nils-herrmann/clean_scirepeval huggingface dataset:
Strengthening Standards for Consent: The Case of U.S. Drone Strikes in Pakistan
Year: 2015

This article situates itself at the intersection of two separate fields of inquiry. This article draws and hopes to contribute to an existing literature on the unique legal interpretations marshaled to explain and justify U.S. armed activities in the Global War on Terror and operationalize its global hegemony. While there is a great deal of work on the erosion of sovereign equality generally, and on how the law has served legal hegemony broadly, there is little attention to the nexus of the two how sovereign equality is negotiated by the US as it pursues its foreign policy. This article seeks to illuminate the set of legal arguments put forth to support the US's armed activities in Pakistan, and their core assumptions and misconceptions with regard to sovereign equality. Additionally, this article also contributes to the multi disciplinary literature on the nature of US Pakistan relations by injecting a critical legal analysis of current bilateral relations. Indeed, drone strikes are the latest chapter of longer history of U.S. involvement in Pakistan, in which it has secured or supported domestic political arrangements favorable to the U.S.'s position and activities during the Cold War, and then during the Global War on Terror.
Thompson Sampling for Bandits with Clustered Arms
Venue: IJCAI
Year: 2021

We propose algorithms based on a multi level Thompson sampling scheme, for the stochastic multi armed bandit and its contextual variant with linear expected rewards, in the setting where arms are clustered. We show, both theoretically and empirically, how exploiting a given cluster structure can significantly improve the regret and computational cost compared to using standard Thompson sampling. In the case of the stochastic multi armed bandit we give upper bounds on the expected cumulative regret showing how it depends on the quality of the clustering. Finally, we perform an empirical evaluation showing that our algorithms perform well compared to previously proposed algorithms for bandits with clustered arms.
Health Professionals Information for Diagnostics Related Groups (DRGs) with the Use of a Website
Venue: ICIMTH
Year: 2015

In recent years the costs of hospital care in developed countries absorbed a high proportion of total health resources. In Greece, after the establishment of the National Health System (NHS) funding for hospitals comes mainly from the state budget, and social insurance, therefore auditing of hospital cost is imperative to ensure their viability, especially at this time when the country is tested by economic crisis. Overtime many factors for the increase of hospital cost have been mentioned, such as the rapid development of medical technology, the low level of organization of the hospital system and the low productivity of human resources. The problem of rising health expenditure over the past decades is a worldwide phenomenon and it has brought about many changes in traditional retrospective methods for financing in the hospital sector, with prospective financial forms that combine patient composition and needs, and the productive activity of hospital need to the rationalization and contain expenditures. The system of Diagnosis Related Groups was a revolutionary success over the past decades and has now achieved worldwide recognition.
Musculoskeletal Pain Of "Batik Tulis" (Writing) Women Worker In Berkah Lestari Batik, Bantul Using Nordic Body Map And Physical Asessment
Year: 2018

Batik is one of the industry in Indonesia which is currently experiencing growth. In 2012 the textile industry (including batik) was 4,553 businesses. The process of making traditional batik tulis still through the manual process, the first step is mencanting (make the pattern on the fabric) pencelupan (dip for coloring) dan penjemuran (drying) The work attitude of batik workers is sitting on a small chair without backrest and doing mencanting activities for 6 hours even more. Many factors can caused the incidence of muscle pain in batik workers such as working in a wrong position or unnatural and doing activities in a monotonous position. This research is a quantitative research using cross sectional research design. The instrument used Nordic Body Map questionnaire and a physical examination to identify musculoskeletal system conditions on batik tulis workers was carried out by the physiotherapist with a palpation test. The study sample consisted of 50 workers using the total population in Batik Berkah Lestari. The characteristics of batik workers are the average age of workers 49.8 years, BMI 22.9, working period of 30.54 years. Nordic Body Map results, from 28 parts of the body, 80% of workers suffered from musculoskeletal pain in the left upper arm, with a percentage of 6% feeling very pain, 30% pain and 44% slightly pain. In the right upper arm 74% of workers suffered from it, with 40% were slightly pain, 26% were pain, and 8% were very pain. On the physical examination, 44% of batik workers experienced scoliosis and 32% had lordosis.
Mild Depressive Symptoms Mediate the Impact of Childhood Trauma on Long Term Functional Outcome in Early Psychosis Patients
Venue: Schizophrenia bulletin
Year: 2017

The mechanism linking childhood trauma (CT) to the functional deficits observed in early psychosis (EP) patients is as yet unknown. We aim to examine the potential mediating effect of depressive symptoms in this well established association.  Two hundred nine EP subjects aged 18 35 were assessed for functioning and psychopathology after 2, 6, 12, 18, 24, 30, and 36 months of treatment. Patients were classified into early trauma if they had faced at least one experience of abuse (physical, sexual, or emotional) or neglect (physical or emotional) before age 12, and late trauma if the exposure had occurred between ages 12 and 16. Diagnosis was based on the criteria of the Diagnostic and Statistical Manual of Mental Disorders (Fourth Edition) Psychopathology was assessed with the Positive and Negative Syndrome Scale and the Montgomery Asberg Depression Rating Scale. Functioning was measured with the Global Assessment of Functioning (GAF) and the Social and Occupational Functioning Assessment Scale (SOFAS) Mediation analyses were performed in order to study whether the relationship between CT and functioning was mediated by depressive symptoms.  When compared with nonexposed patients, early but not late trauma patients showed lower levels of GAF and SOFAS scores over all the time points, excepting after the first assessment. After 30 and 36 months, the effect of early trauma on functioning was completely mediated by depressive symptoms. No mediating effect of positive or negative symptoms was highlighted at those time points.  Mild depressive symptoms mediated the impact of early trauma on long term functional outcome. Intensifying pharmacologic and/or psychotherapeutic treatment, focused on the depressive dimension, may help traumatized EP patients to improve their functioning.
FACTORS INFLUENCING MANAGEMENT TRAINING EFFECTIVENESS IN COMMERCIAL BANKS IN KENYA: A CASE OF CO OPERATIVE BANK OF KENYA, NAIROBI COUNTY.
Year: 2018

This study aimed to undertake an investigation on the factors influencing management training effectiveness in Commercial Banks of Kenya: A case of Co operative Bank of Kenya, Nairobi County. The study was guided by three specific objectives: To determine the influence of training design on management training effectiveness in Co operative Bank, Nairobi County; determine the influence of trainee characteristic on management training effectiveness in Co operative Bank, Nairobi County and to determine the influence of Training environment on management training effectiveness in Co operative Bank, Nairobi County. The target population for the study was therefore 282 respondents. Stratified sampling was used to sample the different categories of management staff at Co operative Bank of Kenya. A questionnaire was used to collect data for this study. The data was analyzed using Statistical Package for the Social Sciences (SPSS) The data was presented in charts, tables and supported with an interpretation and discussion. The findings showed that there were positive and statistically significant associations between training design, trainee characteristics, training environment, and management training effectiveness at Cooperative Bank. The multiple regression analysis showed that the independent variables explained 56.2 of change in management training effectiveness at 95 confidence level. Trainee characteristics were found to have the greatest effect on management training effectiveness followed by training design and training environment. The study concluded that trainee characteristics have the greatest effect on management training effectiveness followed by training design, and training environment. The study recommended that Cooperative Bank human resource managers should conduct needs assessments for training programmes; that there should be continuous monitoring of staff fresh from training and provide incentives to motivate staff to enroll in training programmes offered by the organisation; and that supervisors should be encouraged to create environments where staff can apply skills acquired from training in their day today roles.  Training Design, Trainee Characteristic, Training Environment,
Historical Structure From Motion (HSfM) Automated production of high resolution DEMs from historical aerial photography for long term geodetic change analysis
Year: 2021

Mountain glaciers have lost significant mass over the past century in response to a globally warming climate. However, on interannual to decadal time scales, many glaciers in Western North America show periods of both advance and retreat. To better understand these systems and their sensitivity to climate forcing, we are generating regional records of glacier surface elevation change from scanned historical film photographs acquired between the 1950s to 1990s. Our results will help constrain projections of future glacier change under different climate scenarios, as well as impacts on downstream water resources and geohazard risk.
Tax knowledge for the digital economy
Year: 2020

Because of the digital economy, taxpayers have access to new income streams. These virtual transactions have taxation consequences, and therefore taxpayers need specialised taxation knowledge to understand their tax obligations and act in a tax compliant manner.  The aim of this article was to identify the unique tax knowledge requirements for individuals functioning in the digital economy by systematically reviewing literature on the tax challenges arising from this new economy. Applying a conceptual framework of tax knowledge, these knowledge requirements were categorised as either general, procedural or legal. By identifying these requirements, it was possible to point out the risks within these categories that may cause obstacles to individuals to act fully tax compliant.  Understanding the different knowledge requirements of taxpayers may assist tax authorities to identify the tax compliance risks of these taxpayers in their capacity as individuals functioning in the digital economy.  A qualitative approach was used in the study through a thematic search of appropriate literature such as articles, reports, blogs and media releases. These documents were systematically reviewed to identify the knowledge requirements for individual taxpayers functioning in the digital economy.  The findings suggest that there are specific tax knowledge requirements in different areas that must be in place to ensure tax compliance in the digital economy. Any shortcomings in these areas of knowledge create the risk of non compliance for individuals functioning in the digital economy.  Taxpayers and tax authorities alike should take note of the risk areas identified in each area of knowledge (general, procedural and legal) and devise strategies to deal with taxation issues arising from transactions in the digital economy.  This study applied a tax knowledge framework and identified the general, procedural and legal tax knowledge requirements of individuals functioning in the digital economy. The study also pointed out associated compliance risks, which may assist tax authorities to target strategies for improving taxpayer knowledge in these three areas.
Machine learning algorithms for accurate flow based network traffic classification: Evaluation and comparison
Venue: Perform. Evaluation
Year: 2010

The task of network management and monitoring relies on an accurate characterization of network traffic generated by different applications and network protocols. We employ three supervised machine learning (ML) algorithms, Bayesian Networks, Decision Trees and Multilayer Perceptrons for the flow based classification of six different types of Internet traffic including peer to peer (P2P) and content delivery (Akamai) traffic. The dependency of the traffic classification performance on the amount and composition of training data is investigated followed by experiments that show that ML algorithms such as Bayesian Networks and Decision Trees are suitable for Internet traffic flow classification at a high speed, and prove to be robust with respect to applications that dynamically change their source ports. Finally, the importance of correctly classified training instances is highlighted by an experiment that is conducted with wrongly labeled training data.
Tactical Art of Risk Management in the History of Ninja
Year: 2021

The notion of the innovation system is connected to the role of leaders and decision makers in the management of the scientific and technological environment. Innovative frameworks of analysis, by approaching science and technology systemically (i.e. in relation to the economy, politics, and society) adapt stress for success. Any research and innovation policy must take account of a complex set of problems. The focus of this work is to analyze the application of tactical strategies in the risk management, looking at the historical ninja training. Their worldview, the "way of the warrior" as an art of war but also as a path of inner knowledge, has also had great importance in artistic production, culture, and the construction of social relations.
Predicting Drug Target Interactions Using Dimensionality Reduction with Ensemble Learning
Year: 2019

Drug target interaction is one of the most significant fields of research for drug discovery. The laboratory experiments conducted to identify the drug target interactions are tedious, delayed, and costly. Hence, there is an urgent need to develop highly efficient computational methods for identifying potential drug target interactions that can limit the search space of these laboratory experiments. The existing computational techniques for drug target interaction have been broadly classified into similarity based methods and feature based methods. In this paper, a novel feature based technique to predict drug target interactions has been proposed. The technique uses ensemble learning to determine drug target interactions. Ensemble learning offers greater accuracy in comparison with the traditional classifiers. Thus, the proposed technique aims to improve accuracy using ensemble learning. Also, dimensionality reduction of drug target features is performed using principal component analysis so that the computational time of the method can be reduced. The results indicate an improved performance in comparison with the state of the art methods in the field.
A REINFORCEMENT LEARNING (RL) MODEL FOR DYNAMIC OPTIMIZATION OF SWITCH PORT/QUEUE BUFFER ALLOCATION
Year: 2018

Techniques are described herein for a reinforcement learning (RL) model for dynamic optimization of switch port/queue buffer allocation. According to the described techniques, a trained neural network model can be installed on a switch or on a network management server to dynamically adjust the shared/dedicated buffer allocation for the low/high priority queues in case of a frame loss. The dynamic buffer adjustment continues till the port/queue no longer experience a frame loss.  Typically, switch buffer management algorithms and templates allocate the per port priority queue(s) buffer at the Line Card boot up phase. Thereafter, irrespective of the traffic profile observed, the buffer allocation remains unchanged. Consequently, the high and low priority queues suffer from sub optimal buffer allocation often resulting in frame drops. The techniques described herein provide a reinforcement learning model for dynamic optimization of switch port/queue buffer allocation. In the Artificial Intelligence/Machine Learning (AI/ML) realm, the problem can be framed as a Reinforcement Learning (RL) Model that interacts in a dynamic environment and takes corrective actions to ensure the high/low priority queue buffer allocation is optimized to minimize the frame loss. The dynamic environment monitors the buffer allocation, queue occupancy, queue priority, Quality of Service (QOS) configuration and per port and per queue traffic counters. The environment robustness and sophistication can be enhanced by including more relevant state. The model dynamically adjusts the buffer allocation in reaction to the detection of frame loss. The buffer allocation adjustment continues till the frame loss is 2 Pasha: A REINFORCEMENT LEARNING (RL) MODEL FOR DYNAMIC OPTIMIZATION OF S Published by Technical Disclosure Commons, 2018 2 5756 rectified. The RL model can tap into the available buffer pool or steals from a port/queue that might have excess buffer allocated. The canonical Q learning (model free) RL design for the agent and environment interaction is shown in Figure 1 below.
Aplikasi Parameter Waktu Kontrol PID pada Heat Exchanger dengan Menggunakan Response Surface Methodology (RSM)
Venue: Jurnal Teknologi Kimia Unimal
Year: 2019

Heat exchangers are tools used to transferring or transfer heat energy between a solid surface and fluid or inside different particles and in thermal contact. The objective of the study was to determine the optimum values of Kc, Ti and the best Td which to Heat exchangers of factory phenol design. Proportional Integral Derivative (PID) is a controller to determine the precision of an instrumentation system with the characteristics of feedback on the system. As the methodology of this research is to create a steady state Heat Exchanger model, then change the steady state model into a dynamic model, so create a PID model control, then tuning the PID control and testing PID controls, by disturbing the set point setting the valid point 115 The result of applying PID control system then got the fastest average time with value Kc 8,82, Ti 2,58, Td 0 that is 0,38 minutes. At a temperature of 147.6 air vapor rate of 290.8 Kg hr; at a temperature of 148.6 foundKeywords:heat exchanger, PID, set point, set point
Infrared Thermal Imaging System on a Mobile Phone
Venue: Sensors
Year: 2015

A novel concept towards pervasively available low cost infrared thermal imaging system lunched on a mobile phone (MTIS) was proposed and demonstrated in this article. Through digestion on the evolutional development of milestone technologies in the area, it can be found that the portable and low cost design would become the main stream of thermal imager for civilian purposes. As a representative trial towards this important goal, a MTIS consisting of a thermal infrared module (TIM) and mobile phone with embedded exclusive software (IRAPP) was presented. The basic strategy for the TIM construction is illustrated, including sensor adoption and optical specification. The user oriented software was developed in the Android environment by considering its popularity and expandability. Computational algorithms with non uniformity correction and scene change detection are established to optimize the imaging quality and efficiency of TIM. The performance experiments and analysis indicated that the currently available detective distance for the MTIS is about 29 m. Furthermore, some family targeted utilization enabled by MTIS was also outlined, such as sudden infant death syndrome (SIDS) prevention, etc. This work suggests a ubiquitous way of significantly extending thermal infrared image into rather wide areas especially health care in the coming time.
Addressing the Multistakeholder Impact of Popularity Bias in Recommendation Through Calibration
Venue: ArXiv
Year: 2020

Popularity bias is a well known phenomenon in recommender systems: popular items are recommended even more frequently than their popularity would warrant, amplifying long tail effects already present in many recommendation domains. Prior research has examined various approaches for mitigating popularity bias and enhancing the recommendation of long tail items overall. The effectiveness of these approaches, however, has not been assessed in multistakeholder environments where in addition to the users who receive the recommendations, the utility of the suppliers of the recommended items should also be considered. In this paper, we propose the concept of popularity calibration which measures the match between the popularity distribution of items in a user's profile and that of the recommended items. We also develop an algorithm that optimizes this metric. In addition, we demonstrate that existing evaluation metrics for popularity bias do not reflect the performance of the algorithms when it is measured from the perspective of different stakeholders. Using music and movie datasets, we empirically show that our approach outperforms the existing state of the art approaches in addressing popularity bias by calibrating the recommendations to users' preferences. We also show that our proposed algorithm has a secondary effect of improving supplier fairness.
A Self Rectifying Resistive Switching Device Based on HfO2/TaO $_{{x} Bilayer Structure
Venue: IEEE Transactions on Electron Devices
Year: 2019

To effectively solve the crosstalk issue in high density crossbar array (CBA) high rectifying characteristics should be introduced in the resistance random access memory (ReRAM) device, and in depth understanding of the affecting factors on rectifying properties is essential for the large scale application of ReRAM. In this paper, a high performance self rectifying device with CMOS compatible Pd/HfO<sub>2</sub>/TaO<sub><italic>x</italic>/sub>/Ta structure was demonstrated in a 1 kb CBA. Forming free, self compliance, and high uniformity characteristics were successfully achieved. By modulating the thickness of the HfO<sub>2</sub> rectifying layer, the rectifying ratio of device could be achieved as high as <inline formula> <tex math notation="LaTeX"\sim 2\times 10^\textsf  /tex math>/inline formula> under 3 V at low resistance state (LRS) It was also experimentally confirmed that the selected unit cell in high resistance state (logically the " OFF" state) was stably readable when it was surrounded by unselected LRS (logically the " ON" state) cells, in an array of up to <inline formula> <tex math notation="LaTeX"$32 \times 32$ /tex math>/inline formula> cells. Furthermore, a model based on interfacial barrier modulation and defects trapping/detrapping was proposed to elucidate the impact of the dielectric thickness on the self rectifying characteristics of the device. The results presented in this paper provide a great potential for selector free high density memory applications.