Reviewerly/exHarmony huggingface dataset:
Using Automated Algorithm Configuration for Parameter Control

Dynamic Algorithm Configuration (DAC) tackles the question of how to automatically learn policies to control parameters of algorithms in a data-driven fashion. This question has received considerable attention from the evolutionary community in recent years. Having a good benchmark collection to gain structural understanding on the effectiveness and limitations of different solution methods for DAC is therefore strongly desirable. Following recent work on proposing DAC benchmarks with well-understood theoretical properties and ground truth information, in this work, we suggest as a new DAC benchmark the controlling of the key parameter λ in the (1 + (λ, λ)) Genetic Algorithm for solving OneMax problems. We conduct a study on how to solve the DAC problem via the use of (static) automated algorithm configuration on the benchmark, and propose techniques to significantly improve the performance of the approach. Our approach is able to consistently outperform the default parameter control policy of the benchmark derived from previous theoretical work on sufficiently large problem sizes. We also present new findings on the landscape of the parameter-control search policies and propose methods to compute stronger baselines for the benchmark via numerical approximations of the true optimal policies.
A Framework for Anomaly Detection in Blockchain Networks With Sketches

A blockchain is a distributed ledger composed of immutable blocks of data that often refer to money transfers. As blockchain networks gain popularity, there is a rising concern for security against malicious and hacking users. Detection anomalies and unusual account activities can be based on comparing upcoming activity with recent and historical data. However, the size and rapid growth of the complete blockchain history can result in slow and expensive processing. This paper proposes a solution to this challenge by analyzing summarized block data structures, known as sketches, instead of the entire blockchain. Sketches are commonly used in computer systems and blockchain networks to provide efficient query executions while maintaining a compact data representation. This study explores the use of sketches, such as Bloom Filter and HyperLogLog, to identify suspicious accounts without requiring the examination of the entire blockchain data. We design solutions for anomaly detection of certain goals that may be indications of known attacks. We develop methods to identify accounts with high transaction volume, frequency, and node degree. Furthermore, the innovation of this paper lies in the generalization of sketch-based anomaly detection through a generic solution capable of addressing diverse queries. We conduct experiments based on real Ethereum data and compare the accuracy, time complexity, and memory usage of our algorithms with traditional detection algorithms that rely on the complete blockchain data. Our results indicate that sketch-based anomaly detection methods can provide a practical and scalable solution for detecting anomalies in transactions on blockchain networks. We managed to reduce the amount of memory used by the detection process by 90%-96% and reduce the time complexity by 86% while maintaining high accuracy.
Faculty Perceptions of Online Learning During the Coronavirus Outbreak

This study examined the perceptions of faculty members at Ajman University during the period of COVID-19 to estimate the reality of the experience of employing E-learn is applied during the 2020/2021 academic year at higher education establishments in UAE. A descriptive method was adopted in the research. The study participants consisted of all staff of the faculty (274) of Ajman University who were selected from different faculties with different specializations in the academic year 2020-2021. A questionnaire, consisting of (38) items, was developed as an instrument for the study after its validity and reliability were verified. The results indicated that faculty members' perspective of Ajman university as one of UAE universities as being highly prepared, well trained, and equipped to apply online learning at the period of the Covid-19. The findings also indicated that the faculty members’ perspective related to the process of teaching and learning via online learning reached at a moderate level, and the obstacles encountering the application of online learning reached at a high level, while their perspective concerning suggestions to develop the online learning experience was at mid-level. Faculty members' responses regarding applying online learning differed according to the college variables came with favor for Medicine college, depending on the year of experience (in favor of 5- to less than 10 years), and depending on gender and academic rank of faculty members (the perspective of faculty members is statistically insignificant according to the gender and academic rank variables)
Study in Indonesia Pilot Academy, Why not? : A Website Representation of Critical Discourse Analysis

Indonesia Pilot Academy utilizes language as an impactful tool to affect people’s perceptions. An electronic website is becoming increasingly vital for academy and candidate cadets by employing language as information and representation. The portrayal of Indonesia Pilot Academy represents the use of linguistic representation on the website and the contribution of this to understanding the overlapping factors that reinforce academic practices within the Indonesia flight-social structure. There are unanswered questions related to the study comparing and analyzing the diverse discourses used to represent Indonesia flight academy on its website regarding social constructs (e.g., gender and race). Then, the study aims to determine website language represented as academic quality, product, and social construct. The data was taken from the website of the academy. To that end, critical discourse analysis (CDA) is utilized to describe, analyze, and interpret the textual and visual representations of the Indonesia Pilot Academy website at the local, institutional, and societal levels. What is surprising is that the academy should encourage global political involvement with promotional discourse and visual images. This finding has important implications for fostering international promotional discourse and pictures to emphasize prestige and uniqueness.
A bilateral employment situation prediction model for college students using GCN and LSTM

Due to the prevailing trend of globalization, the competition for social employment has escalated significantly. Moreover, the job market has become exceedingly competitive for students, warranting immediate attention. In light of this, a novel prognostic model employing big data technology is proposed to facilitate a bilateral employment scenario for graduates, aiding college students in promptly gauging the prevailing social employment landscape and providing precise employment guidance. Initially, the focus lies in meticulously analyzing pivotal aspects of college students’ employment by constructing a specialized employment platform. Subsequently, a classification model grounded in a graph convolution network (GCN) is built, leveraging big data technology to comprehensively comprehend graduates’ strengths and weaknesses in the employment milieu. Furthermore, based on the outcomes derived from the comprehensive classification of college students’ qualities, a college students’ employment trend prediction method employing long and short term memory (LSTM) is proposed. This method supplements the analysis of graduates’ employability and enables accurate forecasting of college students’ employment trends. Empirical evidence substantiates that my proposed methodology effectively evaluates graduates’ comprehensive qualities and successfully predicts their employment prospects. The achieved F-values, 82.45% and 69.89%, respectively, demonstrate the efficacy of anticipating the new paradigm in graduates’ dual-line employment.
Homer restored: Virtual reconstruction of Papyrus Bodmer 1

In this paper, we propose a complete method to reconstruct a damaged piece of papyrus using its image annotated at the character level and the original ancient Greek text (known otherwise). Our reconstruction allows us to recreate the written surface, making it readable and consistent with the original one. Our method is in two stages. First, the text is reconstructed by pasting character patches in their possible locations. Second, we reconstruct the background of the papyrus by applying inpainting methods. Two different inpainting techniques are tested in this article, one traditional and one using a GAN.
Extending Morphs in AZee Using Pose Space Deformations

Signing avatars have become increasingly important for sign language synthesis. However, to behave realistically, they must be able to replicate the coordinated activity of human hand movements and facial expressions. Most methods currently evaluate such motion using just kinematic techniques, which can limit the realism of the virtual characters. We propose a new methodology for creating a set of morphs in the AZee language to address this issue. We encapsulate a set of human movements and map their respective pose space deformations within our morphs. This allows us to capture the rigid as well as the non-rigid shape changes of the human anatomy and also addresses the stretching and contracting of the skin at its extremities. We create our pose space deformations based on the study of local avatar movements and a popular cognitive facial model for facial expressions. We integrate our set of morphs in our existing blender addon implementation for AZee with a standard parameterized 3D avatar model, resulting in a fully articulated avatar that can produce more realistic movements with a faster real-time synthesis. The proposed methodology has the potential to enhance the realism of signing avatars and contributes to the development of a more intuitive toolkit for AZee linguists.
Optimization of DeepFake Video Detection Using Image Preprocessing

Deep learning has been evolving recently which allowed it to handle complex problems like big data, computer vision, and human-level control. One of the deep learning-powered applications recently emerged is called “deepfake”. Deepfake algorithms have recently been a controversial development in Artificial Intelligence, because they use deep learning to generate fake yet realistic content based on an input dataset. As a result, many are concerned with the potential risks in terms of cyber-security as it causes threats to privacy, democracy, and national security. Multiple techniques were proposed to detect deepfake videos, however most cannot cope with the variety of the deepfake generation techniques. Therefore, in this study, we optimize one of the best existing deepfake detection methods based on Xception model. In particular, our proposed optimization scheme consists of a pre-processing phase performing advanced image enhancement on the videos in hand for highlighting the face features for better feature extraction as well fake content detection, which is preceded by a close-up dataset cleansing. Our experiments show that the proposed pre-processing optimization scheme had improvemes the performance of the Xception Binary Classifier- Inference model from 94% to 96%.
PEPPER: Precise Privacy-Preserving Contact Tracing with Cheap, BLE/UWB Capable Tokens

Contact Tracing (CT) is an old, recognized epi-demiological tool, and since a digital variant is now within reach, a variety of smartphone-based solutions have been rapidly developed and deployed since 2020, with mixed results and amid controversies. Yet, achieving reliable and effective digital CT at large scale is still an open problem. In this work, we contribute with an open source software platform on top of which various CT solutions can be quickly developed and tested. More specifically, we design PEPPER, which jointly leverages Bluetooth Low Energy (BLE) and Ultra Wide Band (UWB) radios for contact detection, combined with the DESIRE privacy-preserving CT protocol. We show that PEPPER+DESIRE can operate on cheap physical tokens based on low-power microcontrollers, opening new use-cases with less personal, potentially disposable devices, that could be more widely used. We also evaluate the complementarity of Bluetooth and UWB in this context, via experiments mimicking various scenarios relevant for CT. Compared to BLE-only CT, we show that UWB can decrease false negatives (e.g., in presence of human body occlusion), meaning that more actual contacts will be found, a key benefit from an epidemiological viewpoint. Our results suggest that, while PEPPER+DESIRE improves precision over state-of-the-art, further research is required to harness UWB-BLE synergy for CT in practice. To this end, our open source platform (which can run on an open-access testbed) provides a useful playground for the research community.
A constrained Langevin-adapted Particle Filter for Aircraft Engines’ Health Monitoring

We examine the application of particle filter in estimating performance indicators of an aircraft engine; these indicators are a crucial aspect in health monitoring and condition-based maintenance for aeronautics. This approach is flexible and not restricted by rigid assumptions often found in other methods; however, it poses three challenges in our context: (i) high computation cost: classical particle filters require a large number of particles, each of them calling a heavy model; (ii) non-observability: in our system, different system states might provide the same measurements; (iii) constraints: constraints on the estimation are required to be integrated dynamically. We propose a version of particle filter, based on Langevin dynamics, to resolve these challenges.
CENTRAL BANK INTERVENTION FUND AND AGRICULTURAL OUTPUT IN NIGERIA: AN ARDL APPROACH

This paper examines the impact of central bank financial intervention funds on Nigeria's agricultural exports from 1990 to 2020 with the specific objective of determining the extent to which the funds are financed by the Agricultural Credit Guarantee Fund (ACGSF) in the agricultural sector concerned. Agricultural exports both short-term and long-term during the period were under review. The study uses annual time data obtained from the CBN and WDI database. The Auto-Regressive Distributed Lag (ARDL) technique and the ARDL Bound test were used to determine the short-term and long-term relationships of the variables. The results show that ACGSF shows a positive and significant relationship of agricultural export growth in the short run with coefficients of 0.23, 0.33 and 0.19, but a negative and insignificant relationship of agricultural export in the long run. The analysis also shows a positive and insignificance of employment in agriculture in relation to agricultural production in the short term, while negative and significant in the long term. The 68.3% of the change in the dependent variable, as revealed by the adjusted R2, is due to the combined effect of the independent variables. The study therefore seeks for greater and appropriate funding of the government scheme or monetary authority to have a positive impact on long-term agricultural exports in Nigeria and should ensure immediate repayment of loan(s) to the qualified farmer to avoid diversion, political influence and delay in poor implementation process due to bank deposits of money in the country.
FIGURE 3 from Integrative Prognostic Machine Learning Models in Mantle Cell Lymphoma

&lt;p&gt;Feature importance in the combined feature XGBoost models. &lt;b&gt;A,&lt;/b&gt; The VIP for the top 20 features from the “all features” XGBoost model. B2M, beta-2 microglobulin; BM, bone marrow involvement; BMI, body mass index; dx, diagnosis; ECOG, Eastern Cooperative Oncology Group performance status; GI, gastrointestinal; Hgb, hemoglobin; LDH, lactase dehydrogenase; WBC, white blood cell count. &lt;b&gt;B,&lt;/b&gt; SHAP additive values demonstrating feature impact on model output from the “all features” XGBoost model. Colors represent the value of the feature; note continuous features have a color gradient, while the dummy variables values of 0 and 1 separate into two groups “low and high”. &lt;b&gt;C,&lt;/b&gt; The VIP for features that were included in the parsimonious model or iMIPI. The top 20 features were selected from the most important features from the “all features” model; 23 features are represented in the figure due to categorical variables being transformed into dummy variables during data preprocessing. &lt;b&gt;D,&lt;/b&gt; SHAP values from the parsimonious model (iMIPI).&lt;/p&gt;
Exploring a Competency-based Curriculum Development Model Using PDCA in University Education

Objectives The purpose of this study was to explore a competency-based curriculum model and an instructional design plan using PDCA model in university education. Methods To achive this purpose the components and design principles of the draft competency-based curriculum development model using PDCA were derived through a review of prior literature. This study conducted a total of three Delphi surveys on expert panels in the field. Results The components of the finalized competency-based curriculum development model are as follows. A total of 62 components in 21 categories with 11 sub-stages under four stages (Plan, Do, Check, and Act) were developed for the competency-based curriculum using the PDCA cycle. Components of the “Plan” Stage include educational environment analysis, core competency development, and educational goal establishment. The “Do” Stage comprises of needs analysis, university infrastructure analysis, curriculum design, and evaluation method development. The “Check” Stage has components such as competency-based curriculum evaluation, and the assessment of learners’ competency improvement. The components of the “Act” Stage consist of leap-off toward reinforcement and new plan establishment. Conclusions This study is meaningful in that it established a systematic, cyclical competency-based curriculum development model in terms of the quality management of university education curriculum using the PDCA cycle. In addition, for professors to provide competency-based classes, it developed an instructional design model for competency-based curriculum of university education so that it can be used as a basic material for developing instructional strategy.
Sound art before sound art

Click to increase image sizeClick to decrease image size Additional informationNotes on contributorsChristian de Mouilpied SanctoChristian de Mouilpied Sancto is a PhD candidate in Visual and Cultural Studies at the University of Rochester, where he researches environmental aesthetics in post-1945 art, design, and performance. He has degrees in Media and Performance Studies from Utrecht University and music composition from Goldsmiths, University of London. His writing has appeared in Afterimage, ASAP/Journal, PAJ, Resonance, and Film-Philosophy.
Knowledge correlation graph-guided multi-source interaction domain adaptation network for rotating machinery fault diagnosis

Leveraging generalized knowledge from multiple source domains with rich labels to the target domain without labeled data is a more realistic and challenging issue compared with single-source domain adaptation. Furthermore, the distribution discrepancies between each source domain and the expansion of data categories increase the difficulty of aligning each source domain with the target domain. To alleviate these issues, a knowledge correlation graph-guided multi-source interaction domain adaptation network (KCGMIDAN) is developed for rotating machinery fault diagnosis. Firstly, a random mini-batch is randomly selected to update comprehensive feature representations (CFR) extracted from each data category across all domains, thus promoting the knowledge interaction of acquired CFR between the current and the next epochs. Then, a knowledge correlation graph (KCG) is established on all CFR to boost knowledge propagation among various domains. To improve the compactness of characteristics within the same category and the separation of various categories, two losses are designed in this procedure to place constraints on the relationships between categories. Finally, query samples are added into KCG to construct the extended KCG, and the recognition of samples is completed by using built deep graph network based on the extended KCG. Extensive experimental results verify that KCGMIDAN can achieve better recognition performance than existing methods.
Low-Overhead Quantum Bus with Coupling Beyond the Nearest Neighbor via Mediated Effective Capacitance

The design of easy-to-operate high-fidelity two-qubit gates remains an area of ongoing research. Many of the common schemes require dedicated control lines, while others are vulnerable to issues of frequency crowding. Here, we propose a scheme for coupling a chain of transmons acting as logical qubits via a quantum bus of auxiliary qubits. The auxiliary array is made of floating transmons and through the use of mediated interactions we generate effective capacitance between them beyond the nearest neighbor. Logical qubits are not directly coupled to each other but they can be coupled by bringing them closer in frequency to the far-detuned auxiliary arrays. This allows for tunable coupling between non-neighboring logical qubits and for the application of entangling gates to three or more qubits at once.
Efficient method for finding nearest neighbors in flocking behaviors using k-dimensional trees

&lt;p&gt;Flocking is a behavior where a group of objects travel, move or collaborate together. By learning more about flocking behavior, we might be able to apply this knowledge in different contexts such as computer graphics, games, and education. A key steppingstone for understanding flocking behavior is to be able to simulate it. However, simulating behaviors of large numbers of objects is highly compute-intensive task because of the n-squared complexity of nearest neighbor for separating n objects. The work in this paper presents an efficient nearest neighbor method based on the k-dimensional trees (KD trees). To evaluate the proposed approach, we apply it using Unity-3D game engine, together with other conventional nearest neighbor methods. The Unity-3D game simulation engine allows users to utilize interaction design tools for programming and animating flocking behaviors. Results showed that the proposed approach outperform other conventional nearest neighbor approaches. The proposed approach can be used to enhance digital games quality and simulations.&lt;/p&gt;