Reviewerly/exHarmony huggingface dataset:
Lossless Compression of Plenoptic Camera Sensor Images

We propose a codec for the lossless compression of plenoptic camera sensor images. The proposed encoder starts by splitting the input lenslet image into rectangular patches, with each patch corresponding to a microlens image. The encoder and decoder exploit the correlation between the pixels in neighbor patches using a patch-by-patch prediction mechanism where each pixel of a patch has its own dedicated sparse predictor designed to utilize the most relevant pixels from the neighbor patch to the left. An intra-patch prediction mask together with the pixels from the neighbor left patch form the final prediction template. The encoder performs the design of sparse predictors by first finding the relevant regressors in the final template. The patches are classified into M classes according to two possible mechanisms (either based on depth information or based on Bayer mask colors), and the sparse predictor design is performed for each pair (class label; patch pixel index). A relevant context selection mirrors the selection of relevant regressors, thereby providing the arithmetic coding with skewed coding distributions at each context. We show examples of the application of the proposed methods on sensor images from the JPEG Pleno database, thus demonstrating the improved performance of the proposed methods compared to the existing predictive methods for encoding camera sensor images.
An Efficient Pipeline for Pruning Convolutional Neural Networks

Network pruning has achieved significant success in compressing and accelerating CNNs. However, the existing three-step iterative pipeline, which includes ranking, pruning, and fine-tuning, is extremely computationally expensive due to the feed-forward and back-propagation operations conducted in both the ranking and fine-tuning steps. In this paper, we present a computationally efficient framework for structured pruning by exploring the potential of leveraging the intermediate results generated during the fine-tuning step to rank the importance of filters and thus converting a three-step pipeline (with precise ranking) to a two-step pipeline (with coarse ranking), resulting in significant savings in computation time while achieving comparable performance in terms of classification accuracy as compared to the state-of-the-art. The proposed method is evaluated with various benchmark architectures and datasets for the image classification task. Experimental results show that the proposed approach can achieve superior performance in computation efficiency while maintaining the same accuracy level. Our approach would largely facilitate pruning practice, especially on resource-constrained platforms.
Parameterized Temperature Scaling for Boosting the Expressive Power in Post-Hoc Uncertainty Calibration

We address the problem of uncertainty calibration and introduce a novel calibration method, Parametrized Temperature Scaling (PTS). Standard deep neural networks typically yield uncalibrated predictions, which can be transformed into calibrated confidence scores using post-hoc calibration methods. In this contribution, we demonstrate that the performance of accuracy-preserving state-of-the-art post-hoc calibrators is limited by their intrinsic expressive power. We generalize temperature scaling by computing prediction-specific temperatures, parameterized by a neural network. We show with extensive experiments that our novel accuracy-preserving approach consistently outperforms existing algorithms across a large number of model architectures, datasets and metrics (Source code available at: https://github.com/tochris/pts-uncertainty ).
Comparison and evaluation of state-of-the-art LSM merge policies

Abstract Modern NoSQL database systems use log-structured merge (LSM) storage architectures to support high write throughput. LSM architectures aggregate writes in a mutable MemTable (stored in memory), which is regularly flushed to disk, creating a new immutable file called an SSTable . Some of the SSTables are chosen to be periodically merged —replaced with a single SSTable containing their union. A merge policy (a.k.a. compaction policy) specifies when to do merges and which SSTables to combine. A bounded depth merge policy is one that guarantees that the number of SSTables never exceeds a given parameter k , typically in the range 3–10. Bounded depth policies are useful in applications where low read latency is crucial, but they and their underlying combinatorics are not yet well understood. This paper compares several bounded depth policies, including representative policies from industrial NoSQL databases and two new ones based on recent theoretical modeling, as well as the standard Tiered policy and Leveled policy. The results validate the proposed theoretical model and show that, compared to the existing policies, the newly proposed policies can have substantially lower write amplification with comparable read amplification.
A tale of three diagonalizations

In addition to the diagonalization of a normal matrix by a unitary similarity transformation, there are two other types of diagonalization procedures that sometimes arise in quantum theory applications — the singular value decomposition and the Autonne–Takagi factorization. In this pedagogical review, each of these diagonalization procedures is performed for the most general [Formula: see text] matrices for which the corresponding diagonalization is possible, and explicit analytical results are provided in each of the three cases.
An intelligent work order classification model for government service based on multi-label neural network

The classification of work orders for livelihood service play an important role in the e-government system, which goal is to push these work orders to the corresponding processing department. Although many studies have realized the classification of work orders automatically, these work orders are either related to many subjects or related to many departments. Therefore, the problem of work order classification is not a single label classification problem, but a multi-label classification problem. In view of the continuous development of deep neural networks, the goal of this paper is to modify a MultilabelMarginLoss function and design a MultilabelCrossEntropyLoss function to realize the multi-classification system of work orders, which can push the work orders to the related departments(one or more departments) automatically according to their appeal contents (consisting of short texts). We perform deep neural network for text classification including TextCNN, TextRCNN, TextRNN, TextRNN_Att, FastText and DPCNN on the real government hotline data set and compare the performances of these models. The results reveal that TextCNN and TextRCNN have a more stable performance on different loss functions, especially on the MultilabelCrossEntropyLoss function.
Blockchain meets IoT: A Scalable Architecture for Security and Maintenance

Internet of Things, delineated as a network of connected heterogeneous devices is emerging as a widely adopted technology in almost all walks of life today. The massive increase in the number of IoT devices has introduced several issues related to security and management. Blockchains can be a promising technology to make IoT systems secure and distributed for the time to come. However, current blockchain systems are not capable of scaling in accordance to the huge IoT data without a loss in speed and time efficiency. Therefore, we use the concept of sidechains and offline data storage to alleviate the scalability issue of blockchains. In this work, we propose an architectural framework for the security and maintenance of IoT systems using blockchain technology. Smart contracts are used to enforce data authentication, authorization, and keep track of all the activities. Extensive simulation and analysis results demonstrate that the proposed blockchain architecture is highly scalable (in terms of average latency, throughput and cost) and can be applied efficiently in the IoT system.
A progressive CNN in-loop filtering approach for inter frame coding

Convolutional Neural Network (CNN) structures have been designed for in-loop filtering to improve video coding performance. These CNN models are usually trained through learning the correlations between the reconstructed and the original frames, which are then applied to every single reconstructed frame to improve the overall video quality. This direct model training and deployment strategy is effective for intra coding since a locally optimal model is sufficient. However, when applied to inter coding, it causes over-filtering because the intertwined reference dependencies across inter frames are not taken into consideration. To address this issue, existing methods usually resort to the Rate–Distortion Optimization (RDO) to selectively apply the CNN model, but fail to address the limitation of using a local CNN model. In this paper, we propose a progressive approach to train and incorporate the CNN-based in-loop filters to work seamlessly with video encoders. First, we develop a progressive training method to obtain the inter model. Using transfer learning, reconstructed frames using the CNN model are progressively involved back into the training of the CNN model itself, to simulate the reference dependencies in inter coding. Next, we design a frame-level model selection strategy for the high-bitrate coding where the over-filtering effect is diluted. Experimental results show that the proposed method outperforms the RDO method that utilizes only local model. Proposed approach also achieves comparable coding performance but with less computational complexity when integrating our progressive model into the RDO scheme.
HASeparator: Hyperplane-Assisted Softmax

Efficient feature learning with Convolutional Neural Networks (CNNs) constitutes an increasingly imperative property since several challenging tasks of computer vision tend to require cascade schemes and modalities fusion. Feature learning aims at CNN models capable of extracting embeddings, exhibiting high discrimination among the different classes, as well as intra-class compactness. In this paper, a novel approach is introduced that has separator, which focuses on an effective hyperplane-based segregation of the classes instead of the common class centers separation scheme. Accordingly, an innovatory separator, namely the Hyperplane-Assisted Softmax separator (HASeparator), is proposed that demonstrates superior discrimination capabilities, as evaluated on popular image classification benchmarks.
A Unified Approach to Dynamic Decision Problems With Asymmetric Information: Nonstrategic Agents

We study a general class of dynamic multi- agent decision problems with asymmetric information and nonstrategic agents, which include dynamic teams as a special case. When agents are nonstrategic, an agent’s strategy is known to the other agents. Nevertheless, the agents’ strategy choices and beliefs are interdependent over times, a phenomenon known as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">signaling</i> . We introduce the notion of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sufficient information</i> that effectively compresses the agents’ information in a mutually consistent manner. Based on the notion of sufficient information, we propose an information state for each agent that is sufficient for decision-making purposes. We present instances of dynamic multiagent decision problems where we can determine an information state with a time-invariant domain for each agent. Furthermore, we present a generalization of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">the policy-independence property of belief</i> in partially observed Markov decision processes (POMDP) to dynamic multiagent decision problems. Within the context of dynamic teams with asymmetric information, the proposed set of information states leads to a sequential decomposition that decouples the interdependence between the agents’ strategies and beliefs over time and enables us to formulate a dynamic program to determine a globally optimal policy via backward induction.
Offloading Federated Learning Task to Edge Computing with Trust Execution Environment

Federated Learning (FL) takes advantage of distributed data to jointly train a global deep learning model on many clients, without revealing local data to the central server for privacy guarantee. However, due to the heterogeneity of the FL clients, some poor performance clients may become stragglers, impeding the global training process. It is desirable to offload these stragglers' tasks to some high performance servers, but this is at the risk of data privacy leakage. To mitigate such problem, we introduce the edge servers empowered by Trusted Execution Environment (TEE) to securely help the FL clients with poor performance. With the consideration of limited computation resource in TEE, we further investigate how to select the clients for help. Considering the time-varying processing capabilities on the FL clients, we propose an exploration-exploitation based client selection algorithm. Via evaluating our algorithm in a practical FL training task, the experiments show that the proposed algorithm indeed accelerate training process thanks to its efficient client selection.
A hybrid artificial neural network classifier based on feature selection using binary dragonfly optimization for breast cancer detection

Abstract Medical image analysis has become a challenging task as it contributes to disease diagnosis. Breast cancer has been the prominent reason for death among women. While analysing mammogram images, there is a need for clear differentiation of between benign and malignant tissues. Also, early detection of breast masses lead to prediction of breast cancer at the initial stage and minimizes risk of death. In this work, the image is preprocessed using Median filter and is segmented using Fuzzy C Means clustering. Fuzzy C-Means clustering algorithm helps in extracting the region of interest by allocating pixels with similar characteristics into a single group. A pixel may be present in various clusters with different membership values. The belongingness of a pixel to a cluster is decided by the highest membership value. Then the statistical, texture and shape features are extracted from the image. Since there may be many features that are less relevant for classification process, prominent features are selected with the help of Binary Dragonfly Optimization Algorithm and the selected features are fed into a Feed Forward Neural Network trained with Back Propagation Learning to classify the mass as benign or malignant. Experiments are conducted over 320 images from mini-MIAS database out of which 200 ROIs are used in training and 120 ROIs are used in testing phase. The region of interest from given mammogram images are extracted successfully and classified with an accuracy of 98.75%.
Mechanical characterization of self-compacting steel fiber reinforced concrete using digital image correlation

This paper evaluates the mechanical properties, modulus of elasticity (E) and critical stress intensity factor (KIC), of self–compacting concrete with the addition of hook-ended steel fibers, implementing Digital Image Correlation (DIC) to obtain the horizontal and vertical displacement fields for beams under four-point bending test. Based on the vertical displacements and the crack mouth opening displacement, the values of E and KIC were obtained from an inverse analysis using Timoshenko beam and the fracture model proposed by Griffith-Irwin. The results obtained allowed to conclude that DIC is a valid and efficient alternative for the characterization of materials.
Noisy Single Image Super-Resolution Based on Local Fractal Feature Analysis

Generally, most existing super-resolution (SR) methods do not consider noise, which treats SR reconstruction and denoising as two separate problems and performs separately. However, noise is inevitably introduced in the imaging process. Based on analysis of the degraded model, in this paper, the problems of interpolation and denoising are modeled to estimate the noiseless and missing images under the same framework. By applying local fractal dimension (LFD) into image local feature analysis, a noisy single-image SR method is proposed. For each noisy image, we first construct a rational fractal interpolation model containing scaling factors, which can effectively maintain the inherent properties of the data. Furthermore, the original image structure can be well preserved by applying the interpolation model. Considering the local characteristics of the image, scaling factors are calculated on the basis of the LFDs. Then, through further local feature analysis of the interpolated image, a denoising method based on LFD is proposed for recovering a noiseless image. Finally, a high-quality high-resolution image is obtained. Experimental results demonstrate that our method outperforms the state-of-the-art methods both quantitatively and qualitatively.
Calibrating User Response Predictions in Online Advertising

Predicting user response probability such as click-through rate (CTR) and conversion rate (CVR) accurately is essential to online advertising systems. To obtain accurate probability, calibration is usually used to transform predicted probabilities to posterior probabilities. Due to the sparsity and latency of the user response behaviors such as clicks and conversions, traditional calibration methods may not work well in real-world online advertising systems. In this paper, we present a comprehensive calibration solution for online advertising. More specifically, we propose a calibration algorithm to exploit implicit properties of predicted probabilities to reduce negative impacts of the data sparsity problem. To deal with the latency problem in calibrating delayed responses, e.g., conversions, we propose an estimation model to leverage post-click information to approximate the real delayed user responses. We also notice that existing metrics are insufficient to evaluate the calibration performance. Therefore, we present new metrics to measure the calibration performance. Experimental evaluations on both real-world datasets and online advertising systems show that our proposed solution outperforms existing calibration methods and brings significant business values.
Feature-based characterization and extraction of ripple errors over the large square aperture

Freeform surfaces play an important role in modern optical systems with compactness and better performance. The fabrication tools tend to impart a structured signature on optical surfaces, called ripple errors, during the freeform surface manufacturing process. The description and extraction of ripple errors for freeform surface fabrication and testing have attracted extensive attention. In this paper, we develop a fast and accurate method to describe ripple errors for the large aperture based on Fourier model coupling. The polynomial expression is transformed into Fourier series form and surface errors are reconstructed by frequency feature extraction combining with the least square method. The high accuracy and efficiency of the proposed method for representing and filtering ripple errors consuming little computer memory are demonstrated using real experimental data. The proposed method offers a robust and powerful tool not only suitable for surface error characterization but also for image filtering and analysis.
Time is Money: Countering Griefing Attack in Lightning Network

Lightning Network is the most deployed Bitcoin-compatible Payment Channel Network (PCN), ensuring faster execution of transactions. However, this Layer-two solution has its fair share of problems. Topological analysis on Lightning Network reveals that Griefing Attack is a major problem whereby an adversary intentionally exhausts the channel capacity of the network. Though the attack does not always result in a direct monetary gain of the attacker, blocking of channel capacity for several days prevents several nodes from processing any future transaction request, leading to substantial collateral damage. If the attacker is able to lock funds in multiple paths simultaneously, then a major portion of the network may get stalled, reducing the throughput. Mitigating Griefing Attack still remains an open problem. In this paper, we propose an efficient countermeasure for the attack, known as Griefing-Penalty. To realize it, we propose a new payment protocol HTLC-GP or Hashed Timelock Contract with Griefing-Penalty. It not only preserves privacy but also ensures that an attacker cannot ascribe blame on any honest party present in the path relaying the payment. We evaluate the effectiveness of griefing-penalty using different attack strategies and test it on several snapshots of Lightning Network. Our evaluation results show that loss incurred is substantially high for HTLC-GP compared to HTLC.
Anomaly Detection with Autoencoder and Random Forest

This paper proposes AERFAD, an anomaly detection method based on the autoencoder and the random forest, for solving the credit card fraud detection problem. The proposed AERFAD first utilizes the autoencoder to reduce the dimensionality of data and then uses the random forest to classify data as anomalous or normal. Large numbers of credit card transaction data of European cardholders are applied to AEFRAD to detect possible frauds for the sake of performance evaluation. When compared with related methods, AERFAD has relatively excellent performance in terms of the accuracy, true positive rate, true negative rate, and Matthews correlation coefficient.
Using Technology-Flashcard to Encourage Students Learning Mandarin

Abstract The use of learning media in learning Mandarin is very diverse. One of the media used is online-flash card. The objectives of this study are (1) to find out the increase of students interest in learning Mandarin; (2) to determine the ability to master Mandarin vocabulary through a technology flashcard, and (3) to find out the improvement in writing Mandarin characters. This research uses descriptive analytical qualitative method in which the researcher distributes questionnaires and conducts experiments on the use of online-flashcards to elementary school students in Pelita Kasih elementary school. The data obtained comes from the results of the questionnaire and the comparison of values before and after learning by using a flashcard. Based on the analysis of the data obtained by researchers, it can be concluded that the 5th grade students of Pelita Kasih SDS like learning Mandarin by using a flashcard. Through learning with flash cards, the Mandarin scores of 5th grade students at Pelita Kasih SDS have increased. In addition, the students vocabulary mastery also increased. Thus grade 5 elementary school students can learn vocabulary using technology flash card.
Prostate Cancer Classification Using Random Forest and Support Vector Machines

Abstract Nowadays, it gets more types of diseases in the medical sector. For this reason, the role of technology is very important in assisting medical staff to overcome the problem. This research discusses about Prostate Cancer. Prostate Cancer is suffered commonly by males. There are no exact causes how Prostate Cancer occurs in males, but there are several risk factors of a Prostate Cancer, such as age, ethnic group, family history, diet, smoking, and world area. In this research, the classification to diagnose Prostate Cancer is using two methods, those are Random Forest (RF) and Support Vector Machines (SVM). By comparing accuracy of those two methods, we will know which method is better with a dataset that we have from Al-Islam Bandung Hospital, Indonesia. The result is given that Random Forest has a better accuracy than Support Vector Machines. The accuracy shows 97.30% with 80% of data training.