nils-herrmann/clean_scirepeval huggingface dataset:
A REVIEW ON ANALYSIS AND GRADING OF RICE USING IMAGE PROCESSING

This review paper presents the recent developments of image processing and machine vision system in an automated rice grading system. In agricultural field, the efficiency and accuracy in grading process is very essential to increase the productivity of the yield. Rice is cultivated in many regions across India. Rice is a staple food for about 65% of the Indians. There are more than 40,000 different varieties of rice which is cultivated in all continents except Antarctica. The food quality is becoming a major issue in health care. It is tedious for people to analyze the grades and quality in the market. The rice varieties and their quality are assessed through visual inspection by food quality Managers. The decision making capabilities of food quality Managers are subjected to an external influences. In this paper, image processing techniques are implemented to automate the process which overcomes the drawbacks of manual process. Here, various procedures are reviewed to obtain the percentage, quality of rice grains based on its size.
Policy Search in Reproducing Kernel Hilbert Space
Venue: IJCAI

Modeling policies in reproducing kernel Hilbert space (RKHS) renders policy gradient reinforcement learning algorithms non parametric. As a result, the policies become very flexible and have a rich representational potential without a predefined set of features. However, their performances might be either non covariant under reparameterization of the chosen kernel, or very sensitive to step size selection. In this paper, we propose to use a general framework to derive a new RKHS policy search technique. The new derivation leads to both a natural RKHS actor critic algorithm and a RKHS expectation maximization (EM) policy search algorithm. Further, we show that kernelization enables us to learn in partially observable (POMDP) tasks which is considered daunting for parametric approaches. Via sparsification, a small set of "support vectors" representing the history is shown to be effectively discovered. For evaluations, we use three simulated (PO)MDP reinforcement learning tasks, and a simulated PR2's robotic manipulation task. The results demonstrate the effectiveness of the new RKHS policy search framework in comparison to plain RKHS actor critic, episodic natural actor critic, plain actor critic, and PoWER approaches.
The Effects of Group Dynamics on Language Learning and Use in an MMOG.

The use of video games as a learning tool, in particular massively multiplayer online games (MMOGs) continues to grow, as does the research in this field of study; research to date has revealed benefits to the language learner as well as hindrances and research gaps (Godwin Jones, 2014) This study examines participant perspectives on group dynamics, and how comradery and a foreign language guide affect participants' in game production of L2 German while playing World of Warcraft (WoW) as an extramural activity. This study focuses on an eight week long MMOG gaming project, in which two groups of participants played WoW with a native German speaker in order to investigate the effects inter group dynamics have on a participant's real time target language production and willingness to communicate. Participants reported during interviews and written journal entries that a lack of shared interests and motivation (learning and gaming) negatively affected the groups' target language participation. The findings additionally suggest that a trained foreign language instructor paired with a motivated learner may result in increased participation; however these increases may be temporary if a group's relationship does not extend beyond a shared goal of foreign language improvement.
Work Motivation and Operational Risk Assessment: A New Direction for Organizational Behavior Studies

The present study introduces motivation theories into operational risk analysis, exploring a new direction for organizational behavior studies in the risk management field. In particular, it identifies and analyzes one specific type of operational risk: motivational risk. The study asserts that organizations can never know exactly what their employees' work motivations are. Based on motivation theories, individuals are motivated to do their jobs; therefore, employees' work behavior may differ from what organizations expect due to the latter's uncertainty about the former's motivation. Employees' unexpected behaviors may be harmful to a business, leading to financial losses. This study proposes the motivational risk framework (MRF) to assess motivational risk. This theoretical framework will not only improve assessment of operational risk caused by employees, but it will also illustrate how behavior studies can be integrated into risk analysis.
Minimalist Approach to Complexity: Templating the Assembly of DNA Tile Structures with Sequentially Grown Input Strands.
Venue: ACS nano

Given its highly predictable self assembly properties, DNA has proven to be an excellent template toward the design of functional materials. Prominent examples include the remarkable complexity provided by DNA origami and single stranded tile (SST) assemblies, which require hundreds of unique component strands. However, in many cases, the majority of the DNA assembly is purely structural, and only a small "working area" needs to be aperiodic. On the other hand, extended lattices formed by DNA tile motifs require only a few strands; but they suffer from lack of size control and limited periodic patterning. To overcome these limitations, we adopt a templation strategy, where an input strand of DNA dictates the size and patterning of resultant DNA tile structures. To prepare these templating input strands, a sequential growth technique developed in our lab is used, whereby extended DNA strands of defined sequence and length may be generated simply by controlling their order of addition. With these, we demonstrate the periodic patterning of size controlled double crossover (DX) and triple crossover (TX) tile structures, as well as intentionally designed aperiodicity of a DX tile structure. As such, we are able to prepare size controlled DNA structures featuring aperiodicity only where necessary with exceptional economy and efficiency.
Finding feasible harvest zones in mountainous areas using integrated spatial multi criteria decision analysis

Terrain conditions, stand structure attributes, and environmental factors are important elements that determine the success of timber harvests. Planning designs for timber harvesting projects must also be physically feasible, environmentally acceptable, and economically viable and address potentially competing objectives upfront in a multi purpose planning effort. Under mountainous conditions involving highly variable and steep terrain, harvest planning is a particularly complex decision making process. Here, quantitative (objective) information (e.g. stand volume) is jointly considered with qualitative (subjective) information (e.g. expert judgment of slope failure event and preference value of a certain stand) to arrive at a compromise plan that considers conflicting management objectives. The objective of this paper was to evaluate the existing terrain conditions of a large management area to find zones in which harvest operations can be conducted, assess which portions of the terrain can be harvested by different equipment that is available in the region, and locate landing sites for ground based skidding operations. The criteria used in this study included quantitative and qualitative elements that were measured/evaluated across different scales. To address this complex decision making process, we used an integrative approach that combined spatial analyses with multiple criteria decision analysis (SMCDA) Existing geospatial data were combined with information obtained through pairwise evaluations of environmental, topographic, and stand structure elements to create maps in which the terrain was divided into zones of different suitability classes for timber harvests, placement of landing sites, and deployment of different harvesting systems. The results and assessment of this case study indicate that the methodology has great potential for a more integrated policy, in particular, where multiple spatial objectives explicitly need to be taken into consideration when solving complex forest management problems in mountainous conditions. One key finding from this work that needs to be further explored involves efficient ways of incorporating this methodology into traditional harvest scheduling models to better support the operational and tactical phases of the timber harvesting.
Joint Geo Spatial Preference and Pairwise Ranking for Point of Interest Recommendation
Venue: 2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI)

Recommending users with preferred point of interests (POIs) has become an important task for location based social networks, which facilitates users' urban exploration by helping them filter out unattractive locations. Although the influence of geographical neighborhood has been studied in the rating prediction task (i.e. regression) few work have exploited it to develop a ranking oriented objective function to improve top N item recommendations. To solve this task, we conduct a manual inspection on real world datasets, and find that each individual's traits are likely to cluster around multiple centers. Hence, we propose a co pairwise ranking model based on the assumption that users prefer to assign higher ranks to the POIs near previously rated ones. The proposed method can learn preference ordering from non observed rating pairs, and thus can alleviate the sparsity problem of matrix factorization. Evaluation on two publicly available datasets shows that our method performs significantly better than state of the art techniques for the top N item recommendation task.
Knuckle Print Biometrics and Fusion Schemes Overview, Challenges, and Solutions
Venue: ACM Comput. Surv.

Numerous behavioral or physiological biometric traits, including iris, signature, hand geometry, speech, palm print, face, etc. have been used to discriminate individuals in a number of security applications over the last 30 years. Among these, hand based biometric systems have come to the attention of researchers worldwide who utilize them for low to medium security applications such as financial transactions, access control, law enforcement, border control, computer security, time and attendance systems, dormitory meal plan access, etc. Several approaches for biometric recognition have been summarized in the literature. The survey in this article focuses on the interface between various hand modalities, summary of inner and dorsal knuckle print recognition, and fusion techniques. First, an overview of various feature extraction and classification approaches for knuckle print, a new entrant in the hand biometrics family with a higher user acceptance and invariance to emotions, is presented. Next, knuckle print fusion schemes with possible integration scenarios, and traditional capturing devices have been discussed. The economic relevance of various biometric traits, including knuckle print for commercial and forensic applications is debated. Finally, conclusions related to the scope of knuckle print as a biometric trait are drawn and some recommendations for the development of hand based multimodal biometrics have been presented.
Almanac 2015: atrial fibrillation research in Heart
Venue: Heart

Atrial fibrillation continues to attract interest in the cardiovascular community and in Heart. Over 60 original research and review papers published in Heart in 2014 2015 cover various aspects of atrial fibrillation, from associated conditions and precipitating factors to new approaches to management. Here, we provide an overview of articles on atrial fibrillation published in Heart in 2014 2015, highlighting new developments, emerging concepts and novel approaches to treatment.
Low dose rate brachytherapy with I 125 seeds has an excellent 5 year outcome with few side effects in patients with low risk prostate cancer
Venue: Acta oncologica

Low dose rate brachytherapy (LDR BT) has been used in Sweden for more than a decade for treatment of low risk prostate cancer. This study presents the outcome for patients treated with LDR BT at a single institution with focus on the association between dose and biochemical failure free survival (BFFS)  In total 195 patients were treated with LDR BT between 2004 and 2008. The patients were followed systematically for side effects for at least one year. PSA levels were followed regularly from three months and for at least five years. Outcome was analyzed in relation to clinical variables at baseline and to radiotherapy data.  Kaplan Meier estimated BFFS at five years was 95.7% Dose to the prostate in terms of D90% was significantly associated with BFFS [HR 0.90 (95%CI 0.83 0.96) p 0.002]  Out data confirmed that absorbed dose is a predictive factor for BFFS for low risk patients without androgen deprivation therapy. With our treatment routines and dosimetry, a D90% in the range of 170 180 Gy gives excellent outcomes with acceptable toxicity for patients with low risk prostate cancer.
Analysis of Data Mining Techniques for Healthcare Decision Support System Using Liver Disorder Dataset

Accuracy in data classification depends on the dataset used for learning. Now a days the most important cause of death for both men and women is due to the Liver Problem. The healthcare industry collects a huge amount of data which is not properly mined and not put to the optimum use. Discovery of these hidden patterns and relationships often goes unexploited. Our research focuses on this aspect of Medical diagnosis by learning pattern through the collected data of Liver disorder to develop intelligent medical decision support systems to help the physicians. In this paper, we propose the use decision trees J48, Naive Bayes, ANN, ZeroR, 1BK and VFI algorithm to classify these diseases and compare the effectiveness, correction rate among them. Detection of Liver disease in its early stage is the key of its cure. It leads to better performance of the classification models in terms of their predictive or descriptive accuracy, diminishing of computing time needed to build models as they learn faster, and better understanding of the models. In this paper, a comparative analysis of data classification accuracy using Liver disorder data in different scenarios is presented. The predictive performances of popular classifiers are compared quantitatively.
Addressing a Question Answering Challenge by Combining Statistical Methods with Inductive Rule Learning and Reasoning
Venue: AAAI

A group of researchers from Facebook has recently proposed a set of 20 question answering tasks (Facebook's bAbl dataset) as a challenge for the natural language understanding ability of an intelligent agent. These tasks are designed to measure various skills of an agent, such as: fact based question answering, simple induction, the ability to find paths, co reference resolution and many more. Their goal is to aid in the development of systems that can learn to solve such tasks and to allow a proper evaluation of such systems. They show existing systems cannot fully solve many of those toy tasks. In this work, we present a system that excels at all the tasks except one. The proposed model of the agent uses the Answer Set Programming (ASP) language as the primary knowledge representation and reasoning language along with the standard statistical Natural Language Processing (NLP) models. Given a training dataset containing a set of narrations, questions and their answers, the agent jointly uses a translation system, an Inductive Logic Programming algorithm and Statistical NLP methods to learn the knowledge needed to answer similar questions. Our results demonstrate that the introduction of a reasoning module significantly improves the performance of an intelligent agent.
Are Deep Learning Approaches Suitable for Natural Language Processing?
Venue: NLDB

In recent years, Deep Learning (DL) techniques have gained much attention from Artificial Intelligence (AI) and Natural Language Processing (NLP) research communities because these approaches can often learn features from data without the need for human design or engineering interventions. In addition, DL approaches have achieved some remarkable results. In this paper, we have surveyed major recent contributions that use DL techniques for NLP tasks. All these reviewed topics have been limited to show contributions to text understanding, such as sentence modelling, sentiment classification, semantic role labelling, question answering, etc. We provide an overview of deep learning architectures based on Artificial Neural Networks (ANNs) Convolutional Neural Networks (CNNs) Long Short Term Memory (LSTM) and Recursive Neural Networks (RNNs)
Sound tuning of amygdala plasticity in auditory fear conditioning
Venue: Scientific reports

Various auditory tones have been used as conditioned stimuli (CS) for fear conditioning, but researchers have largely neglected the effect that different types of auditory tones may have on fear memory processing. Here, we report that at lateral amygdala (LA) synapses (a storage site for fear memory) conditioning with different types of auditory CSs (2.8 kHz tone, white noise, FM tone) recruits distinct forms of long term potentiation (LTP) and inserts calcium permeable AMPA receptor (CP AMPAR) for variable periods. White noise or FM tone conditioning produced brief insertion <6 hr after conditioning) of CP AMPARs, whereas 2.8 kHz tone conditioning induced more persistent insertion =6 hr) Consistently, conditioned fear to 2.8 kHz tone but not to white noise or FM tones was erased by reconsolidation update (which depends on the insertion of CP AMPARs at LA synapses) when it was performed 6 hr after conditioning. Our data suggest that conditioning with different auditory CSs recruits distinct forms of LA synaptic plasticity, resulting in more malleable fear memory to some tones than to others.
Revisiting dynamic space in film from a semiotic perspective

This paper revisits a particular area of concern in computer generated (CG) visual effects, namely, the problem of the space in dynamic, multiple panels created by digital processes. The paper critiques recent statements made on narrative understanding, positing equivalences between a viewer's navigation across dynamic frames in CG images and human computer interactions, as well as claims of narrative complexity in films using dynamic frames. This paper will argue that it is necessary to approach the meaning construction of cinematic space by distinguishing analytical levels of materiality from their discursive meaning, because the visual effect created by the manipulation of a dynamic spatial layout does not necessarily burden the viewer's linear path for constructing coherent spatial meaning.
Does the Scope of Sell Side Analyst Industry Matter? An Examination of Bias, Accuracy and Information Content of Analyst Reports

Despite broad interest in analysts' activities, our understanding of sell side analysts as a collective group is limited. This paper examines changes in the scope of the sell side analyst industry and whether these changes impact information dissemination. Changes in the number of analysts covering an industry affect analyst competition and have significant spillover effects. When some firms in an industry experience drops in analyst coverage, the aggregate quality of the analyst reports of other firms in an industry (i.e. firms that did not experience a direct change in analyst coverage) decreases along several dimensions: analysts' forecasts are less accurate and more biased, they exert less effort, and their reports have lower information convergence and provide less information to market participants. We further find that the effects of changes in the number of analysts covering an industry are more pronounced in settings where analyst competition is more important. The spill over industry effects we find are incremental to the effects of firm level changes in analyst coverage. Overall, having a more significant sell side analyst industry presence has positive externalities that can result in better functioning capital markets.
Bumbling idiots or evil masterminds? Challenging cold war stereotypes about women, sexuality and state socialism

In academic writing, facts about the past generally require the citation of relevant sources unless the fact or idea is considered "common knowledge:" bits of information or dates upon which there is a wide scholarly consensus. This brief article reflects on the use of "common knowledge" claims in contemporary scholarship about women, families, and sexuality as experienced during 20th century, East European, state socialist regimes. We focus on several key stereo types about the communist state and the situation of women that are often asserted in the scholarly literature, and argue that many of these ideas uncannily resemble American anti communist propaganda. When contemporary scholars make claims about communist intrusions into the private sphere to effect social engineering or the inefficacy of state socialist mass organizations or communist efforts to break up the family or indoctrinate the young, they often do so without citation to previous sources or empirical evidence supporting their claims, thereby suggesting that such claims are "common knowledge." We believe that those wishing to assert such claims should link these assertions to concrete originating sources, lest it turn out the "common knowledge" derives, in fact, from western Cold War rhetoric.
Exploring Cultural Influence on Managerial Communication in Relationship to Job Satisfaction, Organizational Commitment, and the Employees' Propensity to Leave in the Insurance Sector of India

This article identifies the critical importance of effective downward communication in its relationship to job satisfaction, organizational commitment, and the employees' propensity to leave an organization. Employee turnover within the insurance sector of India has become an issue; therefore, a sample of 105 employees from the insurance sector is surveyed to gather information concerning downward communication, job satisfaction, and organizational commitment. The study used various cultural lenses to understand the influence of national culture on norms, values, beliefs, and practices of the Indian employees and managers. Results of the survey suggest that there is a positively significant relationship between downward communication, employee satisfaction, organizational commitment, and the employees' propensity to leave. The results, which have implications and relevance for all kinds of industries all over the world, indicate that managers want to contribute to the effective functioning of the organization and can do so by providing the right, conducive environment for employees. Managers need to send clear, precise, and timely job instructions; communicate constructive feedback related to their job performance; and use multiple channels of communication. Taking these actions will enhance job commitment and reduce the likelihood of employees' leaving their organizations.