Reviewerly/exHarmony huggingface dataset:
Fine-Grained Heterogeneous Execution Framework with Energy Aware Scheduling

The growing convergence of high-performance, data analytics, and machine-learning applications is increasingly pushing computing systems toward heterogeneous processors and specialized hardware accelerators. Hardware heterogeneity, in turn, leads to finer-grained workflows. State-of-the-art server-less computing resource managers do not currently provide efficient scheduling of such fine-grained tasks on systems with heterogeneous CPUs and specialized hardware accelerators (e.g., GPUs and FPGAs). Working with fine-grained tasks presents an opportunity for more efficient energy use via new scheduling models. Our proposed scheduler enables technologies like Nvidia's Multi-Process Service (MPS) to pack multiple fine-grained tasks on GPUs efficiently. Its advantages include better co-location of jobs and better sharing of hardware resources such as GPUs that were not previously possible on container orchestration systems. We propose a Kubernetes-native energy-aware scheduler that integrates with our heterogeneous framework. Combining fine-grained resource scheduling on heterogeneous hardware and energy-aware scheduling results in up to 17.6% improvement in makespan, up to 20.16% reduction in energy consumption for CPU workloads, and up to 58.15% improvement in makespan, and up to 28.92% reduction in energy consumption for GPU workloads.
Teaching Technologies Online: Changes of Experience in Wartime in Ukraine

Online higher education in Ukraine has undergone changes during the Russian-Ukrainian war. Teaching online in wartime is stressful, but also encouraging; it is a way to overcome the stress of war. The purpose of the research is to showcase the changes in experience during wartime in Ukraine with respect to teaching methods and online technologies. To achieve the purpose, the methods of analysis and synthesis, questionnaires, and calculating methods, particularly MS Excel, are applied. There were 49 university teachers from Ukrainian government universities who took part in the study. The survey aims to find out how university teachers are ready to use new online teaching methods and techniques, to determine the priority in the method choice, and to indicate how to organize and have online lectures and practical classes. The following were the results observed: In the current situation, the relationship between teachers and students has become completely virtual. The teachers’ responsibility towards their students has increased significantly. Teachers are convinced of the global trend prospects of perceiving online teaching as one of the relevant and appropriate education forms in wartime Ukraine. It was online teaching that allowed students to continue their learning at Ukrainian universities, as many students were forced to leave their homeland, Ukraine, to save their lives. The conclusion is that teachers became more confident in assessing their skills to organize online teaching, and they tried to develop them by mastering new Internet technologies for transferring educational content.
Iris Detection for Attendance Monitoring in Educational Institutes Amidst a Pandemic: A Machine Learning Approach

Amid the COVID-19 pandemic, the imperative for alternative biometric attendance systems has arisen. Traditionally, fingerprint and facial recognition have been employed; however, these methods posed challenges in adherence to Standard Operational Procedures (SOPs) set during the pandemic. In response to these limitations, iris detection has been advanced as a superior alternative. This research introduces a novel machine learning approach to iris detection, tailored specifically for educational environments. Addressing the restrictions posed by COVID-19 SOPs, which permitted only 50% of student occupancy, an automated e-attendance mechanism has been proposed. The methodology comprises four distinct phases: initial registration of the student's iris, subsequent identity verification upon institutional entry, evaluation of individual attendance during examinations to assess exam eligibility, and the maintenance of a defaulter list. To validate the efficiency and accuracy of the proposed system, a series of experiments were conducted. Results indicate that the proposed system exhibits remarkable accuracy in comparison to conventional methods. Furthermore, a desktop application was developed to facilitate real-time iris detection.
VRPrOE Toolbox for Virtual Pre-occupancy Evaluation: Proof of Concept on a BIM Model of a Conservatory Classroom

Feedback on how people perceive the characteristics of the built environment is very useful in identifying design flaws. Using virtual environment tools, it is possible to show building designs to potential future users before construction begins, allowing for design refinement. Creating experiments to evaluate the built environment is often a laborious and building-specific process. In this paper, we present a toolbox designed to simplify this task. Starting from a 3D model of a building that can be created using Building Information Modeling software, the toolbox allows the creation of different design alternatives with a photorealistic appearance that can be used to perform a variety of pre-occupancy evaluation experiments. Through our tool, it is possible to define the parameters of the experiment through a simple user interface and generate questionnaires that allow the user to evaluate the design of the building in a virtual environment. To demonstrate the usefulness of the toolbox, we successfully conducted a proof-of-concept experiment to evaluate the effect of color and lighting on the perception of the environment in a conservatory classroom. The results of the experiment indicate that there is a significant relationship between the type of lighting (artificial, natural and mixed) and the perception of pleasantness, openness and brightness of the environment. The combined effect of color and lighting also shows a strong relationship with openness and brightness.
Medytox, Inc. v. Galderma S.A.

Biotechnology Law ReportVol. 42, No. 6 Medytox, Inc. v. Galderma S.A.United States Court of Appeals of the Federal Circuit, 2023 71 F.4th 990Edited for Biotechnology Law Report by Christopher M. HolmanEdited for Biotechnology Law Report by Christopher M. HolmanChristopher M. Holman is a Professor of Law at the University of Missouri–Kansas City School of Law; a Senior Scholar at the Center for the Protection of Intellectual Property x Innovation Policy (C-IP) at George Mason University, Antonin Scalia Law School; and the Executive Editor of Biotechnology Law Report.*The synopsis is original content and the decision itself has been condensed for ease of readability.Search for more papers by this authorPublished Online:13 Dec 2023https://doi.org/10.1089/blr.2023.29322.cipAboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookXLinked InRedditEmail View articleFiguresReferencesRelatedDetails Volume 42Issue 6Dec 2023 InformationCopyright 2023, Mary Ann Liebert, Inc., publishersTo cite this article:Edited for Biotechnology Law Report by Christopher M. Holman.Medytox, Inc. v. Galderma S.A..Biotechnology Law Report.Dec 2023.324-331.http://doi.org/10.1089/blr.2023.29322.cipPublished in Volume: 42 Issue 6: December 13, 2023Online Ahead of Print:September 25, 2023PDF download
In re Couvaras

Biotechnology Law ReportVol. 42, No. 6 Case in PointIn re CouvarasUnited States Court of Appeals of the Federal Circuit, 2023 70 F.4th 1374Edited for Biotechnology Law Report by Christopher M. HolmanEdited for Biotechnology Law Report by Christopher M. HolmanChristopher M. Holman is a Professor of Law at the University of Missouri–Kansas City School of Law; a Senior Scholar at the Center for the Protection of Intellectual Property x Innovation Policy (C-IP) at George Mason University, Antonin Scalia Law School; and the Executive Editor of Biotechnology Law Report.*The synopsis is original content and the decision itself has been condensed for ease of readability.Search for more papers by this authorPublished Online:13 Dec 2023https://doi.org/10.1089/blr.2023.29321.cipAboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookXLinked InRedditEmail View articleFiguresReferencesRelatedDetails Volume 42Issue 6Dec 2023 InformationCopyright 2023, Mary Ann Liebert, Inc., publishersTo cite this article:Edited for Biotechnology Law Report by Christopher M. Holman.In re Couvaras.Biotechnology Law Report.Dec 2023.319-323.http://doi.org/10.1089/blr.2023.29321.cipPublished in Volume: 42 Issue 6: December 13, 2023Online Ahead of Print:September 25, 2023PDF download
The Accuracy and Absolute Reliability of a Knee Surgery Assistance System Based on ArUco-Type Sensors

Recent advances allow the use of Augmented Reality (AR) for many medical procedures. AR via optical navigators to aid various knee surgery techniques (e.g., femoral and tibial osteotomies, ligament reconstructions or menisci transplants) is becoming increasingly frequent. Accuracy in these procedures is essential, but evaluations of this technology still need to be made. Our study aimed to evaluate the system's accuracy using an in vitro protocol. We hypothesised that the system's accuracy was equal to or less than 1 mm and 1° for distance and angular measurements, respectively. Our research was an in vitro laboratory with a 316 L steel model. Absolute reliability was assessed according to the Hopkins criteria by seven independent evaluators. Each observer measured the thirty palpation points and the trademarks to acquire direct angular measurements on three occasions separated by at least two weeks. The system's accuracy in assessing distances had a mean error of 1.203 mm and an uncertainty of 2.062, and for the angular values, a mean error of 0.778° and an uncertainty of 1.438. The intraclass correlation coefficient was for all intra-observer and inter-observers, almost perfect or perfect. The mean error for the distance's determination was statistically larger than 1 mm (1.203 mm) but with a trivial effect size. The mean error assessing angular values was statistically less than 1°. Our results are similar to those published by other authors in accuracy analyses of AR systems.
Sentiment Analysis Based on Performance of Linear Support Vector Machine and Multinomial Naïve Bayes Using Movie Reviews with Baseline Techniques

Movies are the better source of entertainment. Every year, a great percentage of movies are released. People comment on movies in the form of reviews after watching them. Since it is difficult to read all of the reviews for a movie, summarizing all of the reviews will help make this decision without wasting time in reading all of the reviews. Opinion mining also known as sentiment analysis is the process of extracting subjective information from textual data. Opinion mining involves identifying and extracting the opinions of individuals, which can be positive, neutral, or negative. The task of opinion mining also called sentiment analysis is performed to understand people’s emotions and attitudes in movie reviews. Movie reviews are an important source of opinion data because they provide insight into the general public’s opinions about a particular movie. The summary of all reviews can give a general idea about the movie. This study compares baseline techniques, Logistic Regression, Random Forest Classifier, Decision Tree, K-Nearest Neighbor, Gradient Boosting Classifier, and Passive Aggressive Classifier with Linear Support Vector Machines and Multinomial Naïve Bayes on the IMDB Dataset of 50K reviews and Sentiment Polarity Dataset Version 2.0. Before applying these classifiers, in pre-processing both datasets are cleaned, duplicate data is dropped and chat words are treated for better results. On the IMDB Dataset of 50K reviews, Linear Support Vector Machines achieve the highest accuracy of 89.48%, and after hyperparameter tuning, the Passive Aggressive Classifier achieves the highest accuracy of 90.27%, while Multinomial Nave Bayes achieves the highest accuracy of 70.69% and 71.04% after hyperparameter tuning on the Sentiment Polarity Dataset Version 2.0. This study highlights the importance of sentiment analysis as a tool for understanding the emotions and attitudes in movie reviews and predicts the performance of a movie based on the average sentiment of all the reviews.
ANALYSIS OF VIDEO DATA COMPRESSION ALGORITHMS AND OPTIMIZED FOR USE IN REMOTELY CONTROLLED DRONES

Due to the widespread use of unmanned aerial vehicles (UAVs) in the civil sphere, there is a need to improve the video image generation quality and transmission technologies, changing the video streams coding to reduce their size and improve quality. Video streaming technologies are moving further towards complicating the video transmission systems used, which helps improve the received video data quality. Higher of transmitted video signal quality – more requirements for frequency band increase used. This fact leads us to use effective video compression algorithms that allow us to combine high image quality with a narrow bandwidth of frequencies used. In this paper, currently existing coding algorithms, their efficiency and computational complexity are studied. Among the algorithms under consideration there will only be those whose effectiveness has been proven by finding their application in modern realities. Based on the research, conclusions will be drawn regarding the feasibility of using each encoding algorithm for transmitting video data in real time.
“Like aspirin for arthritis”: A qualitative study of conditional cyber‐deterrence associated with police crackdowns on the dark web

Abstract Research summary Crackdowns are law enforcement strategies based on the principles of deterrence theory, which stipulates that offenders are rational actors who will refrain from crime if perceived risks are higher than perceived benefits. Studies have shown that the effects of police street drug crackdowns are mostly short termed and followed by considerable displacement. In the early 2010s, an important part of illicit drug trades moved online to cryptomarkets, and law enforcement agencies have responded by engaging in online drug crackdowns. In this study, we focus on the perceptions of dark web users in order to determine, from a qualitative “data‐driven” perspective, whether police online crackdowns may have a cyber‐deterrent effect by analyzing 1796 forum posts. Our results show that these events trigger psychological and practical consequences that participants claim to have a conditional, although minor, deterrent effect. In the majority of cases, dark web users claimed to engage in several forms of spatial and tactical displacement. Policy implications Our study suggests that police crackdowns on the dark web have limited, short‐term effectiveness in curbing illicit activities. It proposes that innovative policing approaches such as problem‐oriented policing and “pulling levers/focused deterrence” strategies, which involve identifying key actors and engaging with them, be potentially extended to the dark web. While this approach is promising, it emphasizes the need for further research to assess its efficacy in the online realm, as it is a largely uncharted territory for law enforcement.
A Bayesian Spatio-Temporal Modeling Approach to the Inverse Heat Conduction Problem

Abstract A wide range of science and engineering problems involve inferring the system behavior and the physical models from indirect observations. These are known as inverse problems. Unlike forward problems (predictive models), inverse problems are difficult to solve due to ill-posedness arising from one or more of non-existence, non-uniqueness (existence of many solutions), or instability (small perturbations in the measurements emerging from noise could lead to large changes in the solution). Therefore, direct inversion of the inverse problem in complex real-world systems is impractical. Several modeling approaches have been developed and adopted in the literature. However, the existing approaches do not consider spatio-temporal relationships. Meanwhile, various real systems and processes exist whose inverse problems can be comfortably modeled using spatio-temporal modeling techniques. We present a spatio-temporal inverse model considering interaction effects for modeling this type of inverse problem. We adopted a Bayesian inference framework for the subsequent parameter estimation problem and developed a Gibbs sampling algorithm to sample from the posterior distribution of the parameter of interest. Finally, we evaluate the proposed modeling technique and solution framework using several case studies of inverse problems in diverse heat conduction domains. Compared with conventional methods, the proposed framework performs better and can be adopted for solving inverse problems with spatio-temporal properties in various scientific and engineering domains.
A call from early‑career Turkish scientists: seismic resilience is only feasible with “earthquake culture”

Despite significant scientific advances in earthquake research and building codes, Türkiye remains vulnerable to earthquakes, as demonstrated by the tragic Kahramanmaraş earthquake in February 2023. In contrast, countries such as Chile and Japan have successfully reduced earthquake damage through strict enforcement of building codes and effective public awareness campaigns. This paper highlights the need for seismic resilience in Türkiye and proposes actionable guidelines to bridge the gap between science and society. These guidelines include comprehensive geoscience education, the establishment of local earthquake centers, effective science communication, preparation for future earthquakes through scenario modeling, and development of an earthquake culture. Geoscience education should be integrated into the education system, and opportunities for geoscientists should be increased. Local earthquake centers can improve seismic monitoring, research, and public outreach. Geoscientists should prioritize science communication training to engage the public and combat misinformation. Scenario modeling and annual preparedness exercises can improve earthquake preparedness across the country, and promoting earthquake memory and awareness initiatives will build a collective consciousness about earthquakes. By implementing these guidelines, Türkiye can build earthquake resilience and mitigate the impact of future earthquakes; however, the active engagement of scientists, institutions, and the public is essential to achieve earthquake resilience.
Goal-Oriented Classification Measure Based on the Game Theory Concepts

In this paper, we present the novel idea of the classification measure based on the combination of precision and recall. The weights of these measures are calculated based on the game-theoretic concept of equilibrium. The classical C4.5 algorithm calculates measures: precision and recall for binary decision classes. In such a game, classification results are used to generate the best weights for the precision and recall classification measures. First, we solve the game and obtain the Nash equilibrium to obtain these weights. Next, we calculate the classification measure according to the measure weights estimated using the Nash equilibrium. Eventually, we compare the results with the more realistic example, where the importance of both decision classes is equal, and only the weights of classification measures can be adjusted. All experiments are performed on several binary classification datasets.
Unsupervised Topic Modeling with BERTopic for Coarse and Fine-Grained News Classification

Transformer models have achieved state-of-the-art results for news classification tasks, but remain difficult to modify to yield the desired class probabilities in a multi-class setting. Using a neural topic model to create dense topic clusters helps with generating these class probabilities. The presented work uses the BERTopic clustered embeddings model as a preprocessor to eliminate documents that do not belong to any distinct cluster or topic. By combining the resulting embeddings with a Sentence Transformer fine-tuned with SetFit, we obtain a prompt-free framework that demonstrates competitive performance even with few-shot labeled data. Our findings show that incorporating BERTopic in the preprocessing stage leads to a notable improvement in the classification accuracy of news documents. Furthermore, our method outperforms hybrid approaches that combine text and images for news document classification.
Investigating the Learning Behaviour of In-Context Learning: A Comparison with Supervised Learning

Large language models (LLMs) have shown remarkable capacity for in-context learning (ICL), where learning a new task from just a few training examples is done without being explicitly pre-trained. However, despite the success of LLMs, there has been little understanding of how ICL learns the knowledge from the given prompts. In this paper, to make progress toward understanding the learning behaviour of ICL, we train the same LLMs with the same demonstration examples via ICL and supervised learning (SL), respectively, and investigate their performance under label perturbations (i.e., noisy labels and label imbalance) on a range of classification tasks. First, via extensive experiments, we find that gold labels have significant impacts on the downstream in-context performance, especially for large language models; however, imbalanced labels matter little to ICL across all model sizes. Second, when comparing with SL, we show empirically that ICL is less sensitive to label perturbations than SL, and ICL gradually attains comparable performance to SL as the model size increases.
Native Conditional <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"><mml:mrow><mml:mtext mathvariant="italic">i</mml:mtext><mml:mrow><mml:mstyle mathsize="0.85em"><mml:mi>swap</mml:mi></mml:mstyle></mml:mrow></mml:mrow></mml:math> Operation with Superconducting Artificial Atoms

Controlling the flow of quantum information is a fundamental task for quantum computers, which is unfeasible to realize on classical devices. Coherent devices, which can process quantum states are thus required to route the quantum states that encode information. In this paper we demonstrate experimentally the smallest quantum transistor with a superconducting quantum processor, which is composed of a collector qubit, an emitter qubit, and a coupler (transistor gate). The interaction strength between the collector and emitter qubits is controlled by the frequency and state of the coupler, effectively implementing a quantum switch. Through the coupler-state-dependent Heisenberg (inherent) interaction between the qubits, a single-step (native) conditional $i$swap operation can be applied. To this end, we find that it is useful to take into consideration the higher-energy level for achieving a native and high-fidelity transistor operation. By reconstructing the quantum process tomography, we obtain an operation fidelity of $92.36\mathrm{%}$ when the transistor gate is open ($i$swap implementation) and $95.23\mathrm{%}$ in the case of closed gate (identity gate implementation). The architecture has strong potential in quantum information processing applications with superconducting qubits.