Reviewerly/exHarmony huggingface dataset:
An In-Depth Comparison Of Software Frameworks For Developing Desktop Applications Using Web Technologies

Today JavaScript is one of the most popular and fastest growing programming languages. Initially designed as a web browser scripting language, its adoption has reached beyond web pages: the Internet of Things, mobile and desktop applications. Lately, an increased interest can be observed over developing desktop software using JavaScript and other web technologies such as HTML and CSS. Many popular software products followed this path: Skype, Visual Studio Code, Atom, Brackets, Light Table, Microsoft Teams, Microsoft SQL Operations Studio, GitHub Desktop, Signal, etc. The aim of this article is to aid developers to choose the right framework for their needs, through a comprehensive side-by-side comparison of Electron and NW.js, the two frameworks available for developing desktop software with JavaScript, HTML and CSS. Electron despite being a younger project. It was concluded in this article that this software framework outperforms NW.js in the matter of capabilities on most areas such as file system, user interface, system integration and multimedia; except printing. However, NW.js is easier to use and debug.
Towards calibrated and scalable uncertainty representations for neural networks

For many applications it is critical to know the uncertainty of a neural network's predictions. While a variety of neural network parameter estimation methods have been proposed for uncertainty estimation, they have not been rigorously compared across uncertainty measures. We assess four of these parameter estimation methods to calibrate uncertainty estimation using four different uncertainty measures: entropy, mutual information, aleatoric uncertainty and epistemic uncertainty. We evaluate the calibration of these parameter estimation methods using expected calibration error. Additionally, we propose a novel method of neural network parameter estimation called RECAST, which combines cosine annealing with warm restarts with Stochastic Gradient Langevin Dynamics, capturing more diverse parameter distributions. When benchmarked against mutilated image data, we show that RECAST is well-calibrated and when combined with predictive entropy and epistemic uncertainty it offers the best calibrated measure of uncertainty when compared to recent methods.
CISPNet: Automatic Detection of Remote Sensing Images from Google Earth in Complex Scenes Based on Context Information Scene Perception

The ability to detect small targets and the speed of the target detector are very important for the application of remote sensing image detection, and in this paper, we propose an effective and efficient method (named CISPNet) with high detection accuracy and compact architecture. In particular, according to the characteristics of the data, we apply a context information scene perception (CISP) module to obtain the contextual information for targets of different scales and use k-means clustering to set the aspect ratios and size of the default boxes. The proposed method inherits the network structure of Single Shot MultiBox Detector (SSD) and introduces the CISP module into it. We create a dataset in the Pascal Visual Object Classes (VOC) format, annotated with the three types of detection targets, aircraft, ship, and oiltanker. Experimental results on our remote sensing image dataset as well as the Northwestern Polytechnical University very-high-resolution (NWPU VRH-10) dataset demonstrate that the proposed CISPNet performs much better than the original SSD and other detectors especially for small objects. Specifically, our network can achieve 80.34% mean average precision (mAP) at the speed of 50.7 frames per second (FPS) with the input size 300 × 300 pixels on the remote sensing image dataset. On extended experiments, the performance of CISPNet in fuzzy target detection in remote sensing image is better than that of SSD.
Forecasting Student Graduation With Classification And Regression Tree (CART) Algorithm

Forecasting Student Graduation with Classification And Regression Tree (CART) Algorithm aims to predict students' graduation in Computer Science Departement of Pakuan University. The method used is the CART Algorithm where this method works by forming a decision tree that can infer the rules of a particular classification of the decision tree. The results of this study resulted ouput in the form of decision trees and determine the students with predetermined criteria are classified in categories such as pass on time or pass not on time. Assessment of the accuracy of the data classification that has been done using confusion matrix validation technique. Once calculated with the matrix, the accuracy rate reached 77.5% in the training data.
Meta Module Generation for Fast Few-Shot Incremental Learning

There are two challenging problems in applying standard Deep Neural Networks (DNNs) for incremental learning with a few examples: (i) DNNs do not perform well when little training data is available; (ii) DNNs suffer from catastrophic forgetting when used for incremental class learning. To simultaneously address both problems, we propose Meta Module Generation (MetaMG), a meta-learning method that enables a module generator to rapidly generate a category module from a few examples for a scalable classification network to recognize a new category. The old categories are not forgotten after new categories are added in. Comprehensive experiments conducted on 4 datasets show that our method is promising for fast incremental learning in few-shot setting. Further experiments on the miniImageNet dataset show that even it is not specially designed for the N-wayK-shot learning problem, MetaMG can sitll perform relatively well especially for 20-way K-shot setting.
A Systematic Approach to Synthesize Underwater Images Benchmark Dataset and Beyond

Underwater imaging suffers from a severe light attenuation. Images captured underwater often appear color cast with limited visibility, which may hinder the performance for underwater vision tasks. To handle this, many underwater image enhancement methods are proposed but their results are evaluated on different datasets. The lack of a large diverse dataset to efficiently evaluate these enhancement methods motivates this work. In this paper, we propose a systematic approach to synthesize a large diversity of underwater images as benchmark dataset. This dataset is generated on the basis of NYU-V2 indoor RGB-D dataset. The intensity of underwater ambient light is simulated based on statistic law and the attenuation coefficients are carefully selected. 1449 indoor images function as the ground-truth and 10 types of underwater scenes are synthesized with large depth range. Based on the benchmark, many image enhancement methods are sufficiently evaluated. Besides, the dataset are also beneficial to develop data-driven-based methods. The synthesized dataset will be made publicly available.
Probabilistic sentence satisfiability: An approach to PSAT

Information analysis often involves heterogeneous sources expressed as logical sentences, numerical models, sensor data, etc. Each of these has its own way to describe uncertainty or error; e.g., frequency analysis, algorithmic truncation, floating point roundoff, Gaussian distributions, etc. A unifying framework is proposed here to represent this information as logical sentences with associated probabilities in order to allow the inference of the probability of a query sentence. Given such a knowledge base in Conjunctive Normal Form (CNF) for use by an intelligent agent, with probabilities assigned to the conjuncts, the probability of any new query sentence can be determined by solving the Probabilistic Satisfiability Problem (PSAT). This involves finding a consistent probability distribution over the atoms (if they are independent) or complete conjunction set of the atoms. For each sentence in the knowledge base, we propose to produce an equation in terms of atoms and conditional probabilities. This system of equations is then solved numerically to get a solution consistent with the sentence probabilities. Finding such a solution is called the Probabilistic Sentence Satisfiability (PS-SAT) problem. In particular, findings include: For independent logical variables: atom probabilities which solve PS-SAT also provide a PSAT solution. numerical experiments demonstrate a q-superlinear convergence rate for most test cases. problems with up to 1,000 variables and 300 sentences are solved. For general knowledge bases (i.e., variables not independent): both atom and a subset of conditional probabilities must be found, a solution to PS-SAT does not guarantee a solution to PSAT, but most empirical results provide such a solution. The convergence rate for equations with non-independent variables also appears q-superlinear.
Justifiable Exceptions in General Contextual Hierarchies

The problem of representing and reasoning with context dependent knowledge has recently gained interest in the area of description logics: among the several proposals, we consider the Contextualized Knowledge Repository (CKR) framework. In CKR applications it is often useful to reason over a hierarchical organization of contexts: for this reason, in our recent work we extended the CKR model to allow for the representation of exception handling in the inheritance of knowledge across contexts. However, to simplify the definition of reasoning procedures, we limited our approach to a particular kind of context organization, i.e. ranked hierarchies. In this paper, we further develop the proposal to extend the reasoning on exception handling for CKRs with general contextual hierarchies. We adapt the semantics (on a core version of CKR) to cope with contextual defeasible axioms in general hierarchies; on the base of this, we define an ASP based reasoning procedure that is complete w.r.t. instance checking under the proposed semantics for general contextual hierarchies.
LapNet : Automatic Balanced Loss and Optimal Assignment for Real-Time Dense Object Detection

Real-time single-stage object detectors based on deep learning still remain less accurate than more complex ones. The trade-off between model performance and computational speed is a major challenge. In this paper, we propose a new way to efficiently learn a single-shot detector which offers a very good compromise between these two objectives. To this end, we introduce LapNet, an anchor based detector, trained end-to-end without any sampling strategy. Our approach aims to overcome two important problems encountered in training an anchor based detector: (1) ambiguity in the assignment of anchor to ground truth and (2) class and object size imbalance. To address the first limitation, we propose a soft positive/negative anchor assignment procedure based on a new overlapping function called Per-Object Normalized Overlap (PONO). This soft assignment can be self-corrected by the network itself to avoid ambiguity between close objects. To cope with the second limitation, we propose to learn additional weights, that are not used at inference, to efficiently manage sample imbalance. These two contributions make the detector learning more generic whatever the training dataset. Various experiments show the effectiveness of the proposed approach.
Heat exchanger simulation and recovery device design of waste heat boiler of gas turbine generator set on ocean platform

In order to study the heat exchanger simulation and recovery device design of waste heat boiler of gas turbine generator set on Ocean platform, the theory of thermal cycle of gas turbine and the principle of steam turbine are introduced. The waste heat of exhaust gas of a certain type of gas turbin e generator set on Ocean platform is taken as the object, to design the waste heat recovery device of this type of gas turbine generator set. According to the characteristics of the selected waste heat boiler, the mathematical model is reasonably simplified, and the steady-state simulation model of the waste heat boiler is established by using the computer simulation software, thus forming the whole model of the waste heat boiler. It is attempted to recover the waste heat from the exhaust gas of gas turbine generator set in Ocean platform by using gas-steam combined cycle power generation, which has certain reference significance for the design, application and improvement of a common gas turbine waste heat recovery device on Ocean platform. The established steady-state model simulation model and thermal efficiency calculation model have good accuracy, and have a certain auxiliary role in design calculation and performance research.
Unsupervised double weight graphs based discriminant analysis for dimensionality reduction

Dimensionality reduction plays an important role in pattern recognition tasks. Locality preserving projection and neighbourhood preserving embedding are popular unsupervised feature extraction methods, which try to preserve a certain local structure in the low-dimensional subspace. However, only considering the local neighbour information will limit the methods to achieve higher recognition accuracy. In this paper, an unsupervised double weight graphs based discriminant analysis method (uDWG-DA) is proposed. First, uDWG-DA considers both similar and dissimilar relationships among samples by using double weight graphs. In order to explore the dissimilar information, a new partitioning strategy is proposed to divide the data set into different clusters, where samples of different clusters are dissimilar. Then, based on L2,1 norm, uDWG-DA finds the optimal projection to not only preserve the similar local structure but also increase the separability among different clusters of the data set. Experiments on four hyperspectral images validate the advantage and feasibility of the proposed method compared with other dimensionality reduction methods.
Complementary and Synergistic Properties of Blockchain and Artificial Intelligence

Artificial intelligence (AI) and blockchain are likely to bring powerful economic and social effects. Blockchain's ability to cryptographically validate identities and transactions and create immutable records can enhance trust, transparency, and accountability. Part of the fascinating character of the AI stems from the fact that computers perform better than humans in repetitive tasks. Their judgment and intelligence are not affected by emotions, feelings, and needs. They have better memories and can process large amounts of information.1 AI, thus, enhances efficiency and provides new opportunities for cost savings and revenue generation. What is even more important is that AI and blockchain have strong complementary capabilities that can have dramatic effects on the performance of industries and markets. Each also has a potential to improve the performance and functioning of the other (see Figure 1).
A Noise Tolerant Auto Resonance Network for Image Recognition

Auto Resonance Network (ARN) is a biologically inspired generic non-linear data classifier with controllable noise tolerance useful in control applications like robotic path planning. It appears that multi-layer ARN can be useful in image recognition but such capabilities have not been studied. This paper presents a multi-layer ARN implementation for an image classification system for handwritten characters from MNIST data set. ARN nodes learn by tuning their coverage in response to statistical properties of the training data. This paper describes the effect of adjusting the resonance parameters like coverage and threshold on accuracy of recognition. ARN systems can be trained with a small training set. It has been possible to attain accuracy up to 93% with as few as 50 training samples. ARN nodes can spawn secondary nodes by perturbing the input and output similar to a mutated cell in a biological system. Use of input transformations to achieve required perturbation of the network is discussed in the paper. Results show that the network is able to learn quickly without any stability issues. The present work shows that reasonable level of image classification can be obtained using small sample training sets using resonance networks.
A Unified Neural Network for Panoptic Segmentation

Abstract In this paper, we propose a unified neural network for panoptic segmentation, a task aiming to achieve more fine‐grained segmentation. Following existing methods combining semantic and instance segmentation, our method relies on a triple‐branch neural network for tackling the unifying work. In the first stage, we adopt a ResNet50 with a feature pyramid network (FPN) as shared backbone to extract features. Then each branch leverages the shared feature maps and serves as the stuff, things, or mask branch. Lastly, the outputs are fused following a well‐designed strategy. Extensive experimental results on MS‐COCO dataset demonstrate that our approach achieves a competitive Panoptic Quality (PQ) metric score with the state of the art.
Cloud Load Balancing Algorithms: A Comparative Assessment

Nowadays, Cloud computing is developing quickly and customers are requesting more administrations and superior outcomes. In the cloud domain, load balancing has turned into an extremely intriguing and crucial research area. Numbers of algorithms were recommended to give proficient mechanism for distributing the cloud user’s requests for accessing pool cloud resources. Also load balancing in cloud should provide notable functional benefits to cloud users and at the same time should prove out to be eminent for cloud services providers. In this paper, the pre-existing load balancing techniques are explored. The paper intends to provide landscape for classification of distinct load balancing algorithms based upon the several parameters and also address performance assessment bound to various load balancing algorithms. The comparative assessment of various load balancing algorithms will helps in proposing a competent load balancing technique for intensify the performance of cloud data centers.
A DCT domain smart vicinity reliant fragile watermarking technique for DIBR 3D-TV

This work presents a vicinity reliant intelligent fragile watermarking scheme for depth image-based rendering technique used for three-dimensional television. Depth map of a centre image is implicitly inserted in the block-based discrete cosine transform (DCT) of the same using an aggregate, which also takes into account the presence of its neighbourhood blocks. Based upon the parity of a Boolean operation on the aggregate, parity is modulated which implicitly embeds the watermark. Genetic algorithm is then utilized to select the appropriate frequency bands in the DCT domain to become eligible for watermark embedding based on imperceptibility requirements. Experimental results demonstrate the usefulness of the proposed scheme in terms of its resistance against a set of fragile watermarking attacks and its ability to detect and localize tempering attempts.
Toward Supporting Food Journaling Using Air Quality Data Mining and a Social Robot

Unhealthy diet is a leading cause of health issues. A powerful means for monitoring and improving nutrition is keeping a food diary. Unfortunately, frail people such as the elderly have a hard time filling food diaries on a continuous basis due to forgetfulness or physical issues. For this reason, in this paper we investigate the integration of nutrition monitoring in a robotic platform. A machine learning module detects cooking activities based on air quality sensor data. When cooking is detected, a social robot interacts with the user to fill the food diary through a conversational interface. We report our experience on the development of a partial prototype of our system. Moreover, we illustrate the results of preliminary experiments with annotated sensor data gathered over one month from a real-world apartment.
Monte Carlo Tree Search on Directed Acyclic Graphs for Object Pose Verification

Reliable object pose estimation is an integral part of robotic vision systems as it enables robots to manipulate their surroundings. Powerful methods exist that estimate object poses from RGB and RGB-D images, yielding a set of hypotheses per object. However, determining the best hypotheses from the set of possible combinations is a challenging task. We apply MCTS to this problem to find an optimal solution in limited time and propose to share information between equivalent object combinations that emerge during the tree search, so-called transpositions. Thereby, the number of combinations that need to be considered is reduced and the search gathers information on these transpositions in a single statistic. We evaluate the resulting verification method on the YCB-VIDEO dataset and show more reliable detection of the best solution as compared to state of the art. In addition, we report a significant speed-up compared to previous MCTS-based methods for object pose verification.
The Medical Deconfounder: Assessing Treatment Effects with Electronic Health Records.

The treatment effects of medications play a key role in guiding medical prescriptions. They are usually assessed with randomized controlled trials (RCTs), which are expensive. Recently, large-scale electronic health records (EHRs) have become available, opening up new opportunities for more cost-effective assessments. However, assessing a treatment effect from EHRs is challenging: it is biased by unobserved confounders, unmeasured variables that affect both patients' medical prescription and their outcome, e.g. the patients' social economic status. To adjust for unobserved confounders, we develop the medical deconfounder, a machine learning algorithm that unbiasedly estimates treatment effects from EHRs. The medical deconfounder first constructs a substitute confounder by modeling which medications were prescribed to each patient; this substitute confounder is guaranteed to capture all multi-medication confounders, observed or unobserved (arXiv:1805.06826). It then uses this substitute confounder to adjust for the confounding bias in the analysis. We validate the medical deconfounder on two simulated and two real medical data sets. Compared to classical approaches, the medical deconfounder produces closer-to-truth treatment effect estimates; it also identifies effective medications that are more consistent with the findings in the medical literature.
MonoForest framework for tree ensemble analysis

In this work, we introduce a new decision tree ensemble representation framework: instead of using a graph model we transform each tree into a well-known polynomial form. We apply the new representation to three tasks: theoretical analysis, model reduction, and interpretation. The polynomial form of a tree ensemble allows a straightforward interpretation of the original model. In our experiments, it shows comparable results with state-of-the-art interpretation techniques. Another application of the framework is the ensemble-wise pruning: we can drop monomials from the polynomial, based on train data statistics. This way we reduce the model size up to 3 times without loss of its quality. It is possible to show the equivalence of tree shape classes that share the same polynomial. This fact gives us the ability to train a model in one tree's shape and exploit it in another, which is easier for computation or interpretation. We formulate a problem statement for optimal tree ensemble translation from one form to another and build a greedy solution to this problem.