nils-herrmann/clean_scirepeval huggingface dataset:
Multimodal MR-imaging reveals large-scale structural and functional connectivity changes in profound early blindness

In the setting of profound ocular blindness, numerous lines of evidence demonstrate the existence of dramatic anatomical and functional changes within the brain. However, previous studies based on a variety of distinct measures have often provided inconsistent findings. To help reconcile this issue, we used a multimodal magnetic resonance (MR)-based imaging approach to provide complementary structural and functional information regarding this neuroplastic reorganization. This included gray matter structural morphometry, high angular resolution diffusion imaging (HARDI) of white matter connectivity and integrity, and resting state functional connectivity MRI (rsfcMRI) analysis. When comparing the brains of early blind individuals to sighted controls, we found evidence of co-occurring decreases in cortical volume and cortical thickness within visual processing areas of the occipital and temporal cortices respectively. Increases in cortical volume in the early blind were evident within regions of parietal cortex. Investigating white matter connections using HARDI revealed patterns of increased and decreased connectivity when comparing both groups. In the blind, increased white matter connectivity (indexed by increased fiber number) was predominantly left-lateralized, including between frontal and temporal areas implicated with language processing. Decreases in structural connectivity were evident involving frontal and somatosensory regions as well as between occipital and cingulate cortices. Differences in white matter integrity (as indexed by quantitative anisotropy, or QA) were also in general agreement with observed pattern changes in the number of white matter fibers. Analysis of resting state sequences showed evidence of both increased and decreased functional connectivity in the blind compared to sighted controls. Specifically, increased connectivity was evident between temporal and inferior frontal areas. Decreases in functional connectivity were observed between occipital and frontal and somatosensory-motor areas and between temporal (mainly fusiform and parahippocampus) and parietal, frontal, and other temporal areas. Correlations in white matter connectivity and functional connectivity observed between early blind and sighted controls showed an overall high degree of association. However, comparing the relative changes in white matter and functional connectivity between early blind and sighted controls did not show a significant correlation. In summary, these findings provide complimentary evidence, as well as highlight potential contradictions, regarding the nature of regional and large scale neuroplastic reorganization resulting from early onset blindness.
Grounded theory research: literature reviewing and reflexivity.

This paper is a report of a discussion of the arguments surrounding the role of the initial literature review in grounded theory.



Researchers new to grounded theory may find themselves confused about the literature review, something we ourselves experienced, pointing to the need for clarity about use of the literature in grounded theory to help guide others about to embark on similar research journeys.



The arguments for and against the use of a substantial topic-related initial literature review in a grounded theory study are discussed, giving examples from our own studies. The use of theoretically sampled literature and the necessity for reflexivity are also discussed. Reflexivity is viewed as the explicit quest to limit researcher effects on the data by awareness of self, something seen as integral both to the process of data collection and the constant comparison method essential to grounded theory.



A researcher who is close to the field may already be theoretically sensitized and familiar with the literature on the study topic. Use of literature or any other preknowledge should not prevent a grounded theory arising from the inductive-deductive interplay which is at the heart of this method. Reflexivity is needed to prevent prior knowledge distorting the researcher's perceptions of the data.
Hidden Markov Models for Forex Trends Prediction

Foreign Exchange (Forex) market is a complex and challenging task for prediction due to uncertainty movement of exchange rate. However, these movements over timeframe also known as historical Forex data that offered a generic repeated trend patterns. This paper uses the features extracted from trend patterns to model and predict the next day trend. Hidden Markov Models (HMMs) is applied to learn the historical trend patterns, and use to predict the next day movement trends. We use the 2011 Forex historical data of Australian Dollar (AUS) and European Union Dollar (EUD) against the United State Dollar (USD) for modeling, and the 2012 and 2013 Forex historical data for validating the proposed model. The experimental results show outperforms prediction result for both years.
Sensorless control of hybrid stepper motor

Today thanks to low cost and high performance DSP's, Kalman filtering (KF) becomes an efficient candidate to avoid mechanical sensors in motor control. We present in this work experimental results by using a steady state KF method to estimate the speed and rotor position for hybrid stepper motor. With this method the computing time is reduced. The Kalman gain is pre-computed from numerical simulation and introduced as a constant in the real time algorithm. The load torque is also on-line estimated by the same algorithm. At start-up the initial rotor position is detected by the impulse current method.
Hierarchical Contextual Attention Recurrent Neural Network for Map Query Suggestion

The query logs from an on-line map query system provide rich cues to understand the behaviors of human crowds. With the growing ability of collecting large scale query logs, the query suggestion has been a topic of recent interest. In general, query suggestion aims at recommending a list of relevant queries w.r.t. users’ inputs via an appropriate learning of crowds’ query logs. In this paper, we are particularly interested in map query suggestions (e.g., the predictions of location-related queries) and propose a novel model Hierarchical Contextual Attention Recurrent Neural Network (HCAR-NN) for map query suggestion in an encoding-decoding manner. Given crowds map query logs, our proposed HCAR-NN not only learns the local temporal correlation among map queries in a query session (e.g., queries in a short-term interval are relevant to accomplish a search mission), but also captures the global longer range contextual dependencies among map query sessions in query logs (e.g., how a sequence of queries within a short-term interval has an influence on another sequence of queries). We evaluate our approach over millions of queries from a commercial search engine (i.e., Baidu Map). Experimental results show that the proposed approach provides significant performance improvements over the competitive existing methods in terms of classical metrics (i.e., Recall@K and MRR) as well as the prediction of crowds’ search missions.
Machine Learning and Sound Design

In this paper I discuss the role of Machine Learning (ML) in sound design. I focus on the modelling of a particular aspect of human intelligence which is believed to play an important role in musical creativity: the Generalisation of Perceptual Attributes (GPA). By GPA I mean the process by which a listener tries to find common sound attributes when confronted with a series of sounds. The paper introduces the basics of GPA and ML in the context of ARTIST, a prototype case study system.  Tools) is a sound design system that works in co-operation with the user, providing useful levels of automated reasoning to render the synthesis tasks less laborious (tasks such as calculating an appropriate stream of synthesis parameters for each single sound) and to enable the user to explore alternatives when designing a certain sound. The system synthesises sounds from input requests in a relatively high-level language; for instance, using attribute-value expressions such as "normal vibrato", "high openness" and "sharp attack". ARTIST stores information about sounds as clusters of attribute-value expressions and has the ability to interpret these expressions in the lower-level terms of sound synthesis algorithms. The user may, however, be interested in producing a sound which is "unknown" to the system. In this case, the system will attempt to compute the attribute values for this yet unknown sound by making analogies with other known sounds which have similar constituents. ARTIST uses ML to infer which sound attributes should be considered to make the analogies.
CSI: A Hybrid Deep Model for Fake News Detection

The topic of fake news has drawn attention both from the public and the academic communities. Such misinformation has the potential of affecting public opinion, providing an opportunity for malicious parties to manipulate the outcomes of public events such as elections. Because such high stakes are at play, automatically detecting fake news is an important, yet challenging problem that is not yet well understood. Nevertheless, there are three generally agreed upon characteristics of fake news: the text of an article, the user response it receives, and the source users promoting it. Existing work has largely focused on tailoring solutions to one particular characteristic which has limited their success and generality.
 In this work, we propose a model that combines all three characteristics for a more accurate and automated prediction. Specifically, we incorporate the behavior of both parties, users and articles, and the group behavior of users who propagate fake news. Motivated by the three characteristics, we propose a model called CSI which is composed of three modules: Capture, Score, and Integrate. The first module is based on the response and text; it uses a Recurrent Neural Network to capture the temporal pattern of user activity on a given article. The second module learns the source characteristic based on the behavior of users, and the two are integrated with the third module to classify an article as fake or not. Experimental analysis on real-world data demonstrates that CSI achieves higher accuracy than existing models, and extracts meaningful latent representations of both users and articles.
Parkinson's Disease Mechanisms and Models

Parkinson's disease (PD) results primarily from the death of dopaminergic neurons in the substantia nigra. Current PD medications treat symptoms; none halt or retard dopaminergic neuron degeneration. The main obstacle to developing neuroprotective therapies is a limited understanding of the key molecular events that provoke neurodegeneration. The discovery of PD genes has led to the hypothesis that misfolding of proteins and dysfunction of the ubiquitin-proteasome pathway are pivotal to PD pathogenesis. Previously implicated culprits in PD neurodegeneration, mitochondrial dysfunction and oxidative stress, may also act in part by causing the accumulation of misfolded proteins, in addition to producing other deleterious events in dopaminergic neurons. Neurotoxin-based models (particularly MPTP) have been important in elucidating the molecular cascade of cell death in dopaminergic neurons. PD models based on the manipulation of PD genes should prove valuable in elucidating important aspects of the disease, such as selective vulnerability of substantia nigra dopaminergic neurons to the degenerative process.
Human activity prediction: Early recognition of ongoing activities from streaming videos

In this paper, we present a novel approach of human activity prediction. Human activity prediction is a probabilistic process of inferring ongoing activities from videos only containing onsets (i.e. the beginning part) of the activities. The goal is to enable early recognition of unfinished activities as opposed to the after-the-fact classification of completed activities. Activity prediction methodologies are particularly necessary for surveillance systems which are required to prevent crimes and dangerous activities from occurring. We probabilistically formulate the activity prediction problem, and introduce new methodologies designed for the prediction. We represent an activity as an integral histogram of spatio-temporal features, efficiently modeling how feature distributions change over time. The new recognition methodology named dynamic bag-of-words is developed, which considers sequential nature of human activities while maintaining advantages of the bag-of-words to handle noisy observations. Our experiments confirm that our approach reliably recognizes ongoing activities from streaming videos with a high accuracy.
The sequence manipulation suite: JavaScript programs for analyzing and formatting protein and DNA sequences.

JavaScript is an object-based scripting language that can be interpreted by most commonly used Web browsers, including Netscape® Navigator® and Internet Explorer®. In conjunction with HTML form elements, JavaScript can be used to make flexible and easy-to-use applications that can be accessed by anyone connected to the Internet (3). The Sequence Manipulation Suite (http://www.ualberta.ca/~stothard/javascript/) is a collection of freely available JavaScript applications for molecular biologists. It consists of over 30 utilities for analyzing and manipulating sequence data, including the following:
Neural Message Passing with Edge Updates for Predicting Properties of Molecules and Materials

Neural message passing on molecular graphs is one of the most promising methods for predicting formation energy and other properties of molecules and materials. In this work we extend the neural message passing model with an edge update network which allows the information exchanged between atoms to depend on the hidden state of the receiving atom. We benchmark the proposed model on three publicly available datasets (QM9, The Materials Project and OQMD) and show that the proposed model yields superior prediction of formation energies and other properties on all three datasets in comparison with the best published results. Furthermore we investigate different methods for constructing the graph used to represent crystalline structures and we find that using a graph based on K-nearest neighbors achieves better prediction accuracy than using maximum distance cutoff or the Voronoi tessellation graph.
Compactions in Apache Cassandra : Performance Analysis of Compaction Strategies in Apache Cassandra

The global communication system is in a tremendous growth, leading to wide range of data generation. The Telecom operators in various Telecom Industries, that generate large amount of data has a need to manage these data efficiently. As the technology involved in the database management systems is increasing, there is a remarkable growth of NoSQL databases in the 20 century. Apache Cassandra is an advanced NoSQL database system, which is popular for handling semi-structured and unstructured format of Big Data. Cassandra has an effective way of compressing data by using different compaction strategies. This research is focused on analyzing the performances of different compaction strategies in different use cases for default Cassandra stress model. The analysis can suggest better usage of compaction strategies in Cassandra, for a write heavy workload.  In this study, we investigate the appropriate performance metrics to evaluate the performance of compaction strategies. We provide the detailed analysis of Size Tiered Compaction Strategy, Date Tiered Compaction Strategy, and Leveled Compaction Strategy for a write heavy (90/10) work load, using default cassandra stress tool.  A detailed literature research has been conducted to study the NoSQL databases, and the working of different compaction strategies in Apache Cassandra. The performances metrics are considered by the understanding of the literature research conducted, and considering the opinions of supervisors and Ericsson’s Apache Cassandra team. Two different tools were developed for collecting the performances of the considered metrics. The first tool was developed using Jython scripting language to collect the cassandra metrics, and the second tool was developed using python scripting language to collect the Operating System metrics. The graphs have been generated in Microsoft Excel, using the values obtained from the scripts.  Date Tiered Compaction Strategy and Size Tiered Compaction strategy showed more or less similar behaviour during the stress tests conducted.  Tiered Compaction strategy has showed some remarkable results that effected the system performance, as compared to date tiered compaction and size tiered compaction strategies. Date tiered compaction strategy does not perform well for default cassandra stress model. Size tiered compaction can be preferred for default cassandra stress model, but not considerable for big data.  With a detailed analysis and logical comparison of metrics, we finally conclude that Level Tiered Compaction Strategy performs better for a write heavy (90/10) workload while using default cassandra stress model, as compared to size tiered compaction and date tiered compaction strategies.
Recurrent Models for Auditory Attention in Multi-Microphone Distance Speech Recognition

Integration of multiple microphone data is one of the key ways to achieve robust speech recognition in noisy environments or when the speaker is located at some distance from the input device. Signal processing techniques such as beamforming are widely used to extract a speech signal of interest from background noise. These techniques, however, are highly dependent on prior spatial information about the microphones and the environment in which the system is being used. In this work, we present a neural attention network that directly combines multichannel audio to generate phonetic states without requiring any prior knowledge of the microphone layout or any explicit signal preprocessing for speech enhancement. We embed an attention mechanism within a Recurrent Neural Network based acoustic model to automatically tune its attention to a more reliable input source. Unlike traditional multi-channel preprocessing, our system can be optimized towards the desired output in one step. Although attention-based models have recently achieved impressive results on sequence-to-sequence learning, no attention mechanisms have previously been applied to learn potentially asynchronous and non-stationary multiple inputs. We evaluate our neural attention model on the CHiME-3 task, and show that the model achieves comparable performance to beamforming using a purely data-driven method.
A Framework to Construct Knowledge Base for Cyber Security

There are some independent cyber security knowledge bases for different aspect now. In the internet, there is also much cyber security related content which exists in the form of text. Fusion of these cyber security related information can be a meaningful work. In this paper, we propose a framework to integrate existing cyber security knowledge base and extract cyber security related information from text. In the framework, we construct a vulnerability-centric ontology and train a Stanford named entity recognizer to extract cyber security related entity from text. Finally, through experiment, we verify the importance of one feature named useGazette to training a named entity recognizer in cyber security domain.
DKN: Deep Knowledge-Aware Network for News Recommendation

Online news recommender systems aim to address the information explosion of news and make personalized recommendation for users. In general, news language is highly condensed, full of knowledge entities and common sense. However, existing methods are unaware of such external knowledge and cannot fully discover latent knowledge-level connections among news. The recommended results for a user are consequently limited to simple patterns and cannot be extended reasonably. Moreover, news recommendation also faces the challenges of high time-sensitivity of news and dynamic diversity of users’ interests. To solve the above problems, in this paper, we propose a deep knowledge-aware network (DKN) that incorporates knowledge graph representation into news recommendation. DKN is a content-based deep recommendation framework for click-through rate prediction. The key component of DKN is a multi-channel and word-entity-aligned knowledge-aware convolutional neural network (KCNN) that fuses semantic-level and knowledge-level representations of news. KCNN treats words and entities asmultiple channels, and explicitly keeps their alignment relationship during convolution. In addition, to address users’ diverse interests, we also design an attention module in DKN to dynamically aggregate a user’s history with respect to current candidate news. Through extensive experiments on a real online news platform, we demonstrate that DKN achieves substantial gains over state-of-the-art deep recommendation models. We also validate the efficacy of the usage of knowledge in DKN.
Music type classification by spectral contrast feature

Automatic music type classification is very helpful for the management of digital music database. In this paper, Octavebased Spectral Contrast feature is proposed to represent the spectral characteristics of a music clip. It represented the relative spectral distribution instead of average spectral envelope. Experiments showed that Octave-based Spectral Contrast feature performed well in music type classification. Another comparison experiment demonstrated that Octave-based Spectral Contrast feature has a better discrimination among different music types than Mel-Frequency Cepstral Coefficients (MFCC), which is often used in previous music type classification systems.
Essential Oils in Combination and Their Antimicrobial Properties 

Essential oils (EOs) have been long recognized for their antibacterial, antifungal, antiviral, insecticidal and antioxidant properties. They are widely used in medicine and the food industry for these purposes. The increased interest in alternative natural substances is driving the research community to find new uses and applications of these substances. EOs and their components show promising activities against many food-borne pathogens and spoilage microorganisms when tested in vitro. In food systems, higher concentrations of EOs are needed to exert similar antibacterial effects as those obtained in in vitro assays. The use of combinations of EOs and their isolated components are thus new approaches to increase the efficacy of EOs in foods, taking advantage of their synergistic and additive effects. The purpose of this review is to provide an overview on the antimicrobial efficacy of these combinations. A survey of the methods used for the determination of the interactions and mechanisms involved in the antimicrobial activities of these combinations are also reported.
Internet Addiction in Students: A Cause of Concern

The Internet was originally designed to facilitate communication and research activities. However, the dramatic increase in the use of the Internet in recent years has led to pathological use (Internet addiction). This study is a preliminary investigation of the extent of Internet addiction in school children 16-18 years old in India. The Davis Online Cognition Scale (DOCS) was used to assess pathological Internet use. On the basis of total scores obtained (N = 100) on the DOCS, two groups were identified--dependents (18) and non-dependents (21), using mean +/- 1/2 SD as the criterion for selection. The UCLA loneliness scale was also administered to the subjects. Significant behavioral and functional usage differences were revealed between the two groups. Dependents were found to delay other work to spend time online, lose sleep due to late-night logons, and feel life would be boring without the Internet. The hours spent on the Internet by dependents were greater than those of non-dependents. On the loneliness measure, significant differences were found between the two groups, with the dependents scoring higher than the non-dependents.