Reviewerly/exHarmony huggingface dataset:
SCD: A Stacked Carton Dataset for Detection and Segmentation

Carton detection is an important technique in the automatic logistics system and can be applied to many applications such as the stacking and unstacking of cartons and the unloading of cartons in the containers. However, there is no public large-scale carton dataset for the research community to train and evaluate the carton detection models up to now, which hinders the development of carton detection. In this article, we present a large-scale carton dataset named Stacked Carton Dataset (SCD) with the goal of advancing the state-of-the-art in carton detection. Images were collected from the Internet and several warehouses, and objects were labeled for precise localization using instance mask annotation. There were a total of 250,000 instance masks from 16,136 images. Naturally, a suite of benchmarks was established with several popular detectors and instance segmentation models. In addition, we designed a carton detector based on RetinaNet by embedding our proposed Offset Prediction between the Classification and Localization module (OPCL) and the Boundary Guided Supervision module (BGS). OPCL alleviates the imbalance problem between classification and localization quality, which boosts AP by 3.1∼4.7% on SCD at the model level, while BGS guides the detector to pay more attention to the boundary information of cartons and decouple repeated carton textures at the task level. To demonstrate the generalization of OPCL for other datasets, we conducted extensive experiments on MS COCO and PASCAL VOC. The improvements in AP on MS COCO and PASCAL VOC were 1.8∼2.2% and 3.4∼4.3%, respectively.
Adaptive Rank and Structured Sparsity Corrections for Hyperspectral Image Restoration

Hyperspectral images (HSIs) are inevitably contaminated by the mixed noise (such as Gaussian noise, impulse noise, deadlines, and stripes), which could influence the subsequent processing accuracy. Generally, HSI restoration can be transformed into the low-rank matrix recovery (LRMR). In the LRMR, the nuclear norm is widely used to substitute the matrix rank, but its effectiveness is still worth improving. Besides, the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$l_{0}$ </tex-math></inline-formula> -norm cannot capture the sparse noise’s structured sparsity property. To handle these issues, the adaptive rank and structured sparsity corrections (ARSSC) are presented for HSI restoration. The ARSSC introduces two convex regularizers, that is: 1) the rank correction (RC) and 2) the structured sparsity correction (SSC), to, respectively, approximate the matrix rank and the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$l_{2,0}$ </tex-math></inline-formula> -norm. The RC and the SSC can adaptively offset the penalization of large entries from the nuclear norm and the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$l_{2,1}$ </tex-math></inline-formula> -norm, respectively, where the larger the entry, the greater its offset. Therefore, the proposed ARSSC achieves a tighter approximation of the noise-free HSI low-rank structure and promotes the structured sparsity of sparse noise. An efficient alternative direction method of multipliers (ADMM) algorithm is applied to solve the resulting convex optimization problem. The superiority of the ARSSC in terms of the mixed noise removal and spatial-spectral structure information preserving, is demonstrated by several experimental results both on simulated and real datasets, compared with other state-of-the-art HSI restoration approaches.
Hierarchical median narrow band for level set segmentation of cervical cell nuclei

This paper presents a novel hierarchical nuclei segmentation algorithm for isolated and overlapping cervical cells based on a narrow band level set implementation. Our method applies a new multiscale analysis algorithm to estimate the number of clusters in each image region containing cells, which turns into the input to a narrow band level set algorithm. We assess the nuclei segmentation results on three public cervical cell image databases. Overall, our segmentation method outperformed six state-of-the-art methods concerning the number of correctly segmented nuclei and the Dice coefficient reached values equal to or higher than 0.90. We also carried out classification experiments using features extracted from our segmentation results and the proposed pipeline achieved the highest average accuracy values equal to 0.89 and 0.77 for two-class and three-class problems, respectively. These results demonstrated the suitability of the proposed segmentation algorithm to integrate decision support systems for cervical cell screening.
The Impact and the Potential Disruption of the Blockchain Technology on Marketing

The blockchain (a distributed ledger) is commonly known as the backbone of the emerging digital cryptocurrencies, such as Bitcoin, and potentially disruptive technology for the monetary system and the financial industry. However, the impact of the blockchain technology goes beyond financial sector and could also influence supply chain management, commerce, health care, real estate, data storage, supercomputing power, decentralized notarization, marketing etc. In this paper we examine the potential of blockchain technology to influence and even disrupt marketing. The areas of influence of the blockchain on marketing include but are not limited to creation of new products and services, ensuring trust and transparency, disintermediation, privacy and data ownership, digital identity, customer relationship management, loyalty programs, advertising, fighting click fraud, supply chain management, etc. Since the application of the blockchain in marketing is novel, mostly conceptual, unmatured and still in development the influence and the potential disruption of this technology is envisioned for the years to come. Anyway, there are already promising projects that have potential to change the landscape in this industry and to provide first-movers advantage for the existing or the emerging brands.&nbsp;&nbsp
Fake Review Detection in Big Data Using Parallel BBO

Online reviews are increasingly used by the customers while purchasing a product or service. In the last few years, significant growth in the number of fake reviews has been observed due to the increasing competition among the e-commerce sites. Thus, fake review detection is an open and challenging problem. However, the majority of research in this field has focused on sequential algorithms which provide inferior results when scaled on the big datasets. To mitigate this problem, this paper presents a novel parallel bio-geography optimization based method to unfold the fake review detection in the big data environment. The experimental analysis is performed on two standard fake review datasets and compared with K-means and 4 state-of-the-art methods in terms of accuracy. The results affirmed that the proposed method has surpassed all the other considered methods on both the dataset. Further, the parallel performance potency of the proposed method is validated by analyzing the speedup performance.
Analysis of College English Teaching in Application Oriented Colleges and Universities Based on the Effect Oriented Concept

In order to cultivate more and more applied talents, promote the employment rate of college students, and improve the National English level, this paper, through theoretical analysis, reading and understanding the relevant literature, combined with practical work experience, through the design of teaching objectives, improving the curriculum evaluation system, and Innovating Curriculum teaching methods, achieves the effect-based teaching purpose.
Improved Color Image Encryption using Hybrid Modulus Substitution Cipher and Chaotic Method

This study proposes the hybrid substitution cryptography method followed by chaotic methods to improve the security of digital image transactions on the internet.The substitution method used is a hybrid of the modification of the Vigenère and Beaufort cipher algorithm.To do a hybrid, two random keys are used.The first random key is a matrix with 8-bits integer values, while the second random key is a matrix that contains binary values.The modulus operation is used in the main process of substitution methods.In this study, 24-bits true-color images were used as a dataset for the testing experiment.Measurement of encryption quality is measured by Entropy, MSE, PSNR, SSIM, UACI, and NPCR, whereas tic toc functions for measuring the computational time needed.To ensure the decryption process runs perfectly, measuring instruments such as SSIM, MSE, and PSNR are used to compare the decrypted image with the original image, the tic toc function is also used to measure computing performance.Based on the results of testing the quality of image encryption proved to have excellent results where the entropy value is very close to 8, the ideal NPCR and UACI values, as well as excellent visual values based on MSE, PSNR, and SSIM measurements.The image can also be decrypted perfectly with relatively fast computing time in the encryption and decryption process.
TAMEC: Trusted Augmented Mobile Execution on Cloud

Cloud computing has emerged as an attractive platform for individuals and businesses to augment their basic processing capabilities. Mobile devices with access to Internet are also turning towards clouds for resource-intensive tasks by working out a trade-off between resources required for performing computation on-device against those required for off-loading task to the cloud. However, as with desktop clients, mobile clients face significant concerns related to confidentiality and integrity of data and applications moved to and from the cloud. Cloud-related security solutions proposed for desktop clients could not be readily ported to mobile clients owing to the obvious limitation in their processing capabilities and restrained battery life. We address this problem by proposing architecture for secure exchange and trusted execution between mobile devices and cloud hosts. We establish a symmetric-key-based secure communication channel between mobile and cloud, backed by a trusted coordinator. We also employee a Trusted Platform Module- (TPM-) based attestation of the cloud nodes on which the data and applications of mobile device will be hosted. This gives a comprehensive solution for end-to-end secure and trusted interaction of the mobile device with cloud hosts.
Application of Virtual Reality Technology to Display of “Maritime Silk Route” Culture

Scenic vistas of Quanzhou are displayed in a 720° perspective as an immersive live 3D display to provide a real digitalized virtual reality experience.In this paper, we report our research on simulating and exploring the extensive scenery of the Quanzhou Maritime Silk Route to support cultural tourism.Users can access the "Maritime Silk Route" applet on their mobile phone by approaching a sensor or scanning a QR code.
Application of an airborne hyper-spectral survey system CASI/SASI in the gold-silver-lead-zinc ore district of Huaniushan, Gansu, China

The airborne hyper-spectral survey system CASI/SASI, which has an integrated system for gathering both image an spectral data, is at the cutting edge developments in the remote-sensing field. It can be used to directly identify surface objects based on diagnostic spectral characteristics. In this paper, the CASI/SASI were used in the Huaniushan gold-silver-lead-zinc ore district–Gansu to produce a lithologic map, identify altered minerals, and map the mineralized-alteration zones. Radiometric correction, radiometric calibration, atmospheric correction (spectral reconstruction), and geometric corrections were carried out in ENVI to pre-process the measured data. A FieldSpec ® Pro FR portable spectrometer was used to obtain the spectral signatures of all types of rock samples, ore deposits, and mineralized-alteration zones. We extracted and analyzed the spectral characteristics of typical alteration minerals. On the basis of hyper-spectral data, ground-spectral data processing, and comparative analysis of the measured image spectrum, we used the spectral-angle-mapping (SAM) and mixture-tuned matchedfiltering (MTMF) methods to perform hyperspectral-alteration mineral mapping of wall rock and mineralized-alteration-zone hyperspectral identification. Hyperspectral- remote- sensing geological- classification maps were produced as well as distribution maps of all kinds of alteration minerals and mineralized-alteration zones. Based on geological comprehensive analysis and field investigations, the range of mineral alteration was proven to be the same as shown by the remote-sensing imagery. Indications are that airborne hyperspectral- remote-sensing -image CASI/SASI offer good application results and show a promising potential as a tool in geological investigations. The results will provide the basis for hyperspectral remote-sensing prospecting in the same or similar unexplored areas.
Towards SDN and Blockchain based IoT Countermeasures: A Survey

Security vulnerabilities have become significant concerns due to growing demand of Internet of Things (IoT) not only for home automation systems, but also for various industrial applications. Central security technologies are vulnerable to single-point failure that could reduce attack handling technology efficiency. Distributed security frameworks are used in resource-constrained heterogeneous IoT networks. Conventional security strategies are insufficient to protect the distributed and dynamic existence of IoT networks. Conversely, Software Define Network (SDN) and Blockchain are two emerging techniques expected to solve heterogeneous IoT network security. This analysis work therefore mainly seeks to examine IoT network features, security specifications, and challenges. Thereafter, well-known threats or attacks are analyzed in IoT. SDN and blockchain-based countermeasures are addressed for IoT network security with a case study. Finally, to enhance security and privacy , numerous open issues are discussed.
Sentiment Analysis of Tweets Using Supervised Machine Learning Techniques Based on Term Frequency

World of technology provides everyone with a great outlet to give their opinion, using social media like Twitter and other platforms. This paper employs machine learning methods for text analysis to obtain sentiments of reviews by the people on twitter. Sentiment analysis of the text uses Natural language processing, a machine learning technique to tell the orientation of opinion of a piece of text. This system extracts attributes from the piece of writing such as a) The polarity of text, whether the speaker is criticizing or appreciating, b) The topic of discussion, subject of the text. A comparison of the work done so far on sentiment analysis on tweets has been shown. A detailed discussion on feature extraction and feature representation is provided. Comparison of six classifiers: Naive Bayes, Decision Tree, Logistic Regression, Support Vector Machine, XGBoost and Random Forest, based on their accuracy depending upon type of feature, is shown. Moreover, this paper also provides sentiment analysis of political views and public opinion on lockdown in India. Tweets with ‘#lockdown’ are analysed for their sentiment categorically and a schematic analysis is shown.
Micro-MetaStream: Algorithm selection for time-changing data

Data stream mining needs to deal with scenarios where data distribution can change over time. As a result, different learning algorithms can be more suitable in different time periods. This paper proposes micro-MetaStream, a meta-learning based method to recommend the most suitable learning algorithm for each new example arriving in a data stream. It is an evolution of MetaStream, which recommends learning algorithms for batches of examples. By using a unitary granularity, micro-MetaStream is able to respond more efficiently to changes in data distribution than its predecessor. The meta-data combines meta-features, characteristics describing recent data, with base-level features, the original variables of the new example. In experiments on real-world regression data streams, micro-metaStream outperformed MetaStream and a baseline method at the meta-level and frequently improved the predictive performance at the base-level.
Visualize as You Randomize: Design-Based Statistical Graphs for Randomized Experiments

A good statistical graph for a randomized experiment simultaneously conveys the study's design, analysis, and results. It reveals the experimental design by mapping design elements to aesthetic parameters. It illuminates the analysis by plotting the statistical model in “dataspace.” When the design and analysis of an experiment are encoded in a plot, the interpretation of the experimental results is clarified. “Analyze as you randomize” is a dictum attributed to Fisher that guides interpretations of experimental data. This chapter extends that principle to visualizations of randomized experiments. While not every experiment requires a visualization, those that do should be visualized in ways that communicate the design and results together.
Automated brain structures segmentation from PET/CT images based on landmark-constrained dual-modality atlas registration

Automated brain structures segmentation in positron emission tomography (PET) images has been widely investigated to help brain disease diagnosis and follow-up. To relieve the burden of a manual definition of volume of interest (VOI), automated atlas-based VOI definition algorithms were developed, but these algorithms mostly adopted a global optimization strategy which may not be particularly accurate for local small structures (especially the deep brain structures). This paper presents a PET/CT-based brain VOI segmentation algorithm combining anatomical atlas, local landmarks, and dual-modality information. The method incorporates local deep brain landmarks detected by the Deep Q-Network (DQN) to constrain the atlas registration process. Dual-modality PET/CT image information is also combined to improve the registration accuracy of the extracerebral contour. We compare our algorithm with the representative brain atlas registration methods based on 86 clinical PET/CT images. The proposed algorithm obtained accurate delineation of brain VOIs with an average Dice similarity score of 0.79, an average surface distance of 0.97 mm (sub-pixel level), and a volume recovery coefficient close to 1. The main advantage of our method is that it optimizes both global-scale brain matching and local-scale small structure alignment around the key landmarks, it is fully automated and produces high-quality parcellation of the brain structures from brain PET/CT images.
Isogenies on twisted Hessian curves

Abstract Elliptic curves are typically defined by Weierstrass equations. Given a kernel, the well-known Vélu's formula shows how to explicitly write down an isogeny between Weierstrass curves. However, it is not clear how to do the same on other forms of elliptic curves without isomorphisms mapping to and from the Weierstrass form. Previous papers have shown some isogeny formulas for (twisted) Edwards, Huff, and Montgomery forms of elliptic curves. Continuing this line of work, this paper derives explicit formulas for isogenies between elliptic curves in (twisted) Hessian form. In addition, we examine the numbers of operations in the base field to compute the formulas. In comparison with other isogeny formulas, we note that our formulas for twisted Hessian curves have the lowest costs for processing the kernel and our X -affine formula has the lowest cost for processing an input point in affine coordinates.
I-GANs for Infrared Image Generation

The making of infrared templates is of great significance for improving the accuracy and precision of infrared imaging guidance. However, collecting infrared images from fields is difficult, of high cost, and time-consuming. In order to address this problem, an infrared image generation method, infrared generative adversarial networks (I-GANs), based on conditional generative adversarial networks (CGAN) architecture is proposed. In I-GANs, visible images instead of random noise are used as the inputs, and the D-LinkNet network is also utilized to build the generative model, enabling improved learning of rich image textures and identification of dependencies between images. Moreover, the PatchGAN architecture is employed to build a discriminant model to process the high-frequency components of the images effectively and reduce the amount of calculation required. In addition, batch normalization is used to optimize the training process, and thereby, the instability and mode collapse of the generated adversarial network training can be alleviated. Finally, experimental verification is conducted on the produced infrared/visible light dataset (IVFG). The experimental results reveal that high-quality and reliable infrared data are generated by the proposed I-GANs.
An Improved Convolutional Neural Network Based on a Parameter Modification of the Convolution Layer

Convolutional Neural Network models represent a powerful tool that can be applied in image classification tasks. Convolutional Neural Networks have a wide variety of parameters and these can produce different results for the same tasks depending on the particular parameter settings. This paper presents an improved Convolutional Neural Networks model based on changing the number of layers and convolution filters weights with the aim of analyzing the impact in the image classification and recognition systems. The Handwritten digits (MNIST), the American Sign Language (ASL MNIST) and, the Mexican Sign Language (MSL) databases were selected to perform this study. The proposed approach achieved greater precision than the accuracy obtained without varying the parameters; therefore, the modification of the parameters in the convolution layer represents a significant impact on the results.
Zoom on the Keystrokes: Exploiting Video Calls for Keystroke Inference Attacks

Due to recent world events, video calls have become the new norm for both personal and professional remote communication.However, if a participant in a video call is not careful, he/she can reveal his/her private information to others in the call.In this paper, we design and evaluate an attack framework to infer one type of such private information from the video stream of a call -keystrokes, i.e., text typed during the call.We evaluate our video-based keystroke inference framework using different experimental settings and parameters, including different webcams, video resolutions, keyboards, clothing, and backgrounds.Our relatively high keystroke inference accuracies under commonly occurring and realistic settings highlight the need for awareness and countermeasures against such attacks.Consequently, we also propose and evaluate effective mitigation techniques that can automatically protect users when they type during a video call.
Multi-Scale Ensemble Learning for Thermal Image Enhancement

In this study, we propose a multi-scale ensemble learning method for thermal image enhancement in different image scale conditions based on convolutional neural networks. Incorporating the multiple scales of thermal images has been a tricky task so that methods have been individually trained and evaluated for each scale. However, this leads to the limitation that a network properly operates on a specific scale. To address this issue, a novel parallel architecture leveraging the confidence maps of multiple scales have been introduced to train a network that operates well in varying scale conditions. The experimental results show that our proposed method outperforms the conventional thermal image enhancement methods. The evaluation is presented both quantitatively and qualitatively.