Reviewerly/exHarmony huggingface dataset:
Penguins Don't Fly: Reasoning about Generics through Instantiations and Exceptions

Generics express generalizations about the world (e.g., birds can fly) that are not universally true (e.g., newborn birds and penguins cannot fly). Commonsense knowledge bases, used extensively in NLP, encode some generic knowledge but rarely enumerate such exceptions and knowing when a generic statement holds or does not hold true is crucial for developing a comprehensive understanding of generics. We present a novel framework informed by linguistic theory to generate exemplars -- specific cases when a generic holds true or false. We generate ~19k exemplars for ~650 generics and show that our framework outperforms a strong GPT-3 baseline by 12.8 precision points. Our analysis highlights the importance of linguistic theory-based controllability for generating exemplars, the insufficiency of knowledge bases as a source of exemplars, and the challenges exemplars pose for the task of natural language inference.
Survey of Model-Based Design Reviews: Practices &amp; Challenges?

Abstract The design of large-scale engineered systems relies on the extensive use of models. Although there are few papers that study design review practices, we did not find any on model-based design reviews. Thus, we undertook a survey to collect current practices and challenges. We found that reviews are mainly synchronous co-located or remote meetings that involve various profiles who need to access and share models, but that they pragmatically give them up and prefer to comment on PowerPoint-like slides containing screenshots of models except when the meeting gathers only experts in model-based design.
Top Popular Python Libraries in Research

Python is one of the most popular programming languages in research. The structure of the language and its object-oriented approach help programmers to write logical and clear code for small and large projects. Python libraries (packages) effectively simplify many important processes such as analysing and visualizing data, retrieving unstructured data from the web, image processing, building machine learning models, and textual information [1-4]. In this article, some of the most important and popular libraries and packages in Python are described.
Trade Openness-Inflation Nexus in Sierra Leone: Testing Romer Hypothesis using ARDL Approach

This study is based on Romer’s proposition that there is an inverse relationship between inflation and trade openness as opposed to the narrative that argues otherwise. Therefore, the objective of this study is to examine Romer hypothesis for Sierra Leone. To achieve this objective, the study utilized the ARDL bounds testing approach to cointegration using data for the period 1980-2020. In addition, a battery of diagnostic tests were conducted to confirm the robustness of the model. The bounds test result confirm that there is long-run association between inflation and the dependent variables in this study. The associated equilibrium correction term was also significant, further confirming the existence of long-run relationship. The key findings of the study is that the Romer hypothesis holds for Sierra Leone in both the long-run and the short-run, as inflation tend to ease with increase trade openness. The control variables, exchange rate and gross domestic product were found to have a positive and significant impact on inflation in both the short-run and long-run, whereas domestic credit to the private sector impacts inflation positively in the long-run but have a disinflationary impact in the short-run. Finally, money supply and real interest rate were found to have no effects on inflation in both the short-run and long-run. At the policy level, these results show that encouraging more trade and imports that embody technology or intermediate inputs is essential in taming inflation in both the short-run and in the long–run. This outcome suggests that implementing trade liberalization policies by deepening the integration of the Sierra Leone economy to the global economy would support the price stability objective of the Bank of Sierra Leone.
Prediction of Hospitalization Using Machine Learning for Emergency Department Patients

The objective of this study was to evaluate the predictive capability of five machine learning models regarding the admission or discharge of emergency department patients. A Random Forest classifier outperformed other models with respect to the area under the receiver operating characteristic curve (AUC ROC).
Mapping Submarine Geomorphology of the Philippine and Mariana Trenches By an Automated Approach Using GMT Scripts

Abstract This paper presents a geospatial analysis of two oceanic trenches using a GMT (Generic Mapping Tools) cartographic method that exploits the scripting approach to visualisation of their geometric shapes. To this end, the research applies the high-resolution datasets GEBCO and ETOPO1 and ETOPO5 for modelling of the submarine relief. This allows taking into account the 2D and 3D shape deviations in the geomorphology of the two selected segments of the trenches by transecting a series of the cross-section profiles. A scripting algorithm of spatial data processing based on the GMT techniques visualised the topography of the submarine objects in 2D and 3D forms and extracted the topographic data from raster grids for statistical analysis of depth using the cross-section transect profiles of both trenches. The bathymetry of the Mariana Trench was evaluated in the southern segment located near the Challenger Deep area, southwest of Guam Island, in comparison with the segment of the Philippine Trench, which was transected in the surroundings of Mindanao Island. The study presented a comparative submarine geomorphic modelling and spatial analysis of the Philippine Sea basin area. The bathymetric analysis of the relief in the Mariana and Philippine trenches showed effective performance of the GMT scripting toolset in advanced cartographic data analysis and visualisation.
JOINT SUPER-RESOLUTION AND IMAGE RESTORATION FOR PLÉIADES NEO IMAGERY

Abstract. Modern Earth Observation optical satellite systems, such as Airbus’s Pleiades Neo (PNeo) push the boundaries of high spatial resolution by providing commercial imagery products with up to 30cm ground sampling distance (GSD). To further enhance the quality of the images, the in-space imaging system is usually complemented by on-ground image restoration processing, such as deconvolution and denoising (Latry et al., 2012). Recent advances leverage Convolutional Neural Networks (CNNs) to improve the image restoration quality (K. Zhang et al., 2021a).Single Image Super-Resolution (SISR), or Zoom, the process of obtaining a higher resolution (HR) image from a lower resolved (LR) source, has recently gained traction for both medium resolution sensors such as Sentinel 2 (Lanaras et al., 2018) and high resolution such as Pléiades and GeoEye-1 (Zhu et al., 2020). This process further enhances the resolution of the image to improve downstream applications such as mapping (L. Zhang et al., 2021) and small objects recognition (Shermeyer and Van Etten, 2019). While SISR for remote sensing has been successfully tackled using CNNs (Rohith and Kumar, 2021) the main challenge for reaching acceptable image quality performance lies in the generation of realistic LR/HR training pairs (K. Zhang et al., 2021b). In this paper, we propose:a dedicated simulation chain leveraging extremely-high-resolution (EHR) aerial imagery to generate realistic 30cm Pléiades Neo images and their corresponding fully restored HR equivalent at 15cm GSDA residual-based CNN architecture which we train to jointly restore and zoom the images All contributions are assessed on real PNEO images.We deployed the trained models in a production context, to enhance the full Pléiades Neo products – with a swath of 47k pixels – in an efficient and scalable manner.
EFFECTS OF KNOWLEDGE CAPTURE AND ACQUISITION PRACTICES ON ORGANIZATIONAL PERFORMANCE

Purpose: SMEs are an important component of the economy, especially with regard to absorbing a large percentage of the workforce. Good performance of these entities is therefore critical so that they can continue with their economic contribution. However, despite county government efforts of Kisumu and the government of Kenya to promote SME activity, not much progress seems to have been achieved. The general objective of this study was to investigate the influence of knowledge management practices on the performance of small and medium enterprises in Kisumu City County.&#x0D; Methodology: The study was based on descriptive research design. The target population will be 532 SMEs firms in Kisumu City County. The sample size was 150 SMEs covering different sectors. The study will use questionnaires, containing both open ended and closed ended questions to obtain primary data. Descriptive statistics including the means and standard deviations will be used to analyse the data and capture the characteristics of the variables under the study. The conclusions of this study were informed by the findings based on each study objective and also findings of other similar studies.&#x0D; Results: The findings of the study revealed that knowledge creation, knowledge acquisition, knowledge sharing and knowledge implementation are positively related with performance of SMEs in Kisumu County. Knowledge creation, knowledge acquisition, knowledge sharing and knowledge implementation were found to be satisfactory variables in explaining performance of SMEs in Kisumu County as supported by coefficient of determination of 51.2%. This shows that knowledge creation, knowledge acquisition, knowledge sharing and knowledge implementation explained 5.2% of the performance of SMEs in Kisumu County.&#x0D; Unique contribution to theory, practice and policy: The study recommends that SMEs in Kisumu County should adopt knowledge management strategy in running their business so that they can gain improved performance, knowledge management strategies to be adopted are: knowledge sharing, knowledge acquisition, knowledge creation and knowledge implementation.
Will Blockchain Really Impact Your Business Model? Empirical Evidence from Slovenian SMEs

In this paper, we use the widely popular business model canvas, which was previously adapted for the blockchain environment, to assess the experiences and perceptions of Slovenian small and medium-sized companies (SMEs) pertaining to blockchain technologies.The results reveal that SMEs expect applications built on blockchain technologies to have a highly positive effect on numerous aspects of their business model.This positive evaluation is even stronger among companies that already have experience with blockchain technologies.
Automatic resolution of model merging conflicts using quality-based reinforcement learning

Modeling is an activity in the software development life cycle in which different experts and stakeholders collaborate as a team. In collaborative modeling, adhering to the optimistic versioning paradigm allows users to apply concurrent changes to the same model. In such a situation, conflicts may arise. To have an integrated yet consistent merged model, conflicts have to be resolved. To this end, automation is currently at its limit or is not supported at all, and user interaction is often required. To alleviate this flaw, there is an opportunity to apply Artificial Intelligence techniques in a collaborative modeling environment to empower the provisioning of automated and intelligent decision-making. In this paper, we propose the use of reinforcement learning algorithms to achieve merging conflict resolution with a high degree of automation. This enables the personalized and quality-based integration of model versions. To evaluate our idea, we demonstrate the resolution of UML class diagram conflicts using a learning process in an illustrative modeling scenario. We also show the applicability of our approach through a proof of concept implementation and assess its accuracy compared to the greedy and search-based algorithms. Moreover, we conducted an experience with five experts to evaluate the satisfaction of actual users with the selection of resolution actions for different conflicts. The result of the assessment validates our proposal with various syntactic and semantic conflicts.
Supporting Developers in Creating Web Apps for Education via an App Development Framework

As more educational activities are conducted online, the need for interactive applications (apps) that can effectively support educators in their practice is increasing. These apps are often created by web developers or by researchers, educators, and even students with programming experience. While a large body of work has focused on incorporating these apps into educational contexts, fewer studies have focused on their development. In this paper, we first present the design and implementation of an app development framework aimed at supporting developers in creating apps for education. We then report the results of a study comprising interviews with 12 developers who used the framework. Our findings highlight that while the creation of web apps for education can be facilitated by a purely software-based app development framework, effectively exploiting such a framework requires domain knowledge that could be acquired through in-depth documentation, tutorials, and collaboration between developers and educators.
Teaching Data-Enabled Design: Student-led Data Collection in Design Education

Designers are increasingly engaging with data practices in their day-to-day work as design practice becomes increasingly connected. To facilitate this practice, design education needs to evolve and embrace data design. In this paper we introduce a Master-level elective course around the Data-enabled Design methodology. This challenge-based learning activity aims to teach how to use data as a creative material in addressing a real-life design challenge from selected industrial partners. In this article we demonstrate how our master students learn how to prototype, conduct data-enabled interviews, adapt their prototypes, and introduce design interventions via a multi-step approach, leveraging their growing knowledge and skills around contextual data. We share how we use a strong collaboration with our industrial partners and a predefined data infrastructure to help our students use data for sharing valid research findings and presenting experiential interventions.
REAL-TIME MOVING VEHICLE COUNTER SYSTEM USING OPENCV AND PYTHON

— In view of highway management, intelligent vehicle recognition, and counting is becoming increasingly crucial. In highway monitoring, it is a crucial part that every vehicle should be observed and should go through vehicle monitoring and detection system. With today’s technology have great effect on this area the road surveillance camera gives a large library of traffic video footage has been available for examination. By taking these video footage from the cameras the paper will present a system that will achieve to calculate the vehicle their direction with using image subtractor and counters. The computer vision will advance the capability of the system to process on the videos and give desired output. The main idea behind using computer vision and open CV is that they will not disturb the traffic and monitoring part will be done smoothly. The vehicle counter-offered in this study is primarily based totally on an aggregate of image processing algorithms which includes item detection, edges detection, and frames differentiation. The machine has been applied with the use of Python. This paper explains how to use multiple libraries for real-time image processing to achieve traffic flow counting and classification. The proposed system differs the existing system by providing the feature to count the cars in the day and night mode.
Payroll Information System Design Using Waterfall Method

The Sorong Islands District Office has problems in terms of employee payroll, because it is still carried out by means of recording using an employee job record book and tax deductions, etc. So it takes a very long time to record the salaries of employees according to class and position. This problem can be solved by utilizing ICT, which is to create a payroll information system according to class or position and the deductions evenly. The method used to design this payroll information system is the waterfall method, with Unified Modeling Language (UML), Macromedia Dreamweaver CS 6 for interface design and XAMPP for database design. The purpose of this research is to be able to process all employee salary data at the Sorong Kepulauan District office in detail so that it is effective and efficient in order to produce an information system that is fast, precise, and accurate.
Leveraging Artificial Intelligence in blockchain-based E-health for safer decision making framework

Abstract Machine learning-based (ML) systems are becoming the primary means of achieving the highest levels of productivity and effectiveness. Incorporating other advanced technologies, such as the Internet of Things (IoT), or e-Health systems, has made ML the first choice to help automate systems and predict future events.The execution environment of ML is always presenting contrasting types of threats, such as adversarial poisoning of training datasets or model parameters manipulation. Blockchain technology is known as a decentralized network of blocks that symbolize means of protecting block content integrity and ensuring secure execution of operations.Existing studies partially incorporated Blockchain into the learning process. This paper propose a more extensive secure way to protect the decision process of the learning model. Using smart contracts, this study executed the model’s decision by the reversal engineering of the learning model’s decision function from the extracted learning parameters. We deploy Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP) classifiers decision functions on-chain for more comprehensive integration of Blockchain. The effectiveness of this proposed approach is measured by applying a case study
Efficient Parallel Processing of k-Nearest Neighbor Queries by Using a Centroid-based and Hierarchical Clustering Algorithm

The k-Nearest Neighbor method is one of the most popular techniques for both classification and regression purposes. Because of its operation, the application of this classification may be limited to problems with a certain number of instances, particularly, when run time is a consideration. However, the classification of large amounts of data has become a fundamental task in many real-world applications. It is logical to scale the k-Nearest Neighbor method to large scale datasets. This paper proposes a new k-Nearest Neighbor classification method (KNN-CCL) which uses a parallel centroid-based and hierarchical clustering algorithm to separate the sample of training dataset into multiple parts. The introduced clustering algorithm uses four stages of successive refinements and generates high quality clusters. The k-Nearest Neighbor approach subsequently makes use of them to predict the test datasets. Finally, sets of experiments are conducted on the UCI datasets. The experimental results confirm that the proposed k-Nearest Neighbor classification method performs well with regard to classification accuracy and performance.
Impact of information security threats on small businesses during the Covid-19 pandemic

Information is a significant asset of any organization. The increased information demand by all parties has gained attention and raised security concerns – especially in this digital era where everyone depends heavily on the Internet. The Internet and online platforms expose valuable information to various information threats. These pervasive threats compromise information privacy, safety, and security. Legitimate people and criminals compete to access information. Criminals use innovative ways to gradually increase information security threats, especially in the small business sector with only a minimal budget for proactive security measures. Due to the scarcity of academic research on information security threats for small businesses, this study presents the impact of security threats on businesses during the global Covid-19 pandemic.&#x0D; A qualitative survey within the interpretive approach was used to gather data from 20 small businesses in Western Cape, South Africa, to fill this gap. The study used judgmental sampling to select research participants who are business owners. Data were analyzed using thematic analysis. The results indicated the knowledge gap relating to information threats, even though most businesses are familiar with the costly and negative impact of threats on business operations, resulting in business discontinuity. However, some small business sectors showed minimal awareness a6nd understanding of information security threats, their impact, and proactive mitigation strategies. The study concluded with recommendations to protect against information security threats.
A decentralized and trustless e-voting system based on blockchain technology

Voting is essential for any modern society. E-voting has been grabbed remarkable attention in recent years as it reduces the cost of the process and increases transparency. Blockchain technology opened the door to a new approach to evoting. Some studies were conducted to design e-voting schemes utilizing blockchain as a public bulletin board, providing the ability for verifiability and self-tallying. In this paper, we propose a novel trustless e-voting scheme based on blockchain technology that minimizes the intervention of authorities in the process, and voters can vote remotely using their own mobile devices. This scheme prevents voters from selling their votes, and anyone can cast his/her vote for free. In addition, everyone in the network can tally the final result and verify the correctness of the votes.
Platform regulation of hate speech – a transatlantic speech compromise?

This paper argues that the binary opposition in the treatment of hate speech in the US and Europe hides non-binary preoccupations that reflect different primary fears which do not fall along the same ‘scale’. European liberal democracies fear the consequences of hate speech being left uncensored in the public domain (a WHAT concern) whilst America fears the consequences of content interventions by government (a WHO concern). The paper then proposes that the German Network Enforcement Law of 2017 builds a bridge between American and European speech traditions. NetzDG requires major platforms to moderate content in response to user takedown notices based on legally imposed speech standards. The mechanism of public standards being enforced through private processes is arguably uniquely adept at simultaneously assuaging the primary European fear about the absence of effective speech controls in the public domain and the primary American fear about the presence of governmental censorship.