Reviewerly/exHarmony huggingface dataset:
Automatic geometry, metrology, and visualisation techniques for 3D scanned vessels

The CVA is an international project for research of ancient pottery. Presently, CVA-Austria plays a key role in the use of non-invasive digital documentation of antique vessels. An online database for ancient pottery with born digital assets, with a special focus on 3D models of vessels and their capacities, is set up by the ODEEG project. To this end, more than four hundred ancient Greek and Cypriot vessels (10th-4th c. BCE), stored at different museum collections, have been 3D scanned. Calculating the capacity of ancient vessels is an important question for many archaeologists, as this helps understanding manufacture, function, transportation, and trade issues. In this paper, we address this problem by presenting the ODEEG App Package, which enables to compute automatically the capacity and wall thickness of vessels, to automatically reconstruct the inner surfaces of incomplete 3D scanned vessels, and to (semi-)automatically generate profile sections and scientific illustrations.
DIAGNOSIS OF BREAST CANCER USING MULTISCALE CONVOLUTIONAL NEURAL NETWORK

The diagnosis of benign and malignant breast cancer is a challenging issue today. Breast cancer is the most common cancer that women suffer from. The sooner the cancer is detected, the easier and more successful it is to treat it. The most common diagnostic method is the mammography of a simple radiographic picture of the chest. The use of image processing techniques and identifying patterns in the detection of breast cancer from mammographic images reduces human errors in detecting tumors, and speeds up diagnosis time. Artificial Neural Network (ANN) has been widely used to detect breast cancer, and has significantly reduced the percentage of errors. Therefore, in this paper, Convolution as Neural Network (CNN), which is the most effective method, is used for the detection of various types of cancers. This study presents a Multiscale Convolutional Neural Network (MCNN) approach for the classification of tumors. Based on the structure of MCNN, which presents mammography picture to several deep CNN with different size and resolutions, the classical handcrafted features extraction step is avoided. The proposed approach gives better classification rates than the classical state-of-the-art methods allowing a safer Computer-Aided Diagnosis of pleural cancer. This study reaches the diagnosis accuracy of [Formula: see text] using multiscale convolution technique which reveals the efficient proposed method.
No-reference prediction of quality metrics for H.264-compressed infrared sequences for unmanned aerial vehicle applications

We propose a no-reference (NR) method for estimating the scores of metrics assessing the quality of infrared (IR) sequences compressed with H.264 for low-complexity unmanned aerial vehicle (UAV) applications. The scenario studied is to estimate the quality on an on-ground computer to avoid performing the processing onboard, due to the computational and memory limitations of the onboard hardware. For low complexity and fast feedback, a bitstream-based (BB) approach was chosen. The original IR sequences are captured by UAV, and then BB and pixel-based (PB) features are computed. Thereafter, a feature selection process is applied and the selected features are mapped using support vector regression, to predict the quality scores of full reference metrics. The method is evaluated for the NR prediction of four image and one video quality metrics. A set of five UAV- and three ground-IR sequences are used for evaluation. The proposed NR method consistently achieves robust results for the different objective metrics tested (Spearman rank-order correlation coefficients ranging from 0.91 to 0.99). A comparison with estimations based on features from three NR models from the literature proves to be in favor of the proposed method.
Transform Facial Expression Space to Euclidean Space Using Riemann Normal Coordinates and Its Applications

It is reported recently in [1] on construction of the psychophysical space of facial expressions through measurement of JND thresholds in the space. It is shown that the facial expression space is, in fact, not a Euclidean space but a Riemann space of which the Riemann metric is defined by the JND thresholds. In this paper, we shown how to transform the facial expression space to Euclidean space in a way to preserve geometry such as distances and angles in the Riemannian space. We build the Riemann normal coordinate system in the facial expression space, which can be regarded as a generalized polar coordinate system consisted of geodesics emanating from the origin and concentric circles with the radius measured by geodesic distances. Results by applying the above method to the JND data in [1] are shown together with application to expression recognition.
Relative coordinates constraint for face alignment

We present a practical approach to improve the precision of face alignment for a single image. Recently, face alignment is deemed as a regression problem, and convolutional neural networks (CNNs) or recurrent neural networks (RNNs) are utilized to predict the coordinates of facial landmarks. However, most existing methods only adopt Euclidean loss as the optimization target for each landmark, and neglect the correlations between them, which we think may be inappropriate. To address this issue, in this paper, we introduce a novel Relative Coordinates Constraint (RCC) loss function for face alignment, which considers the relative coordinates between any pairs of landmarks as a new supervision signal. More importantly, we prove that the proposed RCC loss function is trainable and can be easily incorporated in existing CNNs optimization procedure. With the joint supervision of Euclidean loss and RCC loss, we train a robust and light CNNs framework for face alignment. Extensive experimental results on several datasets show that the precision of face alignment improved significantly by the proposed RCC loss and quantitative results are comparable to state-of-the-art methods (mean error 5.39 on 300-W and 6.99 on AFLW). In addition, the proposed framework is also an efficient solution (300 FPS on CPU). We share the implementation code of our proposed methods at https://github.com/nianfudong/RCC-loss.
A Dataflow Application Deployment Strategy for Hierarchical Networks

Massive dataflows are generated from IoT devices for sensing and controlling real-world environments. At the same time, reducing network traffic and delay utilizing hierarchical network characteristics including edge resources becomes important for maintaining long-running IoT application costs. A dataflow platform considering hierarchical networks is an ongoing research area, and it enables application developers to develop a dataflow application which usually consists of loosely coupled reusable software components for easy and flexible developments. However, the existing dataflow platform does not mention component management including software deployment and placement selection over the hierarchical network. In this paper, we proposed a component management method using a major open source product and a component deployment layer selection method considering network and resource conditions. During the definition of the component placement problem, we also extracted parameters required to estimate component deployment costs. Furthermore, we implemented a simple dataflow application based on two use cases and confirmed the validity of the proposed method.
English Character Recognition Using Robust Back Propagation Neural Network

OCR deals with the handwritten or printed character recognition with the help of digital computers and soft computing. The scanned images of characters and numbers are used as input for system which is analyzed and transformed it into character codes, normally in ASCII format, which is taken for the data processing. Presently, there are lots of issues in recognition of characters and numbers, which can degrade the performance of the system in various ways. Mainly, the rate of recognition is not improved due to distributed neighborhood pixels of an image. Also, there are some techniques used for the OCR are having lack of contrast levels which is well known by fading of the image. So in this paper, the most important concern is to take such measures to enhance the performance of the system for automatic recognition of characters. Here, the operations are performed on the handwritten English alphabets. The dataset is selected from the Chars74K with different shapes and preprocessed it which deals with filtering and edge detection. Then, in the feature extraction process, features are extracted by using independent component analysis and swarm intelligence is used for feature vector selection. Classification of images are done with the back propagation neural network which gives an effective learning approach. The precise contribution which is evaluated in this research work is the uniqueness of classifications using a combination of the feature extraction and feature optimization (instance selection) using extraction of feature vectors. The performance of the developed system is measured in terms of recognition rate, sensitivity and specificity compared with the benchmark.
A Comprehensive Persona Template to Understand Citizens’ Mobility Needs

In the city of Kaiserslautern, an urban district is being built, that aims to be climate-neutral. To achieve this aim, innovative concepts and technologies for energy generation and mobility services are to be applied. For the mobility concept, we needed personas as inhabitants and stakeholders are not living there yet. Since we could not find a persona template for the documentation of travelers' needs, we designed a persona template that provides a comprehensive picture of travel needs. We took a template based on Alan Cooper's idea and customized it to fit the topic of mobility, adding personality traits that are the basis of concrete mobility requirements. We also added characteristics of a person according to their stage of life, because personality traits hardly change over a person's lifetime. In addition, we included attitudes towards sustainability and technology. A description of the life situation together with the usual travel destinations and trip companions is also part of the persona template. The design of our template allows it to be used in other projects as well. We created personas based on our template and used them in two different workshops. The participants were able to use the personas without much explanation. A plan for further evaluation is presented in the discussion.
An Improved Steganalysis Method Using Feature Combinations

This paper proposes a new feature extraction method for steganalysis. In the improved method, the feature set is equipped with two parts. The first part is obtained by employing an existing feature extraction method on a suspicious image. With a content-correlative operation, differences of the image features are enlarge between the cover and the stego. The second part is obtained by employing the same feature extraction method on the obtained image. The two parts are combined together to form the new feature set. With the new feature set, the modifications made by steganography can be recognized more efficiently. Experimental results show that the detection accuracy of existing feature extraction methods is increased using the proposed method.
Aspects of Binary Images Spectral Analysis

This article discusses the compression of documents for storage and transmission in information systems, allowing to eliminate information redundancy, the images contained in them, depending on the purpose of the system and the characteristic indicators of the recovered images.
The use of unmixing technique in stream sediment geochemical exploration

This research has aimed at employing unmixing method as a sediment tracing technique and its use in stream sediment geochemical exploration. Sediment tracing could be applied to determine the relative contributions of source units (Cij) to the metal contents of stream sediment samples. This is the first attempt to use the “fingerprinting” technique for estimating the background values of stream sediment samples. In catchment basin analysis of stream sediment data, the relative area sizes of lithological units (Xij) were used as the proxies for their contributions to the metal contents of stream sediment samples. The Hughes mixing model was solved using the particle swarm optimization (PSO) method to assign the contributions of 7 lithological units to 69 sample catchment basins. The results revealed some similarities between the estimated Cij values and the relative area sizes of lithological units (Xij) in all catchment basins, demonstrating the representativeness of area sizes as the proxies for the relative contributions of upstream sources to the metal content of stream sediments. The background values at the basin outlet were estimated using both Xij and Cij values. The results, therefore, revealed the superiority of the mixing model in estimating the background values at the basin outlet for most of the elements. The mixing model accurately estimated the elemental concentrations of Fe, Mo, Sb, Th, Ti, Y, and Yb; however, the unmixing approach failed to accurately predict the metal contents of elements such as Cr, Ni, Pb, Li, and Zn.
Applying Indications and Warning Frameworks to Cyber Incidents

Despite significant advancements in academia and public policy on identifying, deterring, and mitigating cyber incidents, there is a general discontent among NATO agencies, member states' governments, and intelligence agencies that their strategy against cyber incidents is primarily reactive and implemented post factum, rather than proactive and executed before such attacks occur. This issue could be addressed through the design and application of appropriate indications and warning (I&W) frameworks for the cyber domain. Currently, there is a lack of comprehensive understanding and generally accepted practice of how governments and international organizations can apply such I&W methodologies and integrate them with their existing capabilities and processes. A survey of the classic warning methodologies used by the U.S. intelligence community to address a range of non-cyber threats can inform the design of such robust frameworks. These mature intelligence methods can be adapted and perfected to adequately address threats in cyberspace. In this article, we examine some of these I&W frameworks and propose a high-level practical approach to cyber I&W that governments, NATO agencies and the private sector can use to design and structure their prevention, detection, and response mechanisms in order to effectively anticipate and defend against cyber threats. To demonstrate the utility of this approach, we apply it to an actual case: the November 14, 2018 spearphishing campaign by Russia's APT29 against U.S. government agencies, think tanks, and businesses.
Survey on Strategies to Increase Customer Value in Brazilian Agile Software Development Companies

Previous empirical studies have demonstrated the relevance of the concept of customer value to the software industry and the state-of-the-art of strategies to increase customer value in Agile Software Development (ASD) has been recently documented in a Systematic Literature Review (SLR). However, to date, there are no studies on the state of practice for this area focusing on issues similar to those investigated in the SLRs mentioned above. The objective of this article was to report on the state of the practice of adopting strategies to increase customer value in the ASD and to detail these strategies concerning goals pursued, customer value metrics, challenges and usage impacts, and the reasons for non-adoption. A survey was performed using a web questionnaire answered by software professionals who are members of agile teams. Data were collected from 378 respondents from 123 Brazilian software intensive organizations. Prioritizing the development of high-value customer features and validating them frequently (92.3%), as well as focusing on value aspects of the client's market segment and applying the most relevant agile practices to make them viable (64.8%), appeared to be the strategies most frequently practiced by agile teams. Generally, the experiences of using these strategies appear to be very positive, although challenges have also been identified, such as defining what is customer value and customer collaboration.
Experience of Incorporating NIST Standards in a Digital Forensics Curricula

Recently, Bowling Green State University (BGSU) has started to offer a Digital Forensics specialization program for Computer Science undergraduate students. We (the authors of this paper) actively took part in developing and evaluating the curricula for this program. The overarching goal of the specialization program is to build a digital forensics workforce for the state and the nation. Realizing the importance of standards of digital forensics tools in real-life forensic examinations, we made an effort to incorporate lessons on standardization in the curricula. In particular, so far we incorporated National Institute of Standards and Technology (NIST) standards for three digital forensics topics (Hardware Write Blocker, Deleted File Recovery, and Mobile Forensics) in the curricula. We faced many challenges over the journey but also attained some success. In this paper we share our experience to the community. We believe this account may be helpful to others who are about to begin such a journey.
Multimodal 2D and 3D for In-The-Wild Facial Expression Recognition

In this paper, unlike other in-the-wild facial expression recognition (FER) studies which only focused on 2D information, we present a fusion approach for 2D and 3D facial data in FER. In particular, the 3D facial data are first reconstructed from image datasets. The 3D information are then extracted by deep learning technique that could exploit the meaningful facial geometry details for expression. We further demonstrate the potential of using 3D facial data by taking the 2D projected images of 3D face as an additional input for FER. These features are fused with that of 2D features from a typical network. Following the experiment procedure in recent studies, the concatenated features are classified by linear support vector machines (SVMs). Comprehensive experiments are further conducted on integrating facial features for expression prediction. The results show that the proposed method achieves state-of-the-art recognition performances on both RAF database and SFEW 2.0 database. This is the first time such a deep learning combination of 3D and 2D facial modalities is presented in the context of in-the-wild FER.
Quintuple-Media Joint Correlation Learning With Deep Compression and Regularization

Multi-media data including image, video, text, audio, and 3D model, has been fast emerging on the Internet. Jointly correlating the data of various media types is a challenging task. With the considerable learning ability of deep network, existing works mainly construct multi-pathway network to learn cross-media correlation, where each pathway is for one media type. However, with number of media types increasing, existing methods face the problems of high repetition and complexity, leading to overfitting and poor generalization ability, which makes adverse effect on correlation learning. For addressing the above issues, we propose cross-media deep compression and regularization (CDCR) approach for quintuple-media joint correlation learning: 1) cross-media partial weight-sharing networks is proposed, where a part of parameters are commonly shared among multiple pathways, to exploit common characteristics across different media types for capturing intrinsic cross-media correlation; 2) we propose media-adaptive network pruning to drop connections between weakly-correlated neurons, which can emphasize media-specific characteristics adaptively; and 3) cross-media network regularization is proposed to utilize relationships among quintuple-media data, which can guarantee generalization ability and enhance intra-media and inter-media correlation. The experiments verify the effectiveness of our approach, which outperforms the state-of-the-art methods on two very challenging datasets, including a large-scale dataset PKU XMediaNet with more than 100 000 quintuple-media instances.
Virtual Test Environment for Efficient Verification of Software Updates for Variant-Rich Automotive Systems

The complexity of modern automotive systems is mainly due to its rising electronic and software portions. In premium cars, more than one hundred Electronic Control Units (ECUs) are connected within a complex network to realize a wide variety of tasks ranging from infotainment and navigation to safety-critical functions such as driving assistance systems. In addition, the immense number of existing system variants and configurations makes the development, testing and integration of software a real challenge. A simulation environment that allows to test different system variants and configurations of automotive embedded systems is presented in this work. The considered proof of concept is a network of multiple ECUs with appropriate sensors and actuators realizing the function of an Adaptive Cruise Control (ACC). The virtual test environment, which has been implemented using Matlab/Simulink, includes a simulation of the vehicle dynamics and the external environment of the host car in form of a velocity profile of the lead vehicle. Further, it allows to configure, load and test different configurations of the system using a convenient variant management method. Besides, a virtual platform can be co-simulated allowing a virtual hardware-in-the-loop (vHiL) simulation which encloses the target C-code of one or more ECUs.
The Exploration on Interacting Teaching Mode of Augmented Reality Based on HoloLens

In recent years, the applications of augmented reality and the visualization of mixed reality for education are popular. However the lacking of multi-modal interaction for teaching and learning results in the teaching mode staying at the teacher-centered knowledge-feeding pattern, and the ability of the AR technology underutilized. This paper proposes an interactive teaching mode of augmented reality based on HoloLens, taking full advantage of the device which combines 3D scenes, audio, video and teaching content. Applying gesture, voice, holographic interaction and viewpoint tracking technology to practical teaching, this paper demonstrates a new student-centered teaching mode which includes the dynamic classroom teaching, multi-modal interactive practicing and the multi-form feedback learning. This paper carries out an empirical research in senior one students. The results indicate that the interest and initiative of students in learning are increased, the ability of understanding abstract knowledge is significantly improved, and the academic performance of students is promoted. This paper is devoted to promoting the deep integration of augmented reality and teaching, the innovation in teaching process and method has been explored positively.