adaptive weighted nuclear norm minimization is developed, and a $K$ -means clustering analysis is embedded into this forecasting process to improve the prediction accuracy. First, to ensure that sensors in one group have similar patterns of measurement, we use a traditional machine learning algorithm, called $K$ -means clustering algorithm to separate sensors into different groups. Second, considering the correlations among multiple attributes of sensor data and its joint low-rank characteristics, we propose an algorithm based on the matrix rank-minimization method of automatic weighted nuclear norm minimization, which adaptively assigns different weights to each singular value simultaneously. Moreover, we use the alternating direction method of multipliers to obtain its optimal solution. Finally, we evaluate the proposed method by using a real sensor data set from the Intel Berkeley Research Laboratory with two missing patterns, namely, random missing pattern and consecutive missing pattern. The simulation results prove that our algorithm performs well even with a small number of samples, and it can propagate through a structure to fill in large missing regions.
[Research Paper] Combining Obfuscation and Optimizations in the Real World

Code obfuscation is the de facto standard to protect intellectual property when delivering code in an unmanaged environment. It relies on additive layers of code tangling techniques, white-box encryption calls and platform-specific or tool-specific countermeasures to make it harder for a reverse engineer to access critical pieces of data or to understand core algorithms. The literature provides plenty of different obfuscation techniques that can be used at compile time to transform data or control flow in order to provide some kind of protection against different reverse engineering scenarii. Scheduling code transformations to optimize a given metric is known as the pass scheduling problem, a problem known to be NP-hard, but solved in a practical way using hard-coded sequences that are generally satisfactory. Adding code obfuscation to the problem introduces two new dimensions. First, as a code obfuscator needs to find a balance between obfuscation and performance, pass scheduling becomes a multi-criteria optimization problem. Second, obfuscation passes transform their inputs in unconventional ways, which means some pass combinations may not be desirable or even valid. This paper highlights several issues met when blindly chaining different kind of obfuscation and optimization passes, emphasizing the need of a formal model to combine them. It proposes a non-intrusive formalism to leverage on sequential pass management techniques. The model is validated on real-world scenarii gathered during the development of an industrial-strength obfuscator on top of the LLVM compiler infrastructure.
Building Corporate Reputation on the Internet: Is the Claimed Sales-Ranking Trustworthy?

This paper aims to develop corporate reputation on the Internet by collecting various online information related with a specific company. The online corporate reputation is then used to verify whether the sales-ranking claimed by this company is trustworthy. Firstly, a distributed and generic Web crawler is developed for automatically collecting public opinion data related with a specific company. Meanwhile, basic information of enterprise and open financial data are also collected for auxiliary analysis. Secondly, a filter method is proposed to filter the noisy documents from the amount of public opinion set with word embedding and KL-Divergence methods. Thirdly, a Maximal Marginal Relevance (MMR) based index model is presented for computing the confidence score of the sales-ranking claim. That is, a company with a great deal of positive comments posted by customers is more likely to announce a reliable claim. Unlike traditional media, many negative news about a company cannot be concealed by the user-generated contents, which indicates the objectivity of the corporate reputation on the Internet. The experimental results demonstrate that the corporate reputation built based on public opinion data has an implicit correspondence with the trustworthy of a company.
Story Understanding with External Knowledge Based Attention

How we use commonsense knowledge to guide the learning of the model is critical for natural language understanding. In this paper, we introduce a keywords extraction weight based word-level attention and a sentiment analysis based sentence-level attention network so as to achieve a better understanding of the story. We train on the publicly available ROC story cloze task data. We mainly focus on two aspects of the narrative: topic correlation and sentiment polar. Since the train data set contains a lot of distractor, we train our model on the validation set and achieve the state of art accuracy 80.5%. We mainly provide a way to use external knowledge to guide training process.
Towards a CAD System for Breast Cancer Based on Individual Microcalcifications?

Microcalcifications (MCs) are small calcium deposits which are often present in breast tissue. Although they may be an early indicator of breast cancer, they are often present in healthy women. Judging on the malignancy of MCs based on their appearance on mammograms is a challenging task for radiologists as mammography projects a 3D object onto a 2D plane, as a consequence, superposition of tissue may alter their appearance. Although Computer Aided Detection and Diagnosis (CAD) systems have been developed to assist radiologists in detecting and characterizing MCs, they mainly focus on analyzing MCs’ spatial configuration rather than their individual characteristics. In this study, we propose a CAD system for the characterization of individual MCs, whose shape is visualized in 3D by scanning breast tissue with micro-CT, a high resolution 3D imaging modality. By calculating a large amount of radiomic features we are able to classify MCs as benign or malignant with 75.88% accuracy, 62.13% sensitivity and 86.39% specificity, outperforming the state of the art and proving that there is a difference regarding the shape and the texture of individual MCs. Although this methodology is not applicable in vivo, its potential for use in intra operative imaging is high, as it can reduce the waiting time between tissue extraction and result of the anatomopathological investigation, facilitating and accelerating the procedure.
An Algorithm for Discrimination Prevention in Data Mining: Implementation Statistics and Analysis

Automated data gathering such as cataloging have covered the way to creating automated results, like loan allowing/renunciation, insurance premium calculation, etc. If the teaching datasets are prejudiced in what favors prejudiced elements like gender, race, religion, etc. prejudiced results may arise. For this purpose, anti-discrimination techniques with discrimination Discovery and anticipation have been presented in data mining. Discernment can be whichever direct or indirect. Direct discernment ensues when results are made grounded on discriminatory attributes. Indirect discernment occurs when decisions are complete built on non-discriminatory attributes which are powerfully associated with partial discriminatory ones. In this Paper, it is mentioned the discrimination deterrence in data mining and given new systems applicable for direct or indirect discernment deterrence individually or together at the similar stage. Also mention how to fresh working out datasets and subcontracted datasets in such a technique that direct and/or indirect discriminatory conclusion instructions are transformed to valid (non-discriminatory) ordering instructions. The tentative estimations validate that the these methods are actual at eliminating direct and/or indirect perception partialities in the unique dataset while conserving data excellence. risks should be undertaken together.
The Relationship between Gestational Weight Gain and Postpartum Depression in Normal and Overweight Pregnant Women

Objective To investigate the relationship between weight gain in pregnancy and postpartum depression (PPD) in normal and overweight pregnant women. Methods The participants of this prospective cohort study were 223 healthy pregnant women with the first trimester body mass index (BMI) between 18.5 and 30 and the gestational age of 10-14 weeks and depressed women were excluded with Beck questionnaire in the first trimester. The evaluation included weight gain at the end of the second and third trimesters and the screening of PPD in 6-8 weeks after delivery by Edinburgh scale. Results 49 participants were excluded from the study and data from 174 people were analyzed. 32.2% of mothers were scored above 12 in the Edinburgh scale. The only variable associated with depression was the third trimester weight gain (OR 1.17, 95%CI 1.04-1.32). Conclusion In addition to considering other risk factors for postpartum depression, health care providers should consider the higher probability of PPD in prepregnancy normal and overweight women who have excessive weight gain especially in the third trimester of pregnancy.
Frequency-Quadrupled Microwave Signal Generation with Tunable Phase Shift Employing No Optical Filter

A novel approach for producing frequency-quadrupled microwave or millimeter–wave (mm-wave) signal with tunable phase shift and employing no optical filter is presented and attested. The advantage of the scheme is no utilization of optical filter and sideband separation device which result in the restriction of frequency bandwidth. In the proposed approach, two first-order sidebands possessing two orthogonal polarization states are generated using only a dual-parallel polarization modulator (DP-PolM) and a three wave-plates polarization controller (TWPC). Then an integrated component called dual-parallel dual-polarization Mach-Zhender modulator (DP-DPMZM) is applied to conduct the two first-order sidebands for generating two two-order sidebands with two orthogonal polarization states, and one of the two-order sidebands has a tunable phase. After that, a 45-degree polarizer is utilized to make the two two-order sidebands the same polarization direction. In the end, an mm-wave signal with tunable phase is obtained after a photodiode (PD). The simulation experiments are fulfilled, which generate a 20, 40, 60 or 76GHz microwave signal employing a 5, 10, 15 or 19GHz radio frequency (RF) signal respectively, and the phases of the generated signals can realize full 360-degree tuning.
Focusing on the Big Picture: Insights into a Systems Approach to Deep Learning for Satellite Imagery

Deep learning tasks are often complicated and require a variety of components working together efficiently to perform well. Due to the often large scale of these tasks, there is a necessity to iterate quickly in order to attempt a variety of methods and to find and fix bugs. While participating in IARPA’s Functional Map of the World challenge, we identified challenges along the entire deep learning pipeline and found various solutions to these challenges. In this paper, we present the performance, engineering, and deep learning considerations with processing and modeling data, as well as underlying infrastructure considerations that support large-scale deep learning tasks. We also discuss insights and observations with regard to satellite imagery and deep learning for image classification.
Efficient Reconstruction Techniques for Disaster Recovery in Secret-Split Datastores

Increasingly, archival systems are relying on authentication-based techniques that leverage secret-splitting rather than encryption to secure data for long-term storage. Secret-splitting data across multiple independent repositories reduces complexities in key management, eliminates the need for updates due to encryption algorithm deprecation over time, and reduces the risk of insider compromise. While reconstruction of stored data objects is straightforward if a user-maintained index is available, the system must also support disaster recovery incase the index is unavailable. Designing a mechanism for efficient index-free reconstruction, that does not increase the risk of attacker compromise, is a challenge. Reconstruction requires the association of chunks that make up an object, which is the kind of information attackers can use to identify chunks they must steal to illicitly obtain data. We propose two new techniques, the set-subset reconstruction and secret-split secure hash (S3H) reconstruction, which allow chunks of data to be correlated and quickly reconstructed without providing useful information to an attacker. Both techniques operate on the entire collections of secret-split chunks in the archive. While they can efficiently rebuild an entire archive, they are inefficient and impractical for rebuilding single objects, making them useless for attackers that do not have access to all of the data. These techniques can each be tuned to trade-off between reconstruction performance and security, reducing overall runtime from O(N^K) (for N objects requiring K recombined chunks each to return the original object) to between O(N) and O(N^2). These runtimes are practical for archives containing as many as 10^7 objects for the secret-split secure hash method and 10^9 objects for the set-subset method. Larger archives can run these techniques with manageable runtimes by grouping data into separate smaller collections and running the algorithms on each collection in parallel.
Vehicle-Based Cloudlet Relaying for Mobile Computation Offloading

This paper studies computation offloading for mobile users in legacy vehicles that are not equipped with powerful computing devices. While running a time-sensitive and computation-intensive application on a mobile device (MD) of these users, the MD may offload part or all the applications to a nearby smart vehicle that can serve as a cloudlet. This provides a possibility to complete the application in time while saving the battery energy of the MD. However, high mobility of the vehicles causes short-lived communication links between the vehicles. As a result, the MD may have to offload tasks to a new cloudlet each time it loses the connection to the current cloudlet. In this paper, we propose a vehicle-based cloudlet relaying (VCR) scheme for mobile computation offloading. The objective is to effectively utilize the computation resources available in surrounding smart vehicles of the MD in the highly dynamic network environment, where each vehicle-based cloudlet may only be available to help the MD execute a small portion of the application. Instead of offloading tasks directly from the MD to individual cloudlets, which may consume high power and a long time, especially when the link just becomes available or is about to disconnect, we propose computation relaying that allows the current executing cloudlet to offload tasks to the next executing cloudlet on behalf of the MD. A set of conditions are established to enable the inter-cloudlet offloading so that the computation relaying can be achieved between the vehicle cloudlets, while the results can be returned to the MD. In addition, the computation relaying is always backed up by direct offloading between the MD and individual cloudlets. An algorithm is proposed to find the time to switch to a new cloudlet and select the best cloudlet as the next execution site or return the execution back to the MD, in order to minimize the energy consumption of the MD while satisfying the delay constraint of the application. Extensive simulation results are shown to demonstrate the effectiveness of the proposed VCR scheme.
Identifying Spoofing Accounts on Twitter Based on Relationships of Accounts

According to the spread of the Twitter service, spoofing accounts of famous people (i.e., a malicious user makes an account to pretend famous people) are increasing. To prevent such spoofing, Twitter, Inc. provides verified accounts that are guaranteed as genuine accounts of themselves by Twitter, Inc. However, not all famous people use the verified accounts. Instead of that, famous people sometimes embed the link of their twitter accounts on their official website. But their Twitter accounts are not always embedded on that. So, it is not always easy to distinguish whether the accounts are genuine or spoofing. To identify spoofing Twitter accounts, we propose a new discrimination method focusing on the following functions. The basic idea of the method is based on the PageRank citation ranking. An important assumption of our method is that famous people accounts often have many followers, but not have many follows. In the experiment, we applied our method to 27 genuine accounts and 37 spoofing accounts. As a result, 96% of genuine accounts and 75% of spoofing accounts are identified correctly.
Energy-Efficient Sparse Beamforming in Cloud Radio Access Networks

In this paper, the optimization of energy efficiency (EE) and the improvement of user data rate in cloud radio access networks (C-RANs) are studied by introducing a weighted sparse beamforming (WSB) method. Using stochastic geometry tools, analytical expressions are derived to describe downlink ergodic rate and coverage probability for coordination multipoint joint transmission in C-RAN. Based on these expressions and by taking advantage of central processing and coordination in baseband unit pool, power allocation among remote radio heads is optimized using the WSB. The WSB optimization algorithm is based on water-filling power allocation strategy and redistributing the power among RRHs. The improvement in both EE and ergodic rate is achieved. The improvement in EE is pronounced up to 28% using WSB technique compare to the equal power beamforming method in the proposed C-RAN network.
Multi-Scale Fusion of Two Large-Exposure-Ratio Images

Existing multiscale exposure fusion (MEF) algorithms cannot preserve relative brightness in an image fused from two large-exposure-ratio images if high-light regions in the dark image are darker than shadow regions in the bright image. In this letter, a strategy by synthesizing a virtual image with a medium exposure is presented to brighten the high-light regions in the dark image and to darken the darkest regions in the bright image. The virtual image is generated via intensity mapping functions. In order to avoid possible color distortion in the virtual image due to one-to-many mapping, two intermediate virtual images with the same exposure time are generated by the two input images, and then merged together to produce the desired virtual image using properly defined weights. The final image is obtained by fusing the original two input images and the virtual image via a state-of-the-art MEF algorithm. Experimental results show that the relative brightness is preserved much better and the MEF-SSIM is significantly improved by the proposed algorithm.
Quantitative Comparison of Dynamic Treemaps for Software Evolution Visualization

Dynamic treemaps are one of the methods of choice for displaying large hierarchies that change over time, such as those encoding the structure of evolving software systems. While quality criteria (and algorithms that optimize for them) are known for static trees, far less has been studied for treemapping dynamic trees. We address this gap by proposing a methodology and associated quality metrics to measure the quality of dynamic treemaps for the specific use-case and context of software evolution visualization. We apply our methodology on a benchmark containing a wide range of real-world software repositories and 12 well-known treemap algorithms. Based on our findings, we discuss the observed advantages and limitations of various treemapping algorithms for visualizing software structure evolution, and propose ways for users to choose the most suitable treemap algorithm based on the targeted criteria of interest.
Learning the invisible: a hybrid deep learning-shearlet framework for limited angle computed tomography

The high complexity of various inverse problems poses a significant challenge to model-based reconstruction schemes, which in such situations often reach their limits. At the same time, we witness an exceptional success of data-based methodologies such as deep learning. However, in the context of inverse problems, deep neural networks mostly act as black box routines, used for instance for a somewhat unspecified removal of artifacts in classical image reconstructions. In this paper, we will focus on the severely ill-posed inverse problem of limited angle computed tomography, in which entire boundary sections are not captured in the measurements. We will develop a hybrid reconstruction framework that fuses model-based sparse regularization with data-driven deep learning. Our method is reliable in the sense that we only learn the part that can provably not be handled by model-based methods, while applying the theoretically controllable sparse regularization technique to the remaining parts. Such a decomposition into visible and invisible segments is achieved by means of the shearlet transform that allows to resolve wavefront sets in the phase space. Furthermore, this split enables us to assign the clear task of inferring unknown shearlet coefficients to the neural network and thereby offering an interpretation of its performance in the context of limited angle computed tomography. Our numerical experiments show that our algorithm significantly surpasses both pure model- and more data-based reconstruction methods.
Discreteness-Aware Approximate Message Passing for Discrete-Valued Vector Reconstruction

This paper considers the reconstruction of a discrete-valued random vector from possibly underdetermined linear measurements using sum-of-absolute-value (SOAV) optimization. The proposed algorithm, referred to as discreteness-aware approximate message passing (DAMP), is based on the idea of approximate message passing (AMP), which has been originally proposed for compressed sensing. The DAMP algorithm has low computational complexity and its performance in the large system limit can be predicted analytically via state evolution framework, where we provide a condition for the exact reconstruction with DAMP in the noise-free case. From the analysis, we also propose a method to determine the parameters of the SOAV optimization. Moreover, based on the state evolution, we provide Bayes optimal DAMP, which has the minimum mean-square-error at each iteration of the algorithm. Simulation results show that the DAMP algorithms can reconstruct the discrete-valued vector from underdetermined linear measurements and the empirical performance agrees with our theoretical results in large-scale systems. When the problem size is not large enough, the SOAV optimization with the proposed parameters can achieve better performance than the DAMP algorithms for high signal-to-noise ratio.
New Design of Polygonal Antenna for Ka-band Applications

this article presents a new design of polygonal planar antenna for Ka-band applications, where the effectiveness of planar antenna determined by a numerical simulation with 9.67 dB of antenna gain at 25.1 GHz and return loss less than −10 dB at a frequency band [24.8-26.1 GHz], where we can invest that antenna in wireless system. After systematic analysis of the simulation of this type of antenna by CST, a validation of the results by Momentum Agilent software will be achieved.