Reviewerly/exHarmony huggingface dataset:
Private Posterior Inference Consistent with Public Information: A Case Study in Small Area Estimation from Synthetic Census Data

Methods for generating differentially-private (DP) synthetic data have received recent attention as large government agencies such as the U.S. Census have decided to release DP synthetic data for public usage. In the synthetic data generation process, it is common to post-process the privatized results so that the final synthetic data agrees with what the data curator considers public information. Our contributions are three fold: 1) we show empirically that using post-processing to incorporate public information in contingency tables can lead to sub-optimal inference, 2) we propose an alternative Bayesian sampling framework that directly incorporates both noise due to DP and public information constraints, leading to improved inference, and 3) we demonstrate the proposed methodology on a study of the relationship between mortality rate and race in small areas given priviatized data from the CDC and U.S. Census.
Deep Inhomogeneous Regularization For Transfer Learning

Fine-tuning is an effective transfer learning method to achieve ideal performance on target task with limited training data. Some recent works regularize parameters of deep neural networks for better knowledge transfer. However, these methods enforce homogeneous penalties for all parameters, resulting in catastrophic forgetting or negative transfer. To address this problem, we propose a novel Inhomogeneous Regularization (IR) method that imposes a strong regularization on parameters of transferable convolutional filters to tackle catastrophic forgetting and alleviate the regularization on parameters of less transferable filters to tackle negative transfer. Moreover, we use the decaying averaged deviation of parameters from the start point (pre-trained parameters) to accurately measure the transferability of each filter. Evaluation on the three challenging benchmarks datasets has demonstrated the superiority of the proposed model against state-of-the-art methods.
An End-to-End Adversarial Hashing Method for Unsupervised Multispectral Remote Sensing Image Retrieval

Unlike natural images, remote sensing images are usually multispectral. And the lack of sufficient labeled data puts a limit on supervised learning for remote sensing image retrieval. In this paper, we propose a novel method for unsupervised multispectral remote sensing image retrieval. The proposed method makes use of the unsupervised representation learning ability of GAN. Meanwhile, a new reconstruction loss exploits the latent codes in GAN to make the final output informative and representative. Transfer learning and color histograms are used to generate an estimated similarity matrix to further guide the training. Hash constraints can make the output codes binary and compact. In the testing stage, the hash codes of multispectral images can be computed in an end-to-end manner. Experiments on a multispectral remote sensing image dataset, EuroSAT [1], show the superiority of the proposed method over other state-of-the-art methods.
Evolving Secret Sharing with Essential Participants

Komargodski et al. introduced Evolving Secret Sharing which allows an impartial participant, called dealer, to share a secret among unbounded number of participants over any given access structure. In their construction for evolving secret sharing over general access structure, the size of share of the ith participant happens to be exponential \((\mathscr {O}(2^{i-1}))\). They also provided constructions for \((k,\infty )\) threshold secret sharing. We consider the problem of evolving secret sharing with t essential participants, namely, over t-\((k,\infty )\) access structure, a generalization of \((k,\infty )\) secret sharing \((t=0)\). We further generalize this access structure to a possible case of unbounded number of essential participants and provide a construction for secret sharing on it. Both the constructions are information theoretically secure and reduce the share size of the construction due to Komargodski et al. over general access structure, exponentially. Moreover, the essential participants receive ideal (and hence, optimal) shares in the first construction.
Pre-trained Language Models with Limited Data for Intent Classification

Intent analysis is capturing the attention of both the industry and academia due to its commercial and noncommercial significance. The rapid growth of unstructured data of micro-blogging platforms, such as Twitter and Facebook, are amongst the important sources for intent analysis. However, the social media data are often noisy and diverse, thus making the task very challenging. Further, the intent analysis frequently suffers from lack of sufficient data because the labeled datasets are often manually annotated. Recently, BERT (Bidirectional Encoder Representation from Transformers), a state-of-the-art language representation model, has attracted attention for accurate language modelling. In this paper, we investigate the application of BERT for its suitability for intent analysis. We study the fine-tuning of the BERT model through inductive transfer learning and investigate methods to overcome the challenges due to limited data availability by proposing a novel semantic data augmentation approach. This technique generates synthetic sentences while preserving the label-compatibility using the semantic meaning of the sentences, to improve the intent classification accuracy. Thus, based on the considerations for finetuning and data augmentation, a systematic and novel step-by-step methodology is presented for applying the linguistic model BERT for intent classification with limited data available. Our results show that the pre-trained language can be effectively used with noisy social media data to achieve state-of-the-art accuracy in intent analysis under low labeled-data regime. Moreover, our results also confirm that the proposed text augmentation technique is effective in eliminating noisy synthetic sentences, thereby achieving further performance improvements.
Continuous Empirical Wavelets Systems

The recently proposed empirical wavelet transform was based on a particular type of filter. In this paper, we aim to propose a general framework for the construction of empirical wavelet systems in the continuous case. We define a well-suited formalism and then investigate some general properties of empirical wavelet systems. In particular, we provide some sufficient conditions to the existence of a reconstruction formula. In the second part of the paper, we propose the construction of empirical wavelet systems based on some classic mother wavelets.
Scale-Invariant Siamese Network For Person Re-Identification

Most existing methods for person re-identification (ReID) almost match people at a single scale and ignore that people are often distinguishable at the right spatial locations and scales. Unlike previous works designing complex convolutional neural network (CNN) architecture or concatenating multi-branch scale-specific features, we aim to employ a simple network to learn scale-invariant features. Concretely, we first propose a shared two-branch framework with two-scale images from the same identity as inputs, which is beneficial for ReID network to focus on common features in different-scale images. Furthermore, we introduce a novel attention loss to enforce discriminative regions between two branches more consistent in the visual level. Finally, we conduct extensive evaluations on three largescale datasets and report competitive performance.
Classifier with Deep Deviation Detection in PoE-IoT Devices

With the rapid growth in diversity of PoE-IoT devices and concept of "Edge intelligence", PoE-IoT security and behavior analysis is the major concern. These PoE-IoT devices lack visibility when the entire network infrastructure is taken into account. The IoT devices are prone to have design faults in their security capabilities. The entire network may be put to risk by attacks on vulnerable IoT devices or malware might get introduced into IoT devices even by routine operations such as firmware upgrade. There have been various approaches based on machine learning(ML) to classify PoE-IoT devices based on network traffic characteristics such as Deep Packet Inspection(DPI). In this paper, we propose a novel method for PoE-IoT classification where ML algorithm, Decision Tree is used. In addition to classification, this method provides useful insights to the network deployment, based on the deviations detected. These insights can further be used for shaping policies, troubleshooting and behavior analysis of PoE-IoT devices.
Diversity-Aware Weighted Majority Vote Classifier for Imbalanced Data

In this paper, we propose a diversity-aware ensemble learning based algorithm, referred to as DAMVI, to deal with imbalanced binary classification tasks. Specifically, after learning base classifiers, the algorithm i) increases the weights of positive examples (minority class) which are "hard" to classify with uniformly weighted base classifiers; and ii) then learns weights over base classifiers by optimizing the PAC-Bayesian C-Bound that takes into account the accuracy and diversity between the classifiers. We show efficiency of the proposed approach with respect to state-of-art models on predictive maintenance tasks, credit card fraud detection, webpage classification and medical applications.
The Speculative (In)Efficiency of the CME Bitcoin Futures Market

The launch of Bitcoin futures on the Chicago Board Options Exchange (CBOE) and the Chicago Mercantile Exchange (CME) in December 2017 marked a notable milestone in the development of cryptoassets. Yet while the speculative efficiency of commodity markets has been extensively investigated, relatively little analysis has been undertaken on the speculative efficiency of Bitcoin markets. In this paper we investigate the speculative efficiency of the Bitcoin market, leveraging an approach based on non-overlapping data samples, which has been previously employed to the same end in the context of the London Metal Exchange (LME). Using non-overlapping data on Bitcoin spot and futures prices as traded on the CME, we find that the 1-month futures price is not an unbiased predictor of the spot price, suggesting that the market is inefficient: it may be possible for a speculator to make excess returns. In contrast, with 2-week and 1-week futures we are unable to reject the null hypothesis of market efficiency. Moreover, we find that the futures price becomes a more accurate indicator of the spot price as the futures contract becomes shorter.
Efficient Covisibility-based Image Matching for Large-Scale SfM

Obtaining accurate and sufficient feature matches is crucial for robust large-scale Structure-from-Motion. For unordered image collections, a traditional feature matching method with geometric verification requires a huge cost to find sufficient feature matches. Although several methods have been proposed to speed up this stage, none of them makes full use of existing matches. In this paper, we propose a novel efficient image matching method by using the transitivity of region covisibility. The overlapping image pairs can be efficiently found in an iterative matching strategy even only with few inlier feauture matches. The experimental results on unordered image datasets demonstrate that the proposed method is three times faster than the state-of-the-art and the matching result is high-quality enough for robust SfM.
Multiple Elimination of Base Classifiers in Ensemble Learning Using Accuracy and Diversity Comparisons

When generating ensemble classifiers, selecting the best set of classifiers from the base classifier pool is considered a combinatorial problem and an efficient classifier selection methodology must be utilized. Different researchers have used different strategies such as evolutionary algorithms, genetic algorithms, rule-based algorithms, simulated annealing, and so forth to select the best set of classifiers that can maximize overall ensemble classifier accuracy. In this article, we present a novel classifier selection approach to generate an ensemble classifier. The proposed approach selects classifiers in multiple rounds of elimination. In each round, a classifier is given a chance to be selected to become a part of the ensemble, if it can contribute to the overall ensemble accuracy or diversity; otherwise, it is put back into the pool. Each classifier is given multiple opportunities to participate in rounds of selection and they are discarded only if they have no remaining chances. The process is repeated until no classifier in the pool has any chance left to participate in the round of selection. To test the efficacy of the proposed approach, 13 benchmark datasets from the UCI repository are used and results are compared with single classifier models and existing state-of-the-art ensemble classifier approaches. Statistical significance testing is conducted to further validate the results, and an analysis is provided.
Sharing Knowledge in Digital Ecosystems Using Semantic Multimedia Big Data

The use of formal representations has a basic importance in the era of big data. This need is more evident in the context of multimedia big data due to the intrinsic complexity of this type of data. Furthermore, the relationships between objects should be clearly expressed and formalized to give the right meaning to the correlation of data. For this reason the design of formal models to represent and manage information is a necessary task to implement intelligent information systems. Approaches based on the semantic web need to improve the data models that are the basis for implementing big data applications. Using these models, data and information visualization becomes an intrinsic and strategic task for the analysis and exploration of multimedia Big Data. In this article we propose the use of a semantic approach to formalize the structure of a multimedia Big Data model. Moreover, the identification of multimodal features to represent concepts and linguistic-semantic properties to relate them is an effective way to bridge the gap between target semantic classes and low-level multimedia descriptors. The proposed model has been implemented in a NoSQL graph database populated by different knowledge sources. We explore a visualization strategy of this large knowledge base and we present and discuss a case study for sharing information represented by our model according to a peer-to-peer(P2P) architecture. In this digital ecosystem, agents (e.g. machines, intelligent systems, robots,...) act like interconnected peers exchanging and delivering knowledge with each other.
PSO-PS:Parameter Synchronization with Particle Swarm Optimization for Distributed Training of Deep Neural Networks

Parameter updating is an important stage in parallelism-based distributed deep learning. Synchronous methods are widely used in distributed training the Deep Neural Networks (DNNs). To reduce the communication and synchronization overhead of synchronous methods, decreasing the synchronization frequency (e.g., every n mini-batches) is a straightforward approach. However, it often suffers from poor convergence. In this paper, we propose a new algorithm of integrating Particle Swarm Optimization (PSO) into the distributed training process of DNNs to automatically compute new parameters. In the proposed algorithm, a computing work is encoded by a particle, the weights of DNNs and the training loss are modeled by the particle attributes. At each synchronization stage, the weights are updated by PSO from the sub weights gathered from all workers, instead of averaging the weights or the gradients. To verify the performance of the proposed algorithm, the experiments are performed on two commonly used image classification benchmarks: MNIST and CIFAR10, and compared with the peer competitors at multiple different synchronization configurations. The experimental results demonstrate the competitiveness of the proposed algorithm.
A Low Complexity Decentralized Neural Net with Centralized Equivalence using Layer-wise Learning

We design a low complexity decentralized learning algorithm to train a recently proposed large neural network in distributed processing nodes (workers). We assume the communication network between the workers is synchronized and can be modeled as a doubly-stochastic mixing matrix without having any master node. In our setup, the training data is distributed among the workers but is not shared in the training process due to privacy and security concerns. Using alternating-direction-method-of-multipliers (ADMM) along with a layer-wise convex optimization approach, we propose a decentralized learning algorithm which enjoys low computational complexity and communication cost among the workers. We show that it is possible to achieve equivalent learning performance as if the data is available in a single place. Finally, we experimentally illustrate the time complexity and convergence behavior of the algorithm.
A Novel Design Reversible Logic Based Configurable Fault-Tolerant Embryonic Hardware

With the advancement of advanced node technology beyond sub-10 nm nodes, high-performance computing is facing a great challenge in the form of excessive levels of heat. Against this limitation, we can re-synthesis any complex digital circuits using reversible logic only, known for ideally Zero-heat dissipation. This paper proposes a novel reversible logic based on Configurable Fault-Tolerant Embryonic Hardware. We have reinvestigated the concept of Self-healing for hardware systems in the context of reversible logic and circuits. This paper presents a comparative analysis between conventional and proposed quantum approach on various parameters such as area-overhead, power dissipation and quantum cost along with the limitations of conventional computing. The reliability of the proposed approach is analyzed against other existing classical approaches with different failure rates. The overall power dissipation is almost 19% lower for the proposed approach compared to other conventional approaches using digital gates with cell number 32. The proposed approach is implemented for the ALU array using VHDL on Altera 10 GX FPGA.
Messaging Beyond Texts with Real-time Image Suggestions

While people primarily communicate with text in mobile chat applications, they are increasingly using visual elements such as images, emojis, and memes. Using such visual elements could help users communicate clearly and make chatting experience enjoyable. However, finding and inserting contextually appropriate images during the chat can be both tedious and distracting. We introduce MilliCat, a real-time image suggestion system that recommends images that match the chat content within a mobile chat application (i.e., autocomplete with images). MilliCat combines natural language processing (e.g., keyword extraction, dependency parsing) and mobile computing (e.g., resource and energy-efficiency) techniques to autonomously make image suggestions when users might want to use images. Through multiple user studies, we investigated the effectiveness of our design choices, the frequency and motivation of image usage by the participants, and the impact of MilliCat on mobile chat experiences. Our results indicate that MilliCat's real-time image suggestion enables users to quickly and conveniently select and display images on mobile chat by significantly reducing the latency in the image selection process (3.19 × improvement) and consequently more frequent image usage (1.8 ×) than existing solutions. Our study participants reported that they used images more often with MilliCat as the images helped them convey information more effectively, emphasize their opinion, express emotions, and have fun chatting experience.
A Robust Approach for Application of Morphological Operations on MRI

Morphological operators are used in many applications like segmentation, edge detection and image analysis. These operations play a major role in medical image analysis. Therefore a novel approach has been proposed in this paper which improves the results of morphological operations, which in turn lead to improvement in the whole process of medical analysis. The proposed method makes use of effective pre-processing and improves the results of erosion and dilation. Noise from various sources gets introduced into Magnetic Resonance (MR) images. The MR images are firstly denoised and then morphological operations are applied, which results in better erosion and dilation process of MR images. The performance of proposed method is compared qualitatively as well as quantitatively on basis of metrics such as Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE) and Structure Similarity Index (SSIM) values.
An Automated Pipeline for Robust Image Processing and Optical Character Recognition of Historical Documents

In this paper we propose a pipeline for processing of scanned historical documents into the electronic text form that could then be indexed and stored in a database. The nature of the documents presents a substantial challenge for standard automated techniques – not only there is a mix of typewritten and handwritten documents of varying quality but the scanned pages often contain multiple documents at once. Moreover, the language of the texts alternates mostly between Russian and Ukrainian but other languages also occur. The paper focuses mainly on segmentation, document type classification, and image preprocessing of the scanned documents; the output of those methods is then passed to the off-the-shelf OCR software and a baseline performance is evaluated on a simplified OCR task.
Feature extraction and feature selection in smartphone-based activity recognition

Nowadays, smartphones are gradually being integrated in our daily lives, and they can be considered powerful tools for monitoring human activities. However, due to the limitations of processing capability and energy consumption of smartphones compared to standard machines, a trade-off between performance and computational complexity must be considered when developing smartphone-based systems. In this paper, we shed light on the importance of feature selection and its impact on simplifying the activity classification process which enhances the computational complexity of the system. Through an in-depth survey on the features that are widely used in state-of-the-art studies, we selected the most common features for sensor-based activity classification, namely conventional features. Then, in an experimental study with 10 participants and using 2 different smartphones, we investigated how to reduce system complexity while maintaining classification performance by replacing the conventional feature set with an optimal set. For this reason, in the considered scenario, the users were instructed to perform different static and dynamic activities, while freely holding a smartphone in their hands. In our comparison to the state-of-the-art approaches, we implemented and evaluated major classification algorithms, including the decision tree and Bayesian network. We demonstrated that replacing the conventional feature set with an optimal set can significantly reduce the complexity of the activity recognition system with only a negligible impact on the overall system performance.
STAT: Subtle Typing Around the Thigh for Head-Mounted Displays

In head-mounted display (HMD) interaction, text entry is frequently supported via some form of virtual touch, controller, or ray casting keyboard. While these options effectively support text entry, they often incur costs of additional external hardware, awkward movements, and hand encumbrance. We propose STAT, a low-cost, mobile, touch typing technique that leverages a smartphone screen located at the thigh, to support both tap and word gesture text input for HMDs. Through a controlled laboratory study, we explore the efficacy of our technique – including a comparison of typing in and out of an enclosed pocket – and present design recommendations for the opportunistic use of a personal touchscreen device positioned at a user’s thigh for HMD text entry.