Reviewerly/exHarmony huggingface dataset:
Sustainability of Higher Education: Study of Student Opinions about the Possibility of Replacing Teachers with AI Technologies

The rapid development of artificial intelligence (AI) has affected higher education. Students now receive new tools that optimize the performance of current tasks. Universities have also begun implementing AI technologies to help university teachers and improve the quality of educational services and solve the Sustainable Development Goal 4. Hypothetically, it is possible to replace university teachers by using AI technologies. This is a hidden conflict of Sustainable Development Goal 4 and Sustainable Development Goal 8. This research aimed to examine the perceptions of Eastern European students about the possibility of replacing university teachers through AI technologies. The authors used an information study with a bibliometric analysis of 2000 sources, planning the experiments and compiling the questionnaire, surveying 599 students using an electronic questionnaire and cloud technologies, statistical processing questionnaires using Excel tables, and verifying statistical hypotheses. Verification of statistical hypotheses for replies of 599 respondents showed that more than 10% of the surveyed students from Eastern European universities are confident that AI will replace university teachers in five years. It was shown that the opinions of students in the 1st stage (undergraduate study) from the countries of the European Union and countries outside the European Union have significant differences. The obtained results were proven using one-sided testing and standard hypothesis testing level, α = 0.05. The article was completed with multilevel managerial and pedagogical recommendations. These recommendations are designed to increase higher education’s sustainability in AI implementation.
Are cryptocurrencies currencies? Bitcoin as legal tender in El Salvador

A currency's essential feature is to be a medium of exchange. This study explores the potential of cryptocurrencies to be used in daily transactions in El Salvador, the first country to make bitcoin legal tender. The government's "big push" introduced "Chivo Wallet," a digital wallet sharing features with Central Bank Digital Currencies (CBDCs), with perks to use it for trading bitcoins and US dollars. Through a nationally representative, face-to-face survey of 1800 households and blockchain data encompassing all Chivo Wallet transactions, we document a pattern of low and decreasing usage of digital payments and bitcoin. Privacy and security concerns are key adoption barriers, which speaks to a policy debate on crypto and CBDCs with anonymity at its core. Additionally, we estimate Chivo Wallet's adoption cost and complementarities among adopters.
Does digital transformation curb the formation of zombie firms? A machine learning approach

Based on the dynamic capability view theory, we examined the impact of digital transformation (DT) on zombie firm formation using a sample of listed Chinese firms. Through textual analysis of firms' annual reports, we categorized DT into three dimensions: strategy, technology (i.e. artificial intelligence, blockchain, cloud computing, and big data), and application. Then, we employed 11 machine learning algorithms to detect zombie firms, compared the prediction performance, and calculated the contribution of each DT indicator. The results show that DT can effectively curb the formation of zombie firms. Specifically, big data contributes the most to suppressing the prevalence of zombie firms, followed by artificial intelligence, cloud computing, and DT application. Nevertheless, DT strategy and blockchain cannot reduce zombie likelihood. Finally, our research offers valuable insights for policymakers to address the issues of zombie firms.
GANDiffFace: Controllable Generation of Synthetic Datasets for Face Recognition with Realistic Variations

Face recognition systems have significantly advanced in recent years, driven by the availability of large-scale datasets. However, several issues have recently came up, including privacy concerns that have led to the discontinuation of well-established public datasets. Synthetic datasets have emerged as a solution, even though current synthesis methods present other drawbacks such as limited intraclass variations, lack of realism, and unfair representation of demographic groups. This study introduces GAN-DiffFace, a novel framework for the generation of synthetic datasets for face recognition that combines the power of Generative Adversarial Networks (GANs) and Diffusion models to overcome the limitations of existing synthetic datasets. In GANDiffFace, we first propose the use of GANs to synthesize highly realistic identities and meet target demographic distributions. Subsequently, we fine-tune Diffusion models with the images generated with GANs, synthesizing multiple images of the same identity with a variety of accessories, poses, expressions, and contexts. We generate multiple synthetic datasets by changing GANDiffFace settings, and compare their mated and non-mated score distributions with the distributions provided by popular real-world datasets for face recognition, i.e. VGG2 and IJB-C. Our results show the feasibility of the proposed GANDiffFace, in particular the use of Diffusion models to enhance the (limited) intra-class variations provided by GANs towards the level of real-world datasets.
Infinitely Many Coexisting Attractors and Scrolls in a Fractional-Order Discrete Neuron Map

The neural network activation functions enable neural networks to have stronger fitting abilities and richer dynamical behaviors. In this paper, an improved fractional-order discrete tabu learning neuron (FODTLN) model map with a nonlinear periodic function as the activation function is proposed. The fixed points of the map are discussed. Then, the rich and complex dynamical behaviors of the map under different parameters and order conditions are investigated by using some common nonlinear dynamical analysis methods combined with the fractional-order approximate entropy method. Furthermore, it is found that fractional-order differential operators affect the generation of multiscrolls, and the model has infinitely many coexisting attractors obtained by changing the initial conditions. Interestingly, attractor growth and state transition are found. Finally, the map is implemented on the DSP hardware platforms to verify the realizability. The results show that the map exhibits complex and interesting dynamical behaviors. It provides a fundamental theory for the research of artificial neural networks.
ImDiffusion: Imputed Diffusion Models for Multivariate Time Series Anomaly Detection

Anomaly detection in multivariate time series data is of paramount importance for large-scale systems. However, accurately detecting anomalies in such data poses significant challenges due to the need for precise data modeling capability. Existing forecasting and reconstruction-based methods struggle to address these challenges effectively. To overcome these limitations, we propose a novel anomaly detection framework named ImDiffusion, which combines time series imputation and diffusion models to achieve accurate and robust anomaly detection. The imputation-based approach employed by ImDiffusion leverages the information from neighboring values in the time series, enabling precise modeling of temporal and inter-correlated dependencies, reducing uncertainty in the data, thereby enhancing the robustness of the anomaly detection process. ImDiffusion further leverages diffusion models as time series imputers to accurately capture complex dependencies. We leverage the step-by-step denoised outputs generated during the inference process to serve as valuable signals for anomaly prediction, resulting in improved accuracy and robustness of the detection process. We evaluate the performance of ImDiffusion via extensive experiments on benchmark datasets. The results demonstrate that our proposed framework significantly outperforms state-of-the-art approaches in terms of detection accuracy and timeliness. ImDiffusion is further integrated into the real production system in Microsoft and observes a remarkable 11.4% increase in detection F1 score compared to the legacy approach. To the best of our knowledge, ImDiffusion represents a pioneering approach that combines imputation-based techniques with time series anomaly detection, while introducing the novel use of diffusion models to the field.
Experimental Study on Chess Board Setup Using Delta Parallel Robot Based on Deep Learning

In recent years, robotics and object detection applications have seen remarkable improvements in executing routine tasks. In the near future, it is expected that most of the tasks can be performed by means of these two foregoing tools. This paper presents a system that combines these two approaches in order to automate the setup of a chessboard using a Delta parallel robot equipped with a two-finger gripper designed for grasping small objects. This system utilizes the so-called YOLO model for object detection in order to detect the chess pieces. The system begins by taking a picture of a scene, where the chess pieces are placed randomly around a board. The location of the chessboard is predefined and YOLO is used to detect and locate chess pieces in the given picture. The final object detection model used to execute this task is YOLOv5m, although this model encountered some challenges in detecting four types of chess pieces from a top view, these challenges were addressed by increasing the instances of those objects in the dataset. Upon extracting the location of each piece, the coordinates are sent to the robot, and the Delta parallel robot arranges the initial setup of a chess game. The model achieved an overall mAP of 0.99, indicating excellent precision and the grasp success rate in the process of grasping the chess pieces was 95%. This study demonstrates the potential of automating everyday tasks by exploring the intersection of robotics and artificial intelligence and can be well extended to tasks, namely, sorting medications and pills in hospitals, sorting different wastes like plastic and papers for recycling, identifying and removing spoiled fruits in agricultural industries, etc.
An efficient PDE framework for satellite image classification

In this paper, we present a method based on coupling between shock filter and diffusion process for image restoration. This method smoothes homogeneous part of the image, while it enhances edges efficiently. This selective smoothing propriety is used as a pretreatment step for an efficient classification of infrared satellite images, in order to cluster the solar field.
Distributional Semantic Models

This chapter contains a synoptic view of the different types and generations of distributional semantic models (DSMs), including the distinction between static and contextual models. Part II then focuses on static DSMs, since they are still the best known and widely studied family of models, and they learn context-independent distributional representations that are useful for several linguistic and cognitive tasks.
A Comparison of Statistical Tools for Identifying Modality in Body Mass Distributions

The assessment of modality or \bumps in distributions is of in- terest to scientists in many areas. We compare the performance of four statistical methods to test for departures from unimodality in simulations, and further illustrate the four methods using well-known ecological datasets on body mass published by Holling in 1992 to illustrate their advantages and disadvantages. Silverman's kernel density method was found to be very conservative. The excess mass test and a Bayesian mixture model approach showed agreement among the data sets, whereas Hall and York's test pro- vided strong evidence for the existence of two or more modes in all data sets. The Bayesian mixture model also provided a way to quantify the un- certainty associated with the number of modes. This work demonstrates the inherent richness of animal body mass distributions but also the diculties for characterizing it, and ultimately understanding the processes underlying them.
The Psychology of Thinking: Embedding Artifice in Nature

This chapter contains sections titled: Psychology as a Science of the Artificial, Search Strategies, The Limits on Performance, Limits on Speed of Concept Attainment, The Parameters of Memory-Eight Seconds per Chunk, The Parameters of Memory-Seven Chunks, or Is It Two?, The Organization of Memory, Stimulus Chunking, Visual Memory, The Mind’s Eye, Processing Natural Language, Semantics in Language Processing, Conclusion
Constraint Based Solid Modeling: What Do Employers Want Students To Know?

Over the last twenty years, engineers, technologists, technicians and educators have watched the development of three-dimensional modeling go from wireframe to solid.More recently, constraint-based modelers have replaced 2D CAD and constructive solid geometry modelers as the tool of choice for many engineering applications.These modelers place the 3D model at the center of the design process database.Over the last several years, engineering graphics educators have been adjusting their curricula to better prepare students to secure employment in environments where constraint-based modelers are used.One of the big concerns in engineering graphics education is the importance of documentation in the curriculum.How much time should be spent covering multiview drawings, standards for dimensioning and tolerancing, sectional views, conventional practices, auxiliary views, or geometric dimensioning and tolerancing?Do employers want students to know these "drawing" practices?Do they want students to be proficient in constraint-based modeling?What do they expect students to know when they leave the university and what do they want them to learn on the job?This paper will summarize research in engineering graphics education related to constraint-based modeling, present survey results of employers who utilize constraint-based modeling software, and make recommendations related to changes in the engineering graphics education curriculum.
Work-in-Progress: Creating an Intrusion Detection Experimental Environment Using Cloud-based Virtualization Technology

This paper discusses the latest capabilities of lab automation software and how such software could enhance the learning environment for an Intrusion Detection class.A lab automation system with virtual networking would enable a more realistic environment akin to the real world.Our design utilizes VMware software such as ESXi server, Virtual Center and Lab Manager to provide a robust virtual environment.The experimental results suggest an improved classroom environment for learning Intrusion Detection Systems and related software; in addition, the classroom environment requires much less setup time on the part of the student.
Bridging the Gap between the Social and Semantic Web: Extracting domain-specific ontology from folksonomy

Folksonomies have become very popular as means to organize large sets of resources shared over the Social Web. The bottom-up nature of folksonomies has proved to be an interesting alternative to the current effort at semantic web ontologies since folksonomies provide a rich terminology generated by large user-communities. Besides, ontologies extracted from folksonomies can represent the intelligence collective of social communities. Such ontologies also represent a core element of a new feature of the Web, the Internet of Things. Many research studies have captured semantics in folksonomies, some of which have developed ontologies from folksonomy. However, the formal specific-domain ontology consisting of domain-dependent relations has not been researched yet. This paper introduces an algorithm for deriving a domain-specific ontology from folksonomy tags. The proposed algorithm starts by collecting a domain-specific terminology; next, discovering a pre-defined set of conceptual relationships among the domain terminologies. The evaluation of the algorithm, using a dataset extracted from BibSonomy, demonstrated that the algorithm could effectively learn domain ontologies consisting of domain concepts linked by meaningful and high accurate relationships. Furthermore, the proposed algorithm can help reduce common issues related to tag ambiguity and synonymous tags.
Internet of Things: An Experiment with Residential Automation for Robotics Classes

This study presents the results obtained in a technological and applied research for robotics disciplines, using the Internet of Things with the device Arduino Uno to automate the lights of a residence, to have the ease of control during the day to day, the panel of control is accessed by a browser software on a mobile phone or computer, providing greater convenience for people. During the research were analyzed the different devices available in the national market, the results allowed to conclude the development of a simple solution that could contribute to robotics classes, the main interest related to the results involves the application experience, which can be expanded according to with the need of the user and its environment, proposing new applications in disciplines that involve robotics, database, internet, programming and electronics.
Speaking Performance of Islamic Boarding School and Public School-Based Graduates: A Comparative Study

This study aims 1) to compare the speaking performance of university students who graduated from Islamic boarding school and public school-based senior high schools and 2) to investigate the potential factors affecting the difference of their speaking performance. First year students of a State Islamic University in Lampung - Indonesia participated as the subjects of research. The data of students’ speaking performance was obtained from the documentation of students’ speaking entrance test score. Then, a semi-closed-ended questionnaire investigating the curricular, co-curricular, and extra-curricular programs in their previous high schools covering the sorts, the frequency, and the duration of the activities was distributed to disclose the underlying reasons of the speaking performance differences. The results revealed that generally the Islamic boarding school-based senior high school graduates’ speaking performance is slightly better than the public-based one with a minor difference on the five aspects of speaking assessment. The former showed better achievement in fluency, vocabulary and comprehension, whereas the latter got better result in grammar and pronunciation. Dissimilar variations of the co-curricular and extra-curricular activities at both types of school and the frequency of the activities might be the potential factors affecting the differences.
Semantic localisation via globally unique instance segmentation

© 2018. The copyright of this document resides with its authors. In this work we propose a novel approach to semantic localisation. Our work is motivated by the need for environment perception techniques which not only perform self-localisation within a map but also simultaneously recognise surrounding objects. Such capabilities are crucial for computer vision applications which interact with the environment: autonomous driving, augmented reality or robotics. In order to achieve this goal we propose a solution which consists of three key steps. Firstly, a database of panoramic RGB images and corresponding globally unique, per-pixel object instance labels is built for the desired environment where we typically consider objects from static categories such as building or tree. Secondly, a semantic segmentation network capable of predicting more than 3000 labels is trained on the collected data. Finally, for a given panoramic query image, the corresponding instance label image predicted by the network is used for semantic matching within the database. The matching is performed in two stages: (i) a fast retrieval of a small subset of database images (~100) with highly overlapping instance label histograms, followed by (ii) an explicit approximate 3 DoF (yaw, pitch, roll) alignment of the selected subset of images and the query image. We evaluate our approach in challenging indoor and outdoor navigation scenarios, achieving better or similar performance when compared to state-of-the-art image retrieval-based localisation approaches using key-point matching [29, 63] and image level embedding [3]. Our contribution includes: (i) a description of a novel semantic localisation approach using globally unique instance segmentation, (ii) corresponding quantitative and qualitative analysis and (iii) a novel CamVid-360 dataset containing 986 labelled instances of buildings, trees, road signs and poles.
Exploring Highly Dependable and Efficient Datacenter Power System Using Hybrid and Hierarchical Energy Buffers

The massive and irregular load surges challenge datacenter power infrastructures. As a result, power mismatching between supply and demand has emerged as a crucial availability issue in modern datacenters which are either under-provisioned or powered by intermittent power sources. Recent proposals have employed energy storage devices such as the uninterruptible power supply (UPS) to address this issue. However, current approaches lack the capacity of efficiently handling the irregular and unpredictable power mismatches. In this paper, we propose Hybrid and Hierarchical Energy Buffering (HHEB), a novel heterogeneous and adaptive scheme that could enable various energy storage devices (ESDs) to be efficiently integrated into existing datacenters for dynamically dealing with power mismatches. Our techniques exploit the diverse characteristics of different ESDs and intelligent load assignment algorithms to improve the dependability and efficiency of datacenter power systems. We evaluate the HHEB design with a prototype. Compared with a homogenous battery energy buffering system, HHEB could improve energy efficiency by 39.7 percent, extend UPS lifetime by 4.7X, promote energy availability by 3.2X, reduce system downtime by 41 percent, and effectively improve the energy availability of various energy buffers in different hierarchies. It allows datacenters to adapt to various power supply anomalies, thereby improving operational efficiency, dependability and availability.
Red-Black Heuristics for Planning Tasks with Conditional Effects

Red-black planning is a state-of-the-art approach to satisficing classical planning. Red-black planning heuristics are at the heart of the planner Mercury, the runner-up of a satisficing track in the International Planning Competition (IPC) 2014 and a major component of four additional planners in IPC 2018, including Saarplan, the runner-up in the agile track. Mercury’s exceptional performance is amplified by the fact that conditional effects were handled by the planner in a trivial way, simply by compiling them away. Conditional effects, however, are important for classical planning, and many domains require them for efficient modeling.Consequently, we investigate the possibility of handling conditional effects directly in the red-black planning heuristic function, extending the algorithm for computing red-black plans to the conditional effects setting. We show empirically that red-black planning heuristics that handle conditional effects natively outperform the variants that compile this feature away, improving coverage on tasks where black variables exist by 19%.