Reviewerly/exHarmony huggingface dataset:
Entanglement Robustness in Trace Decreasing Quantum Dynamics

Trace decreasing dynamical maps are as physical as trace preserving ones; however, they are much less studied. Here we overview how the quantum Sinkhorn theorem can be successfully applied to find a two-qubit entangled state which has the strongest robustness against local noises and losses of quantum information carriers. We solve a practically relevant problem of finding an optimal initial encoding to distribute entangled polarized qubits though communication lines with polarization dependent losses and extra depolarizing noise. The longest entanglement lifetime is shown to be attainable with a state that is not maximally entangled.
Detecting and Removing Text in the Wild

Scene text removal is a challenging task that aims to erase wild text regions that include text strokes and their ambiguous boundaries, such as embossing, shade, or flare. The challenging issues raised in the wild are not completely addressed by the existing methods. To address these issues, we propose a new loss function for blending two tasks in a new network structure that depicts wild text regions in a soft mask and selectively inpaints them into a sensible background. The proposed loss function aids the learning of two seemingly separate tasks in a synergistic way via the soft mask to achieve remarkable performance in scene text removal. We validate our method through qualitative and quantitative comparisons, and region-wise analysis, showing that our method outperforms existing methods.
A Capability Token Selection Algorithm for Lightweight Information Flow Control in the IoT

The CBAC (Capability-Based Access Control) model where device owners issue subjects capability tokens, i.e., a set of access rights on objects in devices, is adopted to the IoT (Internet of Things). Objects are data resource in a device which are used to store sensor data and action data. A subject $$sb_i$$ can get data of an object $$o_m^k$$ in a device $$d_k$$ by accessing an object $$o_n^l$$ in another device $$d_l$$ even if the subject $$sb_i$$ is not allowed to get the data from the object $$o_m^k$$ . Here, information of the object $$o_m^k$$ illegally flows to the subject $$sb_i$$ . In addition, a subject may get data of an object generated at time $$\tau $$ when the subject is not allowed to get the data from the object. Here, since the data come to the subject later than expected by the subject to get the data, i.e., the data flow late to the subject. In our previous studies, protocols are implemented to interrupt operations implying both illegal and late types of information flow. Here, the request processing time takes longer as the number of capability tokens whose signatures are verified in devices increases. Hence, in this paper, an MRCTS (Minimum Required Capability Token Selection) algorithm to reduce the number of capability tokens used is proposed. In the evaluation, it is shown that the request processing time is shortened by the MRCTS algorithm.
Purging Data from Backups by Encryption

Data retention laws establish rules intended to protect privacy. These define both retention durations (how long data must be kept) and purging deadlines (when the data must be destroyed in storage). To comply with the laws and to minimize liability, companies should destroy data that must be purged or is no longer needed. However, database backups generally cannot be edited to purge "expired" data and erasing the entire backup is impractical. To maintain compliance, data curators need a mechanism to support targeted destruction of data in backups.
Ensemble Wrapper Subsampling for Deep Modulation Classification

Subsampling of received wireless signals is important for relaxing hardware requirements as well as the computational cost of signal processing algorithms that rely on the output samples. We propose a subsampling technique to facilitate the use of deep learning for automatic modulation classification in wireless communication systems. Unlike traditional approaches that rely on pre-designed strategies that are solely based on expert knowledge, the proposed data-driven subsampling strategy employs deep neural network architectures to simulate the effect of removing candidate combinations of samples from each training input vector, in a manner inspired by how wrapper feature selection models work. The subsampled data is then processed by another deep learning classifier that recognizes each of the considered 10 modulation types. We show that the proposed subsampling strategy not only introduces drastic reduction in the classifier training time, but can also improve the classification accuracy for the considered dataset. An important feature herein is exploiting the transferability property of deep neural networks to avoid retraining the wrapper models and obtain superior performance through an ensemble of wrappers over that possible through solely relying on any one of them.
Automatic Segmentation and Detection System for Varicocele in Supine Position

Image analysis is an important technique that can help specialists localize, detect and segment objects in different types of medical images such as MRI, CTs, and Ultrasounds (US).In this research, we use US images to identify and segment the enlarged veins in the pampiniform venous plexus, which is called varicocele.The proposed method aims to determine whether a potential patient is affected or not.This method was evaluated on 90 US images that were taken to the left testicles of 90 patients using the Supine position.This system analyzes US images in three stages which are; preprocessing, processing, and edge detection.The Region Of Interest (ROI) of the pampiniform plexus area was extracted using Otsu segmentation with different parameters 0.1, 0.2, and 0.17, and different color modes (Grayscale, YCbCr, RGB).In the processing stage, different denoising filters were used.Eventually, in the edge detection stage, four edge detectors were applied which are Canny, Soble, Prewitt, and Roberts.Results showed that the best accuracy in detecting varicocele was 78% when YCbCr color mode yellow (y) channel is used with 0.1 Otsu segmentation and the Canny edge detector.The system also showed a Sensitivity of 91%, the test was able to detect 91% of the people with Varicocele, and a Specificity of 39%.
The Upstream Matters: Impact of Uplink Performance on YouTube 360° Live Video Streaming in LTE

Live video streaming services are gaining momentum as network and terminal capabilities improve. However, 360° live video streaming services pose new challenges due to its high bandwidth and computational requirements both on the user and service provider. In this paper, a study of the impact of the uplink of a cellular network on the performance of 360° live video streaming in YouTube is presented. Unlike previous works, the analysis focuses on the upstream between the video source and the server, not on the downstream between the server and viewers. To this end, a measurement campaign is conducted where a live video feed is transmitted and received through YouTube 360° platform in a pilot Long Term Evolution (LTE) system. During the tests, a large dataset of real traces is collected at different protocol layers, both in upstream and downstream, to check the correlation between TCP/IP metrics and key service performance indicators (e.g., video segment quality and end-to-end latency). Results show that uplink performance has a strong impact on the latency perceived by the user, which is critical for the considered live services.
2D Self-attention Convolutional Recurrent Network for Offline Handwritten Text Recognition

Offline handwritten text recognition is still a big challenging problem due to various backgrounds, noises, diversity of writing styles, and multiple touches between characters. In this paper, we propose a model of 2D Self-Attention Convolutional Recurrent Network (2D-SACRN) for recognizing handwritten text lines. The 2D-SACRN model consists of three main components: 1) a 2D self-attention based convolutional feature extractor that extracts a feature sequence from an input image; 2) a recurrent encoder that encodes the feature sequence into a sequence of label probabilities; and 3) a CTC-decoder that decodes the sequence of label probabilities into the final label sequence. In this model, we present a 2D self-attention mechanism in the feature extractor to capture the relationships between widely separated spatial regions in an input image. In the experiment, we evaluate the performance of the proposed model on the three datasets: IAM Handwriting, Rimes, and TUAT Kondate. The experimental results show that the proposed model achieves similar or better accuracy when compared to state-of-the-art models in all datasets.
A Survey on Statistical, Information, and Estimation—Theoretic Views on Privacy

Privacy has become an emerging challenge in both information theory and computer science due to massive (centralized) collection of user data. In this article, we overview privacy-preserving mechanisms and metrics from the lenses of information theory, and unify different privacy metrics, including f-divergences, Rényi divergences, and differential privacy (DP), in terms of the probability likelihood ratio (and its logarithm). We review recent progress on the design of privacy-preserving mechanisms according to the privacy metrics in computer science, where DP is the standard privacy notion which controls the output shift given small input perturbation, and information theory, where the privacy is guaranteed by minimizing information leakage. In particular, for DP, we include its important variants (e.g., Rényi DP, Pufferfish privacy) and properties, discuss its connections with information-theoretic quantities, and provide the operational interpretations of its additive noise mechanisms. For information-theoretic privacy, we cover notable frameworks, including the privacy funnel, originated from rate-distortion theory and information bottleneck, to privacy guarantee against statistical inference/guessing, and information obfuscation on samples and features. Finally, we discuss the implementations of these privacy-preserving mechanisms in current data-driven machine learning scenarios, including deep learning, information obfuscation, federated learning, and dataset sharing.
Suspicious Activity Detection for Security Cameras

Human activity recognition, in recent years, has become a very important topic in artificial intelligence and computer vision. The need for automated surveillance in security cameras has grown since the human eye cannot detect suspicious activities accurately. Detection of suspicious activities and automatic reporting can prevent crime before it occurs, thus avoiding collateral damage. In this paper, we have classified human activities into two: abnormal and normal. Normal activities include sitting, walking, hand waving, etc. Abnormal activities include kicking, punching, pointing a gun, wielding a knife, etc. We achieve this classification by using convolutional and recurrent neural networks. First, the convolutional neural network is used to extract high level features from images. The final classification of the convolutional network is not considered, instead, the result of the last pooling layer is extracted and passed to the recurrent neural network to make the final prediction.
Texture Preserving Photo Style Transfer Network

Photo style transfer aims to change the style of a given photo to a reference style image with the constraint by retaining the broad and faithful conservation of the content of the input image. Most previous algorithms still have challenging issues on how to exactly extract and represent the style of the image to avoid the interruption of human visual perception. In this paper, we present a texture preserving photo style transfer algorithm by separating the input image into texture and structure and then applying the deep structure style transfer network to effectively change the extracted style characteristics of the structure. The texture preserving photo style transfer overcomes the main drawback of the previous approaches like distortion and saturation of the boundary of the objects. The quantitative and qualitative experimental results including user study prove that the proposed photo style transfer is universally applicable comparing to remarkable previous approaches.
Task Scheduling Optimization in Cloud Computing Based on Genetic Algorithms

Task scheduling is the main problem in cloud computing that reduces system performance; it is an important way to arrange user needs and perform multiple goals. Cloud computing is the most popular technology nowadays and has many research potential in various areas like resource allocation, task scheduling, security, privacy, etc. To improve system performance, an efficient task-scheduling algorithm is required. Existing task-scheduling algorithms focus on task-resource requirements, CPU memory, execution time, and execution cost. In this paper, a task scheduling algorithm based on a Genetic Algorithm (GA) has been presented for assigning and executing different tasks. The proposed algorithm aims to minimize both the completion time and execution cost of tasks and maximize resource utilization. We evaluate our algorithm’s performance by applying it to two examples with a different number of tasks and processors. The first example contains ten tasks and four processors; the computation costs are generated randomly. The last example has eight processors, and the number of tasks ranges from twenty to seventy; the computation cost of each task on different processors is generated randomly. The achieved results show that the proposed approach significantly succeeded in finding the optimal solutions for the three objectives; completion time, execution cost, and resource utilization.
Fashion Classification and Object Detection Using CNN

Shopkeepers once were the best choice for product details and were also guiding consumers to navigate the buying process. But technology has turned this scenario in reverse. With every minute of change in technology, information right from product specification to product analysis is all available online. With the help of practical policy of return on offer for E-commerce sites offering flexible return policies, it has become imperative for sellers to provide premium quality and customer service well. In this paper, we have demonstrated the effectiveness of computer vision for object detection methods. We have put forth the best method to get the dress type in the picture and give the exact name of that particular dress from the different journal papers. Here, we have used the VGG16 layer ConvNet architecture using multiple layers to get more effective results. In the proposed methodology, we came up with the few basic steps that are data preprocessing (Yamaguchi et al in 2012 IEEE conference on computer vision and pattern recognition, pp 3570–357, 2012 [1]), training (Lao and Jagadeesh in Convolutional neural networks for fashion classification and object detection, 2015 [2]), compilation, validation and model performance. The proposed method is experimentally tested, verified and found acceptable by achieving accuracy of 95%.
Modal analysis of free archtop guitar top plates

We analyze the modal response of the top plates of archtop guitars using free boundary conditions. Starting from the three-dimensional scan of a real archtop guitar, we build a mesh of its top plate using a non-invasive process. Once the mesh of the plate is built, we compute its vibrational response by finite element method simulations and perform many different analyses. The outer surface of the mesh matches the scan, while we retain the freedom to control the shape of the inner surface. This way we can change some of its aspects (e.g., thickness distribution) depending on what we intend to study. We investigate the similarities of its mode shapes with those of similar instruments (e.g., violin and classical guitar), analyze the carving process of the plate's inner surface and study the influence of resonant holes on its final vibratory response.
Interactive Inference under Information Constraints

We consider the problem of distributed estimation and testing of discrete distributions under local information constraints that include communication and privacy as special cases. Our main result is a unified method that establishes tight bounds for interactive protocols under both the constraints and both the problems. Our main technical contribution is an average information bound which connects learning and testing and handles correlations due to interactivity. While we establish that for learning and testing under both the constraints above, interactivity does not help, we also illustrate a natural family of “leaky query” local constraints under which interactive protocols strictly outperform the noninteractive ones for identity testing.
Learning to teach and learn for semi-supervised few-shot image classification

This paper presents a novel semi-supervised few-shot image classification method named Learning to Teach and Learn (LTTL) to effectively leverage unlabeled samples in small-data regimes. Our method is based on self-training, which assigns pseudo labels to unlabeled data. However, the conventional pseudo-labeling operation heavily relies on the initial model trained by using a handful of labeled data and may produce many noisy labeled samples. We propose to solve the problem with three steps: firstly, cherry-picking searches valuable samples from pseudo-labeled data by using a soft weighting network; and then, cross-teaching allows the classifiers to teach mutually for rejecting more noisy labels. A feature synthesizing strategy is introduced for cross-teaching to avoid clean samples being rejected by mistake; finally, the classifiers are fine-tuned with a few labeled data to avoid gradient drifts. We use the meta-learning paradigm to optimize the parameters in the whole framework. The proposed LTTL combines the power of meta-learning and self-training, achieving superior performance compared with the baseline methods on two public benchmarks.
A selective ensemble learning algorithm for imbalanced dataset

Network intrusion behaviour data is the imbalanced data. It includes a large amount of normal behavior data and a small amount of intrusion behavior data. The traditional selective ensemble learning algorithm will lead to high false negative rate. This paper proposes a selective ensemble learning algorithm for imbalanced data based on under sampling (SELAUS). First of all, the algorithm uses Bootstrap method to extract samples equal to the number of samples of a few classes from majority class samples to construct multiple balanced training subsets. Then, in order to ensure that the obtained base classifiers have great differences, several features are randomly selected on the training subset and a decision tree is constructed as the base classifier using CART algorithm. This method can also make some base classifiers have poor performance, so it can select and integrate base classifiers instead of all base classifiers. In order to accurately evaluate the generalization error of the classifier for imbalanced dataset, this paper defines the performance evaluation method in the imbalanced dataset and the difference evaluation method between the base classifiers. Then the generalization error of each base classifier is calculated, and the base classifier is selected according to the generalization error. In the integration of weighted voting, the weight of each base classifier is calculated by the weight calculation method for imbalanced data. Finally, the validity of the algorithm is verified by UCI dataset and applied to network intrusion detection. The simulation results show that the algorithm can improve the detection rate of minority class samples, that is to say, reduce the false negative rate.
Deep Learning Models for Recognizing the Simple Human Activities Using Smartphone Accelerometer Sensor

In recent years, Deep Learning (DL) models, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory (LSTM), have been widely used for Human Activities Recognition (HAR). They have achieved considerable performance improvements over classical Machine Learning (ML) approaches due to their excellent feature representation capabilities. Simple human activities are performed in sequential order with no overlaps or concurrent actions. The paucity of labeled training activity samples, high computational cost, and system resource requirements of deep learning architectures compared to lightweight models are some of the research challenges confronting the HAR community. We framed the lightweight DL-based CNNs, RNNs, and LSTM model for HAR to tackle these research challenges. The RNNs and LSTM have single layers with softmax activation functions, whereas the one-dimensional CNNs have two convolutional and single max-pooling layers. To evaluate the performance of our models, we used the publicly available benchmark WISDM experimental dataset, which includes the reading of six activities (walking, jogging, upstairs, downstairs, sitting, and standing) performed by 36 participants using a single accelerometer sensor. The experimental result illustrates the efficacy of our models in activity recognition, demonstrating that they attain higher accuracy while being computationally efficient.
Joint Representation Learning with Relation-Enhanced Topic Models for Intelligent Job Interview Assessment

The job interview is considered as one of the most essential tasks in talent recruitment, which forms a bridge between candidates and employers in fitting the right person for the right job. While substantial efforts have been made on improving the job interview process, it is inevitable to have biased or inconsistent interview assessment due to the subjective nature of the traditional interview process. To this end, in this article, we propose three novel approaches to intelligent job interview by learning the large-scale real-world interview data. Specifically, we first develop a preliminary model, named Joint Learning Model on Interview Assessment (JLMIA), to mine the relationship among job description, candidate resume, and interview assessment. Then, we further design an enhanced model, named Neural-JLMIA, to improve the representative capability by applying neural variance inference. Last, we propose to refine JLMIA with Refined-JLMIA (R-JLMIA) by modeling individual characteristics for each collection, i.e., disentangling the core competences from resume and capturing the evolution of the semantic topics over different interview rounds. As a result, our approaches can effectively learn the representative perspectives of different job interview processes from the successful job interview records in history. In addition, we exploit our approaches for two real-world applications, i.e., person-job fit and skill recommendation for interview assessment. Extensive experiments conducted on real-world data clearly validate the effectiveness of our models, which can lead to substantially less bias in job interviews and provide an interpretable understanding of job interview assessment.