Reviewerly/exHarmony huggingface dataset:
Introducing Teenagers to Machine Learning through Design Fiction: An Exploratory Case Study

In this paper, we describe an exploratory study of how design fiction can be used to introduce machine learning (ML) to 14-15 year old students in an engaging way. The study describes three sessions conducted online that combined hands-on and design fiction activities related to supervised ML, focusing on facial analysis. Based on semi-structured interviews and field notes, we discuss how the design fiction approach seemed to be engaging and supportive for students' reflection on their relationship with technology. In future work, we will expand the study to include a larger number of students, assess their learning, and further explore connections between ML and its societal implications.
A New Approach to Characterizing Deposit Type Using Mineral Inclusion Assemblages in Gold Particles

Abstract Mineral inclusions within native gold are features of lode gold occurrences that are preserved in detrital particles. Inclusion assemblages in populations of gold particles in placers from specific localities are revealed through inspection of polished sections, and assimilation of robust data sets permits reconstruction of the lode source mineralogy. Inclusion assemblages differ considerably according to the source deposit type, and various approaches have been employed to graphically represent inclusion mineralogy. We present a simple method for depicting and comparing inclusion assemblages using a single standardized radar diagram template that illustrates the proportions of 11 metal and five nonmetal (and metalloid) elements in each inclusion assemblage. The Canadian Cordillera hosts many different gold-bearing deposit types and is an ideal terrane in which to develop a globally applicable methodology. Although placer gold is widespread, the location and nature of source mineralization is commonly unclear. This study is based on the inclusion suites recorded in 37 sample sets of gold particles from both placer and lode localities. Radar diagrams describing inclusion assemblages show clear generic differences according to deposit type. Diagnostic signatures have been established and act as templates against which samples of unknown origin may be compared. This approach permits differentiation between populations of gold particles formed in different magmatic systems (low-sulfidation epithermal, calc-alkalic porphyry, and alkalic porphyry), which may all be distinguished from gold formed in orogenic (amagmatic) mineralization. Metallic element signatures are most useful in differentiating gold from different magmatic hydrothermal systems, whereas nonmetallic elements allow for classification of orogenic gold subtypes. Comparisons of mineral inclusion signatures from gold in the Canadian Cordillera to samples from similar geologic settings worldwide suggest that this approach to gold fingerprinting is globally applicable. Therefore, the geochemical signatures of inclusion assemblages provide a robust indication of deposit type and may be applied in exploration to illuminate regional metallogeny in areas where relationships between placer deposits and their source(s) may be unclear.
Privacy Concerns Go Hand in Hand with Lack of Knowledge: The Case of the German Corona-Warn-App

The German Corona-Warn-App (CWA) is one of the most controversial tools to mitigate the Corona virus spread with roughly 25 million users. In this study, we investigate individuals’ knowledge about the CWA and associated privacy concerns alongside different demographic factors. For that purpose, we conducted a study with 1752 participants in Germany to investigate knowledge and privacy concerns of users and non-users of the German CWA. We investigate the relationship between knowledge and privacy concerns and analyze the demographic effects on both. Our results indicate that knowledge about the CWA significantly reduces the privacy concerns about it. Non surprisingly, users have far lower privacy concerns than non-users, but they also have more knowledge about the app. We also find a positive significant effect of education and income and a small negative effect of age on the participants’ knowledge. Furthermore, we find a significant negative effect of income and education on the privacy concerns. Our study has important implications for political decision-makers aiming at increasing adoption rates for helpful technologies to mitigate the severe effects of the pandemic. Most relevant here is to acknowledge the results regarding education, knowledge, privacy concerns and CWA use and devise effective strategies to reach certain groups in the society which are currently not using the CWA.
Self-supervised Video Representation Learning with Cross-Stream Prototypical Contrasting

Instance-level contrastive learning techniques, which rely on data augmentation and a contrastive loss function, have found great success in the domain of visual representation learning. They are not suitable for exploiting the rich dynamical structure of video however, as operations are done on many augmented instances. In this paper we propose "Video Cross-Stream Prototypical Contrasting", a novel method which predicts consistent prototype assignments from both RGB and optical flow views, operating on sets of samples. Specifically, we alternate the optimization process; while optimizing one of the streams, all views are mapped to one set of stream prototype vectors. Each of the assignments is predicted with all views except the one matching the prediction, pushing representations closer to their assigned prototypes. As a result, more efficient video embeddings with ingrained motion information are learned, without the explicit need for optical flow computation during inference. We obtain state-of-the-art results on nearest-neighbour video retrieval and action recognition, outperforming previous best by +3.2% on UCF101 using the S3D backbone (90.5% Top-1 acc), and by +7.2% on UCF101 and +15.1% on HMDB51 using the R(2+1)D backbone. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Hand Gesture Recognition Based Virtual Mouse Events

This paper proposes a virtual mouse application based on the tracking of different hand gestures. The system eliminates the dependency on any external hardware required to perform mouse actions. A built-in camera tracks the user's hands, predefined gestures are recognized and the corresponding mouse events are executed. This system has been implemented in Python using OpenCV and PyAutoGUI. Researchers have studied background conditions, effects of differences in illuminance and skin colour individually. However, the proposed system aims to take into account all the above factors to build an application most suitable in the real world.
Agricultural trade, foreign direct investment and inclusive growth in developing countries: evidence from West Africa

This study examines how agricultural trade and Foreign Direct Investment (FDI) influence inclusive growth in developing countries, using the case of West Africa. It engages data obtained from various World Bank sources for 15 West African countries that are members of the Economic Community of West African States (ECOWAS) for the period 2000–2019. The study calculates inclusive growth using the Principal Component Analysis (PCA) and applies the Two-stage Least Squares (2SLS) to resolve the possible issue of endogeneity. The findings show, among others, that agricultural trade is significant in explaining the level of inclusive growth. It implies that a 1% increase in agricultural trade may increase inclusive growth by 0.88% (first stage) and 0.99% (second stage), respectively. In contrast, FDI is insignificant in explaining inclusive growth. Therefore, the study recommends that effective policies such as flexible trade policies to enhance the exchange of goods and services should be implemented, which is crucial given the need for more resilience in post-COVID-19 ECOWAS.
A deep convolutional neural network model for hand gesture recognition in 2D near-infrared images

The hand gesture recognition (HGR) process is one of the most vital components in human-computer interaction systems. Especially, these systems facilitate hearing-impaired people to communicate with society. This study aims to design a deep learning CNN model that can classify hand gestures effectively from the analysis of near-infrared and colored natural images. This paper proposes a new deep learning model based on CNN to recognize hand gestures improving recognition rate, training, and test time. The proposed approach includes data augmentation to boost training. Furthermore, five popular deep learning models are used for transfer learning, namely VGG16, VGG19, ResNet50, DenseNet121, and InceptionV3 and compared their results. These models are applied to recognize 10 different hand gestures for near-infrared images and 24 ASL hand gestures for colored natural images. The proposed CNN model, VGG16, VGG19, Resnet50, DenseNet121, and InceptionV3 models achieve recognition rates of 99.98%, 100%, 99.99%, 91.63%, 82.42% and 81.84%, respectively on near-infrared images. For colored natural ASL images, the models achieve recognition rates of 99.91%, 99.31%, 98.67%, 91.97%, 93.37%, and 93.21%, respectively. The proposed model achieves promising results spending the least amount of time.
Effective Network Communication Based on Blockchain Based Trusted Networks

Blockchain technology has become increasingly popular as the internet of things (IoT) has grown in popularity. Over NDN(Named Data Networking), blockchain provides an innovative technique to preserve reliable records without relying on a community blockchain machine. A decentralized network and data management has been established throughout the community by utilizing blockchain is proposed in this paper. Moreover, this research work has developed a trust model to enhance the trustworthiness of messages by relying on the popularity of the sender based on parallel mining. Blockchain has been determined as growing generation for establishing decentralized and transactional information sharing throughout a big community of untrusted people. It permits the advent of decentralized surroundings, in which transaction and data below the manipulation of any one third of the party organization. Some transactions are always completed in a public account in a checkable, visible, and permanent manner, accompanied by a stamp and other data. The work gives the comparative analysis on serial mining and parallel mining of data by using blockchain. Also in the study, it is proven that, parallel mining outperforms well when compared to the serial mining of data.
Scalable Fuzzy Keyword Ranked Search Over Encrypted Data on Hybrid Clouds

Searchable encryption (SE) is a powerful technology that enables keyword-based search over encrypted data becomes possible. However, most SE schemes focus on exact keyword search which can not tolerate misspellings and typos. Existing fuzzy keyword search schemes only support fuzzy search within a limited similarity threshold <inline-formula><tex-math notation="LaTeX">$d$</tex-math></inline-formula> , the storage cost will grow exponentially or the precision of search results will greatly decrease as <inline-formula><tex-math notation="LaTeX">$d$</tex-math></inline-formula> increases. Moreover, the current fuzzy keyword ranked search schemes consider only the keyword weight, and disregard the influence of keyword morphology similarity on the ranking. In this article, we propose a scalable fuzzy keyword ranked search scheme over encrypted data under hybrid clouds architecture. We use the edit distance to measure the similarity of keywords and design an edit distance algorithm over encrypted data, in which our scheme achieves fuzzy keyword search for any similarity threshold <inline-formula><tex-math notation="LaTeX">$d$</tex-math></inline-formula> with a constant storage size and accurate search results. Furthermore, we design a two-factor ranking function combining keyword weight with keyword morphology similarity, which is utilized to rank the search results and enhance system usability. Extensive experiments are performed to demonstrate the trade-off of efficiency and security of the proposed scheme.
Support vector machines with the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si349.svg"><mml:mrow><mml:mi>ε</mml:mi></mml:mrow></mml:math>-insensitive pinball loss function for uncertain data classification

Due to the use of the quantile function, support vector machines with the pinball loss (PinSVMs) have good properties such as noise insensitivity and stability of re-sampling. In this paper we propose a novel model with the ε-insensitive pinball loss function for uncertain data classification. The original model we propose involves the high-dimensional integral problem. In order to make the optimization model become tractable, we transform the original model into the simplified one by using some techniques. We theoretically analyze some properties of the proposed optimization model including noise insensitivity and scatter minimization. Based on the probability of uncertain samples in the halfspaces, we make a rule to classify uncertain samples. In the proposed model a probabilistic degree of the uncertain sample that belongs to the positive or negative class can be given. In addition, we use the kernel trick to extend the proposed model to deal with nonlinear problems. The experiments on artificial data and real-world data sets are carried out to confirm the effectiveness of the proposed model in handling uncertain data.
Agile Methods in Higher Education: Adapting and Using eduScrum with Real World Projects

This Innovative Practice Full Paper presents our learnings of the process to perform a Master of Science class with eduScrum integrating real world problems as projects. We prepared, performed, and evaluated an agile educational concept for the new Master of Science program Digital Transformation organized and provided by the department of business computing at the University of Applied Sciences and Arts - Hochschule Hannover in Germany. The course deals with innovative methodologies of agile project management and is attended by 25 students. We performed the class due the summer term in 2019 and 2020 as a teaching pair. The eduScrum method has been used in different educational contexts, including higher education. During the approach preparation, we decided to use challenges, problems, or questions from the industry. Thus, we acquired four companies and prepared in coordination with them dedicated project descriptions. Each project description was refined in the form of a backlog (list of requirements). We divided the class into four eduScrum teams, one team for each project. The subdivision of the class was done randomly. Since we wanted to integrate realistic projects into industry partners' implementation, we decided to adapt the eduScrum approach. The eduScrum teams were challenged with different projects, e.g., analyzing a dedicated phenomenon in a real project or creating a theoretical model for a company's new project management approach. We present our experiences of the whole process to prepare, perform and evaluate an agile educational approach combined with projects from practice. We found, that the students value the agile method using real world problems. However, the results are mainly based on the summer term 2019, this paper also includes our learnings from virtual distance teaching during the Covid 19 pandemic in summer term 2020. The paper contributes to the distribution of methods for higher education teaching in the classroom and distance learning.
Multisensor registration using phase matching for large-scale fringe projection profilometry

• Multisensor registration for large-scale fringe projection profilometry. • The proposed method combines phase matching and bundle adjustment to improve registration accuracy and flexibility. • Registration implements during measuring the object without auxiliary devices or reference targets. Multisensor systems are widely used in large-scale fringe projection profilometry, where the accuracy and efficiency of registration play an important role. However, existing methods using reference targets or auxiliary devices lengthen the coordinate transformation chain and are difficult to achieve high accuracy and efficiency. In this study, we propose a method that uses the absolute phase retrieved from sinusoidal fringe patterns for the registration of different sensors, which can shorten the coordinate transformation chain. First, the corresponding pixels of cameras of different sensors are determined through phase matching and used to calculate the corresponding points on the basis of triangulation, which determines corresponding points between the sensors directly. The coordinate transformation is then calculated. Furthermore, the registration accuracy can be improved by combining the epipolar constraint and bundle adjustment. Experimental results show that the proposed method can be used for accurate and efficient registration without auxiliary devices and reference targets.
RSCA: Real-time Segmentation-based Context-Aware Scene Text Detection

Segmentation-based scene text detection methods have been widely adopted for arbitrary-shaped text detection recently, since they make accurate pixel-level predictions on curved text instances and can facilitate real-time inference without time-consuming processing on anchors. However, current segmentation-based models are unable to learn the shapes of curved texts and often require complex label assignments or repeated feature aggregations for more accurate detection. In this paper, we propose RSCA: a Real-time Segmentation-based Context-Aware model for arbitrary-shaped scene text detection, which sets a strong baseline for scene text detection with two simple yet effective strategies: Local Context-Aware Upsampling and Dynamic TextSpine Labeling, which model local spatial transformation and simplify label assignments separately. Based on these strategies, RSCA achieves state-of-the-art performance in both speed and accuracy, without complex label assignments or repeated feature aggregations. We conduct extensive experiments on multiple benchmarks to validate the effectiveness of our method. RSCA-640 reaches 83.9% F-measure at 48.3 FPS on CTW1500 dataset.
Towards Accurate Visual and Natural Language-Based Vehicle Retrieval Systems

In this work, we consider two tracks of the 2021 NVIDIA AI City Challenge, the City-Scale Multi-Camera Vehicle Re-identification and Natural language-based Vehicle Retrieval. For the vehicle re-identification task, we employ the state-of-art Excited Vehicle Re-Identification deep representation learning model coupled with best training practices and domain adaptation techniques to obtain robust embeddings. We further refine the re-identification results through a series of post-processing steps to remove camera and vehicle orientation bias that is inherent in the task of re-identification. We also take advantage of multiple observations of a vehicle using track-level information and finally obtain fine-grained retrieval results. For the task of Natural language-based vehicle retrieval we leverage the recently proposed Contrastive Language-Image Pre-training model and propose a simple yet effective text-based vehicle retrieval system. We compare our performance against the top submissions to the challenge and our systems are ranked 8 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> in the public leaderboard for both tracks.
Troubleshooting Blind Image Quality Models in the Wild

Recently, the group maximum differentiation competition (gMAD) has been used to improve blind image quality assessment (BIQA) models, with the help of full-reference metrics. When applying this type of approach to troubleshoot "best-performing" BIQA models in the wild, we are faced with a practical challenge: it is highly nontrivial to obtain stronger competing models for efficient failure-spotting. Inspired by recent findings that difficult samples of deep models may be exposed through network pruning, we construct a set of "self-competitors," as random ensembles of pruned versions of the target model to be improved. Diverse failures can then be efficiently identified via self-gMAD competition. Next, we fine-tune both the target and its pruned variants on the human-rated gMAD set. This allows all models to learn from their respective failures, preparing themselves for the next round of self-gMAD competition. Experimental results demonstrate that our method efficiently troubleshoots BIQA models in the wild with improved generalizability.
Multi-objective optimization technique for trajectory planning of multi-humanoid robots in cluttered terrain

Humanoid robots hold a decent advantage over wheeled robots because of their ability to mimic human exile. The presented paper proposes a novel strategy for trajectory planning in a cluttered terrain using the hybridized controller modeled on the basis of modified MANFIS (multiple adaptive neuro-fuzzy inference system) and MOSFO (multi-objective sunflower optimization) techniques. The controller works in a two-step mechanism. The input parameters, i.e., obstacle distances and target direction, are first fed to the MANFIS controller, which generates a steering angle in both directions of an obstacle to dodge it. The intermediate steering angles are obtained based on the training model. The final steering angle to avoid obstacles is selected based on the direction of the target and additional obstacles in the path. It is further works as input for the MOSFO technique, which provides the ultimate steering angle. Using the proposed technique, various simulations are carried out in the WEBOT simulator, which shows a deviation under 5% when the results are validated in real-time experiments, revealing the technique to be robust. To resolve the complication of providing preference to the robot during deadlock condition in multi-humanoids system, the dining philosopher controller is implemented. The efficiency of the proposed technique is examined through the comparisons with the default controller of NAO based on toques produces at various joints that present an average improvement of 6.12%, 7.05% and 15.04% in ankle, knee and hip, respectively. It is further compared against the existed navigational strategy in multiple robot systems that also displays an acceptable improvement in travel length. In comparison in reference to the existing controller, the proposed technique emerges to be a clear winner by portraying its superiority.
Object Identification and Localization Using Grad-CAM++ with Mask Regional Convolution Neural Network

One of the fundamental advancements in the deployment of object detectors in real-time applications is to improve object recognition against obstruction, obscurity, and noises in images. In addition, object detection is a challenging task since it needs the correct detection of objects from images. Semantic segmentation and localization are an important module to recognizing an object in an image. The object localization method (Grad-CAM++) is mostly used by researchers for object localization, which uses the gradient with a convolution layer to build a localization map for important regions on the image. This paper proposes a method called Combined Grad-CAM++ with the Mask Regional Convolution Neural Network (GC-MRCNN) in order to detect objects in the image and also localization. The major advantage of proposed method is that they outperform all the counterpart methods in the domain and can also be used in unsupervised environments. The proposed detector based on GC-MRCNN provides a robust and feasible ability in detecting and classifying objects exist and their shapes in real time. It is found that the proposed method is able to perform highly effectively and efficiently in a wide range of images and provides higher resolution visual representation than existing methods (Grad-CAM, Grad-CAM++), which was proven by comparing various algorithms.