Reviewerly/exHarmony huggingface dataset:
Safe-Planner: A Single-Outcome Replanner for Computing Strong Cyclic Policies in Fully Observable Non-Deterministic Domains

Replanners are efficient methods for solving nondeterministic planning problems. Despite showing good scalability, existing replanners often fail to solve problems involving a large number of misleading plans, i.e., plans that do not lead to strong solutions, however, due to their minimal lengths, are likely to be found at every replanning iteration. The poor performance of replanners in such problems is due to their all-outcome determinization. That is, when compiling from non-deterministic to classical, they include all compiled classical operators in a single deterministic domain which leads replanners to continually generate misleading plans. We introduce an offline replanner, called Safe-Planner (SP), that relies on a single-outcome determinization to compile a nondeterministic domain into a set of classical domains, and ordering heuristics for ranking the obtained classical domains. The proposed single-outcome determinization and the heuristics allow for alternating between different classical domains. We show experimentally that SP avoids generating misleading plans, but rather generates weak plans that directly lead to strong solutions. The experiments show that SP outperforms state-of-the-art non-deterministic solvers by solving a broader range of problems. We also validate the practical utility of SP in real-world non-deterministic robotic tasks.
High-Resolution Recurrent Gated Fusion Network for 3D Pancreas Segmentation

Pancreas segmentation has been challenging due to its large variations in size, shape, localization, and indistinguishable boundary. Current mainstream pancreas segmentation methods are based on the deep encoder-decoder structure, which recover high-resolution representations from encoded low-resolution representations to generate pancreas voxel masks. However, the details of the pancreas are easily lost in the encoding stage. In this paper, we propose a 3D high-resolution network (3D HRNet) to extract pancreas features, which maintains high-resolution representations throughout the whole process. We use a novel recurrent gated fusion (RGF) head to fuse high-resolution features and generate pancreas voxel masks. To reduce variable background interference, we crop the pancreas area from abdominal CT images for segmentation with a pancreas localization network. We evaluate the above propsed method on the public NIH and MSD pancreas segmentation datasets, and experiments show a competitive result with a mean Dice-Srensen Coefficient (DSC) of 85.82±4.01% on NIH and 84.22±5.91% on MSD, respectively. The lowest variance and the highest mean DSC reveal the stability of our method among current methods and its potential in the clinical setting.
Deep Root Memory Optimized Indexing Methodology for Image Search Engines

Digitization has created an abundance of new information sources by altering how pictures are captured.Accessing large image databases from a web portal requires an opted indexing structure instead of reducing the contents of different kinds of databases for quick processing.This approach paves a path toward the increase of efficient image retrieval techniques and numerous research in image indexing involving large image datasets.Image retrieval usually encounters difficulties like a) merging the diverse representations of images and their Indexing, b) the low-level visual characters and semantic characters associated with an image are indirectly proportional, and c) noisy and less accurate extraction of image information (semantic and predicted attributes).This work clearly focuses and takes the base of reverse engineering and de-normalizing concept by evaluating how data can be stored effectively.Thus, retrieval becomes straightforward and rapid.This research also deals with deep root indexing with a multidimensional approach about how images can be indexed and provides improved results in terms of good performance in query processing and the reduction of maintenance and storage cost.We focus on the schema design on a non-clustered index solution, especially cover queries.This schema provides a filter predication to make an index with a particular content of rows and an index table called filtered indexing.Finally, we include non-key columns in addition to the key columns.Experiments on two image data sets 'with and without' filtered indexing show low query cost.We compare efficiency as regards accuracy in mean average precision to measure the accuracy of retrieval with the developed coherent semantic indexing.The results show that retrieval by using deep root indexing is simple and fast.
Entity-Aware Biaffine Attention for Constituent Parsing

Constituency parsing is the process of analyzing a sentence by breaking it down into sub-phrases also known as constituents. Although many deep neural models have achieved state-of-the-art results on this task, few consider entity-violating issue, i.e. an entity cannot form a complete sub-tree in the resultant constituent parsing tree. To attack this issue, this paper proposes an entity-aware biaffine attention model for constituent parsing. It leverages entity information for a potential phrase when conducting biaffine attention between the start and end words of the phrase. In the absence of the proper metric for comparison, the entity violating rate (EVR) as a new metric is introduced here to evaluate how many the final parsing trees suffer from entity violating issue. The lower the EVR, the better the model. This metric from a brand perspective helps us understand the potential of existing arts. Experiments on three publicly popular datasets including ONTONOTES, PTB and CTB show that our model achieves the lowest EVR while almost achieving the same performance in terms of the three conventional metrics, i.e., precision, recall, and F1-score. Moreover, extensive experiments of sentence sentiment analysis as a downstream application further exhibit the efficacy of our model and the validity of the proposed metric EVR.
Out-of-Distribution Detection with Uncertainty Enhanced Attention Maps

Out-of-Distribution Detection (OOD) has attracted a lot of attention in the past decade, since its practical importance in safety-critical applications. Various methods have been proposed for solving this problem and most existing classifier-based methods have focused on the entire image when detecting the anomalies. However, in an image, there would be uncertain regions that confuse the model, these regions prevent us from detecting anomalies even classifying correctly. To address this issue, we propose a novel model namely Uncertainty Enhanced Attention for OOD Detection (UEAOD) that consider the uncertainty estimate of various regions and specify emphases placed on each region during OOD detection. Specifically, we propose to use a generative adversarial network (GAN) to generate surrogate OOD examples and estimate uncertainty of discriminator to obtain a gradient-based certainty attention map for feature. With the weighted feature, classifier will be focused on the certain regions so that we can improve OOD detection ability. We provide detailed empirical analysis of the method for OOD task and convincingly demonstrate the effectiveness of the proposed approach.
An Unsupervised Domain Adaptation Being Aware of Domain-specific and Label Information

Domain adaptation aims to leverage the knowledge in a label-rich source domain to facilitate the learning task in an unlabeled target domain with a different distribution. Adversarial-based domain adaptation methods have attracted increasing attention due to their remarkable performance. However, most methods learn the invariant features by aligning distribution between domains, while ignoring the domain-specific and label information. It will lead to unsatisfying invariant features and cause improper label alignment when there is much specific information. Therefore, in this paper, we propose an unsupervised method to learn more robust and discriminative invariant features for domain adaptation by using the specific features and label information. Specially, a separate batch normalization layer is introduced to replace the completely shared layer to capture domain-specific information, which will benefit the learning of invariant features. Then, to make the adaptation more sufficiently, a symmetric design of classifier and the corresponding adversarial training loss are used to realize domain-wise and label-wise alignment. The two steps are optimized iteratively to improve the performance of the model. The extensive experiments on three benchmark datasets have demonstrated the effectiveness of our method.
An Improved LeNet-5 Model Based on Encrypted Data

In recent years, the problem of privacy leakage has attracted increasing attentions. Therefore, machine learning privacy protection becomes crucial research topic. In this paper, the Paillier homomorphic encryption algorithm is proposed to protect the privacy data. The original LeNet-5 convolutional neural network model was first improved. Then the activation function was modified and the C5 layer was removed to reduce the number of model parameters and improve the operation efficiency. Finally, by mapping the operation of each layer in the convolutional neural network from the plaintext domain to the ciphertext domain, an improved LeNet-5 model that can run on encrypted data was constructed. The purpose of using machine learning algorithm was realized and privacy was ensured at the same time. The analysis shows that the model is feasible and the efficiency is improved.
Who are Vulnerability Reporters?

(Background) Software vulnerabilities pose a serious threat to the security of computer systems. Hence, there is a constant race for defenders to find and patch them before attackers are able to exploit them. Measuring different aspects of this process is important in order to better understand it and improve the odds for defenders. (Aims) The human factor of the vulnerability discovery and patching process has received limited attention. Better knowledge of the characteristics of the people and organizations who discover and report security vulnerabilities can considerably enhance our understanding of the process, provide insights regarding the expended effort in vulnerability hunting, contribute to better security metrics, and help guide practical decisions regarding the strategy of projects to attract vulnerability researchers.
Optimizing QoE and Latency of Live Video Streaming Using Edge Computing and In-Network Intelligence

Live video streaming traffic and related applications have experienced significant growth in recent years. More users have started generating and delivering live streams with high quality (e.g., 4K resolution) through popular online streaming platforms such as YouTube, Twitch, and Facebook. Typically, the video contents are generated by streamers and watched by many audiences, which are geographically distributed in various locations far away from the streamers' locations. The resource limitation in the network (e.g., bandwidth) is a challenging issue for network and video providers to meet the users' requested quality. In this thesis, we will investigate optimizing QoEand end-to-end (E2E) latency of live video streaming by leveraging edge computing capabilities and in-network intelligence. We present four main research questions aiming to address the various challenges in optimizing live streaming QoE and E2E latency by employing edge computing and in-network intelligence.
A Detailed Analysis of Word Sense Disambiguation Algorithms and Approaches for Indian Languages

Word sense disambiguation (WSD) could be a difficult exploration-research issue in computational etymology that was perceived toward the commencement of the exact interest in machine translation (MT) and artificial intelligence (AI). WSD is the process of detecting the right meaning of a word with various senses and requires depth knowledge of various sources. Phrases that contain multifunctional words, easily present a different parsing structure and provoke different understanding, and are always referred to as ambiguous. Much effort is required to resolve this problem using machine translation, but the hard work is still continuing. Numerous techniques have been used in disambiguation process and executed on various frames for approximately all dialects. This article presents a detailed analysis of WSD algorithms and their different approaches adopted by researchers in their researches for many Indian languages. In this paper, we present forward an examination of directed, undirected and information-based methodology and calculations accessible in word sense disambiguation.
Analyzing Interpretability Semantically via CNN Visualization

Deep convolutional neural networks are widely used in image recognition, but the black box property is always perplexing. In this paper, a method is proposed using visual annotation to interpret the internal structure of CNN from the semantic perspective. First, filters are screened in the high layers of the CNN. For a certain category, the important filters are selected by their activation values, frequencies and classification contribution. Then, deconvolution is used to visualize the filters, and semantic interpretations of the filters are labelled by referring to the visualized activation region in the original image. Thus, the CNN model is interpreted and analyzed through these filters. Finally, the visualization results of some important filters are shown, and the semantic accuracy of filters are verified with reference to the expert feature image sets. In addition, the results verify the semantic consistency of the same important filters under similar categories, which indicates the stability of semantic annotation of these filters.
Do Users Appreciate Explanations of Recommendations? An Analysis in the Movie Domain

In this paper, we provide insights into users’ needs regarding the inclusion of explanations in a movie recommender system. We have developed different variants of a movie recommender system prototype corresponding to different types of explanations and conducted an online user study to evaluate related explanations. The experimental results show that users do not always appreciate explanations. They want to see explanations when they are not satisfied with the recommended items. They expect to see explanations showing how well the recommended item meets their preferences. Moreover, explanation goals are interdependent and affect the overall satisfaction of users with the recommender system.
Dynamic Prioritization of Software Requirements for Incremental Software Development

Requirements keep changing within an evolving business environment. In incremental software development, where functionalities are identified and delivered incrementally, a need is felt toward capturing and analyzing the impact of the changes in the system development process. This may require refactoring the order of the development of the system components based on newly added dependencies and conflicts among functional and non-functional requirements. In this paper, we propose a dynamic framework that re-prioritizes the requirements based on newly added (and updated) requirement dependencies and conflicts. We also provide an effective tool for our framework that supports software developers during incremental delivery within a changing business environment.
Broker-Based Collaborative Auction Method for Resource Scheduling in Cloud Computing

The computing world is quickly changing toward creating programming for millions to utility as a model, instead of to execute on their separate computers. The cloud environment has become a well-organized model to execute concurrent and difficult applications to execute and deal with the huge measure of information. Scheduling assumes a significant part in dealing with the implementation of continuous requests in distributed computing form. Cloud supplier offers types of models to execute the jobs in cloud environment and keep up the quality of service (QoS) for its clients. To guarantee the cloud supplier’s QoS, we propose a novel broker-dependent scheduling method to execute the clients’ jobs in the cloud and give efficient resources. In broker-based allocation, the jobs are planned progressively in a proficient way. The broker-based distributed system scheduling essentially on the grouping of market-related methods is utilized through the multi-broker frameworks. Additionally, allocation is completed in distributed framework which permits parallel computing and also enhances the implementation of the framework. In broker-based scheduling, we present a collaborative declaration-based auction method to distribute jobs and resources effectively. Likewise, it comprises of three stages, i.e., fundamental coordinating stage, forward declaration auction stage, and in reverse declaration auction stage. But elasticity is essential when allocation is achieved dynamically including virtual systems which provide scalability. We had planned computation policies for forward and in reverse declaration auction system which help in choosing contractual workers. Our broker-based scheduling method gives better execution evaluated with existing techniques.
Explicit Control of the Level of Expressiveness in DNN-Based Speech Synthesis by Embedding Interpolation

The paper proposes a method for controlling the level of expressiveness of speech synthesis by linear interpolation between neural network embeddings corresponding to neutral and fully emotional speech. The deep neural network based speech synthesis model learns the properties of a speech style from a multi-speaker, multi-style speech database. The input features related to speaker and style ID are presented to the model through appropriate embeddings, which allows the model not only to learn the acoustic distance between styles, but also to synthesize speech with an arbitrary level of expressiveness of a particular style. The feasibility of the method has been confirmed by listening tests aimed at establishing the perceived level of style expressiveness, and ensuring that general quality of synthesis from interpolated embeddings is unaffected. The model was trained on approximately 26 h of American English speech by 23 speakers, and the listening tests used speech data of one male and one female speaker. Speech styles considered included happy, angry and apologetic. The results fully confirm that the method is quite successful in controlling the level of style expressiveness, which is one of the key elements of highly natural speech synthesis.
Fake Product Monitoring System using Artificial Intelligence

The problem of recognizing counterfeit (fake) products is a tedious task in certain cases and can be dangerous when it comes to medical products. It becomes easier to produce and sell fake products if an individual does not check the details of the product properly. This paper suggests a better solution using Artificial Intelligence for non-tech-savvy customers who can scan the product with the help of a mobile application to identify, if the product received is fake or original. The major focus will be on the detection of logos (both image and textual representation).
Network Pruning via Annealing and Direct Sparsity Control

Artificial neural networks (ANNs) especially deep convolutional neural networks are very popular these days and have been proved to successfully offer quite reliable solutions to many vision problems. However, the use of deep neural networks is widely impeded by their intensive computational and memory cost. In this paper, we propose a novel efficient network pruning framework that is suitable for both non-structured and structured channel-level pruning. Our proposed method tightens a sparsity constraint by gradually removing network parameters or filter channels based on a criterion and a schedule. The attractive fact that the network size keeps dropping throughout the iterations makes it suitable for the pruning of any untrained or pre-trained network. Because our method uses a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$L_{0}$</tex> constraint instead of the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$L_{1}$</tex> penalty, it does not introduce any bias in the training parameters or filter channels. Furthermore, the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$L_{0}$</tex> constraint makes it easy to directly specify the desired sparsity level during the network pruning process. Finally, experimental validation on extensive synthetic and real vision datasets show that the proposed method obtains better or competitive performance compared to other states of art network pruning methods.
Experiments for Linking the Complexity of the Business UML Class Diagram to the Quality of the Associated Code

A relevant goal of software engineering is to assure the quality of object oriented software from the conceptual modeling phase. UML Class diagrams constitute a key artifact at that stage. Among existing models for iterative and incremental development of software systems, Model Driven Architecture (MDA) has reached a leadership position. MDA enables model-driven software development which treats models as primary development artifacts. The present empirical study answers the following three research questions: (RQ1) are there available in the literature class complexity metrics that can be adopted at the business level? (RQ2) Is it possible to adopt those metrics (if any) to predict the quality of the code returned by xGenerator? The latter is a Java technology platform for the creation of MVC Web applications, which implements the model-driven approach. (RQ3) Is it possible to identify a threshold for the adopted metrics (if any) that might suggest when a business Class diagram should be refactored?
Visible and infrared missile-borne image registration based on improved SIFT and joint features

Abstract For the acquired visible and infrared missile-borne images, in order to provide accurate and reliable image data for the subsequent processing of images to achieve complementary information, we carried out visible and infrared missile-borne image registration research. Firstly, the shape context is used to extract the rough edge features of the image. Then we improve the SIFT gradient definition to overcome the difference in image gray value, and combine with the coherent point drift algorithm to increase the number of correct matching points to achieve global image registration. Experimental results show that, compared with the existing registration methods, the proposed method has a good visual effect of registration, greatly improves the correct registration points and registration accuracy, and can better solve the problem of visible infrared missile-borne image registration.
Anomaly detection in air conditioners using IoT technologies

Anomaly prediction and diagnosis of various machinery using IoT technology have been widely studied. Although air conditioners are commonly used in many facilities, few studies have focused on anomaly diagnosis, especially for small and medium-sized air conditioners. As the installation of necessary IoT sensors is not easy for such air conditioners, the design of the anomaly diagnosis and detection system is not straightforward. In this study, we propose an approach for installing a data collection and diagnosis system for existing air conditioners. Some of the challenges faced in detecting and diagnosing anomalies in air conditioners include noisy environments, cost constraints, and seasonal changes of targets. Therefore, careful coordination of the data collection system and the diagnosis method, and the seasonal tuning of the diagnosis model could realize an effective and inexpensive anomaly detection system for air conditioners.