nils-herrmann/clean_scirepeval huggingface dataset:
Dihydroorotate dehydrogenase: A drug target for the development of antimalarials.
Venue: European journal of medicinal chemistry
Year: 2017

Malaria is a critical human disease with extensive exploration yet unestablished due to occurrence of frequent drug resistance. This aspect of malaria pharmacology calls for the introduction of new antimalarial. The drugs reported till date targeted different stages of the parasites in order to stop their growth and proliferation. Beside this, various drugs that could inhibit the imperative enzymes of the parasite have also been reported. Amid them, dihydroorotate dehydrogenase (DHODH) has a key worth. DHODH is involved in the de novo pyrimidine biosynthesis of the malarial parasite which acts as a primary source of energy for its survival. Since life of the parasite utterly depends on pyrimidine biosynthesis, so it can be used as an apt drug target for malaria eradication. In addition to this, DHODH is also present in human and their active sites have significant structural dissimilarities, so the development of selective inhibitors may prove to be a milestone in search of new antimalarials. Inhibitors of human DHODH have been used to treat autoimmune diseases such as, rheumatoid arthritis or multiple sclerosis and have been investigated in the treatment of cancer, viral diseases, as well as in plant pathology. Here, we have reviewed the important role of DHODH as a viable drug target against malaria, its importance for the survival of the parasite, and DHODH inhibitors reported so far. The rate of success of the reported DHODH inhibitors and further required improvements have also been accounted.
SemEval 2019 Task 3: EmoContext Contextual Emotion Detection in Text
Venue: *SEMEVAL
Year: 2019

In this paper, we present the SemEval 2019  Contextual Emotion Detection in Text. Lack of facial expressions and voice modulations make detecting emotions in text a challenging problem. For instance, as humans, on reading "Why don't you ever text me!" we can either interpret it as a sad or angry emotion and the same ambiguity exists for machines. However, the context of dialogue can prove helpful in detection of the emotion. In this task, given a textual dialogue i.e. an utterance along with two previous turns of context, the goal was to infer the underlying emotion of the utterance by choosing from four emotion classes Happy, Sad, Angry and Others. To facilitate the participation in this task, textual dialogues from user interaction with a conversational agent were taken and annotated for emotion classes after several data processing steps. A training data set of 30160 dialogues, and two evaluation data sets, Test1 and Test2, containing 2755 and 5509 dialogues respectively were released to the participants. A total of 311 teams made submissions to this task. The final leader board was evaluated on Test2 data set, and the highest ranked submission achieved 79.59 micro averaged F1 score. Our analysis of systems submitted to the task indicate that Bi directional LSTM was the most common choice of neural architecture used, and most of the systems had the best performance for the Sad emotion class, and the worst for the Happy emotion class.
Social Media: A New Way of Public and Political Communication in Digital Media
Venue: Int. J. Interact. Mob. Technol.
Year: 2017

Today, social media are the new way of public and political communication in digital marketing. Companies or organizations are no longer the exclusive owners of the relation of consumers with their products/services; instead, the survival of the organizations depends of the effective utilization of the social media. New web technologies have made it easy for anyone to create and distribute their own content. A tweet can be viewed by virtually millions of people for free, and advertisers don't have to pay publishers huge sums of money to embed their messages. More consumers are on social media than ever before, and every second a company is not engaged is a wasted opportunity  Consumers trust other people to provide recommendations about products and services in a very active way and it is important to know how and why social media influence organizations. This study analyzes through a literature review the importance of public and political communication through social media and proposes a model of business for successful marketing strategies.
Space Based Earth Observations for Disaster Risk Management
Venue: Surveys in Geophysics
Year: 2020

As space based Earth observations are delivering a growing amount and variety of data, the potential of this information to better support disaster risk management is coming into increased scrutiny. Disaster risk management actions are commonly divided into the different steps of the disaster management cycle, which include: prevention, to minimize future losses; preparedness and crisis management, often focused on saving lives; and post crisis management aiming at re establishing services supporting human activities. Based on a literature review and examples of studies in the area of coastal, hydro meteorological and geohazards, this review examines how space based Earth observations have addressed the needs for information in the area of disaster risk management so far. We show that efforts have essentially focused on hazard assessments or supporting crisis management, whereas a number of needs still remain partly fulfilled for vulnerability and exposure mapping, as well as adaptation planning. A promising way forward to maximize the impact of Earth observations includes multi risk approaches, which mutualize the collection of time evolving vulnerability and exposure data across different hazards. Opportunities exist as programmes such as the Copernicus Sentinels are now delivering Earth observations of an unprecedented quality, quantity and repetitiveness, as well as initiatives from the disaster risk science communities such as the development of observatories. We argue that, as a complement to this, more systematic efforts to (1) build capacity and (2) evaluate where space based Earth observations can support disaster risk management would be useful to maximize its societal benefits.
Urban Environmental Governance: Bangladesh as a Case in Point
Year: 2015

Given the rapid urbanisation trends and the strategic and economic significance of cities in Asia, sustainably managing urban environments needs to be an urgent priority. Taking Bangladesh as a case in point, the authors have sought to validate their core premise that effective urban environmental governance at all levels is crucial for building sustainable and climate resilient cities. Bangladesh prides itself with exemplar macro level environmental policy and governance instruments, but these have failed to translate into practice, due to weak policy enforcement and implementation on account of a number of systemic barriers and constraints. This paper maps the role and dimensions of urban environmental governance and the systemic barriers, opportunities and pathways for strengthening this at all levels in the Bangladesh context.
Worker overconfidence: Field evidence and implications for employee turnover and firm profits
Year: 2020

Combining weekly productivity data with weekly productivity beliefs for a large sample of truckers over 2 years, we show that workers tend to systematically and persistently overpredict their productivity. If workers are overconfident about their own productivity at the current firm relative to their outside option, they should be less likely to quit. Empirically, all else equal, having higher productivity beliefs is associated with an employee being less likely to quit. To study the implications of overconfidence for worker welfare and firm profits, we estimate a structural learning model with biased beliefs that accounts for many key features of the data. While worker overconfidence moderately decreases worker welfare, it also substantially increases firm profits. Overconfidence biased learning turnover D03 J24 J41 M53
Studi Fenomenologi Work Family Conflict dalam Kehidupan Guru Honorer Wanita
Year: 2020

Tujuan penelitian ini adalah untuk mengetahui latar belakang seorang ibu rumah tangga untuk bekerja di luar sebagai guru honorer, mengetahui kondisi finansial keluarga guru honorer wanita, dan untuk mengetahui work family conflict yang dialami wanita selama menjadi guru honorer. Penelitian ini adalah penelitian kualitatif dengan pendekatan studi fenomenologi dengan metode pengumpulan data, yaitu wawancara, observasi dan studi literatur. Narasumber yang ada dalam penelitian ini adalah ibu rumah tangga yang bekerja di luar rumah sebagai guru honorer di Tangerang. Untuk mengetahui keabsahan data digunakan uji kredibilitas data atau kepercayaan terhadap data. Metode pengujian data menggunakan triangulasi sumber dan member check. Metode analisis data menggunakan data reduction, dan drawing conclusion. Penelitian ini menemukan bahwa faktor utama ibu rumah tangga untuk bekerja di luar rumah adalah karena faktor finansial dan pendidikan. Faktor lainnya adalah faktor mengisi waktu luang dan untuk bersosialisasi dengan rekan kerja. Solusi menangani work family conflict dibahas mendalam dalam kajian ini.
Intelligent Distributed Computing VIII Proceedings of the 8th International Symposium on Intelligent Distributed Computing, IDC 2014, Madrid, Spain, September 3 5, 2014
Venue: IDC
Year: 2015

This book represents the combined peer reviewed proceedings of the Eight International Symposium on Intelligent Distributed Computing IDC'2014, of the Workshop on Cyber Security and Resilience of Large Scale Systems WSRL 2014, and of the Sixth International Workshop on Multi Agent Systems Technology and Semantics MASTS 2014. All the events were held in Madrid, Spain, during September 3 5, 2014. The 47 contributions published in this book address several topics related to theory and applications of the intelligent distributed computing and multi agent systems, including: agent based data processing, ambient intelligence, collaborative systems, cryptography and security, distributed algorithms, grid and cloud computing, information extraction, knowledge management, big data and ontologies, social networks, swarm intelligence or videogames amongst others.
Multi Criteria Decision Analysis and Cost Benefit Analysis: Comparing alternative frameworks for integrated valuation of ecosystem services
Year: 2016

Multi Criteria Decision Analysis (MCDA) methods has been promoted as an alternative approach to monetary economic valuation of ecosystem services in Cost Benefit Analysis framework (CBA) We discuss the potential of MCDA in providing a framework for integrated valuation of ecosystem services. We conclude that MCDA does in general perform better than CBA and associated monetary valuation techniques in several aspects that are essential in ecosystem service valuation. These include the ability of a valuation method to account for multiple dimensions of well being, including ecological and economic as well as cultural and moral aspects of a policy or management problem and to facilitate open and transparent public debate on the pros and cons of alternative courses of action, including the distribution of gains and losses across beneficiaries of ecosystem services. The capacity of MCDA to articulate values related to ecosystem services depends on individual methods used in the MCDA process. More importantly, it depends of the ways in which the process is organized and facilitated. However, MCDA cannot provide representative information of the values of wider population. Further empirical and theoretical research is needed on the potential of hybrid methodologies to combine monetary valuation and MCDA in fruitful ways.
Label Free Distant Supervision for Relation Extraction via Knowledge Graph Embedding
Venue: EMNLP
Year: 2018

Distant supervision is an effective method to generate large scale labeled data for relation extraction, which assumes that if a pair of entities appears in some relation of a Knowledge Graph (KG) all sentences containing those entities in a large unlabeled corpus are then labeled with that relation to train a relation classifier. However, when the pair of entities has multiple relationships in the KG, this assumption may produce noisy relation labels. This paper proposes a label free distant supervision method, which makes no use of the relation labels under this inadequate assumption, but only uses the prior knowledge derived from the KG to supervise the learning of the classifier directly and softly. Specifically, we make use of the type information and the translation law derived from typical KG embedding model to learn embeddings for certain sentence patterns. As the supervision signal is only determined by the two aligned entities, neither hard relation labels nor extra noise reduction model for the bag of sentences is needed in this way. The experiments show that the approach performs well in current distant supervision dataset.
Estimation of Ground Reaction Forces and Moments During Gait Using Only Inertial Motion Capture
Venue: Sensors
Year: 2017

Ground reaction forces and moments (GRF&M) are important measures used as input in biomechanical analysis to estimate joint kinetics, which often are used to infer information for many musculoskeletal diseases. Their assessment is conventionally achieved using laboratory based equipment that cannot be applied in daily life monitoring. In this study, we propose a method to predict GRF&M during walking, using exclusively kinematic information from fully ambulatory inertial motion capture (IMC) From the equations of motion, we derive the total external forces and moments. Then, we solve the indeterminacy problem during double stance using a distribution algorithm based on a smooth transition assumption. The agreement between the IMC predicted and reference GRF&M was categorized over normal walking speed as excellent for the vertical (r 0.992, rRMSE 5.3% anterior (r 0.965, rRMSE 9.4% and sagittal (r 0.933, rRMSE 12.4% GRF&M components and as strong for the lateral (r 0.862, rRMSE 13.1% frontal (r 0.710, rRMSE 29.6% and transverse GRF&M (r 0.826, rRMSE 18.2% Sensitivity analysis was performed on the effect of the cut off frequency used in the filtering of the input kinematics, as well as the threshold velocities for the gait event detection algorithm. This study was the first to use only inertial motion capture to estimate 3D GRF&M during gait, providing comparable accuracy with optical motion capture prediction. This approach enables applications that require estimation of the kinetics during walking outside the gait laboratory.
A Kansei evaluation Study on the Uncanny Valley of Real world Robots
Year: 2017

Roboticists believe that highly, but not perfectly, realistic human looking robots elicit negative feelings in humans. This is the so called 'uncanny valley' response. Most research on the uncanny valley has used the morphing technique and the morphing rates as the objective scale. However, those findings might come from unnatural and/or unexistable appearance of morphed faces. The present study investigated the uncanny valley by measuring participants' impressions of some existing robots whose degree of human likeness was evaluated by Scheffe's paired comparison method, a kansei engineering approach. The survey demonstrated the uncanny valley like Mori's (1970) hypothetical graph. Furthermore, as the X axis, 'experience' one of the two independent dimensions in mind perception (Gray, Gray, Wegner, 2007) showed the pattern of the uncanny valley, but 'agency' did not. These results suggest that robots become unnerving when people ascribe to them experience, rather than agency.
New secure partial encryption method for medical images using graph coloring problem
Year: 2015

The traffic of digital images has been quickly increased in the network. Security of image processing became important for many sectors, namely for medical applications. Currently, the transmission of medical images is a daily routine. The large volume of data exchange has motivated the development of new methods to reduce the cost. Partial encryption is an approach to reduce the computational resources for huge volumes of multimedia. This paper introduces a new and secure approach, called graph coloring problem cryptography, to encrypt partially the medical images using the graph coloring problem (GCP) Before encrypting the medical images using advanced encryption standard algorithm, we use a GCP algorithm to localize and select some optimal positions of the pixels in the original images. Thus, the key of the cryptographic method is hard to be detected and extracted by the hackers. We get an acceptable percentage of the encrypted data for better security with a lower cost compared with the total image encryption.
A statistical approach to the identification of diploid cellular automata based on incomplete observations
Venue: Biosyst.
Year: 2019

In this paper, the identification problem of diploid Cellular Automata is considered, in which, based on a series of incomplete observations, the underlying cellular automaton rules and the states of missing cell states are to be uncovered. An algorithm for identifying the rule, based on a statistical parameter estimation method using a normal distribution approximation, is presented. In addition, an algorithm for filling the missing cell states is formulated. The accuracy of these methods is examined in a series of computational experiments.
RENCANA PROGRAM KEGIATAN PEMBELAJARAN SEMESTER (RPKPS)
Year: 2011

(1) Minggu Ke (2) Materi Pembelajaran (3) Bentuk Pembelajaran (4) Kemampuan akhir yang diharapkan (5) Kreteria Penilaian (6) Bobot Nilai 1 Revolusi Sistem Informasi Ceramah Mahasiswa mampu 1. Memahami Pengertian Sistem Komponen Sistem Bagian Komponen Sistem Klasifikasi Dan Jenis Sistem Open Loop System Open Versus Closed Systems Sistem Terdiri Dari Subsistem Elemen Sistem Konseptual Sistem Fisik Pendekatan Sistem Data Dan Informasi Fungsi Sistem Informasi Pemecahan Masalah Informasi Dalam Banyak Bentuk 2. Menjelaskan Computer Literacy Versus Sistem Informasi Literacy 3. Menjelaskan Dampak SI Pada Organisasi Dan Manajemen 1. Kemampuan mahasiswa dalam menjelaskan Definisi Sistem Informasi, Sistem Informasi Manajemen, Manajemen Sistem Informasi Dan Perbedaannya (Fokus) 2. Quiz 10%
Public Oversight and Reporting Credibility: Evidence from the PCAOB Audit Inspection Regime
Year: 2019

This paper examines how audit oversight by a public sector regulator affects investors' assessments of reporting credibility. We analyze whether the introduction of the Public Company Accounting Oversight Board (PCAOB) and its inspection regime have strengthened capital market responses to unexpected earnings releases, as theory predicts when reporting credibility increases. To identify the effects, we use a difference in differences design that exploits the staggered introduction of the inspection regime, which affects firms at different points in time depending on their fiscal year ends, auditors, and the timing of PCAOB inspections. We find that capital market responses to unexpected earnings increase significantly following the introduction of the PCAOB inspection regime. Corroborating these findings, we also find an increase in abnormal volume responses to firms' 10 K filings after the new regime. Overall, our results are consistent with public audit oversight increasing the credibility of financial reporting.
ISOCC 2019
Year: 2019

This paper presents a novel method to implement ring learning with errors (ring LWE) cryptography for video based face encryption and decryption on a graphics processing unit (GPU) By conducting ring arithmetic operations in parallel on a GPU, the processing time of these operations is significantly reduced. Consequently, ring LWE encryption and decryption operations are remarkably improved. The simulation results conducted on GPU and CPU platforms using CUDA C+ show that the ring LWE based face encryption and decryption operations implemented on a GPU are approximately 100 times faster than that implemented on a CPU.
Motion Estimation by Hybrid Optical Flow Technology for UAV Landing in an Unvisited Area
Venue: Sensors
Year: 2019

The capability of landing on previously unvisited areas is a fundamental challenge for an unmanned aerial vehicle (UAV) In this paper, we developed a vision based motion estimation as an aid to improve landing performance. As an alternative to the common scenarios accompanying by external infrastructures or well defined marker, the proposed hybrid framework can successfully land on a new area without any prior information about guiding marks. The implementation was based on the optical flow technique associated with a multi scale strategy to overcome the decreasing field of view during the UAV descending. Compared with a commercial Global Positioning System (GPS) through a sequence of flight trials, the vision aided scheme can effectively minimize the possible sensing error, thus, leading to a more accurate result. Moreover, this work has potential to integrate the fast growing image learning process and yields more practical versatility for UAV applications in the future.
Destination Image Components and Word of Mouth Intentions in Urban Tourism: A Multigroup Approach
Year: 2018

The purpose of this study was to explore differences among three distinct groups, namely local residents, past tourists, and prospective tourists, in their perceptions of cognitive, affective, and overall image of a city destination and their future behavior. Analysis of data generally confirmed previously established structural relationships of cognitive and affective image, overall destination image, and word of mouth intentions. However, differences were identified among the three groups in terms of their destination image perceptions and their behavioral intentions to engage in word of mouth communications. Specifically, residents who engaged in word of mouth were primarily influenced by the cognitive and affective destination image components, while tourists relied on overall image perceptions.
An Extension of FBS Three Phase Power Flow for Handling PV Nodes in Active Distribution Networks
Venue: IEEE Transactions on Smart Grid
Year: 2014

Forward/backward sweep power flow (FBS) is widely used in distribution network analysis because it is very efficient for radial and weakly meshed networks. In these types of methods, sensitivity based approaches are commonly used to account for PV nodes in active distribution system; however, the convergence deteriorates as the number of PV nodes increases, and may even diverge for large branch R/X ratios. We propose an efficient method for handling PV nodes based on loop analysis incorporated in the FBS framework. Here, PV nodes refer to nodes connected by distributed generators with constant voltage control. As an extension of FBS, the proposed method's convergence remains satisfactory when the number of PV nodes increases for a wide range of branch R/X ratios. Numerical simulations with three phase models were carried out to quantify the performance of the method.
Full characterization for material constants of a promising KNN based lead free piezoelectric ceramic
Year: 2020

Potassium sodium niobate (K1 xNaxNbO3, referred to as KNN) solid solutions, which are an important type of lead free piezoelectric materials possessing environmentally friendly features, good piezoelectric response and high Curie temperature, have attracted considerable attention in replacing lead based ceramics. In order to promote the application of KNN based ceramics in piezoelectric devices, we characterized a complete set of material constants of a high performance KNN based ceramic, that is 0.965(K0.48Na0.52) (Nb0.96Sb0.04)O3 0.035Bi0.5Na0.5Zr0.15Hf0.75O3 (KNNS BNZH) whose Curie temperature is 235 degC, piezoelectric coefficient d33 is 380 pC/N and electromechanical coupling factor k33 is 70% These results will benefit the design of piezoelectric transducers and actuators using lead free piezoelectric ceramics.