nils-herrmann/clean_scirepeval huggingface dataset:
Twin Delayed DDPG: A Deep Reinforcement Learning Technique to Model a Continuous Movement of an Intelligent Robot Agent
Venue: ICVISP
Year: 2019

In this current research, Twin Delayed DDPG (TD3) algorithm has been used to solve the most challenging virtual Artificial Intelligence application by training a 4 ant legged robot as an Intelligent Agent to run across a field. Twin Delayed DDPG (TD3) is an incredibly smart AI model of a Deep Reinforcement Learning which combines the state of the art methods in Artificial Intelligence. These includes Policy gradient, Actor Critics, and continuous Double Deep Q Learning. These Deep Reinforcement Learning approaches trained an Intelligent agent to interact with an environment with automatic feature engineering, that is, necessitating minimal domain knowledge. For the implementation of the TD3, we used a two layer feedforward neural network of 400 and 300 hidden nodes respectively, with Rectified Linear Units (ReLU) as an activation function between each layer for both the Actor and Critics. We, then added a final tanh unit after the output of the Actor. The Critic receives both the state and action as input to the first layer. Both the network parameters were updated using Adam optimizer. The idea behind the Twin Delayed DDPG (TD3) is to reduce overestimation bias in Deep Q Learning with discrete actions which are ineffective in an Actor Critic domain setting. Based on the Maximum Average Reward over the evaluation time step, our model achieved an approximate maximum of 2364. Therefore, we can truly say that, TD3 has obviously improved on both the learning speed and performance of the Deep Deterministic Policy Gradient (DDPG) in a challenging environment in a continuous control domain.
Combined heat and power system intelligent economic dispatch: A deep reinforcement learning approach
Year: 2020

This paper proposed a Deep Reinforcement learning (DRL) approach for Combined Heat and Power (CHP) system economic dispatch which obtain adaptability for different operating scenarios and significantly decrease the computational complexity without affecting accuracy. In the respect of problem description, a vast of Combined Heat and Power (CHP) economic dispatch problems are modeled as a high dimensional and non smooth objective function with a large number of non linear constraints for which powerful optimization algorithms and considerable time are required to solve it. In order to reduce the solution time, most engineering applications choose to linearize the optimization target and devices model. To avoid complicated linearization process, this paper models CHP economic dispatch problems as Markov Decision Process (MDP) that making the model highly encapsulated to preserve the input and output characteristics of various devices. Furthermore, we improve an advanced deep reinforcement learning algorithm: distributed proximal policy optimization (DPPO) to make it applicable to CHP economic dispatch problem. Based on this algorithm, the agent will be trained to explore optimal dispatch strategies for different operation scenarios and respond to system emergencies efficiently. In the utility phase, the trained agent will generate optimal control strategy in real time based on current system state. Compared with existing optimization methods, advantages of DRL methods are mainly reflected in the following three aspects: 1) Adaptability: under the premise of the same network topology, the trained agent can handle the economic scheduling problem in various operating scenarios without recalculation. 2) High encapsulation: The user only needs to input the operating state to get the control strategy, while the optimization algorithm needs to re write the constraints and other formulas for different situations. 3) Time scale flexibility: It can be applied to both the day ahead optimized scheduling and the real time control. The proposed method is applied to two test system with different characteristics. The results demonstrate that the DRL method could handle with varieties of operating situations while get better optimization performance than most of other algorithms.
Bridging the Gap Between Growth Theory and Policy in Asia: An Extension of the Solow Growth Model
Year: 2014

Although economic growth is amongst the oldest of debates in Economics, there has been little advancement in growth theory since the classic works of Solow. As such, clear cut answers to the context specific determinants of growth, especially for developing countries, are rare. to the works of Solow by showing how his original model can be extended and used for policy, discussing issues in growth econometrics, including the theoretical underpinnings of growth models, and some of the important challenges in empirical studies on economic growth. This simpler manual will be highly useful for applied economists, policy makers, and graduate students.  This premier publication is essential for all academic and research library reference collections. It is a crucial tool for academicians, researchers, and practitioners. Ideal for classroom use.
Machine learning a probabilistic perspective
Venue: Adaptive computation and machine learning series
Year: 2012

Today's Web enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package PMTK (probabilistic modeling toolkit) that is freely available online. The book is suitable for upper level undergraduates with an introductory level college math background and beginning graduate students.
Repurposing Fumaric Acid Esters to Treat Conditions of Oxidative Stress and Inflammation: A Promising Emerging Approach with Broad Potential
Year: 2020

The medicinal benefit of salts of fumaric acid and its esters (FAE) known as fumarates (mono and dimethyl fumarate) was realized many years ago. Early on, FAE were derived from plants and mushrooms (e.g. Fumaria officinalis, Boletus fomentarius var. pseudo igniarius) The FAE containing formulation Fumaderm(r) was licensed in Germany for the treatment of psoriasis in 1994. Recently, a clinical formulation of dimethyl fumarate known as BG12 (Tecfidera) was approved for use in the United States, New Zealand, Australia, European Union, Switzerland, and Canada for the treatment of multiple sclerosis. Others and we have assessed the potential benefit of FAE in a number of disease conditions that are diverse with respect to etiology but unified with regard to the involvement of inflammation and oxidative stress. Hence, a FAE based drug with robust anti oxidative and anti inflammatory effects that is already US FDA approved is a perfect contender for repurposing and rapid clinical implementation for their management. There is a burgeoning literature on the use of FAE in the prevention and treatment of diseases, other than psoriasis and MS, in which oxidative stress and/or inflammation are prominent. This chapter highlights critical information gleaned from these studies, exposes lacunae of potential importance, and provides related perspectives.
Predictive Models for Game Outcomes in Women's Lacrosse
Venue: Applied Mathematics and Sciences: An International Journal (MathSJ)
Year: 2019

This research presents a predictive model for determining the game outcome of a Women's (Female) Lacrosse game. This is important to coaches regardless of if their team appears to be winning or losing the game. Coaches make decisions throughout the game based upon the belief that they are winning or losing. The model is a Logistic Regression model and can be used with very little data from a game: time remaining and difference between the scores. This could be a valuable tool to coaches that can be used during the game. It is more than 89% accurate. Data used in this research comes from direct matchup games between BigTen Women's Lacrosse teams. The win probability equations, including coefficients, are presented.
Kinesthetic Mind Reader A Method to Identify Image Schemas in Natural Language
Year: 2017

Natural language understanding remains one of the weak spots of Artificial Intelligence and cognitive systems in general. In cognitive linguistics, image schemas were introduced as spatio temporal relations learned from sensorimotor processes that constitute conceptual building blocks for highlevel cognition, such as language and reasoning. In this role, they have been successfully employed in computational concept invention and conceptual metaphor research. However, due to their abstract nature identifying them in natural language is an open challenge. To address this issue, this paper proposes a spectral clustering method combined with semantic role labeling to semi automatically detect image schemas in natural language. In the majority of identified spatial clusters from the Europarl corpus the proposed method detected image schemas, which shows that it works effectively on large corpora. The outcome of this method is a repository of natural language expressions annotated with image schemas that can be used to improve spatial language understanding and provide examples to supervised machine learning approaches.
Black face with an invisible white mask a phenomenological study of self understanding through investigating co researchers' psychological lived experiences before, during and after gang involvement
Year: 2016

Research into gang members is dominated by behavioural and criminal perspectives with limited in depth, meaningful understanding of the experience of gang members' sense of being. I set out to investigate five male African Caribbean former gang members' psychological lived experiences before, during and after gang involvement using the qualitative methodology of Heuristic Hermeneutic Phenomenological Inquiry (HHPI) I approached this study based on my experience as a teenager having been associated with gang members. I utilised the principles of triple hermeneutics as a way of understanding myself through the co researchers' experiences which are incorporated into the findings. The analysis is divided into three phases; before, during and after gang involvement. The main themes of before gang involvement are: (1) Dysfunctional Lifeworld, which revealed the effect of domestic violence, an emotionally and frequently absent father, separation and relational racism; (2) Safe Place uncovered the only lived space which induced calmness and routine; and (3) Psychological Survival illuminated the fight and flight response to perceived threatening situations. In phase two, during gang involvement, the theme Psychological Survival illustrated the adversities experienced being within White mainstream society that consequently opened the pathway to life being within an alternative world, gang society. In the final phase, after gang involvement, the central theme is Functional Lifeworld, which highlighted post traumatic growth, a positive psychological adjustment. Overall, the findings suggest a connection with the legacy of slavery and how it led me to consider the possibility of living with posttraumatic slavery disorder and intergenerational trauma (Reid et al. 2005) Based on the findings, I introduced the therapeutic model; Heuristic Existential Post Slavery Therapy (HEPST) which contributed to the knowledge and clinical practice for counselling and psychotherapy. Due to the sensitivity of the study, it should be noted that by the end of the research, only one co researcher remained in the study.
"At Least I Didn't Get Raped" A Qualitative Exploration of IPV and Reproductive Coercion among Adolescent Girls Seeking Family Planning in Mexico.
Venue: Journal of interpersonal violence
Year: 2020

Adolescent girls who report intimate partner violence (IPV) are at an increased risk of experiencing reproductive coercion (RC) both these forms of gender based violence (GBV) are associated with unintended pregnancy. Yet little is known about these experiences among adolescent girls in Mexico. Qualitative data were collected as part of formative research for the adaptation of an evidence based intervention to address RC and IPV in community health centers in Tijuana, Mexico. From September, 2017 to January, 2018, adolescent girls aged 16 to 20 years old (n 20) seeking voluntary family planning (FP) services were identified and recruited from two publicly funded community health centers. We conducted semi structured, in depth interviews and analyzed the transcripts using inductive and deductive techniques. Participants in this sample commonly described experiencing IPV and RC (including pregnancy coercion and contraceptive sabotage) which many girls reported resulted in unintended pregnancy. Further, participants' narratives and general lack of knowledge on how to cope with IPV or RC illuminated the acceptability of offering GBV prevention intervention within FP clinics serving this population. Findings highlight an urgent need to prevent IPV and RC, and reduce risk for unintended pregnancy among adolescent girls in this region and the potential of FP clinics to serve as a safe space for intervention delivery. Findings contribute to the limited qualitative evidence from Mexico, describing adolescent girl's experiences of IPV and RC, strategies for preventing pregnancy in the context of RC, and opportunities for support from FP providers.
Estudio de prefactibilidad del proyecto fotovoltaico para el municipio de Waspan
Year: 2019

El estudio tiene como finalidad evaluar la prefactibilidad del uso de paneles solares fotovoltaicos en las familias de escasos recursos de sectores rurales de Waspan, rio coco arriba. Se pretende incorporar una alternativa para el diseno y calculo de estos sistemas fotovoltaicos y de esta manera reemplazar la generacion de energia a partir de combustibles fosiles como el diesel, minimizando asi los impactos ambientales negativos que estas producen.
Online Trajectory Planning of RLV Terminal Area Energy Management Phase
Venue: 2020 Chinese Automation Congress (CAC)
Year: 2020

The terminal area energy management (TAEM) phase of the reusable launch vehicle (RLV) determines whether it can reach the landing window safely and accurately. Compared with offline trajectory planning, online trajectory planning is more robust to the uncertainties of actual flight. This paper investigates the online trajectory planning algorithm of TAEM phase. Firstly, the trajectory profile design method based on fixed range to go is proposed, and the non linear least squares optimization algorithm is used to automatically design a trajectory profile that satisfies the fixed range to go. Secondly, in order to adapt to high speed and high sinking flight, according to the approximate linear characteristics of the dynamic pressure transition point with the fixed range to go, a dual optimization algorithm is used to quickly find the dynamic pressure transition point with the shortest time and satisfying the range to go. By establishing the trim data table, the planning time is less than 1s, which fully meets the real time requirements of the online planning. The simulation results show that the online planning algorithm is simple, efficient and robust.
ICT and transport infrastructure development: an empirical analysis of complementarity
Venue: Applied Economics
Year: 2019

This paper provides an empirical framework to assess the nonlinear complementary linkage effects that arise from the interaction between motorway capital and information and communications technology (ICT) capital in developed economies. Using panel data from the Organisation for Economic Co operation and Development (OECD) member countries and controlling for endogeneity, the paper finds that there exists a critical mass for ICT capital such that if the capital grows beyond the critical mass, the marginal contribution of motorway capital to productivity growth increases as the motorway is extended. This empirical result explains variations in the productivity contributions of transport infrastructure across countries that differ in their ICT infrastructure and has implications for setting the investment priorities of key components of infrastructure.
Revised Models of the Photo earning APP's Pricing Plan
Year: 2019

This paper aims at the existing pricing platform of photo making platform.By studying the reasons for the unfinished tasks,synthesizing the impact of each indicator,proposing a new plan and explaining its impact,the pricing plan of the platform is completed and revised.The completed projects are clustered by latitude and longitude,and the selected indicators are quantified and then gradually regressed with the project pricing to obtain the relational display pricing law.Divide the members,formulate a new quota plan,and then establish a correction function for the comprehensive indicators to obtain a new pricing plan.The packaging model is integrated to correct the pricing model.After the pricing is revised,the task completion degree and the number of tasks in each city reflect the impact of the packaging factors on the task completion.Finally,the new and old pricing models are applied to the new project,and the task pricing under the new and old pricing schemes is obtained,and the effect of the implementation of the scheme is evaluated from the analysis of members and the platform.
A Priority based Round Robin CPU Scheduling Algorithm for Real Time Systems
Year: 2012

The main objective of this paper is to develop a new approach for round robin C P U scheduling a l g o r i t h m which improves the performance of CPU in real time operating system. The proposed Priority based Round Robin CPU Scheduling algorithm is based on the integration of round robin and priority scheduling algorithm. It retains the advantage of round robin in reducing starvation and also integrates the advantage of priority scheduling. The proposed algorithm also implements the concept of aging by assigning new priorities to the processes. Existing round robin CPU scheduling algorithm cannot be implemented in real time operating system due to their high context switch rates, large waiting time, large response time, large turnaround time and less throughput. The proposed algorithm improves all the drawbacks of round robin C P U scheduling algorithm. The paper also presents the comparative analysis of proposed algorithm with existing round robin scheduling algorithm on the basis of varying time quantum, average waiting time, average turnaround time and number of context switches.
Visual Comfort Based Algorithmic Control for Roller Shade and Assessment of Potential Energy Savings
Year: 2018

Offices which are mostly operated during day lit hours are fascinated toward incorporation of solar daylighting systems so as to get benefitted of energy savings along with intangible benefits like good health, well being, and productivity of their occupants. But discomfort glare is usually ignored in front of rigorous energy load optimization practices and researches, or even if considered, is not properly quantified, which leads to a false evaluation of the performance of daylighting in the context of lighting energy savings. Blinds when operated with optimum visual comfort prevention measures, then only can create a functional day lit environment. The present study includes modeling and analysis of an east facing office building located in New Delhi, India. It utilizes drafting tool Rhinoceros 3D 5.0 and simulation tool DIVA 4.0 to mathematically analyze the utilization of natural lighting for the office. The analysis includes properly arranged viewpoints similar to that experienced by any occupant, being placed in simulation environment so as to visualize and evaluate realistic glare scenario. The study further deals with the observation and analysis of the illuminance patterns inside the test space and glare values for different blind positions modeled. An algorithm has been developed to analyze the required visual comfort level of the space and suggests an appropriate blind position for every occupancy hour that eventually leads to estimate the potential energy savings through the utilization of daylight. It was observed that even after providing visual comfort to the occupants, the reduction in savings was marginal, measured to be 1% as compared to the case when visual discomfort is overlooked in daylighting utilization.
European Cybersecurity Perspectives
Year: 2019

2018 is a difficult year to summarize for Infosec. After the initial flurry of activity around Spectre and Meltdown in the beginning of January, we ended the year with global supply chain concerns brought about by the Super Micro story. Throughout the year we saw the geopolitical dilemmas of 2018 manifest in cyber security issues. Technology giants like Facebook and Google had a security reckoning. However in pure scariness the medical data breaches of MyHeritage (DNA) and MyFitnessPal (health) rank higher. The Starwood Marriot Hotel breach made every travelling executive nervous for the rest of the year, but probably not as nervous as the incident of CEO Fraud at Pathe. In an effort to alleviate some of that impact we are proud to publish the 6th European Cyber Security Perspectives (ECSP) report. The 2019 issue is filled with great articles from our partners ranging from government, universities and private companies. Special thanks goes out to all the partners who have submitted an article for the 6th edition of the ECSP. Also huge hugs to first time authors from de Piratenpartij, de Volksbank, Leiden University, University of Illinois, Hack in the Box and QuSoft. If IoT was the buzzword in 2017 then Artificial Intelligence (AI) was most definitely in 2018. AI and security seem to be intertwined and that is why you will find several articles about AI in this issue. This year the organization of Hack in the Box created a challenge which you can find at the bottom of the centerfold. There are great prizes involved so make sure to try your luck.