Reviewerly/exHarmony huggingface dataset:
New Results on the Storage-Retrieval Tradeoff in Private Information Retrieval Systems

In a private information retrieval (PIR) system, the user needs to retrieve one of the possible messages from a set of storage servers, but wishes to keep the identity of the requested message private from any given server. Existing efforts in this area have made it clear that the efficiency of the retrieval will be impacted significantly by the amount of the storage space allowed at the servers. In this work, we consider the tradeoff between the storage cost and the retrieval cost. We first present three fundamental results: 1) a regime-wise approximate characterization of the optimal tradeoff with a factor of two, 2) a cyclic permutation lemma that can produce more sophisticated codes from simpler base codes, and 3) a relaxed entropic linear program (LP) lower bound that has a polynomial complexity. Equipped with the cyclic permutation lemma, we then propose two novel code constructions, and by applying the lemma, obtain new storage-retrieval points. Furthermore, we derive more explicit lower bounds by utilizing only a subset of the constraints in the relaxed entropic LP in a systematic manner. Though the new upper bound and lower bound do not lead to a better approximation factor uniformly, they are significantly tighter than the existing art in some regimes.
Big Data Clustering Using Improvised Fuzzy C-Means Clustering

Clustering emerged as powerful mechanism to analyze the massive data generated by modern applications; the main aim of it is to categorize the data into clusters where objects are grouped into the particular category. However, there are various challenges while clustering the big data recently. Deep Learning has been powerful paradigm for big data analysis, this requires huge number of samples for training the model, which is time consuming and expensive. This can be avoided though fuzzy approach. In this research work, we design and develop an Improvised Fuzzy C-Means (IFCM)which comprises the encoder decoder Convolutional Neural Network (CNN) model and Fuzzy C-means (FCM) technique to enhance the clustering mechanism. Encoder decoder based CNN is used for learning feature and faster computation. In general, FCM, we introduce a function which measure the distance between the cluster center and instance which helps in achieving the better clustering and later we introduce Optimized Encoder Decoder (OED) CNN model for improvising the performance and for faster computation. Further in order to evaluate the proposed mechanism, three distinctive data types namely Modified National Institute of Standards and Technology (MNIST), fashion MNIST and United States Postal Service (USPS) are used, also evaluation is carried out by considering the performance metric like Accuracy, Adjusted Rand Index (ARI) and Normalized Mutual Information (NMI). Moreover, comparative analysis is carried out on each dataset and comparative analysis shows that IFCM outperforms the existing model.
Bayesian Neural Networks with Maximum Mean Discrepancy regularization

Bayesian Neural Networks (BNNs) are trained to optimize an entire distribution over their weights instead of a single set, having significant advantages in terms of, e.g., interpretability, multi-task learning, and calibration. Because of the intractability of the resulting optimization problem, most BNNs are either sampled through Monte Carlo methods, or trained by minimizing a suitable Evidence Lower BOund (ELBO) on a variational approximation. In this paper, we propose an optimized version of the latter, wherein we replace the Kullback–Leibler divergence in the ELBO term with a Maximum Mean Discrepancy (MMD) estimator, inspired by recent work in variational inference. After motivating our proposal based on the properties of the MMD term, we proceed to show a number of empirical advantages of the proposed formulation over the state-of-the-art. In particular, our BNNs achieve higher accuracy on multiple benchmarks, including several image classification tasks. In addition, they are more robust to the selection of a prior over the weights, and they are better calibrated. As a second contribution, we provide a new formulation for estimating the uncertainty on a given prediction, showing it performs in a more robust fashion against adversarial attacks and the injection of noise over their inputs, compared to more classical criteria such as the differential entropy.
Deep action learning enables robust 3D segmentation of body organs in various CT and MRI images

Abstract In this study, we propose a novel point cloud based 3D registration and segmentation framework using reinforcement learning. An artificial agent, implemented as a distinct actor based on value networks, is trained to predict the optimal piece-wise linear transformation of a point cloud for the joint tasks of registration and segmentation. The actor network estimates a set of plausible actions and the value network aims to select the optimal action for the current observation. Point-wise features that comprise spatial positions (and surface normal vectors in the case of structured meshes), and their corresponding image features, are used to encode the observation and represent the underlying 3D volume. The actor and value networks are applied iteratively to estimate a sequence of transformations that enable accurate delineation of object boundaries. The proposed approach was extensively evaluated in both segmentation and registration tasks using a variety of challenging clinical datasets. Our method has fewer trainable parameters and lower computational complexity compared to the 3D U-Net, and it is independent of the volume resolution. We show that the proposed method is applicable to mono- and multi-modal segmentation tasks, achieving significant improvements over the state-of-the-art for the latter. The flexibility of the proposed framework is further demonstrated for a multi-modal registration application. As we learn to predict actions rather than a target, the proposed method is more robust compared to the 3D U-Net when dealing with previously unseen datasets, acquired using different protocols or modalities. As a result, the proposed method provides a promising multi-purpose segmentation and registration framework, particular in the context of image-guided interventions.
A Dual Layer Secure Data Encryption and Hiding Scheme for Color Images Using the Three-Dimensional Chaotic Map and Lah Transformation

The rapid increase in the usage of multimedia content and transmission over the internet demands data securing techniques. In this work, a data encryption technique using a three-dimensional chaotic map and highly nonlinear substitution boxes for the encryption of secret images, followed by a data hiding scheme based on Lah transformation i-e the secret image is first encrypted and then hide in the cover image. For data hiding, integer’s polynomial sequence is generated using addition and multiplication in coefficient form by evaluating pixels of images, secret bits of Lah transformed encrypted image are embedded in the LhTs coefficient of cover image partitioned into the cluster of four bits. To ensure minimum distortion coefficient adjustment is done before the embedding process. To obtain the stego image inverse Lah transformation is applied after the embedding process. The security analysis of the proposed dual layer data security scheme is performed, the result shows that the proposed system bearing strong immunity against various cryptographic attacks.
Design, FPGA implementation and statistical analysis of a high-speed and low-area TRNG based on an AES s-box post-processing technique

The statistical weakness problem occurring as a result of physical randomness is an important shortcoming of TRNGs. Post-processing techniques are generally used in the literature to overcome this shortcoming. In this study, the hardware implementation of Advanced Encryption Standard (AES) substitution box (s-box)-based novel post-processing technique is presented. The low-cost novel method is based on the substitution s-box transformations and can successfully remove the statistical weakness problem of TRNGs. The real-time verification of the proposed post-processing is done by applying ring oscillator (RO) based TRNG architecture in four different scenarios on Field Programmable Gate Array (FPGA) environment. Successful statistical results obtained from bias, correlation, entropy and NIST 800-22 tests confirm the usability of the proposed method for cryptographic purposes. The low area-energy requirement, practicality and compressionless properties of the post-processing provide better tradeoff for TRNG compared to known methods in the literature. For this reason, TRNG's performance is high. Furthermore, the presented study is important in demonstrating that s-boxes with good mathematical encryption properties can also be used for different cryptographic purposes.
Object manipulation in immersive virtual environments: Hand Motion tracking technology and snap-to-fit function

There have been recent efforts to use virtual reality and manipulation to train construction workers and inspection. However, there is a lack of research efforts comparing and evaluating virtual manipulation hardware for construction tasks. Moreover, the current practice of virtual manipulation has limited functionality to guide users with the placement of objects in virtual environments. To address these issues, this paper presents 1) a detailed case study that compares three types of manipulation hardware (image-based, infrared-based, and magnetic-based) for construction applications and 2) a snap-to-fit method that improves the VM through solving limitations of the advanced virtual reality interaction metaphors. The latter enhances the placement process for manipulation by comparing two models (e.g., scan and BIM models) for proper placement in assembly scenarios. The case study results show that magnetic-based systems outperform others in construction scenarios. Lastly, the snap-to-fit function was validated in terms of accuracy and time performance.
Tangible VR Book: Exploring the Design Space of Marker-Based Tangible Interfaces for Virtual Reality

Tangible User Interface (TUI) represents a huge potential for Virtual Reality (VR) because tangibles can naturally provide rich haptic cues which are often missing in VR experiences that make use of standard controllers. We are particularly interested in implementing TUIs for smartphone-based VR, given the lower usage barrier and easy deployment. In order to keep the overall system simple and accessible, we have explored object detection through visual markers, using the smartphone’s camera. In order to help VR experience designers, in this work we present a design space for marker-based TUI for VR. We have mapped this design space by developing several marker-based tangible interaction prototypes and through a formative study with professionals with different backgrounds. We then instantiated the design space in a Tangible VR Book which we evaluate with remote user studies inspired by the vignette methodology.
Evaluation of the Use of Single- and Multi-Magnification Convolutional Neural Networks for the Determination and Quantitation of Lesions in Nonclinical Pathology Studies

Digital pathology platforms with integrated artificial intelligence have the potential to increase the efficiency of the nonclinical pathologist’s workflow through screening and prioritizing slides with lesions and highlighting areas with specific lesions for review. Herein, we describe the comparison of various single- and multi-magnification convolutional neural network (CNN) architectures to accelerate the detection of lesions in tissues. Different models were evaluated for defining performance characteristics and efficiency in accurately identifying lesions in 5 key rat organs (liver, kidney, heart, lung, and brain). Cohorts for liver and kidney were collected from TG-GATEs open-source repository, and heart, lung, and brain from internally selected R&amp;D studies. Annotations were performed, and models were trained on each of the available lesion classes in the available organs. Various class-consolidation approaches were evaluated from generalized lesion detection to individual lesion detections. The relationship between the amount of annotated lesions and the precision/accuracy of model performance is elucidated. The utility of multi-magnification CNN implementations in specific tissue subtypes is also demonstrated. The use of these CNN-based models offers users the ability to apply generalized lesion detection to whole-slide images, with the potential to generate novel quantitative data that would not be possible with conventional image analysis techniques.
A Doctor Recommendation Based on Graph Computing and LDA Topic Model

A C TDoctor recommendation technology can help patients filter out large number of irrelevant doctors and find doctors who meet their actual needs quickly and accurately, helping patients gain access to helpful personalized online healthcare services.To address the problems with the existing recommendation methods, this paper proposes a hybrid doctor recommendation model based on online healthcare platform, which utilizes the word2vec model, latent Dirichlet allocation (LDA) topic model, and other methods to find doctors who best suit patients' needs with the information obtained from consultations between doctors and patients.Then, the model treats these doctors as nodes in order to construct a doctor tag cooccurrence network and recommends the most important doctors in the network via an eigenvector centrality calculation model on the graph.This method identifies the important nodes in the entire effective doctor network to support the recommendation from a new graph computing perspective.An experiment conducted on the Chinese healthcare website Chunyuyisheng.com proves that the proposed method a good recommendation performance.
Conservative Safety Critics for Exploration

Safe exploration presents a major challenge in reinforcement learning (RL): when active data collection requires deploying partially trained policies, we must ensure that these policies avoid catastrophically unsafe regions, while still enabling trial and error learning. In this paper, we target the problem of safe exploration in RL, by learning a conservative safety estimate of environment states through a critic, and provably upper bound the likelihood of catastrophic failures at every training iteration. We theoretically characterize the tradeoff between safety and policy improvement, show that the safety constraints are satisfied with high probability during training, derive provable convergence guarantees for our approach which is no worse asymptotically then standard RL, and empirically demonstrate the efficacy of the proposed approach on a suite of challenging navigation, manipulation, and locomotion tasks. Our results demonstrate that the proposed approach can achieve competitive task performance, while incurring significantly lower catastrophic failure rates during training as compared to prior methods. Videos are at this URL https://sites.google.com/view/safe-exploration/
Multi-Purpose Ontology-Based Visualization of Spatio-Temporal Data: A Case Study on Silk Heritage

Due to the increasing use of data analytics, information visualization is getting more and more important. However, as data get more complex, so does visualization, often leading to ad hoc and cumbersome solutions. A recent alternative is the use of the so-called knowledge-assisted visualization tools. In this paper, we present STMaps (Spatio-Temporal Maps), a multipurpose knowledge-assisted ontology-based visualization tool of spatio-temporal data. STMaps has been (originally) designed to show, by means of an interactive map, the content of the SILKNOW project, a European research project on silk heritage. It is entirely based on ontology support, as it gets the source data from an ontology and uses also another ontology to define how data should be visualized. STMaps provides some unique features. First, it is a multi-platform application. It can work embedded in an HTML page and can also work as a standalone application over several computer architectures. Second, it can be used for multiple purposes by just changing its configuration files and/or the ontologies on which it works. As STMaps relies on visualizing spatio-temporal data provided by an ontology, the tool could be used to visualize the results of any domain (in other cultural and non-cultural contexts), provided that its datasets contain spatio-temporal information. The visualization mechanisms can also be changed by changing the visualization ontology. Third, it provides different solutions to show spatio-temporal data, and also deals with uncertain and missing information. STMaps has been tested to browse silk-related objects, discovering some interesting relationships between different objects, showing the versatility and power of the different visualization tools proposed in this paper. To the best of our knowledge, this is also the first ontology-based visualization tool applied to silk-related heritage.
Tax evasion risk management using a Hybrid Unsupervised Outlier Detection method

Big data methods are becoming an important tool for tax fraud detection around the world. Unsupervised learning approach is the dominant framework due to the lack of label and ground truth in corresponding datasets, although these methods suffer from lower interpretability and precision compared with supervised approaches. In contrast to prior research works, we examine the possibility of a hybrid unsupervised method for tax evasion risk management that is able to internally validate and explain outliers detected in a given tax dataset. The proposed method, HUNOD (Hybrid UNsupervised Outlier Detection), 1 1 HUNOD is an abbreviation for “Hybrid UNsupervised Outlier Dection”. combines clustering and representation learning for robust outlier detection, additionally allowing its users to incorporate relevant domain knowledge into both constituent outlier detection approaches in order to identify outliers relevant for a given economic context. The interpretability of obtained outliers is achieved by training explainable-by-design surrogate models over internally validated outliers. The experimental evaluation of the HUNOD method is conducted on two datasets derived from the database on individual personal income tax declarations collected by the Tax Administration of Serbia. The obtained results show that the method indicates between 90% and 98% internally validated outliers depending on the clustering configuration and employed regularization mechanisms for representational learning. • A novel hybrid unsupervised outlier detection method (HUNOD) for tax evasion risk management is proposed. • HUNOD combines clustering and representational learning to detect and internally validate outliers. • HUNOD can be enhanced by relevant domain knowledge reflecting underlying economic context. • HUNOD relies on explainable-by-design surrogate models to increase interpretability of its results. • Experimental evaluation shows that HUNOD detects between 90% and 98% of internally validated outliers.
Context-aware item attraction model for session-based recommendation

Session-based recommendation uses existing items in users’ interaction sessions to predict the next items with which users will interact. The existing items in sessions usually have different degrees of relevance with each other, and this item relevance also reflects users’ interests. Moreover, when sessions are represented in different structural forms, there will be different types of relevance between items, an aspect typically neglected by previous work. In this paper, we propose a novel Context-aware Item Attraction Model (CIAM) for session-based recommendation, which is capable of capturing different types of relevance between items in order to obtain users’ general and temporal interests and predict the next items in sessions. First, we convert sessions into local and global undirected graphs to mine the item adjacency relevance within and across sessions in order to better determine users’ general interests. Second, we retain the natural sequence structure of sessions, and model the transition relevance between items in sessions to get users’ temporal interests. Third, we design a context-aware item embedding method to obtain the embedding of each item; this method utilizes superposition and a weighted graph convolutional network to aggregate the context information from both the item’s features and the item’s neighborhood. Finally, based on users’ general and temporal interests, as well as the context-aware embeddings of items, we predict the next items with which users will interact during a session. The proposed model is then extensively evaluated on two real-world datasets. Experimental results show that our model outperforms the state-of-the-art baseline methods. Through the analysis of the experiments, we prove that our model can effectively capture the different types of relevance between items within and across sessions for accurately modeling user interests, therefore improving recommendation performance.
HHVM Jump-Start: Boosting Both Warmup and Steady-State Performance at Scale

Just-In-Time (JIT) compilation is often employed in Virtual Machines (VMs) to translate their virtual-machine languages into real-machine code. This approach not only brings portability, but it also enables aggressive compiler optimizations based on runtime behavior observed via profiling. The downside of JIT compilation, compared to Ahead-Of-Time native compilation, is that the profiling and compilation overheads are incurred during execution. To mitigate these overheads, previous work have proposed sharing either profile data or final JIT compiled code across VM executions. Unfortunately, these techniques have drawbacks, including steady-state performance degradation and difficulty of use. To address these issues, this paper presents the Jump-Start mechanism implemented inside the Hip Hop Virtual Machine (HHVM). Jump-Start is a practical approach to share VM profile data at a large scale, being used to power one of the largest websites in the world. In this paper, we argue for HHVM's Jump-Start approach, describe it in detail, and present steady-state optimizations built on top of it. Running the Facebook website, we demonstrate that Jump-Start effectively solves the warmup problem in HHVM, reducing the server capacity loss during warmup by 54.9%, while also improving steady-state performance by 5.4%.
On embedding sequence correlations in attributed network for semi-supervised node classification

Graph Neural Networks(GNNs) has dominated the semi-supervised node classification task by its neighborhood aggregation mechanism over traditional network embedding methods. However, GNNs still encounters with two vital problems when scales to large networks. It requires the whole graph as input and aggregates attribute only, which brings out-of-memory(OOM) problem on single machine scenery and attribute over-smoothing respectively. To tackle these issues, from the traditional view, we propose a Sequence correlation preserving method for Attributed Network Embedding (SANE) which transforms the network properties into three types of sequences and preserves the correlations among them. Firstly, SANE extracts three types of sequences in attributed network, namely node sequence, attribute sequence, and label sequence, which provide distinct insight into networks. Secondly, the proposed method preserves attribute-node sequence correlation and attribute-label sequence correlation by (1) extracting dual-directional features from attribute sequence and (2) exploiting the extracted features to decode node sequence and label sequence. SANE can scale to large networks and relieve the over-smoothing causing by attribute only aggregation. Experimental results on five real-world datasets demonstrate that SANE outperforms the state-of-the-art methods.