Reviewerly/exHarmony huggingface dataset:
Compressive Sensing Image Watermarking Orthogonal Matching Pursuit

Ease of access to digital data on the internet makes piracy more rampant, especially of copyright and ownership in the multimedia industry. Therefore, we need a system to secure the copyright of data ownership. Watermarking is one solution to overcome piracy that occurs. Watermarking allows the embedded confidential information to be solved by other parties. This study discusses image watermarking using discrete wavelet transform (DWT) method to decompose the host image as an image where the watermark is embedded, the spread spectrum (SS) method used in the insertion process by spreading watermark bits on the host image and compressive sensing (CS) which is used to increase the efficiency of the watermark technique to increase the capacity and perceptibility of the watermarking process. The reconstruction process was carried out using orthogonal matching pursuit (OMP). The result is that the host image has a watermark embedded in the best sub-band. The test results showed the performance of the PSNR value was 71.2 dB, MSE was close to 0 and BER was 0.0001068.
Construction of Computer Curriculum Resource Platform Based on SSM Framework

The online teaching resource platform of “University Computer” course is designed based on JSP technology, using MySQL database and Tomcat server. SSM framework is adopted in the system development framework, and B/S technology is used to construct the platform resource organization mode. MySQL is used to persist the data in the database, and the online teaching resource platform database is gridded and managed according to the actual needs, and all kinds of data are stored in a standardized and orderly manner. Through further analysis of online teaching resources platform, the functions including online registration and login, browsing of learning resources, teaching video on demand, suggestion and feedback, and collecting resources are designed and implemented, and the platform is used to improve teaching efficiency and teaching methods.
Dynamic Determinants of Access to Weapons: Global Evidence

This study investigates the determinants of and persistence in access to weapons using a global sample of 163 countries for the period 2010 to 2015. The empirical evidence is based on Generalised Method of Moments (GMM). Hysteresis in access to weapons is consistently more apparent in countries with below-median levels in access to weapons, compared to their counterparts with above-median levels in access to weapons. The hysteresis hypothesis within this context is the propensity of past values of access to weapons to influence future values of access to weapons. Factors that consistently drive access to weapons are: perceptions of crime; criminality; conflict intensity; political instability; military expenditure, violent demonstrations and terrorism. The effects of these drivers are contingent on initial levels of access to weapons. Policy recommendations for managing access to weapons are discussed.
Feature Understanding Based on Deconvolution Visualization

Convolutional neural network has been widely used in many fields, such as image recognition, target detection, image segmentation, and so on. But the process of training a neural network is very complex, which will consume a lot of time and resources. The process of input to output is similar to a “Black Box,” and its specific working process cannot be seen. Visualization of the internal work of neural network is helpful to understand the working process of neural network. The method of deconvolution visualization is used to extract the features of the middle layer of the convolutional neural network and explain the extracted features, which can help to understand what features are extracted from each layer of the convolutional neural network. In this paper, the method of deconvolution visualization is further analyzed, Sobel operator and LBP operator are used to explain the extracted features, and a more detailed understanding of the internal network is obtained.
Towards Document Panoptic Segmentation with Pinpoint Accuracy: Method and Evaluation

In this paper we study the task of document layout recognition for digital documents, requiring that the model should detect the exact physical object region without missing any text or containing any redundant text outside objects. It is the vital step to support high-quality information extraction, table understanding and knowledge base construction over the documents from various vertical domains (e.g. financial, legal, and government fields). Here, we consider digital documents, where characters and graphic elements are given with their exact texts, positions inside document pages, compared with image documents. Towards document layout recognition with pinpoint accuracy, we consider this problem as a document panoptic segmentation task, that each token in the document page must be assigned a class label and an instance id. Considering that two predicted objects may intersect under traditional visual panoptic segmentation method, like Mask R-CNN, however, document objects never intersect because most document pages follow manhattan layout. Therefore, we propose a novel framework, named document panoptic segmentation (DPS) model. It first splits the document page into column regions and groups tokens into line regions, then extracts the textual and visual features, and finally assigns class label and instance id to each line region. Additionally, we propose a novel metric based on the intersection over union (IoU) between the tokens contained in predicted and the ground-truth object, which is more suitable than metric based on the area IoU between predicted and the ground-truth bounding box. Finally, the empirical experiments based on PubLayNet, ArXiv and Financial datasets show that the proposed DPS model obtains 0.8833, 0.9205 and 0.8530 mAP scores on three datasets. The proposed model obtains great improvement on mAP score compared with Faster R-CNN and Mask R-CNN models.
CNN-Based Forensic Method on Contrast Enhancement with JPEG Post-Processing

As one of the most popular digital image manipulations, contrast enhancement (CE) is frequently applied to improve the visual quality of the forged images and conceal traces of forgery, therefore it can provide evidence of ta... | Find, read and cite all the research you need on Tech Science Press
A Study-Level Dual-Axis Active Solar Tracker

The exhaustion of non-renewable energy resources, increased carbon footprints, and global promises lead to renewable energy resources for energy needs. Photovoltaic solar energy is the leading contender with the advantages of easy and abundant availability, simplicity in implementation from small to significant energy demands. The fixed position of solar panels generates low energy in rising and setting hours of Sun. Energy generation from the photovoltaic plant can be increased by tracking the Sun and getting the maximum incident radiation. This paper shows a step by step process of making a study level, active two-degree of freedom solar tacker using low-cost components such as Arduino UNO and LDR (Light Dependent Resistance) sensors. A detailed discussion on components used, control mechanism, simulation, and hardware connections have been provided in this paper. The paper aims to show the method of development of the solar tracker so that anyone can make it by going through the write-up.
Radiometric mapping for coltan prospection in shallow alluvial deposits using in situ gamma ray spectrometry with BGO detector

PreviousNext No AccessFirst International Meeting for Applied Geoscience & Energy Expanded AbstractsRadiometric mapping for coltan prospection in shallow alluvial deposits using in situ gamma ray spectrometry with BGO detectorAuthors: Emilio DarribaHaydn BarrosEmilio DarribaNuclear Physics Laboratory, Simón Bolívar UniversitySearch for more papers by this author and Haydn BarrosNuclear Physics Laboratory, Simón Bolívar UniversityLabNuclear Technical Services LLCSearch for more papers by this authorhttps://doi.org/10.1190/segam2021-3594445.1 SectionsSupplemental MaterialAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractColtan is one of the main ores for obtaining tantalum and niobium, two elements with very particular physicochemical characteristics and of great interest for technological development since the 20th century (Hughes et al. 2011). The concentrations of uranium and thorium in Venezuelan coltan samples, previously measured by the Nuclear Physics Laboratory of the Simón Bolívar University (LFN-USB), and the description of some alluvial deposits in the Parguaza region (South of Venezuela) show the potential feasibility of locating superficial mineral deposits using gamma ray spectrometry by mapping the radiation associated with U and Th contained in these minerals. In this work we explore this possibility by using a high-density scintillator detector (BGO 3”×3” Crismatec Scintibloc 76S76). Coltan samples were used on a test field conditioned to emulate a shallow alluvial deposit, as those observed in some areas of the Parguaza region, obtaining that for detailed prospecting a 2.5×2.5 m2 spaced survey grid using a BGO detector is effective in detecting significant quantities of coltan (kilograms), even at a depth of 40 cm (depth at which artisanal extractions have been described in recent years). The need to prioritize the quantification of thorium and especially uranium was also evidenced, using their more energetic emission lines.Keywords: mining, radiometrics, passive acquisition, nuclear, South AmericaPermalink: https://doi.org/10.1190/segam2021-3594445.1FiguresReferencesRelatedDetails First International Meeting for Applied Geoscience & Energy Expanded AbstractsISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2021 Pages: 3561 publication data© 2021 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished: 01 Sep 2021 CITATION INFORMATION Emilio Darriba and Haydn Barros, (2021), "Radiometric mapping for coltan prospection in shallow alluvial deposits using in situ gamma ray spectrometry with BGO detector," SEG Technical Program Expanded Abstracts : 1300-1304. https://doi.org/10.1190/segam2021-3594445.1 Plain-Language Summary Keywordsminingradiometricspassive acquisitionnuclearSouth AmericaPDF DownloadLoading ...
Inductive Conformal Out-of-distribution Detection based on Adversarial Autoencoders

Machine learning components are used extensively to cope with various complex tasks in highly-uncertain environments. However, Out-Of-Distribution (OOD) data may lead to predictions with large errors and degrade performance considerably. This paper first introduces different types of OOD data and then presents an approach for OOD detection for classification problems efficiently. Our approach utilizes an Adversarial Autoencoder (AAE) for representing the training distribution and Inductive Conformal Anomaly Detection (ICAD) for online detecting OOD high-dimensional data. Experimental results using several datasets demonstrate that the approach can detect various types of OOD data with a small number of false alarms. Moreover, the execution time is very short, allowing for online detection.
Automated Gesture Based Wireless Wheelchair Control by Means of Accelerometer

According to a research there are about 6 million populations in the world who are paralysed and needs a wheelchair for their mobility. Earlier the wheel chairs had to be moved and be externally supported by any person. To help overcome this “joystick-controlled wheelchairs” are developed. But in regular use, these joystick-controlled wheelchairs became difficult to use. Especially in the case of paralysed people, the use of joystick became more difficult due to the hard buttons and unidirectional use of the joysticks. To overcome these problems, we’ve tried to develop a “gesture-controlled wheelchair” which can be moved with a slight tilt of the hand. This can be used in both hands and can be controlled to come to the user from a distance. The current work is implemented with Arduino based devices such as Arsuino NANO and UNO processors and programmed through Arduino ID
Preliminary Design Concept of Garbage Collecting Machine

Abstract Malaysia can only recycle about 1,150 tons of solid waste than 23,000 tons of solid waste disposed of every day. This amount is equivalent to 5% of the total solid waste disposed of. At the same time, 19% of waste is dumped into water drainages such as rivers, trench, and drains. This activity contributes to clogged drains, mosquito breeding, and the transmission of other diseases. Waste management is an important issue that is commonly discussed and highlighted in the media. The most common issue is how to efficiently and effectively collect garbage waste. The current method of collecting garbage is very time-consuming and requires many workforces. In addition, the existing machine is expensive, and its usage is limited to certain areas only. To save the energy of human resources and time, a new garbage collector design should be proposed. Usage of machines focused on the big flat area such as at a field and open area like parking lot area. This area is commonly used for significant events like a night market, expo, concert, or exhibition. Therefore, this concept design is intended to solve and surmount the stated problem by designing and developing the new design garbage collector machine. The concept design follows an essential design process that identifies needs, conceptual design, embodiment design, and detail design. This design can be developed for the expected result and proceed for proof-of-concept testing to test the functionality and capability.
Efficient Monocular Depth Estimation with Transfer Feature Enhancement

Estimating the depth of the scene from a monocular image is an essential step for image semantic understanding. Practically, some existing methods for this highly ill-posed issue are still in lack of robustness and efficiency. This paper proposes a novel end-to-end depth esti- mation model with skip connections from a pre- trained Xception model for dense feature extrac- tion, and three new modules are designed to im- prove the upsampling process. In addition, ELU activation and convolutions with smaller kernel size are added to improve the pixel-wise regres- sion process. The experimental results show that our model has fewer network parameters, a lower error rate than the most advanced networks and requires only half the training time. The evalu- ation is based on the NYU v2 dataset, and our proposed model can achieve clearer boundary de- tails with state-of-the-art effects and robustness.
A Multi-level Progressive Rectification Mechanism for Irregular Scene Text Recognition

Rectifying irregular texts into regular ones is a promising approach for improving scene text recognition systems. However, most existing methods only perform rectification at the image level once. This may be insufficient for complicated deformations. To this end, we propose a multi-level progressive rectification mechanism, which consists of global and local rectification modules at the image level and a refinement rectification module at the feature level. First, the global rectification module roughly rectifies the entire text. Then, the local rectification module focuses on local deformation to achieve a more fine-grained rectification. Finally, the refinement rectification module rectifies the feature maps to achieve supplementary rectification. In this way, the text distortion and interference from the background are gradually alleviated, thus benefiting subsequent recognition. The entire rectification stage is trained in an end-to-end weakly supervised manner, requiring only images and their corresponding text labels. Extensive experiments demonstrate that the proposed rectification mechanism is capable of rectifying irregular scene texts flexibly and accurately. The proposed method achieves state-of-the-art performance for three testing datasets including IIIT5K, IC13 and SVTP.
Product-lining the elinvar wealthtech microservice platform

Software product lining is the act of providing different but related software products under the same brand, known as a software product line (SPL). As engineering, management and validation of SPLs is far from trivial, special solutions for software product line engineering (SPLE) have a continuous momentum in both academic and industry. In general, it is hard to judge when to reasonably favor SPLE over alternative solutions that are more common in the industry. In this paper, we illustrate how we as Elinvar manage variability within our WealthTech Platform as a Service (PaaS) at different granularity levels, and discuss methods for SPLE in this context. More in detail, we share our techniques and concepts to address configuration management, and show how we manage a single microservice SPL including inter-service communication. Finally, we provide insights into platform solutions by means of packages for our clients. We end with a discussion on SPLE techniques in context of service SPLs and our packaging strategy. We conclude that while we are good to go with industry-standard approaches for microservice SPLs, the variability modeling and analysis advantages within SPLE is promising for our packaging strategy.
Comparing Regularization Techniques Applied to a Perceptron

Overfitting is a common problem that is faced when dealing with neural networks, especially as computers continue to get more powerful, and we have the capability to train larger networks with many free parameters. As a result there is a pressing need to develop and explore different techniques to reduce overfitting; we explore the impact of different regularization terms, and their combinations, in the training phase of a single-perceptron neural network.
Catch a blowfish alive

Policy-aware differential privacy (DP) frameworks such as Blowfish privacy enable more accurate query answers than standard DP. In this work, we build the first policy-aware DP system for interactive data exploration, BlowfishDB, that aims to (i) provide bounded and flexible privacy guarantees to the data curators of sensitive data and (ii) support accurate and efficient data exploration by data analysts. However, the specification and processing of customized privacy policies incur additional performance cost, especially for datasets with a large domain. To address this challenge, we propose dynamic Blowfish privacy which allows for the dynamic generation of smaller privacy policies and their data representations at query time. BlowfishDB ensures same levels of accuracy and privacy as one would get working on the static privacy policy. In this demonstration of BlowfishDB, we show how a data curator can fine-tune privacy policies for a sensitive dataset and how a data analyst can retrieve accuracy-bounded query answers efficiently without being a privacy expert.
A Hybrid Framework for Class-Imbalanced Classification

Data classification is a commonly used data processing method in the fields of networks and distributed systems, and it has attracted extensive attention in recent years. Nevertheless, the existing classification algorithms are mainly aimed at relatively balanced datasets, while the data in reality often exhibits imbalanced characteristics. In this paper, we propose a novel Hybrid Resampling-based Ensemble (HRE) model, which aims to solve the classification problem of highly skewed data. The main idea of the HRE is to leverage the resampling approach for tackling class imbalance, and then twelve classifiers are further adopted to construct an ensemble model. Besides, a novel combination of under-sampling and over-sampling is elaborately proposed to balance the heterogeneity among different data categories. We decide the resampling rate in an empirical manner, which provides a practical guideline for the use of sampling methods. We compare the effect of different resampling methods based on the imbalanced network anomaly detection dataset, where few abnormal data need to be distinguished from a large number of common network traffics. The results of extensive experiments show that the HRE model achieves better accuracy performance than the methods without hybrid resampling.
SAPN: Spatial Attention Pyramid Network for Cross-Domain Person Re-Identification

The background differences between domains poses a huge challenge for cross-domain person re-identification (Re-ID). And the attention mechanism is usually used to select regions of interest and suppress irrelevant background noises. However, many realizations of the attention mechanism do not deeply dig the relevance of the internal information of the features, and the attention information at different stages in the model is independent of each other. In response to the above problems, we propose the Spatial Attention Pyramid Network (SAPN), which can fuse the saliency information at different stages, thereby enhancing the model’s adaptability to cross-domain Re-ID. First, the Instance Normalization (IN) layer is inserted in the backbone to eliminate the style differences of images by normalizing the data distribution. Secondly, a novel Spatial Attention Block (SAB) is designed to accurately locate the salient features of pedestrains and suppress background noises. Finally, we draw on the idea of Feature Pyramid Network (FPN) to design the Attention Embedded Pyramid Module (AEPM), which combines high-level semantic information with low-level location information, improves the cross-domain generalization capability of the model. We validate our SAPN through extensive experiments on three datasets which usually used in person Re-ID, the experiments show consistent improvements in a variety of cross-domain scenarios.
Supply Chain Management by Blockchain

This paper first presents a formal model of the transactions activated in a blockchain used to manage a supply chain composed by Small-Mid Enterprises (SME). Such a formal model is based on a correspondence between the decision to be taken by a SME each time a new order is arrived from a downstream stage of the supply chain, and the formulation of a transaction in a blockchain. Based on this correspondence, a model of the blockchain management of all SMEs included in a supply chain is formulated according to the basic concepts of an event-driven production scheduling problem. The resulting blockchain model can be represented by a table showing the interactions between a provider and a consumer in terms of smart contract, thus connecting two stages of the supply chain. The transaction security is assured by secret codes of both the contract and the two actors, namely the provider and the consumer. Applications of blockchain to two SMEs operating in the agro-food sector is briefly described, thus showing the real impact of blockchain-based management in practice situations.
Deflate‐inflate: Exploiting hashing trick for bringing inference to the edge with scalable convolutional neural networks

Abstract With each passing year, the compelling need to bring deep learning computational models to the edge grows, as does the disparity in resource demand between these models and Internet of Things edge devices. This article employs an old trick from the book "deflate and inflate" to bridge this gap. The proposed system uses the hashing trick to deflate the model. A uniform hash function and a neighborhood function are used to inflate the model at runtime. The neighborhood function approximates the original parameter space better than the uniform hash function according to experimental results. Compared to existing techniques for distributing the VGG‐16 model over the Fog‐Edge platform, our deployment strategy has a 1.7 ‐ 7.5 speedup with only 1–4 devices due to decreased memory access and better resource utilization.