Reviewerly/exHarmony huggingface dataset:
Adversarial Privacy Preservation under Attribute Inference Attack

With the prevalence of machine learning services, crowdsourced data containing sensitive information poses substantial privacy challenges. Existing work focusing on protecting against membership inference attacks under the rigorous framework of differential privacy are vulnerable to attribute inference attacks. In light of the current gap between theory and practice, we develop a novel theoretical framework for privacy-preservation under the attack of attribute inference. Under our framework, we propose a minimax optimization formulation to protect the given attribute and analyze its privacy guarantees against arbitrary adversaries. On the other hand, it is clear that privacy constraint may cripple utility when the protected attribute is correlated with the target variable. To this end, we also prove an information-theoretic lower bound to precisely characterize the fundamental trade-off between utility and privacy. Empirically, we extensively conduct experiments to corroborate our privacy guarantee and validate the inherent trade-offs in different privacy preservation algorithms. Our experimental results indicate that the adversarial representation learning approaches achieve the best trade-off in terms of privacy preservation and utility maximization.
URGENSI LITERASI DIGITAL DALAM MENANGKAL HOAX INFORMASI KESEHATAN DI ERA DIGITAL

Many as the number of the community of being exposed to hoax various kinds of health information in this digital era. This clearly need the consciousness of the community itself and requires support to overcome this phenomenon because it can to be harmful and made anxious the doers the public related to false news or hoax in social media. A support for to overcome problems related to this can be conducted one of them is by digital literacy. Even though the community is therefore skillful in digital or social media but also need to be given digital education related to the use of the information that spread in the digital era so that into society who is wise and critical in using social media.
Research in Fast Modular Exponentiation Algorithm Based on FPGA

Modular exponentiation of large number is widely applied in public-key cryptosystem, also the bottleneck in the computation of public-key algorithm. Modular multiplication is the key calculation in modular exponentiation. An improved Montgomery algorithm is utilized to achieve modular multiplication and converted into systolic array to increase the running frequency. A high efficiency fast modular exponentiation structure is developed to bring the best out of the modular multiplication module and enhance the ability of defending timing attacks and power attacks. For 1024-bit key operands, the design can be run at 170MHz and finish a modular exponentiation in 4,402,374 clock cycles.
Pancreas Segmentation in Abdominal CT Scans using Inter-/Intra-Slice Contextual Information with a Cascade Neural Network

Automatic pancreas segmentation with high precision in Computed Tomography (CT) images is a fundamental issue in both medical image analysis and computer-aided diagnosis (CAD). However, pancreas segmentation is challenging because of the high variability in location and anatomy of the organs, while occupying only a very small part of the entire abdominal CT scans. Due to the rapid development of the CAD system and the urgent need for clinical treatment, the pancreas image segmentation with high precision is demanded. In this paper, we propose a new approach for automatic pancreas segmentation of CT images using inter-/intra-slice contextual information with a cascade neural network. Fully convolutional neural networks (FCN) are used to extract intra-slice contextual information for pancreas segmentation. Recurrent neural networks (RNNs) is introduced to extract inter-slice contextual information. With the setting bounding boxes, the proposed method outperforms the state-of-the-arts with an average Dice Similarity Coefficient (DSC) of 87.72 for NIH dataset with 4-fold cross-validation.
Single-photon interference using silica-based AMZI with phase modulation

Abstract An asymmetric Mach-Zehnder interferometer (AMZI) which comprised a variable optical attenuator (VOA), a thermo-optic phase modulator (TOPM) and a delay line of 740 ps was designed and fabricated using silica-based planar lightwave circuit technology. Two identical AMZI chips were connected in series to demonstrate the single photon interference in the middle pulse. The experimental results showed the TOPM can modulate the phase difference between the double pulses. The single photon interference visibility was dependent on the temperature and the polarization of the input light. The observed maximum visibility was 98.38% when the temperature of temperature controller (TEC) was 10 °C. And the interference visibility was dependent on the polarization of the input light. The AMZI could form the basis of the passive decoder in the future quantum cryptography systems.
Generalized linear mixed models with Gaussian mixture random effects: Inference and application

We propose a new class of generalized linear mixed models with Gaussian mixture random effects for clustered data. To overcome the weak identifiability issues, we fit the model using a penalized Expectation Maximization (EM) algorithm, and develop sequential locally restricted likelihood ratio tests to determine the number of components in the Gaussian mixture. Our work is motivated by an application to nationwide kidney transplant center evaluation in the United States, where the patient-level post-surgery outcomes are repeated measures of the care quality of the transplant centers. By taking into account patient-level risk factors and modeling the center effects by a finite Gaussian mixture model, the proposed model provides a convenient framework to study the heterogeneity among the transplant centers and controls the false discovery rate when screening for transplant centers with non-standard performance.
Boundary Detection-Based Density Peaks Clustering

Clustering algorithms have a very wide range of applications on data analysis, such as machine learning, data mining.However, data sets often have problems with unbalanced and non-spherical distribution.Clustering by fast search and find of density peaks (DPC) is a density-based clustering algorithm which could identify clusters with non-spherical data.In real applications, this algorithm and its variants are not very effective for the division of unevenly distributed clusters, because they only use one indicator (the distance of neighbor points) to handle inner points and boundary points at the same time.To this end, we introduce a new indicator named asymmetry measure which enhances the ability of finding boundary points.Then we propose a boundary detection-based density peaks clustering (BDDPC) algorthm that combines the above two indicators, so that different clusters are separated from each other accurately and the purpose of improving the clustering effect is achieved.The BDDPC algorithm can not only cluster uniformly distributed data, but also cluster unevenly distributed data.In real life, the distribution of high-dimensional data sets are always unbalanced, so this algorithm has very important applications.Experimental results with synthetic and real-world data sets illustrate the effectiveness of our algorithm.
Prediction of Negative Conversion Days of Childhood Nephrotic Syndrome Based on PCA and BP-AdaBoost Neural Network

The prognosis of childhood nephrotic syndrome directly hinges on the accurate prediction of negative conversion days (NCDs).Therefore, this paper designs a hybrid approach of principal component analysis (PCA) and backpropagation (BP)-adaptive boosting (AdaBoost) neural network (NN), and applies the method to predict the NCDs for children with nephrotic syndrome.Specifically, PCA method was employed for dimension reduction.Six principal components were extracted from multiple physiological features, and taken as input variables of three-tiered neural networks.The boosted predictor of BP-AdaBoost model, together with three predictors of BP NN, support vector machine (SVM) and radial basis function (RBF) NN, was trained for NCDs prediction.The experimental results show that the predictor of BP-AdaBoost NN achieves the mean absolute error of 0.2334, the mean relative error of 3.2789%, the SD of 4.6804 and the RSD of 50.8053% in NCDs prediction, and it outperforms other predictors of BP NN, SVM and RBF NN on both accuracy and precision.Furthermore, comparison experiments are conducted on PCA processed testing data and raw testing data for BP-AdaBoost NN and demonstrate the excellent effect of PCA.The hybrid approach of PCA and BP-AdaBoost NN is simple and reliable for NCDs prediction of childhood nephrotic syndrome, and it can help pediatricians prognose childhood nephrotic syndrome accurately and further provide patients with better care and treatment.INDEX TERMS Childhood nephrotic syndrome, negative conversion days (NCDs), principal component analysis (PCA), backpropagation (BP)-adaptive boosting (AdaBoost) neural network (NN).
Exactly Decoupled Kalman Filtering for Multitarget State Estimation With Sensor Bias

The problem of multisensor multitarget state estimation in the presence of constant but unknown sensor biases is investigated. The classical approach to this problem is to augment the state vector to include the states of all the targets and the sensor biases, and then implement an augmented state Kalman filter (ASKF). In this paper, we propose a novel decoupled Kalman filtering algorithm. The decoupled Kalman filtering first processes each target in a separate branch, namely the single-target Kalman filtering branch, where the single-target states and the sensor biases are estimated. Then the bias estimate is refined by fusing the former bias estimates across all the single-target Kalman filtering branches. Finally, the refined bias estimate is fed back to each single-target Kalman filtering branch to improve the target state estimation. We prove that the proposed decoupled Kalman filtering is exactly equivalent to the ASKF in terms of the estimation results under a usual initial condition. The equivalence is also confirmed via the numerical example. Moreover, we further validate the proposed algorithm using the field experimental data of a multistatic passive radar.
Augmented and Virtual Reality for Public Space Art

Public space art, usually placed outside and accessible to all, is a proper target for the exploitation of transformative and immersive technologies such as augmented and virtual reality. In the recent years, artists and technology developers have collaborated to make physical art more expressivein the digital world, taking advantage of the democratisation of mixed reality devices and software.In this paper, we report on such development of the ArtTM application, an Android app for the publicspace art in Timisoara, Romania. We show the current state of the application and the innovative features that are currently in development. We present the envisioned roadmap to an augmented and virtual reality-enhanced experience and some initial thoughts on the actual implementation of them.
A Model-Based System for Real-Time Articulated Hand Tracking Using a Simple Data Glove and a Depth Camera

Tracking detailed hand motion is a fundamental research topic in the area of human-computer interaction (HCI) and has been widely studied for decades. Existing solutions with single-model inputs either require tedious calibration, are expensive or lack sufficient robustness and accuracy due to occlusions. In this study, we present a real-time system to reconstruct the exact hand motion by iteratively fitting a triangular mesh model to the absolute measurement of hand from a depth camera under the robust restriction of a simple data glove. We redefine and simplify the function of the data glove to lighten its limitations, i.e., tedious calibration, cumbersome equipment, and hampering movement and keep our system lightweight. For accurate hand tracking, we introduce a new set of degrees of freedom (DoFs), a shape adjustment term for personalizing the triangular mesh model, and an adaptive collision term to prevent self-intersection. For efficiency, we extract a strong pose-space prior to the data glove to narrow the pose searching space. We also present a simplified approach for computing tracking correspondences without the loss of accuracy to reduce computation cost. Quantitative experiments show the comparable or increased accuracy of our system over the state-of-the-art with about 40% improvement in robustness. Besides, our system runs independent of Graphic Processing Unit (GPU) and reaches 40 frames per second (FPS) at about 25% Central Processing Unit (CPU) usage.
An RTP Extension for Reliable User-Data Transmission over VoIP Traffic

Covert channels are those breaking the restrictions of the legitimate channel and transmitting covert messages secretly. According to the modulation methods, the covert channels are separated into two types, which are the covert storage channel and the covert timing channel. Since the covert storage channel modulates a covert message into the shared storage space, both capacity and throughput exceed the covert timing channel, which guarantees the effectiveness of covert communication. In this paper, a covert storage channel over RTP is proposed and evaluated. Since mobile multimedia communication requires low transmission latency, the RTP based on UDP is widely utilized by VoIP applications. Through modifying the packet layout of RTP packets and embedding secret message sections into the target position, the covert message could be delivered to the receiver. However, since the RTP is not a reliable protocol, the transmission procedure could be infected by the network noise. To solve this, a transmission control mechanism with retransmission is designed for the scheme. Besides, the secret message is also encrypted with a secure encryption algorithm, which prevents message leakage. To evaluate the scheme, several experiments are deployed, and the scheme is proved to be reliable and secure.
Rethinking compact abating probability modeling for open set recognition problem in Cyber-physical systems

Machine learning (ML) components, especially convolutional neural network (CNN) based components, are becoming increasingly vital in cyber-physical systems (CPS), existing great influence on the dependability of CPS. Different to the popular ML model with close set hypothesis, which limits the classes of testing samples to seen classes in training samples, ML in CPS should face the problem of real-world recognition. Open set recognition (OSR) is a more suitable model for improving the dependability of ML components, which allows the presence of unknown classes at testing time. However, the ability to reject those unknown classes is concurrently required in the model. In this paper, we find the defect in compact abating probability application to most CNN-based OSR model. In order to mitigate the defect, two different methods, OpenSoftMax and OPEB are respectively proposed. Finally, extensive experiments on caltech256 data set have been conducted to verify the effectiveness and efficiency of our proposals. Compared to the well-known CNN-based OSR method OpenMax, our first method OpenSoftMax achieves 15.55% average accuracy performance gain and 28.35% F1-score performance gain, while our second method OPEB achieves 13.17% average accuracy performance gain and 25.70% F1-score performance gain.
A Fast 3D Brain Extraction and Visualization Framework Using Active Contour and Modern OpenGL Pipelines

Brain extraction is a process of removing non-brain tissue in the brain magnetic resonance (MR) images and serves as a first step towards more delicate brain segmentation. Although many brain extraction methods have been proposed in the literature, most of them are either laborious or time consuming, and lack of instant visualization. This leads to a time lag between image acquisition and comprehensive visualization. Especially for intraoperative image based neurosurgery navigation, the time lag from image acquisition to brain visualization should be reduced as much as possible. In this paper, we propose an end-to-end fast brain extraction and visualization framework. The input is a T1-weighted MR volume and the output is comprehensive brain visualization. An improved brain extraction tool (BET) algorithm is proposed to evolve a 3D active mesh model to fit the brain surface in the 3D image. Then the brain mask is generated per slice using a polygon fill algorithm. At last, a ray-casting volume rendering algorithm is used to visualize the brain surface with the help of the generated mask. All the operations are performed using the modern OpenGL pipelines running on a graphics processing unit (GPU). Experiments were performed on two publicly available datasets and one clinical dataset to compare our method with five state-of-the-art methods including the original BET in terms of segmentation accuracy and time cost. Our method achieved mean Dice coefficients of 96.8%, 97.1%, 98.5% and mean time cost of 361 ms, 341 ms, 502 ms on the three datasets, outperforming all the other methods.
Regenerative Semi-Supervised Bidirectional W-Network-Based Knee Bone Tumor Classification on Radiographs Guided by Three-Region Bone Segmentation

The objective of this study is to develop and evaluate a new deep learning architecture, namely regenerative semi-supervised bidirectional W-network (RSS-BW), to predict the tumor state of the knee bone from radiographic images. First, we constructed an autoencoder model, called Bidirectional W-network (BW), for segmenting three-region (i.e., femur, tibia, and fibula) of knee bone. Using these regions as input data, RSS-BW architecture consisting of the autoencoding model for regenerating the bone structures, the backbone model for extracting features with pretrained ImageNet, and the predicting model for knee bone tumor classification are established. The developed scheme rapidly obtained segmentation results of the three-region of knee bone with a mean dice score of 98.06 ± 0.08%. Moreover, two types of classification are conducted: single-step and double-step. The single-step classifies the bone images into normal, benign, and malignant states. The mean values of accuracy, precision, recall and F-beta score obtained from the segmented images were 85.23±3.91%, 82.23±3.22%, 82.15±3.31%, and 82.21±3.25%, respectively. For the double-step of bone tumor classification, the images are classified first as normal versus abnormal. The second classification is conducted for abnormal images as benign versus malignant. The double-step classification shows an improvement of the mean accuracy by 1.7% compared to the single-step classification. In conclusion, the RSS-BW model presents higher accuracy than conventional models, indicating its potential clinical decision support for bone tumor classification.
New Graph-Theoretic Approach to Social Steganography

In this paper, we introduce a new and novel graph-theoretic steganographic approach applicable to online social networking services (SNSs). The proposed approach translates a secret message to be embedded as an undirected graph called messagegraph, the structural information of which is concealed within a new directed graph. The new directed graph is released in a SNS platform by producing a sequence of ordered multiple-user interaction events. To secure communication, we propose to split a specific vertex to multiple copies and insert new vertices and edges to the directed graph. A receiver is able to reconstruct the directed graph from observations. Both the message-graph and the secret message can be orderly retrieved without error. It is probably the first work deeply focusing on the practical design of interaction based steganography using graph-theoretic approach.
Practicing Genre-Based Language Teaching Model to Improve Students’ Achievement of Writing Skills

This study investigated the use of genre-based language teaching model in the teaching of writing skills to improve students’ low achievement. It was aimed at improving the achievement of the fifth semester of English language education study program. This study was classroom action research. The subjects were the 4th semester in academic year 2017/2018. The procedure of the study included two steps namely the preliminary study and the action. The preliminary study collected information dealing with the students’ problems and the action presented the steps of the classroom action research. The data were collected through observation, questionnaire, and testing. The data were analyzed quantitatively and qualitatively. The result of the study showed that the genre-based language teaching model can improve the students’ writing achievement. Before the action, the average score of students’ writing achievement was 55 and the average score after the action was 76. The result was higher than the subject minimum passing grade which is 75. The improvement was supported by two indicators. First, students felt easy to write a descriptive text because the teaching steps guided them to practice writing much more according to the generic structures of the text. Last but not least, students felt compatible with the topics asked to be written because they have background knowledge of those.
ACCESSIBILITY OF REGIONAL HIGHER EDUCATION SYSTEMS IN ACCORDANCE WITH DEMOGRAPHIC TRENDS

The article analyses the issue of regional higher education systems provision with public funding per students and the degree of the current system public funding distribution correspondence to demographic trends and to the forecast of the number of potential students. The forecast of the population at the age group of 17–21 years showed that starting from 2019 within the Russian higher education system, the applicants’ number will increase. The reason for this is coming out of the 1990s «demographic hole». The current guarantee of financial support for higher education at the expense of public funds (at least 800 students for every 10,000 people from the age group of 17–30 years old) can be carried out until 2024 without increasing the volume of public funding. However, the implementation of this standard is provided only by reducing the age cohort of 25–30 years. The ratio of the predicted values of the number of potential applicants and the value of demand for regional higher education systems allowed us to identify the donor regions, the deficit regions and the recipient regions. The analysis of the current distribution of public funded places between Russian regions made it possible to identify territorial imbalances that negatively affect the ability of school graduates to receive free higher education in their home region.
Optimizing on-demand GPUs in the Cloud for Deep Learning Applications Training

Deep learning (DL) methods have recently gained popularity and been used in commonplace applications; voice and face recognition, among the others. Despite the growing popularity of DL and the associated hardware acceleration techniques, GPU-based systems still have very high costs. Moreover, while the cloud represents a cost-effective and flexible solution, in large settings operations costs can be further optimized by carefully managing and fostering resource sharing. This work addresses the online joint problem of capacity planning of virtual machines (VMs) and DL training jobs scheduling, and proposes a Mixed Integer Linear Programming (MILP) formulation. In particular, DL jobs are assumed to feature a deadline, while multiple VM types are available from a cloud provider catalog, and each VM has, possibly, multiple GPUs. Our solutions optimize the operations costs by (i) right-sizing the VM capacities; (ii) partitioning the set of GPUs among multiple concurrent jobs running on the same VM, and (iii) determining a deadline-aware job schedule. Our approach is evaluated using an ad-hoc simulator and a prototype environment, and compared against first-principle approaches, resulting in a cost reduction of 45-80%.