Reviewerly/exHarmony huggingface dataset:
RETRACTED ARTICLE: Improved policy mechanisms for the promotion of future digital business economy during covid-19 pandemic

Recent advances in the field of digital technology have enabled the expansion of the business market to a new level. Digital business markets encompass eminent features like increased transparency, enhanced customer relationship, flexible business targets, customer participation, etc. It aids in enhancing traditional business quality by bridging the relationship between the suppliers and the customers. The massive development of internet technology in recent years has largely contributed to the emergence of digital business. The digitization of the business world helps small and medium enterprises create a new digital network, which allows overcoming large enterprises' dominance. Since small and medium enterprises play a major role in determining a country's economy, the digital market's emergence and development are very important. The self-organizing capability of the digital market is the main advantage for newly emerging enterprises. Recently, the Covid-19 pandemic has resulted in customers' migration from offline shopping to the online market. These changing business models mandate the need for policies and control mechanisms. Using these policies, digital business projects are developed using an open environment using which the small and medium enterprises can interact with each other. These policies and control mechanisms outline the rules to be followed to avoid the current digital market issues. These policies must be designed to improve and ensure the welfare of future digital business systems. Thus, this paper aims to explore the policies associated with regulating and promoting the digital business economy.
An Image is Worth a Thousand Terms? Analysis of Visual E-Commerce Search

Visual search has become popular in recent years, allowing users to search by an image they are taking using their mobile device or uploading from their photo library. One domain in which visual search is especially valuable is electronic commerce, where users seek for items to purchase. In this work, we present an in-depth comprehensive study of visual e-commerce search. We perform query log analysis of one of the largest e-commerce platforms' mobile search application. We compare visual and textual search by a variety of characteristics, with special focus on the retrieved results and user interaction with them. We also examine image query characteristics, refinement by attributes, and performance prediction for visual search queries. Our analysis points out a variety of differences between visual and textual e-commerce search. We discuss the implications of these differences for the design of future e-commerce search systems.
Data Life Aware Model Updating Strategy for Stream-Based Online Deep Learning

Many deep learning applications deployed in dynamic environments change over time, in which the training models are supposed to be continuously updated with streaming data to guarantee better descriptions of data trends. However, most state-of-the-art learning frameworks support well in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">offline</i> training methods while omitting <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">online model updating</i> strategies. In this work, we propose and implement <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">iDlaLayer</i> , a thin middleware layer on top of existing training frameworks that streamlines the support and implementation of online deep learning applications. In pursuit of good model quality and fast data incorporation, we design a Data Life Aware model updating strategy (DLA), which builds training data samples according to contributions of data from different life stages, and considers the training cost consumed in model updating. We evaluate iDlaLayer's performance through simulations and experiments based on TensorflowOnSpark with three representative online learning workloads. Our experimental results demonstrate that iDlaLayer reduces the overall elapsed time of ResNet, DeepFM and PageRank by 11.3, 28.2, and 15.2 percent compared to the periodic update strategy, respectively. It further achieves an average 20 percent decrease in training cost and brings about a 5 percent improvement in model quality against the traditional continuous training method.
Reduced Biquaternion Convolutional Neural Network for Color Image Processing

Reduced biquaternion is a four dimensional hyper–complex number which is commutative algebra and an extension of complex. Due to this property, the corresponding fast algorithm is designed in time frequency analysis which can better fit the convolution theorem than the non-commutative quaternion. In addition, the reduced biquaternion can be interpreted as a single point in 2-dimensional hyperbolic space with its algebraic structure which has more capacity and variable ability than the Euclidean space. However, the algebra properties of the reduced biquaternion have not yet been well explored in the deep convolutional neural network. In this paper, we develop a new deep network structure, namely reduced biquaternion valued convolutional neural network (RQV-CNN). The proposed RQV-CNN includes basic modules of reduced biquaternion convolution layer and fully connection layer. Extensive experiments on color image classification and color image denoising are conducted to evaluate the promising performance of the RQV-CNN framework. The results show that RQV-CNN outperforms the real-valued CNN (RV-CNN), complex-valued CNN (CV-CNN), and quaternion-valued CNN (QV-CNN) with same structures. The source code can be found at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/tasteimage/RQVCNN</uri> .
Zero-Shot Video Event Detection With High-Order Semantic Concept Discovery and Matching

Multimedia event detection aims to precisely retrieve videos that contain complex semantic events from a large pool. This work addresses this task under a zero-shot setting, where only brief event-specific textural information (such as event names, a few descriptive sentences, etc.) is known yet none positive video example is provided. Mainstream approaches to tackling this task are middle-level semantic concept-based, where meticulously-crafted concept banks (e.g., LSCOM) are adopted. We argue that these concept banks are still inadequate facing video semantic complexity. Existing semantic concepts are essentially first-order, mainly designed for atomic objects, scenes or human actions, etc. This work advocates the utilization of high-order concepts (such as subject-predicate-object triplets or adjective-object). The main contributions are two-fold. First, we harvest a comprehensive albeit compact high-order concept library through distilling information from three large public datasets (MS-COCO, Visual Genome, and Kinetics-600), mainly related to visual relations and human-object interactions. Secondly, zero-shot events are often only briefly and partially described via textual input. The resultant semantic ambiguity makes the pursuit of the most indicative high-order concepts challenging. We thus design a novel query-expanding scheme that enriches ambiguous event-specific keywords by searching over either large common knowledge bases ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , WikiHow) or top-ranked webpages retrieved from modern search engines. This way sets up a more faithful connection between zero-shot events and high-order concepts. To our best knowledge, this is the first work that strives for concept-based video search beyond first-order concepts. Extensive experiments have been conducted on several large video benchmarks (TRECVID 2013, TRECVID 2014, and ActivityNet-1.3). The evaluations clearly demonstrate the superiority of our constructed high-order concept library and its complementariness to existing concepts.
PRICURE: Privacy-Preserving Collaborative Inference in a Multi-Party Setting

When multiple parties that deal with private data aim for a collaborative prediction task such as medical image classification, they are often constrained by data protection regulations and lack of trust among collaborating parties. If done in a privacy-preserving manner, predictive analytics can benefit from the collective prediction capability of multiple parties holding complementary datasets on the same machine learning task. This paper presents PRICURE, a system that combines complementary strengths of secure multi-party computation (SMPC) and differential privacy (DP) to enable privacy-preserving collaborative prediction among multiple model owners. SMPC enables secret-sharing of private models and client inputs with non-colluding secure servers to compute predictions without leaking model parameters and inputs. DP masks true prediction results via noisy aggregation so as to deter a semi-honest client who may mount membership inference attacks. We evaluate PRICURE on neural networks across four datasets including benchmark medical image classification datasets. Our results suggest PRICURE guarantees privacy for tens of model owners and clients with acceptable accuracy loss. We also show that DP reduces membership inference attack exposure without hurting accuracy.
A scheduling-driven approach to efficiently assign bug fixing tasks to developers

The efficient assignment of bug fixing tasks to software developers is of major importance in software maintenance and evolution. When those tasks are not efficiently assigned to developers, the software project might confront extra costs and delays. In this paper, we propose a strategy that minimizes the time and the cost in bug fixing by finding the best feasible developer arrangement to handle bug fixing requests. We enhance therefore a state-of-the-art solution that uses an evolutionary bi-objective algorithm by involving a scheduling-driven approach that explores more parts of the search space. Scheduling is the process of evaluating all possible orders that developers can follow to fix the bugs they have been assigned. Through an empirical study we analyze the performance of the scheduling-driven approach and compare it to state of the art solutions. A non-parametric statistical test with four quality indicator metrics is used to assure its superiority. The experiments using two case-studies (JDT and Platform) showed that the scheduling-driven approach is superior to the state of the art approach in 71% and 74% of cases, respectively. Thus, our approach offers superior performance by assigning more conveniently bug fixing tasks to developers, while still avoiding to overload developers.
On the robustness of median sampling in noisy evolutionary optimization

Evolutionary algorithms (EAs) are a sort of nature-inspired metaheuristics, which have wide applications in various practical optimization problems. In these problems, objective evaluations are usually inaccurate, because noise is almost inevitable in real world, and it is a crucial issue to weaken the negative effect caused by noise. Sampling is a popular strategy, which evaluates the objective a couple of times, and employs the mean of these evaluation results as an estimate of the objective value. In this work, we introduce a novel sampling method, median sampling, into EAs, and illustrate its properties and usefulness theoretically by solving OneMax, the problem of maximizing the number of 1s in a bit string. Instead of the mean, median sampling employs the median of the evaluation results as an estimate. Through rigorous theoretical analysis on OneMax under the commonly used onebit noise, we show that median sampling reduces the expected runtime exponentially. Next, through two special noise models, we show that when the 2-quantile of the noisy fitness increases with the true fitness, median sampling can be better than mean sampling; otherwise, it may fail and mean sampling can be better. The results may guide us to employ median sampling properly in practical applications.
Face Image Feature Extraction based on Deep Learning Algorithm

Abstract In recent years, due to the rapid development of computer technology, artificial intelligence technology in the computer field has begun to integrate into people’s life, and facial recognition, as a unique biometric recognition method, is the core of artificial intelligence technology. Based on this, this paper discusses the local feature extraction and global feature extraction based on the deep learning algorithm, and proposes a training classification method based on the deep learning model combined with local pattern and GLQP representation feature extraction algorithm. In this paper, the local quantization method is used to input the data set preprocessed by the filter into the network. The depth of CNN network is selected as 4 layers, and the network is trained to produce high-resolution features. Experiments show that the accuracy of the trained deep network model is 92.2% in the test set. Therefore, compared with the traditional methods, deep learning has the advantages of powerful visualization and automatic face feature extraction, overcomes the shortcomings of deep learning model in the process of shallow feature learning, and shows higher recognition efficiency and generalization.
MonoPass: A Password Manager without Master Password Authentication

Passwords are the most common user authentication methods. Password policies regulate passwords to a certain degree of complexity, which also makes it difficult for users to create and remember passwords. Password managers improve both security and usability by allowing users to memorize only one master password. However, authenticating to the password manager with the master password has the risk of exposing all passwords when the security of the password manager is breached. We present a password manager, MonoPass, that leverages a master password to regenerate consistent passwords across a variety of devices and passes password metadata through a central server. MonoPass enables users to synchronize passwords without storing user data on the server and without using authentication with the master password.
Development of a Convolutional Neural Network Based Skull Segmentation in MRI Using Standard Tesselation Language Models

Segmentation is crucial in medical imaging analysis to help extract regions of interest (ROI) from different imaging modalities. The aim of this study is to develop and train a 3D convolutional neural network (CNN) for skull segmentation in magnetic resonance imaging (MRI). 58 gold standard volumetric labels were created from computed tomography (CT) scans in standard tessellation language (STL) models. These STL models were converted into matrices and overlapped on the 58 corresponding MR images to create the MRI gold standards labels. The CNN was trained with these 58 MR images and a mean ± standard deviation (SD) Dice similarity coefficient (DSC) of 0.7300 ± 0.04 was achieved. A further investigation was carried out where the brain region was removed from the image with the help of a 3D CNN and manual corrections by using only MR images. This new dataset, without the brain, was presented to the previous CNN which reached a new mean ± SD DSC of 0.7826 ± 0.03. This paper aims to provide a framework for segmenting the skull using CNN and STL models, as the 3D CNN was able to segment the skull with a certain precision.
Optimal Design of Fuzzy Controllers Using the Multiverse Optimizer

In this paper we study the application of metaheuristics in optimization of fuzzy logic controllers, mainly with the multi-verse optimizer and the comparison with other algorithms like PSO. For the main application of the study, we use a common control problem which is the temperature control in a shower, where its control objective is to achieve and maintain a desired temperature and flow, this by controlling the opening and closing speed of the cold and hot water valves. The fuzzy system that controls this problem uses two inputs and two outputs, where the optimization occurs over the antecedent and consequent membership functions, this by only changing the parameters of the main points in every membership function. The objective of this study is to observe the behavior of the multi-verse optimizer over control systems and its promising uses on more complex fuzzy control systems.
Single-scale siamese network based RGB-D object tracking with adaptive bounding boxes

Most of the excellent methods for visual object tracking are based on RGB videos. With the popularity of depth cameras, the research on RGB-D(RGB+depth) tracking has gradually gained extensive attention. The depth map provides more available information for dealing with intractable tracking problems. How to make full use of depth maps to construct a better tracker is the foremost problem to be settled. The fully-convolutional siamese network shows excellent performance in 2D tracking, but still cannot achieve satisfying tracking performance in complex scenarios. Therefore, we have proposed the RGB-D tracker integrated with the single-scale siamese network as well as the adaptive bounding boxes, which achieves stable tracking performance under the challenges such as occlusion, scale variation and background clutter. Our proposed adaptive strategy enables the bounding box to adjust automatically when the target appearance changes during the tracking, instead of multi-scale input in the siamese network. We design an effective algorithm to quickly obtain the target depth and construct the 3D local visual field to eliminate the interference from background and similar objects. In addition, the total occlusion handling approach combined with RGB and depth information has achieved more reliable occlusion detection and target recovery. Our presented object tracker, including the strategies of 3D local visual field, adaptive bounding boxes and occlusion handling, has been evaluated on two widely utilized RGB-D tracking benchmarks and achieves suprior performance especially for the situations of occlusion and pedestrian detection.
RLNF: Reinforcement Learning based Noise Filtering for Click-Through Rate Prediction

Click-through rate (CTR) prediction aims to recall the advertisements that users are interested in and to lead users to click, which is of critical importance for a variety of online advertising systems. In practice, CTR prediction is generally formulated as a conventional binary classification problem, where the clicked advertisements are positive samples and the others are negative samples. However, directly treating unclicked advertisements as negative samples would suffer from the severe label noise issue, since there exist many reasons why users are interested in a few advertisements but do not click. To address such serious issue, we propose a reinforcement learning based noise filtering approach, dubbed RLNF, which employs a noise filter to select effective negative samples. In RLNF, such selected, effective negative samples can be used to enhance the CTR prediction model, and meanwhile the effectiveness of the noise filter can be enhanced through reinforcement learning using the performance of CTR prediction model as reward. Actually, by alternating the enhancements of the noise filter and the CTR prediction model, the performance of both the noise filter and the CTR prediction model is improved. In our experiments, we equip 7 state-of-the-art CTR prediction models with RLNF. Extensive experiments on a public dataset and an industrial dataset present that RLNF significantly improves the performance of all these 7 CTR prediction models, which indicates both the effectiveness and the generality of RLNF.
FPGA Implementation of a Binarized Dual Stream Convolutional Neural Network for Service Robots

In this study, with the aim of installing an object recognition algorithm on the hardware device of a service robot, we propose a Binarized Dual Stream VGG-16 (BDS-VGG16) network model to realize high-speed computations and low power consumption. The BDS-VGG16 model has improved in terms of the object recognition accuracy by using not only RGB images but also depth images. It achieved a 99.3% accuracy in tests using an RGB-D Object Dataset. We have also confirmed that the proposed model can be installed in a field-programmable gate array (FPGA). We have further installed BDS-VGG16 Tiny, a small BDS-VGG16 model in XCZU9EG, a system on a chip with a CPU and a middle-scale FPGA on a single chip that can be installed in robots. We have also integrated the BDS-VGG16 Tiny with a robot operating system. As a result, the BDS-VGG16 Tiny installed in the XCZU9EG FPGA realizes approximately 1.9-times more computations than the one installed in the graphics processing unit (GPU) with a power efficiency approximately 8-times higher than that installed in the GPU.
Prediction of Target-Drug Therapy by Identifying Gene Mutations in Lung Cancer With Histopathological Stained Image and Deep Learning Techniques

Lung cancer is a kind of cancer with high morbidity and mortality which is associated with various gene mutations. Individualized targeted-drug therapy has become the optimized treatment of lung cancer, especially benefit for patients who are not qualified for lung lobectomy. It is crucial to accurately identify mutant genes within tumor region from stained pathological slice. Therefore, we mainly focus on identifying mutant gene of lung cancer by analyzing the pathological images. In this study, we have proposed a method by identifying gene mutations in lung cancer with histopathological stained image and deep learning to predict target-drug therapy, referred to as DeepIMLH. The DeepIMLH algorithm first downloaded 180 hematoxylin-eosin staining (H&amp;amp;E) images of lung cancer from the Cancer Gene Atlas (TCGA). Then deep convolution Gaussian mixture model (DCGMM) was used to perform color normalization. Convolutional neural network (CNN) and residual network (Res-Net) were used to identifying mutated gene from H&amp;amp;E stained imaging and achieved good accuracy. It demonstrated that our method can be used to choose targeted-drug therapy which might be applied to clinical practice. More studies should be conducted though.
Comparative Question Answering System based on Natural Language Processing and Machine Learning

It is very tedious for anyone to go through the whole document to get answers for their queries since there is a need of Question Answering system to make life easier. In this research, we used machine learning architectures in Question Answering field, based on the Stanford Question Answering dataset (SQuAD). In our work, build two models in which first model used unsupervised learning algorithm GloVe to get vector representation of word and trained using bidirectional-LSTM in which the training accuracy is 64.93% and testing accuracy is 60.33% scored with the model. In Second model used InferSent which is a sentence embedding method to get vector representation of data. This vector representation data is used to train model. The machine learning algorithms used are XG Boost and Multinomial Logistic Regression in which scored 70.02 percent in training and 66.03 percent in testing. The aim of this research is to build the best accuracy model using Glove and InferSent to use vector of various dimensionality to represent it numerically so that machine can interpret it.