Reviewerly/exHarmony huggingface dataset:
Research and Design of Intelligent Test Paper System Based On Genetic Algorithm

With the mutual integration of computer technology and education, promoting the reform of education and teaching network, online examination mode has gradually become an important means of new education measurement. The online examination system has basically realized the examinee answer, check and system group, and other functions, but there is a set of performance is not high, the examination such as the imperfection of the auxiliary function, in view of the above problem, this paper analyzes the group's related theory, using genetic algorithm as a smart group volume is determined, and genetic operation process of the group to carry on the design and improvement; At the same time, the system function and framework technology are deeply analyzed, the system function framework is established, and the intelligent test system is finally realized.
Comparison of Digit-Serial and Bit-Level Designs for Acceleration of Convolutional Neural Network Computation

Since the bit-width requirements of weights in different neural network (NN) layers might be different, we can design more efficient NN accelerators considering the dynamic adjustment of bit-width in the involved hardware components. In this paper, two different bit-width adjustable design approaches are designed, digital-serial (DS) and bit-level (BL). In the DS design, the multiplication-accumulation is performed digit-by-digit after radix-4 Booth recoding, leading to fewer execution cycles compared to the conventional bit-serial design. In the BL design, the arithmetic components of higher bit-width is constructed-from sub-units of smaller bit-width, leading to more execution parallelism in case of smaller bit-width. We compare both DS and BL designs for the quantized VGG NN models with previous alternative designs and find that the DS design has significant speed-up against the BL design and other previous NN hardware accelerators which adopt uniform bit-width for all layer of computations.
Machine learning in medicine: Medical droids, tricorders, and a computer named Hal 9000

The usage of artificial intelligence to evaluate histological images was recently explored in many different areas of pathology. Studies focusing on nephropathology demonstrated that algorithms could be trained to identify various structures of the kidney, like glomeruli and interstitium, as well as performing a classification task just as good as highly experienced pathologists. It is conceivable that further development of digitalized pathology in combination with all opportunities that artificial intelligence and machine learning have to offer, will rapidly change the work of the clinical pathologist in a substantial way.
Improving reading rate prediction with word length information: Evidence from Dutch

Previous research in English has suggested that reading rate predictions can be improved considerably by taking average word length into account. In this study, we investigated whether the same regularity holds for Dutch. The Dutch language is very similar to English, but words are on average half a letter longer: 5.1 letters per word (in non-fiction) instead of 4.6. We collected reading rates of 62 participants reading 12 texts with varying word lengths and examined which change in the English equation accounts for the Dutch findings. We observed that predictions were close to the best-fitting curve as soon as the average English word length was replaced by the average Dutch word length. The equation predicts that Dutch texts with an average word length of 5.1 letters will be read at a rate of 238 words per minute (wpm). Texts with an average word length of 4.5 letters will be read at 270 wpm, and texts with an average word length of 6.0 letters will be read at a rate of 202 wpm. The findings are in line with the assumption that the longer words in Dutch do not slow down silent reading relative to English and that the word length effect observed in each language is due to word processing effort and not to low-level visual factors.
A Novel Approach to Radiometric Identification

This paper demonstrates that highly accurate radiometric identification is possible using CAPoNeF feature engineering method. We tested basic ML classification algorithms on experimental data gathered by SDR. The statistical and correlational properties of suggested features were analyzed first with the help of Point Biserial and Pearson Correlation Coefficients and then using P-values. The most relevant features were highlighted. Random Forest provided 99% accuracy. We give LIME description of model behavior. It turns out that even if the dimension of the feature space is reduced to 1, it is still possible to classify devices with 99% accuracy.
Research Findings in the Domain of CI/CD and DevOps on Security Compliance

This chapter studies the mapping of governance and security control objectives impacted by DevOps to the corresponding DevOps control objectives. These DevOps objectives introduce either an opportunity or a risk for the achievement of the security and governance control objectives. Finally, the artifact defines a list of SecDevOps controls that have proven to be effective in combining the agility of the DevOps paradigm with the security compliance assurance. The authors examine in collaboration with experts the multiple frameworks to be suitable. The authors define SecDevOps controls that have proven to be effective in combining the agility of the DevOps paradigm with the security compliance assurance. To design this artefact, four widely-used frameworks/standards (COBIT 5, NIST cybersecurity framework, NIST SP 800-53, and ISO 27002) were reviewed for sufficiently detailed security and privacy control objectives and controls. Based on these criteria, NIST SP 800-53 and ISO 27002 standards were selected for comparison and mapping with (Sec)DevOps controls in this research.
A VQA System for Medical Image Classification Using Transfer Learning

The Visual Question Answering (VQA) is based on Computer Vision and Natural Language Processing (NLP). The goal of VQA system is to predict the textual answer to a question based on the image. The VQA system takes images and questions as input and combines the information of the input to generate readable answers as output. The medical VQA system has the following advantages. The radiologists can use the VQA system for their inference about the medical image. It can also help the patients to get basic information about the clinical image prior to doctor consultation. In this paper, we discuss about the VQA on medical images using the ImageCLEF 2019 VQA dataset. We build a medical VQA system using transfer learning on radiology images using MobileNet for input images on the plane class and predict the answer. The proposed VQA model is evaluated on the test dataset and the accuracy obtained is 80.8% on the plane class.
Avoiding Communication in Logistic Regression

Stochastic gradient descent (SGD) is one of the most widely used optimization methods for solving various machine learning problems. SGD solves an optimization problem by iteratively sampling a few data points from the input data, computing gradients for the selected data points, and updating the solution. However, in a parallel setting, SGD requires interprocess communication at every iteration. We introduce a new communication-avoiding technique for solving the logistic regression problem using SGD. This technique re-organizes the SGD computations into a form that communicates every s iterations instead of every iteration, where s is a tuning parameter. We prove theoretical flops, bandwidth, and latency upper bounds for SGD and its new communication-avoiding variant. Furthermore, we show experimental results that illustrate that the new Communication-Avoiding SGD (CA-SGD) method can achieve speedups of up to 4.97× on a high-performance Infiniband cluster without altering the convergence behavior or accuracy.
Affecting Factors in Information Security Policy Compliance: Combine Organisational Factors and User Habits

Information security policy compliance is one concern of organisations to improve information security, including universities. Previous research has shown that factors that influence user compliance can come from user behaviour and from within the organisation. This study aims to explore the factors of organisation and user habits that affect user compliance with information security policies. The research model proposed used organisational commitment, organisational culture, reward, and habit variables—this research conducted with a case study of public universities in Indonesia by using an online survey. The results indicated that the factors derived from organisational commitment established a positive influence on the user’s respectful behaviour. The insignificant organisational culture and reward affected information security policies. User habits also influenced user behaviour in maintaining information security.
Visibility Restoration in Infra-Red Images

For the last decade, single image defogging has been a subject of interest in image processing. In the visible spectrum, fog and haze decrease the visibility of distant objects. Thus, the objective of the visibility restoration is to remove as much as possible the effect of the fog within the image. Infrared sensors are more and more used in automotive and aviation industries but the effect of fog and haze is not restricted to the visible spectrum and also applies in the infrared band. After recalling the effects of fog in the common sub-bands of the infrared spectrum, we tested if the approach used for single image defogging in the visible spectrum might also work for infrared. This led us to propose a new approach of single image defogging for Long-Wavelength Infra-Red (LWIR) or Thermal Infra-Red. Several experiments are presented showing that the proposed algorithm offers interesting results not only for fog and haze but for bad weather conditions in general, during day and night.
The checkerboard effect and mineral resource reporting of underground mineral resources

The goal of a Mineral Resource statement is to estimate the in-situ tonnage and grade that might reasonably be expected to be extracted using the contemplated mining methods. Despite the transition by the mining industry to the use of computer-aided methods for preparing Mineral Resource estimates, the fundamental realities of complying with the CIM Definition Standards requirement of "Reasonable Prospects for Eventual Economic Extraction" (RPEEE) have not changed. Computer-aided block modeling can result in an irregular, patchwork of blocks above and below cutoff grade, termed the checkerboard effect. In such cases, for underground mining methods, strict application of a block cutoff grade does not consider the impact of any internal dilution blocks that may be present, while also potentially including blocks above cutoff grade that may not have sufficient spatial continuity. Considering the block dimensions relative to the selectivity of the potential underground mining method, this can result in material errors in Mineral Resource statements. The impact of the checkerboard effect varies from deposit to deposit. While several techniques are currently employed by Mineral Resource practitioners to ensure compliance with the RPEEE requirement of a Mineral Resource, practitioners are encouraged to develop additional methods and techniques that provide reasonable results.
Workload Level Assessment of Online Classes of College Students in Technological Institute if the Philippines Manila Using NASA Task Load Index (NASA TLX)

Online classes or the online learning is the new found system that was implemented and absorbed by the education system. Even though the system looks new, this thing was already practiced by other institution before. But today’s educational system was purely online related and technology dependent with the thought that everyone could be at par with the changes in the system. This new system is far different from the traditional education practice since instead of physically going to school to learn, the students can use gadgets to study and learn while at home or anywhere. But this system was forcedly implemented that is why many problem arises, saying that this new system makes it more difficult to cope up compared to traditional system. This issue affected many students, most especially their mental health. The main purpose of this study is to assess the current level of workload given to students and decrease online fatigue experienced by college students with the use of NASA Task Load index.
Technology-driven Service Innovation in University Libraries

Libraries are building a digital environment and preparing for the post-corona era. They attempt to increase operational efficiency and competitiveness by applying new technologies and strive to build a more intelligent and user-friendly environment using big data, the Internet of things (IoT), artificial intelligence (AI), virtual reality (VR), 3D printing, and automated robots. This study aims to present the direction for future university libraries by analyzing technology-based service innovation cases in line with the current era, centering on university libraries
Porting A Convolutional Neural Network For Stereo Vision In Hardware

With the leaps of progress done in the field of machine learning through the last few years, Artificial Neural Networks (ANN) are being used in more and more applications. In the field of computer vision, applications of ANNs include object recognition, motion and object tracking, and obstacle avoidance. Alternatively, ANNs are used to find the solutions of costly problems such as the construction of a depth map for stereoscopic vision. Significant research has been done using Field Programmable Gate Arrays (FPGAs) to accelerate the simulation of ANNs and achieve real-time execution. We seek to develop optimized hardware for embedded systems in order to run pretrained neural networks in real time. In this paper we analyze, reconstruct and reevaluate a pretrained convolutional neural network for stereo matching and develop a hardware architecture to be used in an FPGA so as to compute the stereo estimation of still images in real time in hardware.
Where do Blockchains Fit in the History of Financial Institutions?

In this essay I present an interpretation of economic history through the prism of financial institutions, and analyze the blockchain technology in this context. The role of institutions has gone from defining and enforcing property rights to the more active role of preventing externalities and driving the whole economy out of crises. With two exemples (the Central Clearing Platform regulation and central bank communication) I show that such an active role can introduce inefficiencies related to the cost of trust. I propose an interpretation of blockchains as "autonomous institutions" and show how they can alleviate this cost of trust. In terms of the Coase theorem, their use reduces the cost of transacting thereby making institutions less relevant. If blockchains ever reach broad use, traditional institutions will likely have to adapt. I conclude that we could be on the verge of an important change in the institutional landscape.
Remittances and Value Added across Economic sub-sectors in Sub-Saharan Africa

This research assesses the relevance of enhancing remittances on value added across economic sectors in sub-Saharan Africa for the period 1980 to 2014 using the Generalised Method of Moments. First, no significant net effects on added value to the agricultural sector are apparent. Second, enhancing remittances engenders a positive net effect on added value to the manufacturing sector. Third, there are negative net effects on added value to the service sector. Given that the unfavourable net incidence of remittances to the service sector is associated with a positive marginal or conditional effect, the analysis is extended by computing thresholds at which remittances induce net positive effects on added value to the service sector. The extended analysis shows that a remittance threshold of 48.5% of GDP is the critical mass needed for further enhancement of remittances to engender positive net effects on value added to the service sector.
The application of imperfect information game theory in social games

Abstract The research on game AI has always been one of the focuses of artificial intelligence research. More complex real-world problems can be solved by the study of game AI. Since our cognition and information acquisition of the world are imperfect, we proposed single deep counterfactual regret minimization method with advantage baselines, a deep reinforcement learning algorithm based on imperfect information game. The new approach combines the single deep counterfactual regret minimization approach with the baseline network to achieve a better performance than the benchmark in poker. At last, this paper applies the new algorithm to two Chinese poker games with imperfect information game, and obtains better performance. Meanwhile, it also provides the possibility for the extension to real life.
Region and Relations Based Multi Attention Network for Graph Classification

Graphs are non-euclidean structures that can represent many relational data efficiently. Many studies have proposed the convolution and the pooling operators on the non-euclidean domain. The graph convolution operators have shown astounding performance on various tasks such as node representation and classification. For graph classification, different pooling techniques are introduced, but none of them has considered both neighborhood of the node and the long-range dependencies of the node. In this paper, we propose a novel graph pooling layer R2POOL, which balances the structure information around the node as well as the dependencies with far away nodes. Further, we propose a new training strategy to learn coarse to fine representations. We add supervision at only intermediate levels to generate predictions using only intermediate-level features. For this, we propose the concept of an alignment score. Moreover, each layer's prediction is controlled by our proposed branch training strategy. This complete training helps in learning dominant class features at each layer for representing graphs. We call the combined model by R2MAN. Experiments show that R2MAN has the potential to improve the performance of graph classification on various datasets.
Summarized Applications of Machine Learning in Subsurface Geosciences

Geoscientists have been implementing machine learning (ML) algorithms for several classifications and regression related problems in the last few decades. ML’s implementation in geosciences came in different phases, and often these broadly followed or lagged after certain advances in computer sciences. We can trace back some of the early applications of modern ML techniques to 1980–1990. Geoscientists were mostly dealing with deterministic analytical solutions at that time, and they were encouraged to do so at their organizations. This is also the time when geostatistics started flourishing in reservoir characterization and modeling efforts. Then, the early 2000’s saw a slight uptick in ML applications, mostly neural networks and decision trees. Since 2014–2015, a lot of ML-related work was published. In addition to open-source languages, this also has to do with access to the massive volume of data from unconventional reservoirs. And then, since 2017, there has been an explosion of deep learning related work. This again corresponds to the convolutional neural network architecture published by Goodfellow in 2014. Initially, the ML work in geosciences focused on petrophysics, seismic, and now core and thin section images. Another growing trend is the application of ML in passive geophysical data analysis (seismology, gravity, and magnetic, etc.). As of now, most of the published studies on ML are confined to outlier detection, facies, fracture, and fault classification, rock property (e.g., poro-perm-fluid saturation-total organic carbon-geomechanics) prediction, predicting missing logs/variables, and well log correlation. In this chapter, we will review some of these popular research problems tackled by ML.
ONLINE EDUCATION IN LANGUAGE LEARNING/TEACHING IN UNIVERSITIES: THE COVID-19 AND DIGITAL TRANSFORMATION

The Covid -19 pandemic has no doubt changed and fast-tracked the way digital transformation in language learning and teaching is being conducted in higher institutions of learning. Research questions were asked to students and teachers on a voluntary basis to collect substantial data needed to illustrate the influence of digital transformation in foreign language learning and teaching and reveal the effect of digital practice on students. The participants were 103 students and 35 lecturers at the English department at the Komrat University in Gagavuzia. The instrument used for data collection was survey questions. According to the results obtained, 42% of the participants believed that the online platform was foolproof during exams. On the other hand, 71,7% stated that they did not believe that the online platform was more interactive or convenient. These results reveal that higher education institutions involved in teaching on the digital platform need additional skills to ensure they engage students interactively and professionally.
Research on Three Dimensional Global Path Planning of Unmanned Underwater Vehicle

Abstract The ant colony algorithm is used to solve the global path planning problem of unmanned underwater vehicle (UUV) in three-dimensional environment. On the basis of grid model environment, the traditional ant colony algorithm is optimized. In order to meet the needs of path planning, an improved algorithm is proposed to solve the problem of initial pheromone distribution. Simulation results show that the improved ant colony algorithm has the characteristics of fast convergence.
Calibration and Absolute Pose Estimation of Trinocular Linear Camera Array for Smart City Applications

A method for calibrating a Trinocular Linear Camera Array (TLCA) for traffic surveillance applications, such as towards smart cities, is presented. A TLCA-specific parametrization guarantees that the calibration finds a model where all the cameras are on a straight line. The method uses both a chequerboard close to the camera, as well as measured 3D points far from the camera: points measured in world coordinates, as well as their corresponding 2D points found manually in the images. Superior calibration accuracy can be obtained compared to standard methods using only a single data source, largely due to the use of chequerboards, while the line constraint in the parametrization allows for joint rectification. The improved triangulation accuracy, from 8-12 cm to around 6 cm when calibrating with 30-50 points in our experiment, allowing better road user analysis. The method is demonstrated by a proof-of-concept application where a point cloud is generated from multiple disparity maps, visualizing road user detections in 3D.
An Improved Multiple Hypothesis Tracker Integrated with Unknown Clutter Intensity Estimator

The multiple hypothesis tracker (MHT) is the optimal multi-target data association algorithm in ideal conditions. However, there is a degraded accuracy of data association when the observation scenario containing unknown clutter density. To addressed this problem, this paper proposed a new multiple hypothesis tracker integrated with unknown clutter intensity estimator (MHT-UCIE). In the MHT-UCIE, the clutter intensity is formulated as the product of the average number of clutter points per scan and the clutter spatial density, which are obtained subsequently through the maximum likelihood estimator and the Gaussian kernel estimator. Finally, the clutter intensity and track hypotheses are calculated. The experiment results verify the superior tracking performance of the MHT- UCIE in an unknown clutter intensity tracking scenario.