Miracle-dz/UniFAR-FTUs huggingface dataset:
Automated Detection For The Severity Of Knee Osteoarthritis From Plain Radiographs Using Machine Learning Methods

— Knee joint pain is one of the foremost imperative clinical and debilitating features of osteoarthritis. It may be a late manifestation of osteoarthritis (OA) whose early indication includes joint space narrowing, bone subluxation, subchondral sclerosis, the formation of osteophytes and cartilage degradation. Even though the formation of osteophytes and joint space reduction is the early indications of the knee osteoarthritis (OA). The discovery is based on the Kellgren and Lawrence (KL) classification grades, which compares the distinctive stages of OA. Since the treatments for progressed radiographic knee OA are restricted, clinicians confront a significant challenge of distinguishing patients, who are at a high chance of getting affected by OA in a timely and effected way. Therefore we have created a straight forward self-assessment scoring framework for knee OA. OA is generally analyzed by specialists through manual assessment of patient’s medical images, which are typically gathered in medical clinics. Checking the occurrence of OA to some degree is tedious for patients. In the expansion, the current studies are centred on consequently identifying OA through image-based machine learning algorithms. A right forecast of OA is a fundamental advance to successfully analyse and avoid the severity of OA. The objective of this study is to develop a machine vision approach for investigation of the severity of knee OA. The computation involves LBP (Local Binary Pattern) and PCA (Principal component Analysis) method. The processed PCA with LBP features are computed using FFNN (Feed Forward Neural Network), Multi-SVM and DBN with RBM (Deep Belief Neural Network with Restricted Boltzmann’s Machine) classifiers for evaluating the knee osteoarthritis based on the Kellgren Lawrence grading system. The x-ray images used for the method are taken in stand Posterior Anterior (PA) view. The X rays images are used to obtain a region of interest (Extraction). The ROI extraction images undergo PCA and LBP feature extraction process. The texture features are then calculated from the ROI extraction. The extracted features are given to the ANN, Multi-SVM and DBN with RBM classifiers to classify the images to their grades. The highest accuracy rate of 83% is achieved by the DBN with RBM classifier for the classification of the images into the Grade 0, Grade 1, Grade 2, Grade 3, and Grade 4 osteoarthritis (OA). The goal of this paper is to analyse the severity of the osteoarthritis by using the plain x-ray radiographs.
Design, Synthesis, Antihyperglycemic Studies, and Docking Simulations of Benzimidazole-Thiazolidinedione Hybrids

A simple and cheap three-step procedure for the synthesis of three (5Z)-5-[3(4)-(1H-benzimidazol-2-ylmethoxy)benzylidene]-1,3-thiazolidine-2,4-diones has been described via a SN2 reaction of generally recognized as safe hydroxybenzaldehydes and 2-(chloromethyl)-1H-benzimidazole, followed by a Knoevenagel condensation with thiazolidine-2,4-dione in moderated yields. All the newly synthesized compounds were characterized using analytical and spectral studies. In vitro treatment on adipocytes with compounds increased the mRNA expression of two proteins recognized as strategic targets in diabetes: PPARγ and GLUT-4. In silico studies were conducted in order to explain the interaction binding mode of the synthesized compounds on PPARγ. In vivo studies confirmed that compounds 1–3 have robust antihyperglycemic action linked to insulin sensitization mechanisms. The present study provides three compounds with a promising antidiabetic action.
A Conceptual Framework for Adopting Ambidexterity in Circular Supply Chains

The incorporation of the circular economy principles into Supply Chain Management leads to a Circular Supply Chain (CSC), which fosters the perpetual flow of materials in a cyclical manner without damaging the environment, society or economy. This perpetual flow is essential for organizations to survive in an ever-increasingly dynamic environment and in a natural resource-constrained economy (NRCE). Therefore, organizations, which aim to implement CSC must seek to identify an intermediating factor, which should simultaneously foster reactive supply chain strategies to manage natural resources limitation and proactive supply chain strategies for uncertain environment. The concept of Ambidexterity enabling the simultaneous pursuit of exploration and exploitation aligns with the principles of CSC. However, this concept has not been investigated in the CSC literature to date. Within this context, the aim of this paper is to propose a conceptual framework to adopt Ambidexterity in CSC. The framework has a two-dimensional hierarchy that includes eco-efficiency aimed at reactive strategies, and eco-effectiveness aimed at proactive strategies: both regenerative and restorative from supply chain process perspective, closed-loop and open-loop/cascading from flow perspective. This paper provides a conceptual framework with four propositions that should enable the adoption of Ambidexterity in CSC.
Towards Quantifying Magnitude of Political Bias in News Articles Using a Novel Annotation Schema

Media bias is a predominant phenomenon present in most forms of print and electronic media such as news articles, blogs, tweets, etc. Since media plays a pivotal role in shaping public opinion towards political happenings, both political parties and media houses often use such sources as outlets to propagate their own prejudices to the public. There has been some research on detecting political bias in news articles. However, none of it attempts to analyse the nature of bias or quantify the magnitude ofthe bias in a given text. This paper presents a political bias annotated corpus viz. PoBiCo-21, which is annotated using a schema specifically designed with 10 labels to capture various techniques used to create political bias in news. We create a ranking of these techniques based on their contribution to bias. After validating the ranking, we propose methods to use it to quantify the magnitude of bias in political news articles.
A multi-stage group decision model based on improved Q-learning

The multi-stage group decision making problem is a typical sequential group decision making problem. It is normally utilized to find the optimal solution to the group decision problems in discrete deterministic environment. However, the real life environments faced by decision-makers are usually full of uncertainty, even unknown environments (with unknown state transition matrix). Therefore, it is essential for the decision-makers to obtain more information by interacting with the environment dynamically to achieve an optimal decision strategy with high consensus degree. Due to the advantage of reinforcement learning in handling the sequential decision-making problems, the classical reinforcement learning algorithm (Q-learning) is improved to discover the optimal solution of multi-stage group decision making problems under uncertain environment. Additionally, a theorem is proposed to show that the optimal group decision obtained by using the improved Q-learning algorithm is the group decision with the highest degree of group consensus. Finally, an illustrative example is presented to verify the rationality and feasibility of the proposed algorithm.
Leveraging Real-Time Big Data: Product Market and Stock Market Effects of Idiosyncratic Production Shocks

In this paper, we use big data from a novel methodology (cell phone geolocation) to identify idiosyncratic production shocks (outage) in oil refiners. We posit that such shocks have important implications for both product market consumers and stock market participants. We also examine the relative informativeness of outages gleaned from big data with that of information obtained from traditional media and Bloomberg, an information aggregator. Our evidence suggests that gas prices, at the cities proximate to the refineries experiencing significant outage, increase almost immediately following the outage and the price effect persists for up to 3 months. When compared to big data, the gas price effects are similar in magnitude for Bloomberg but smaller for traditional media. At the firm level, refinery outages based on big data negatively affect sales growth, ROA, and profit margin, whereas the effects based on media and Bloomberg are lower in magnitude with weaker statistical significance. Stock prices do not incorporate information about outages based on big data and Bloomberg until about 3 months following the outage. Big data-based outages generate the most negative returns 9 months out, suggesting that a trading strategy of shorting stocks with high levels of refinery outage based on big data generates significant abnormal returns. ________________ We thank Orbital Insight for providing proprietary data on cell phone pings at refineries. We appreciate many helpful discussions with data experts at Orbital Insight, Bloomberg, and GasBuddy. We also appreciate helpful institutional insights from Dilip Gandhi and multiple industry professionals. We thank John Heater, Suresh Nallareddy, Rodrigo Verdi (Editor), and an anonymous referee for helpful comments and Mark Yue Ma at the University of Oklahoma for excellent research assistance.
University of Birmingham On index calculus algorithms for subfield curves

In this paper we further the study of index calculus methods for solving the elliptic curve discrete logarithm problem (ECDLP). We focus on the index calculus for subfield curves, also called Koblitz curves, defined over F 𝑞 with ECDLP in F 𝑞 𝑛 . Instead of accelerating the solution of polynomial systems during index calculus as was predominantly done in previous work, we define factor bases that are invariant under the 𝑞 -power Frobenius automorphism of the field F 𝑞 𝑛 , reducing the number of polynomial systems that need to be solved. A reduction by a factor of 1 /𝑛 is the best one could hope for. We show how to choose factor bases to achieve this, while simultaneously accelerating the linear algebra step of the index calculus method for Koblitz curves by a factor 𝑛 2 . Furthermore, we show how to use the Frobenius endomorphism to improve symmetry breaking for Koblitz curves. We provide constructions of factor bases with the desired properties, and we study their impact on the polynomial system solving costs experimentally. This work gives an answer to the problem raised in the literature on how the Frobenius endomorphism can be used to speed-up index calculus on subfield curves.
The Case Against B.F. Skinner

A century ago, a voice of British liberalism described the "Chinaman" as "an inferior race of malleable orientals. "[1] During the same years, anthropology became professionalized as a discipline, "intimately associated with the rise of raciology. "[2] Presented with the claims of nineteenth-century racist anthropology, a rational person will ask two sorts of questions: What is the scientific status of the claims? What social or ideological needs do they serve? The questions are logically independent, but the second type of question naturally comes to the fore as scientific pretensions are undermined. The question of the scientific status of nineteenth-century racist anthropology is no longer seriously at issue, and its social function is not difficult to perceive. If the "Chinaman" is malleable by nature, then what objection can there be to controls exercised by a superior race?
initio study Electronic and structural study of hexagonal TiC nanowires : ab

Nanowires are the one-dimensional nanostructures with the diameter order of one to few hundred nanometres. These structures show unique properties other than their bulk structures. In this article, a qualitative first principle discussion of TiC nanowire is reported, indicating the impact of DFT based GGA relativistic corrections on its electronic properties. Here, we analyse the Titanium Carbide (TiC) nanowire of hexagonal structure periodic in Z-direction with the density functional theory (DFT). The GGA with RBBE Correlation analysis of this material shows the metallic characteristics in its bulk but the electronic density of state shows that the hybridization states are different from their bulk when the material is analysed in nanostructure form. Three structures of hexagonal TiC nanowire directed in (1,1,1) plane were analysed to explore diameter (4-18) Å dependent comparative study of electronic, stabilizing and optical property which shows unique different result counterparts to its bulk. Hexagonal TiC nanowire were found to be semiconducting with narrow band gap (0.21-0.34) eV in small diameter while metallic in higher diameter. They are comparable stables as their bulk for higher structure. Similarly, for the same investigation, the structures are cross checked by surface atom passivation to verify the reliability of the result that we found.
TOWARDS SECURITY AND PRIVACY CONCERNS IN THE INTERNET OF THINGS IN THE AGRICULTURE SECTOR

The Internet of Things (IoT) is an important player in the agricultural industry. IoT is developing smart irrigation systems, crop monitoring, crop tagging, crop security, crop disease diagnosis, and crop selection. The potential applications of IoT in agriculture are limitless. The Internet of Things (IoT) allows the global smart city paradigm. Intelligent communities include smart homes, smart farms, smart environments, smart fitness, smart government, and so on. The Internet of Things is also utilized in the oil refining, gas mining, and manufacturing industries. IoT is increasing efficiency, optimizing costs, maximizing energy, sustaining forecasts, and giving people with a great lot of ease. With more diverse systems and data processing, security concerns are increasing. Security and privacy concerns are the primary impediments to the growth of IoT. This article investigates different security and privacy problems in the Internet of Things.
Human-monkey interaction dynamics and their dietary impacts on central american white-faced capuchins (Cebus imitator) at Manuel Antonio National Park, Costa Rica

Wild capuchin monkeys are highly adaptable to anthropogenic environments. We assessed how the interaction dynamics between humans and three groups of Panamanian white-faced capuchin monkeys (Cebus imitator) in Manuel Antonio National Park, the most visited national park of Costa Rica, affected the feeding behavior and diet of these animals in 2008-2009. On average, individual monkeys acquired 2.4 human food items per focal hour during peak hours of park visitation by humans. Although human visitors directly and indirectly provisioned monkeys in the park, 71 % of monkeys’ acquisition of human food items were a result of monkey-initiated interactions (MIIs) rather than human-initiated interactions (HIIs), and adult male monkeys were the most frequent initiators (with 157 adult male MIIs in the study period, compared to 33 initiated by adult females and 84 by juveniles). Adult male monkeys were also the most likely to make direct contact with humans to grab food (35 of 50 direct grabs of food from humans were by adult male monkeys). Adult females acquired food from humans through HIIs at about the same rate as adult males in their group. Secondary acquisition of human food from other monkeys accounted for about one-third of all events of monkey acquisition of human food, and juvenile monkeys had higher median hourly rates than adults in their group to acquire human food through secondary acquisition. Humans frequently offered fruit when provisioning (61.4 % of provisioning events), but monkeys actively acquired fruit, meat, candy, condiments, chips, crackers and dairy items, and only 36.5 % of MIIs were to acquire fruit. Our results suggest that in parks where humans have frequent contact with capuchin monkeys, park regulations should not only prohibit direct provisioning, but require visitors not to bring food into parks. Stronger measures such as fines or park expulsion for those interacting with monkeys might be more effective, and environmental education is necessary as a strategy to inform visitors what to expect from the monkeys and how to avoid accidental provisioning.
Perturbation-based Detection and Resolution of Cherry-picking

In settings where an outcome, a decision, or a statement is made based on a single option among alternatives, it is popular to cherry-pick the data to generate an outcome that is supported by the cherry-picked data but not in general. In this paper, we use perturbation as a technique to design a support measure to detect, and resolve, cherry-picking across different contexts. In particular, to demonstrate the general scope of our proposal, we study cherry picking in two very different domains: (a) political statements based on trend-lines and (b) linear rankings. We also discuss sampling-based estimation as an effective and efﬁcient approximation approach for detecting and resolving cherry-picking at scale.
CHALLENGES IN DETECTING NULL RELATIVIZERS IN AFRICAN AMERICAN LANGUAGE FOR SOCIOLINGUISTIC AND PSYCHOLINGUISTIC APPLICATIONS

Introduction. African American Language (AAL) is a variety of English spoken primarily, but not exclusively, by African Americans. AAL and Mainstream American English (MAE) overlap, but there are notable differences between the two varieties [1]. One difference is in subject-extracted relative clauses (SRCs), where the subject has been extracted from a subject position inside the embedded clause. An overt relativizer (e.g., “that,” “which,” “who”) is optional in AAL but obligatory in MAE. Null relativizers are grammatical in both varieties for object-extracted relative clauses (ORCs), where an object has been extracted from inside the embedded clause.
Data-Driven Structured Thermal Modeling for COTS Multi-core Processors

Thermal awareness is increasingly important for real-time systems deployed in harsh environments. As high chip temperature can cause frequency throttling or shutdown of processor cores at unexpected times, many real-time scheduling techniques have been developed to ensure continuous, fail-safe operation of safety-critical tasks with stringent timing constraints. However, their practical use remains largely limited due to the fact that it is extremely difficult to obtain a precise thermal model of commercial processors without using special measurement instruments or access to proprietary information, such as the power traces of micro-architectural units and detailed floorplans. In this paper, we propose a data-driven structured thermal modeling scheme that is directly applicable to commercial off-the-shelf multi-core processors used in real-time embedded systems. By using a small number of thermal profiles obtained from on-chip temperature sensors, our scheme can accurately predict the processor operating temperature under dynamic real-time workloads at various CPU frequencies and ambient conditions. The thermal model derived from our scheme is fast to converge and robust against different sources of errors. Our scheme is non-intrusive, meaning that it does not require changes to the software code or the hardware packaging of the target system. Furthermore, our scheme can estimate the relative power consumption of the processor for a given workload and clock frequency level. Experimental results from a multi-core ARM platform indicate that our scheme estimates the operating temperature with a maximum error of 2.5% while the latest prior work results in 23% error. This highly accurate modeling enables us to obtain the maximum achievable processor utilization that does not cause a thermal safety violation.
UNDERSTANDING RESISTANCE AND OHM’S LAW WITH ARDUINO-BASED EXPERIMENTS ENTENDIENDO LA RESISTENCIA Y LA LEY DE OHM CON EXPERIMENTOS BASADOS EN ARDUINO

The rapid development of technology has enabled many types of technology to be used frequently by educators in learning and teaching activities. One of these frequently used educational technologies is Arduino. This study aims to determine how resistance changes depending on the cross-sectional area, length, and resistivity of the conductor using Arduino. In order to determine the factors affecting the resistance in the Arduino-based experiment, the experiment utilized the graphite leads that students use in their daily lives. An inexpensive and high mobility experiment system has been developed. The data obtained by the experimental setup complies with theoretical laws. This experiment, along with the use of Arduino, will enable students to experience Ohm’s law and enable them to better understand the concepts of potential, current, and resistance in a series circuit. In addition, it is thought that the experiment will attract more attention from students by offering a technology-supported learning opportunity. Researchers and teachers can turn this experiment into a science, technology, engineering y mathematics (STEM) activity by integrating it into a problematic situation. El rápido desarrollo de la tecnologı́a ha permitido que los educadores utilicen con frecuencia muchos tipos de tecnologı́a en las actividades de aprendizaje y enseñanza. Una de estas tecnologı́as educativas de uso frecuente es Arduino. Este estudio tiene como objetivo determinar cómo cambia la resistencia según el área de la sección transversal, la longitud y la resistividad del conductor que usa Arduino. Para determinar los factores que afectan la resistencia en el experimento basado en Arduino, el experimento utilizó las minas de grafito que los estudiantes usan en su vida diaria. Se ha desarrollado un sistema experimental económico y de alta movilidad. Los datos obtenidos por la configuración experimental cumplen con las leyes teóricas. Este experimento, junto con el uso de Arduino, permitirá a los estudiantes experimentar la ley de Ohm y les permitirá comprender mejor los conceptos de potencial, corriente y resistencia en un circuito en serie. Además, se cree que el experimento atraerá más la atención de los estudiantes al ofrecer una oportunidad de aprendizaje respaldada por tecnologı́a. Los investigadores y profesores pueden convertir este experimento en una actividad ciencia, tecnologı́a, ingenierı́a y matemática (STEM) integrándolo en una situación problemática.
Development and application of a robotic zebra finch (RoboFinch) to study multimodal cues in vocal communication

Understanding animal behaviour through psychophysical experimentation is often limited by insufficiently realistic stimulus representation. Important physical dimensions of signals and cues, especially those that are outside the spectrum of human perception, can be difficult to standardize and control separately with currently available recording and displaying techniques (e.g. video displays). Accurate stimulus control is in particular important when studying multimodal signals, as spatial and temporal alignment between stimuli is often crucial. Especially for audiovisual presentations, some of these limitations can be circumvented by the employment of animal robots that are superior to video presentations in all situations requiring realistic 3D presentations to animals. Here we report the development of a robotic zebra finch, called RoboFinch, and how it can be used to study vocal learning in a songbird, the zebra finch.