nils-herrmann/clean_scirepeval huggingface dataset:
The spinach RNA aptamer as a characterization tool for synthetic biology.

Characterization of genetic control elements is essential for the predictable engineering of synthetic biology systems. The current standard for in vivo characterization of control elements is through the use of fluorescent reporter proteins such as green fluorescent protein (GFP). Gene expression, however, involves not only protein production but also the production of mRNA. Here, we present the use of the Spinach aptamer sequence, an RNA mimic of GFP, as a tool to characterize mRNA expression in Escherichia coli. We show how the aptamer can be incorporated into gene expression cassettes and how co-expressing it with a red fluorescent protein (mRFP1) allows, for the first time, simultaneous measurement of mRNA and protein levels from engineered constructs. Using flow cytometry, we apply this tool here to evaluate ribosome binding site sequences and promoters and use it to highlight the differences in the temporal behavior of transcription and translation.
Time-delay neural networks for time series prediction: an application to the monthly wholesale price of oilseeds in India

Agricultural price forecasting is one of the challenging areas of time series forecasting. The feed-forward time-delay neural network (TDNN) is one of the promising and potential methods for time series prediction. However, empirical evaluations of TDNN with autoregressive integrated moving average (ARIMA) model often yield mixed results in terms of the superiority in forecasting performance. In this paper, the price forecasting capabilities of TDNN model, which can model nonlinear relationship, are compared with ARIMA model using monthly wholesale price series of oilseed crops traded in different markets in India. Most earlier studies of forecast accuracy for TDNN versus ARIMA do not consider pretesting for nonlinearity. This study shows that the nonlinearity test of price series provides reliable guide to post-sample forecast accuracy for neural network model. The TDNN model in general provides better forecast accuracy in terms of conventional root mean square error values as compared to ARIMA model for nonlinear patterns. The study also reveals that the neural network models have clear advantage over linear models for predicting the direction of monthly price change for different series. Such direction of change forecasts is particularly important in economics for capturing the business cycle movements relating to the turning points.
ow does hotel pricing influence guest satisfaction by the moderating nfluence of room occupancy ? hiang

Whether the product price increases customer satisfaction or rather decreases it has been actively debated for some time. A non-linear correlation between the hotel price and guest satisfaction, explored empirically in this paper, implies that the two phenomena are actually not inconsistent with one another At the low price level, room price and food and beverage (F&B) price leads to an increase in guest satisfaction whereas the high price level could have just the opposite effect The results suggests an inverse U shaped otel pricing oom product &B product uest satisfaction relationship between price level and guest satisfaction On the other hand, the room price guest satisfaction link could be affected by the moderating influence of room occupancy to be a U shaped function This paper reports results from a study on the influence of hotel pricing on guest satisfaction in the context of room products and F&B products in Taiwanese international tourist satisfaction It is found that the hotel pricing has a quadratic effect on guest satisfaction
A Survey of Online Activity Recognition Using Mobile Phones

Physical activity recognition using embedded sensors has enabled many context-aware applications in different areas, such as healthcare. Initially, one or more dedicated wearable sensors were used for such applications. However, recently, many researchers started using mobile phones for this purpose, since these ubiquitous devices are equipped with various sensors, ranging from accelerometers to magnetic field sensors. In most of the current studies, sensor data collected for activity recognition are analyzed offline using machine learning tools. However, there is now a trend towards implementing activity recognition systems on these devices in an online manner, since modern mobile phones have become more powerful in terms of available resources, such as CPU, memory and battery. The research on offline activity recognition has been reviewed in several earlier studies in detail. However, work done on online activity recognition is still in its infancy and is yet to be reviewed. In this paper, we review the studies done so far that implement activity recognition systems on mobile phones and use only their on-board sensors. We discuss various aspects of these studies. Moreover, we discuss their limitations and present various recommendations for future research.
Adding Interpretable Attention to Neural Translation Models Improves Word Alignment

Multi-layer models with multiple attention heads per layer provide superior translation quality compared to simpler and shallower models, but determining what source context is most relevant to each target word is more challenging as a result. Therefore, deriving high-accuracy word alignments from the activations of a state-of-the-art neural machine translation model is an open challenge. We propose a simple model extension to the Transformer architecture that makes use of its hidden representations and is restricted to attend solely on encoder information to predict the next word. It can be trained on bilingual data without word-alignment information. We further introduce a novel alignment inference procedure which applies stochastic gradient descent to directly optimize the attention activations towards a given target word. The resulting alignments dramatically outperform the naı̈ve approach to interpreting Transformer attention activations, and are comparable to Giza++ on two publicly available data sets.
Scalable Function Call Graph-based Malware Classification

In an attempt to preserve the structural information in malware binaries during feature extraction, function call graph-based features have been used in various research works in malware classification. However, the approach usually employed when performing classification on these graphs, is based on computing graph similarity using computationally intensive techniques. Due to this, much of the previous work in this area incurred large performance overhead and does not scale well. In this paper, we propose a linear time function call graph (FCG) vector representation based on function clustering that has significant performance gains in addition to improved classification accuracy. We also show how this representation can enable using graph features together with other non-graph features.
Optical Character Recognition

Optical character recognition (OCR) is very popular research field since 1950’s. Character recognition techniques associate a symbolic identity with the image of character. In a typical OCR systems input characters are digitized by an optical scanner. Each character is then located and segmented, and the resulting character image is fed into a pre-processor for noise reduction and normalization. Certain characteristics are the extracted from the character for classification. The feature extraction is critical and many different techniques exist, each having its strengths and weaknesses. After classification the identified characters are grouped to reconstruct the original symbol strings, and context may then be applied to detect and correct errors.  Feature extraction, Segmentation, Template Matching and Correlation.
Automatic recipe cuisine classification by ingredients

With the growth of recipe sharing services, online cooking recipes associated with ingredients and cooking procedures are available. Many recipe sharing sites have devoted to the development of recipe recommendation mechanism. While most food related research has been on recipe recommendation, little effort has been done on analyzing the correlation between recipe cuisines and ingredients. In this paper, we aim to investigate the underlying cuisine-ingredient connections by exploiting the classification techniques, including associative classification and support vector machine. Our study conducted on food.com data provides insights about which cuisines are the most similar and what are the essential ingredients for a cuisine, with an application to automatic cuisine labeling for recipes.
Chapter 1 Enterprise Knowledge Graph : An Introduction

Compared to other knowledge-oriented information systems, the distinctive features of knowledge graphs lie in their special combination of knowledge representation structures, information management processes and search algorithms. The term ‘Knowledge Graph’ became well known in 2012 when Google started to use knowledge graph in their search engine, allowing users to search for things, people or places, rather than just matching strings in the search queries with those in Web documents. Inspired by the success story of Google, knowledge graphs are gaining momentum in the world’s leading information companies. The idea of a knowledge graph is not completely new though. The original idea dates back to the knowledge representation technique called the Semantic Network. Later on, researchers in Knowledge Representation and Reasoning (KR) addressed
THE IMPACT OF CORPORATE SOCIAL RESPONSIBILITY ON CONSUMER LOYALTY

More firms today are beginning to realise the importance of corporate social responsibility (CSR), and its impact on societal well being, apart from the overall governance of the supply chain processes of every organisation. Ratings are being developed to measure performances of most firms with regard to their CSR efforts besides their financial performance. This rating would then be used to measure each firm's progress in the business world. Many studies have been conducted linking CSR to consumer loyalty. It is found that the higher the CSR rating the stronger the bond would be between these two. Given this scenario, the main objective of this study is to find relevant evidence to link consumer loyalty and CSR initiatives within a company. A case study approach was used to empirically test our research model derived from the literature. Quantitative analysis based on a total of 100 responses from a leading electronics manufacturer in Malaysia was used to address the research objectives. The findings of this study suggest that the higher the practice of CSR the stronger is the consumer loyalty towards a particular firm. The study also shows that by investing in CSR, a firm could achieve a positive outcome in terms of its financial performance. CSR can provide the additional competitive edge to a particular firm. However, the linearity of this relationship warrants more testing and empirical research.
Do humans have two systems to track beliefs and belief-like states?

The lack of consensus on how to characterize humans' capacity for belief reasoning has been brought into sharp focus by recent research. Children fail critical tests of belief reasoning before 3 to 4 years of age (H. Wellman, D. Cross, & J. Watson, 2001; H. Wimmer & J. Perner, 1983), yet infants apparently pass false-belief tasks at 13 or 15 months (K. H. Onishi & R. Baillargeon, 2005; L. Surian, S. Caldi, & D. Sperber, 2007). Nonhuman animals also fail critical tests of belief reasoning but can show very complex social behavior (e.g., J. Call & M. Tomasello, 2005). Fluent social interaction in adult humans implies efficient processing of beliefs, yet direct tests suggest that belief reasoning is cognitively demanding, even for adults (e.g., I. A. Apperly, D. Samson, & G. W. Humphreys, 2009). The authors interpret these findings by drawing an analogy with the domain of number cognition, where similarly contrasting results have been observed. They propose that the success of infants and nonhuman animals on some belief reasoning tasks may be best explained by a cognitively efficient but inflexible capacity for tracking belief-like states. In humans, this capacity persists in parallel with a later-developing, more flexible but more cognitively demanding theory-of-mind abilities.
Topological Data Analysis

This paper explores the new and growing field of topological data analysis (TDA). TDA is a data analysis method that provides information about the ’shape’ of data. The paper describes what types of shapes TDA detects and why these shapes having meaning. Additionally, concepts from algebraic topology, the mathematics behind TDA, will be discussed. Specifically, the concepts of persistent homology and barcodes will be developed. Finally, the paper will show how these concepts from algebraic topology can be applied to analyze data. Acknowledgments I would first like to thank my advisor, Scott Taylor. He provided me with guidance and support throughout this year long project. Second, I would like to acknowledge my reader, Jim Scott. His input, especially concerning statistical matters, was very valuable. Additionally, I would like to thank Randy Downer for helping me with Perseus, a software packages for topological data analysis. Finally, I would like to thank Xiaojie (Jackie) Chen and Dan Medici, both of whom provided me with support and helped me with LaTeX, the system that was used to typeset this document.
Dilated Recurrent Neural Networks

Learning with recurrent neural networks (RNNs) on long sequences is a notoriously difficult task. There are three major challenges: 1) complex dependencies, 2) vanishing and exploding gradients, and 3) efficient parallelization. In this paper, we introduce a simple yet effective RNN connection structure, the DILATEDRNN, which simultaneously tackles all of these challenges. The proposed architecture is characterized by multi-resolution dilated recurrent skip connections, and can be combined flexibly with diverse RNN cells. Moreover, the DILATEDRNN reduces the number of parameters needed and enhances training efficiency significantly, while matching state-of-the-art performance (even with standard RNN cells) in tasks involving very long-term dependencies. To provide a theory-based quantification of the architecture’s advantages, we introduce a memory capacity measure, the mean recurrent length, which is more suitable for RNNs with long skip connections than existing measures. We rigorously prove the advantages of the DILATEDRNN over other recurrent neural architectures. The code for our method is publicly available1.
The case of Jeffrey Dahmer: sexual serial homicide from a neuropsychiatric developmental perspective.

Sexual serial homicidal behavior has received considerable attention during the last three decades. Substantial progress has been made in the development of methods aimed at identifying and apprehending individuals who exhibit these behaviors. In spite of these advances, the origins of sexual serial killing behavior remain for the most part unknown. In this article we propose a biopsychosocial psychiatric model for understanding the origins of sexual serial homicidal behavior from both neuropsychiatric and developmental perspectives, using the case of convicted serial killer Jeffrey Dahmer as the focal point. We propose that his homicidal behavior was intrinsically associated with autistic spectrum psychopathology, specifically Asperger's disorder. The relationship of Asperger's disorder to other psychopathology and to his homicidal behavior is explored. We discuss potential implications of the proposed model for the future study of the causes of sexual serial homicidal crime.
Medical Image Retrieval using Deep Convolutional Neural Network

With a widespread use of digital imaging data in hospitals, the size of medical image repositories is increasing rapidly. This causes difficulty in managing and querying these large databases leading to the need of content based medical image retrieval (CBMIR) systems. A major challenge in CBMIR systems is the semantic gap that exists between the low level visual information captured by imaging devices and high level semantic information perceived by human. The efficacy of such systems is more crucial in terms of feature representations that can characterize the high-level information completely. In this paper, we propose a framework of deep learning for CBMIR system by using deep Convolutional Neural Network (CNN) that is trained for classification of medical images. An intermodal dataset that contains twenty four classes and five modalities is used to train the network. The learned features and the classification results are used to retrieve medical images. For retrieval, best results are achieved when class based predictions are used. An average classification accuracy of 99.77% and a mean average precision of 0.69 is achieved for retrieval task. The proposed method is best suited to retrieve multimodal medical images for different body organs. Content Based Medical Image Retrieval (CBMIR); Convolutional Neural Networks (CNNs); Similarity Metric; Deep Learning;
Combining Cognitive Behavioural Therapy and Pharmacotherapy in the Treatment of Anxiety Disorders : True Gains or False Hopes ?

Anxiety disorders such as obsessive-compulsive disorder (OCD), panic disorder, social anxiety disorder (SAD), generalized anxiety disorder (GA) and posttraumatic stress disorder (PTSD) are the most common of the psychiatric disorders worldwide with a prevalence of up to 18% (1). These disorders often only receive little attention by mental health services despite their great contribution to overall clinical burden and economic costs Combining Cognitive Behavioural Therapy and Pharmacotherapy in the Treatment of Anxiety  True Gains or False Hopes?
Computer Support for Collaborative Learning : foundations for a CSCL community : proceedings of CSCL 2002, Boulder, Colorado, USA, January 7-11, 2002

In this paper, I review two studies (Roschelle, 1996; Baker, Hansen, Joiner, & Traum, 1999) which I believe to represent paradigmatic examples of CSCL research. I offer a critique of these studies based on the theory of inquiry developed by the American pragmatist philosopher John Dewey. Inquiry, for Dewey, represented an exceedingly broad category of activity of which joint problem solving is a special case. I conclude by proposing a description of what I think research in CSCL is, or at least should be, about. This description can be used to distinguish what is done in this field from traditional research in education on learning outcomes, research based on classical information processing theory, and conventional research on social interaction.
CURIOSITY-DRIVEN EXPERIENCE PRIORITIZATION

In Reinforcement Learning (RL), an agent explores the environment and collects trajectories into the memory buffer for later learning. However, the collected trajectories can easily be imbalanced with respect to the achieved goal states. The problem of learning from imbalanced data is a well-known problem in supervised learning, but has not yet been thoroughly researched in RL. To address this problem, we propose a novel Curiosity-Driven Prioritization (CDP) framework to encourage the agent to over-sample those trajectories that have rare achieved goal states. The CDP framework mimics the human learning process and focuses more on relatively uncommon events. We evaluate our methods using the robotic environment provided by OpenAI Gym. The environment contains six robot manipulation tasks. In our experiments, we combined CDP with Deep Deterministic Policy Gradient (DDPG) with or without Hindsight Experience Replay (HER). The experimental results show that CDP improves both performance and sampleefficiency of reinforcement learning agents, compared to state-of-the-art methods.
The big data system, components, tools, and technologies: a survey

The traditional databases are not capable of handling unstructured data and high volumes of real-time datasets. Diverse datasets are unstructured lead to big data, and it is laborious to store, manage, process, analyze, visualize, and extract the useful insights from these datasets using traditional database approaches. However, many technical aspects exist in refining large heterogeneous datasets in the trend of big data. This paper aims to present a generalized view of complete big data system which includes several stages and key components of each stage in processing the big data. In particular, we compare and contrast various distributed file systems and MapReduce-supported NoSQL databases concerning certain parameters in data management process. Further, we present distinct distributed/cloud-based machine learning (ML) tools that play a key role to design, develop and deploy data models. The paper investigates case studies on distributed ML tools such as Mahout, Spark MLlib, and FlinkML. Further, we classify analytics based on the type of data, domain, and application. We distinguish various visualization tools pertaining three parameters: functionality, analysis capabilities, and supported development environment. Furthermore, we systematically investigate big data tools and technologies (Hadoop 3.0, Spark 2.3) including distributed/cloud-based stream processing tools in a comparative approach. Moreover, we discuss functionalities of several SQL Query tools on Hadoop based on 10 parameters. Finally, We present some critical points relevant to research directions and opportunities according to the current trend of big data. Investigating infrastructure tools for big data with recent developments provides a better understanding that how different tools and technologies apply to solve real-life applications.
SmartCuckoo: A Fast and Cost-Efficient Hashing Index Scheme for Cloud Storage Systems

Fast query services are important to improve overall performance of large-scale storage systems when handling a large number of files. Open-addressing cuckoo hash schemes have been widely used to support query services due to the salient features of simplicity and ease of use. Conventional schemes are unfortunately inadequate to address the potential problem of having endless loops during item insertion, which degrades the query performance. To address the problem, we propose a costefficient cuckoo hashing scheme, named SmartCuckoo. The idea behind SmartCuckoo is to represent the hashing relationship as a directed pseudoforest and use it to track item placements for accurately predetermining the occurrence of endless loop. SmartCuckoo can efficiently predetermine insertion failures without paying a high cost of carrying out step-by-step probing. We have implemented SmartCuckoo in a large-scale cloud storage system. Extensive evaluations using three realworld traces and the YCSB benchmark demonstrate the efficiency and efficacy of SmartCuckoo. We have released the source code of SmartCuckoo for public use.
Appendix G: Thirty Years of Adaptive Neural Networks: Perceptron, Madaline, and Backpropagation

Fundamental developments in feedfonvard artificial neural networks from the past thirty years are reviewed. The central theme of this paper is a description of the history, origination, operating characteristics, and basic theory of several supervised neural network training algorithms including the Perceptron rule, the LMS algorithm, three Madaline rules, and the backpropagation technique. These methods were developed independently, but with the perspective of history they can a / / be related to each other. The concept underlying these algorithms is the “minimal disturbance principle,” which suggests that during training it is advisable to inject new information into a network in a manner that disturbs stored information to the smallest extent possible.