Reviewerly/exHarmony huggingface dataset:
Financial openness, trade openness and financial development: Evidence from sub-Saharan Africa

The study investigates the impact of financial openness (de facto) and trade openness on financial development for 26 sub-Saharan African countries over the period 1982–2016. A system Generalized Method of Moments methodological approach is utilised with 5-year averaged data. There is no evidence of the combined/joint impact of trade and financial openness towards financial development which disproves the existence of openness hypothesis by Rajan and Zingales [(2003). The great reversals: the politics of financial development in the twentieth century. Journal of Financial Economics 69(1), 5–50]. Comparatively, the region shows that it can positively benefit from trade openness to financial openness. Even though trade openness shows weak positive impact on financial development, the results are not robust, as they vary from one model to the other. Institutions show positive contribution towards financial development, but the impact is only significant towards private credit by banks. It is recommended that the institutional quality in the region be improved so that the region could fully benefit from international business, largely through investor protection.
Causally Attentive Collaborative Filtering

Attention-based recommender models hold the promise of improving performance by learning to discriminate different user/item feature importances. However, due to the existence of the latent confounders, the correlations captured by attention mechanisms may fail to reflect the true influence of the features on the targets (i.e., spurious correlation). In this paper, we propose to empower attention mechanism by the causal inference, which is a powerful tool to identify the real causal effects. Our model is based on the potential outcome framework, where the item features are regarded as the treatment and the outcome is the predicted user preference. In specific, the causal relation of each feature on the outcome is measured by the individual treatment effect (ITE). In order to distill the causal information into the attention learning process, we minimize the distance between the traditional attention weights and the normalized ITE. With such causal regularization, the learned attention weights can capture the real causal effects, which are expected to correct the feature importances for improving performance. We conduct extensive experiments based on three real-world datasets to demonstrate the effectiveness.
Occupancy Grid Mapping with Cognitive Plausibility for Autonomous Driving Applications

This work investigates the validity of an occupancy grid mapping inspired by human cognition and the way humans visually perceive the environment.This query is motivated by the fact that, to date, no autonomous driving system reaches the performance of an ordinary human driver.The mechanisms behind human perception could provide cues on how to improve common techniques employed in autonomous navigation-specifically the use of occupancy grids to represent the environment.We experiment with a neural network that maps an image of the scene onto an occupancy grid representation, and we show how the model benefits from two key (and yet simple) changes: 1) a different format of occupancy grid that resembles the way the brain projects the environment into a warped representation in the cortical visual area; 2) a mechanism similar to human visual attention that filters out non-relevant information from the scene.These effective expedients can potentially be applied to any autonomous driving task requiring an abstract representation of the scenario like the occupancy grids.
Linearly Homomorphic Signatures with Designated Combiner

Linearly homomorphic signatures provide authenticity services for a series of scenarios such as network coding routing mechanisms and verifiable computation mechanisms. However, most of the present constructions are publicly combinable and verifiable. Motivated by the problem proposed by Rivest, we introduce the concept of designated combiner into linearly homomorphic signatures. In the new notion, the verification procedure remains public, nevertheless, the homomorphic operation is infeasible for other entities except the one designated by the signer (we call it the designated combiner). In addition, we present a specific construction with provable security in the random oracle model.
MCMC Computations for Bayesian Mixture Models Using Repulsive Point Processes

Repulsive mixture models have recently gained popularity for Bayesian cluster detection.Compared to more traditional mixture models, repulsive mixture models produce a smaller number of well separated clusters.The most commonly used methods for posterior inference either require to fix a priori the number of components or are based on reversible jump MCMC computation.We present a general framework for mixture models, when the prior of the 'cluster centres' is a finite repulsive point process depending on a hyperparameter, specified by a density which may depend on an intractable normalizing constant.By investigating the posterior characterization of this class of mixture models, we derive a MCMC algorithm which avoids the well-known difficulties associated to reversible jump MCMC computation.In particular, we use an ancillary variable method, which eliminates the problem of having intractable normalizing constants in the Hastings ratio.The ancillary variable method relies on a perfect simulation algorithm, and we demonstrate this is fast because the number of components is typically small.In several simulation studies and an application on sociological data, we illustrate the advantage of our new methodology over existing methods, and we compare the use of a determinantal or a repulsive Gibbs point process prior model.
A Machine Learning Approach for Predicting Student Enrollment in the University

The Matura exam is the final national examination that high school students in many countries must pass to be eligible for admission to a university. This paper discusses the key factors that have the most impact in producing a reliable machine learning model for predicting students' enrollment in the university. These factors include the final grades from each high school year, matura exam results and the university entry exam points. It should be noted that demographic factors were not taken into consideration in this study. Four machine learning (ML) techniques with a total of sixteen algorithms were implemented using the Weka software: Bayes (Bayes Net, Naive Bayes, etc.), Logistic Regression (Logistic and SimpleLogistic), K-Nearest Neighbors (IBK, KStar, and LWL) and Decision Tree (J48, Random Forest, RepTree, etc.). According to the results, The RepTree algorithm performed the best with a True Positive (TP) rate of 0.902 and a False Positive (FP) rate of 0.148. The algorithm with the lowest performance was NaiveMulti with a TP rate of only 0.790. However, the range between the worst and the best-performing algorithms was 14.18%.
Security analysis of confidential-compute instruction set architecture for virtualized workloads

Data in-use protection is critical to the data use lifecycle. Trusted execution environments supported by hardware are a key element of a system that provides data protection. The computing model focused on data protection via hardware-attested trusted execution engines is broadly called confidential computing. In this paper, we describe a security analysis of an Intel ISA extension called Intel <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> Trust Domain Extensions (Intel <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> TDX) to host confidential compute virtual machine workloads (called Trust Domain Virtual Machines or TD VMs) in the cloud. This paper describes the threat model and security architecture for Intel TDX. We describe the architecture principles, design constraints, and outline open problems that should be solved to increase the utility and scale of confidential computing models.
Rethinking Deconvolution for 2D Human Pose Estimation Light yet Accurate Model for Real-time Edge Computing

In this study, we present a pragmatic lightweight pose estimation model. Our model can achieve real-time predictions using low-power embedded devices. This system was found to be very accurate and achieved a 94.5% accuracy of SOTA HRNet 256x192 using a computational cost of only 3.8% on COCO test dataset. Our model adopts an encoder-decoder architecture and is carefully downsized to improve its efficiency. We especially focused on optimizing the deconvolution layers and observed that the channel reduction of the deconvolution layers contributes significantly to reducing computational resource consumption without degrading the accuracy of this system. We also incorporated recent model agnostic techniques such as DarkPose and distillation training to maximize the efficiency of our model. Furthermore, we applied model quantization to exploit multi/mixed precision features. Our FP16'ed model (COCO AP 70.0) operates at ~60-fps on NVIDIA Jetson AGX Xavier and ~200 fps on NVIDIA Quadro RTX6000.
THE EFFECT OF SAVING AND CAPITAL EXPANSION ON FINANCIAL AND TECHNOLOGICAL DEVELOPMENT IN GCC COUNTRIES

This study examined the effect of the relationship between saving and capital expansion on financial and technological development in three GCC countries using panel data from 1990 to 2019. The study used panel least squares, feasible general least squares, dynamic ordinary least squares and fully modified ordinary least squares used in the study. The findings showed that there was a significant positive long-run relationship between capital expansion and financial development and was a positive and insignificant long-run relationship between saving and financial development. Conversely, the study showed that there was a significant positive long-run relationship between saving and technological development. Meanwhile, there was a negative long-run relationship between capital expansion and technological development. Pairwise Granger causality test results showed that there was bidirectional causality between saving and financial development, a single causal direction from Adjusted net national income and financial development and a single causal direction from technological development and saving and Inflation, consumer prices. The main conclusions of the study were saving tends to support technological development, while investment tends to improve financial development. Therefore, GCC countries should formulate a long-term growth strategy in all sectors to determine their development requirements in light of the available resources.
How Compatible is Alexa with Dual Tasking? — Towards Intelligent Personal Assistants for Dual-Task Situations

Previous literature has reported that users consider hands-free and eyes-free interaction as one of the prime features of IPAs (Intelligent Personal Assistants). Hands-free and eyes-free interaction enables dual tasking. Although users prefer dual tasking with IPAs, it is unknown to what degree current IPAs are compatible with dual tasking. To determine IPA efficiency while dual tasking, we investigate cognitive load in dual-task scenarios with IPAs. In our experiment, we selected a rhythm game as the primary task and everyday IPA requests as secondary tasks. The secondary tasks belonged to four common categories: information search, multimedia control, smart home control, and turn-taking conversations. The findings show that IPAs need significant improvement to support dual tasking. Out of the four categories, only tasks in the smart home and multimedia categories were appropriate for dual tasking, whereas turn-taking conversation and information search had a high cognitive load. Task completion time was significantly different between tasks, but the penalty on the accuracy of the primary task was small. In interviews we found that, due to information abundance in IPA responses and high time pressure during task completion, users tended to make several mistakes. Based on our findings and observations we derive four design recommendations that facilitate dual-tasking while using IPAs.
Muography as a new complementary tool in monitoring volcanic hazard: implications for early warning systems

Muography uses muons naturally produced in the interactions between cosmic rays and atmosphere for imaging and characterization of density differences and time-sequential changes in solid (e.g. rocks) and liquid (e.g. melts ± dissolved gases) materials in scales from tens of metres to up to a few kilometres. In addition to being useful in discovering the secrets of the pyramids, ore prospecting and surveillance of nuclear sites, muography successfully images the internal structure of volcanoes. Several field campaigns have demonstrated that muography can image density changes relating to magma ascent and descent, magma flow rate, magma degassing, the shape of the magma body, an empty conduit diameter, hydrothermal activity and major fault lines. In addition, muography is applied for long-term volcano monitoring in a few selected volcanoes around the world. We propose using muography in volcano monitoring in conjunction with other existing techniques for predicting volcanic hazards. This approach can provide an early indication of a possible future eruption and potentially the first estimate of its scale by producing direct evidence of magma ascent through its conduit in real time. Knowing these issues as early as possible buy critically important time for those responsible for the local alarm and evacuation protocols.
Analysis of third-party request structures to detect fraudulent websites

Third-party websites or applications are the key entities in the web eco-system that enable websites to function and offer services. Almost every organization today uses dozens of websites and sub-domains. Each provides essential functions and typically uses dozens of third-parties to produce its capabilities. With the growing problem of illegitimate websites, such as those peddling fake news and selling counterfeit products, the detection of fraudulent websites becomes more and more crucial. While the conventional method of fraudulent website detection mostly relies on the content-based analysis of websites, the method of this study uses third-party request structure features and attributes of third-parties engaged in the structure to predict legitimate and fraudulent websites. This method can be used on a real-time basis to complement current detection methods. Moreover, our approach is not limited to a specific category of websites. In other words, unlike previous studies, our approach is able to increase the likelihood of detecting all kinds of fake and fraudulent websites. The results of this study are largely robust across different predictive models.
Development of a Low-cost Data Glove using Flex Sensors for the Robot Hand Teleoperation

This paper focuses on the control of a simulated robot hand using a data glove, which gathers information from resistors that measure the flexion of the user's fingers. The data glove used was a commercially available controller, using 5 flex sensors on each finger to collect data. Arduino Uno R3 microcontroller board was used to power the data glove which is based on the ATmega328P microcontroller. CoppeliaSim was used as a platform for simulation, with a compatible model for the hand found within the software's community. C++ is used within both the CoppeliaSim and Arduino environments, with a micro-USB connecting the two. This paper allows for real time control with the proposed data glove, which transmit the data in the form of a string of 5 different values. In the future, many adjustments and modifications could be made to allow for more precise control and eventually more degrees of freedom. A video demo for the proposed design can be seen from: https://youtu.be/w-GqzigfkXY.
Automated Interpretation of Air Traffic Control Communication: The Journey from Spoken Words to a Deeper Understanding of the Meaning

Sophisticated Automatic Speech Recognition (ASR) technologies have become increasingly popular and are widely used in all domains over the years. Systems like Google Assistant, Siri®, Alexa® are integrated into our day-to-day lives. These systems offer a wide range of possible applications just by understanding human speech. However, in the Air Traffic Control (ATC) domain, even the most advanced simulators can just partially replace expensive pseudo-pilots. In spite of having a standardized ATC phraseology, it is still a major challenge to recognize and correctly understand the communication between air traffic controllers (ATCo) and pilots. This is because understanding an ATCo-pilot communication requires more than just transforming speech to a sequence of words. For most ATC applications, perfectly recognizing the sequence of words would not be useful, if the meaning behind the word sequence cannot be correctly interpreted. Recently, 20 European partners from Air Traffic Management (ATM) domain have agreed on a common set of rules, i.e., an ontology on how to transform the spoken words into ATC instructions that clearly define the meaning of the words and make them usable for different applications. In this paper, we present an extension of the mentioned ontology to make it usable for pilot speech as well. We also show some of the challenges faced in understanding the meaning of ATCo-pilot communication and describe our approach of tackling them. Furthermore, we present an algorithm to transform words automatically into ontology instructions and describe the interfaces used to ensure a consistent and reliable communication of ATC instructions. This interface includes, besides other information, plausibility values, different speakers, and ambiguous outputs.
Classification of blood cells into white blood cells and red blood cells from blood smear images using machine learning techniques

Classification of blood cells from Peripheral Blood Smear (PBS) images is a crucial step to diagnose blood-related disorders such as leukemia, anemia, an infection, cancer, and polycythemia. In blood cell-based analysis, the hematologists always make a decision based on the total number of cells, their morphology, and distribution using a microscope. Hematology analyzer, flow cytometry provide reliable and exact Complete Blood Count (CBC) indicating abnormalities in the blood smear slide. The methods being used are very expensive, timeconsuming, require manual intervention and not accessible in many medical centers. Therefore there is a necessity for an automatic, inexpensive and robust technique to detect various types of diseases from any PBS images. The automatic classification model improves the hematological procedures, quickens the diagnosis process and enhances the accuracy of the evaluation process. Thus in this paper, we used a semi-automatic method to segment and classify blood cells into White Blood cell (WBC) and Red Blood Cell (RBC). Texture features of a cell are extracted using Gray Level Co-occurrence Matrix (GLCM) and fed to the classifiers like Naive Bayes classifier, K-nearest neighbors, decision tree, K-means clustering, random forest, logistic regression, ANN and SVM. The performance parameters are compared and found that the logistic regression is best suited for the work with the 97% accuracy.
Vehicle Tracking based on an Improved DeepSORT Algorithm and the YOLOv4 Framework

Vehicle tracking plays an important role in traffic surveillance systems in which efficient traffic management is the main objective. During the last several decades, with the rapid growth of the number of vehicles, the task of detecting and tracking vehicles efficiently and accurately has become challenging. Existing algorithms often fail to track vehicles continuously throughout the video stream due to the nonlinear nature of vehicular motion and vehicle occlusion in crowded scenarios. This paper proposes a vehicle tracking algorithm as an improvement on DeepSORT (Simple Online and Realtime Tracking with a Deep Association Metric), based on an optimized YOLOv4 (You Only Look Once version 4) detector, the Unscented Kalman filter, and AlexNet as the feature extraction network which ensures better performance in tracking the non-linear motion of vehicles and tracking through occlusions with a reduced number of ID switches compared to state-of-the-art vehicle trackers.
Design Research of Interactive Picture Books of Cultural Education Based on Augmented Reality Technology

The application of augmented reality technology shows a new thinking of cultural education. What is different from the traditional cultural education is that it enhances user experience not only in the aspect of story but also ideological connotation of culture. In this paper, augmented reality technology will be applied to the design of picture book Xi Shi Dance. By using of Unity development engine and Vuforia SDK, we studied and developed the interactive augmented reality works. Been ranked among the first batch of intangible cultural heritage in 2006, Xi Shi Legend is a household story in china. The classic plot and character of Xi Shi Legend are used in this third-person puzzle solving interactive picture book, so as to achieve the purpose of inheriting excellent Chinese culture and popularizing traditional cultural knowledge.
Does Synthetic Voice alter Social Response to a Photorealistic Character in Virtual Reality?

In this paper, we investigate the effect of a realism mismatch in the voice and appearance of a photorealistic virtual character in virtual reality. While many studies have investigated voice attributes for robots, not much is known about the effect voice naturalness has on the perception of realistic virtual characters. We conducted an experiment in Virtual Reality (VR) with over two hundred participants investigating the mismatch between realistic appearance and unrealistic voice on the feeling of presence, and the emotional response of the user to the character expressing a strong negative emotion (sadness, guilt). We predicted that the mismatched voice would lower social presence and cause users to have a negative emotional reaction and feelings of discomfort towards the character. We found that the concern for the virtual character was indeed altered by the unnatural voice, though interestingly it did not affect social presence.
A predictive and user-centric approach to Machine Learning in data streaming scenarios

Machine Learning has emerged in the last years as the main solution to many of nowadays' data-based decision problems. However, while new and more powerful algorithms and the increasing availability of computational resources contributed to a widespread use of Machine Learning, significant challenges still remain. Two of the most significant nowadays are the need to explain a model's predictions, and the significant costs of training and re-training models, especially with large datasets or in streaming scenarios. In this paper we address both issues by proposing an approach we deem predictive and user-centric. It is predictive in the sense that it estimates the benefit of re-training a model with new data, and it is user-centric in the sense that it implements an explainable interface that produces interpretable explanations that accompany predictions. The former allows to reduce necessary resources (e.g. time, costs) spent on re-training models when no improvements are expected, while the latter allows for human users to have additional information to support decision-making. We validate the proposed approach with a group of public datasets and present a real application scenario.