nils-herrmann/clean_scirepeval huggingface dataset:
Assessing mental health issues on college campuses: preliminary findings from a pilot study

A significant fraction of college students suffer from serious mental health issues including depression, anxiety, self-harm and suicidal thought. The prevalence and severity of these issues among college students also appear to increase over time. However, most of these issues often remain undiagnosed, and as a result, untreated. One of the main reasons of this gap between illness and treatment results from the lack of reliable data over time. While health care services in college campuses have been focusing on detection of illness onset and appropriate interventions, their tools are mostly manual surveys which often fail to capture the granular details of contexts and behaviors which might provide important cues about illness onset. To overcome the limitations of these manual tools, we deployed a smartphone based tool or unobtrusive and continuous data collection from 22 students during an academic semester. In this paper, we present the preliminary findings from our study about assessing mental health on college campuses using passively sensed smartphone data.
Generation of usable electric power from available random sound energy

In this work, a relatively less explored source of green energy is proposed. Random sound energy around us can be treated as a source of electric power after their efficient conversion using suitable transducer. An effective way of producing usable electric power from available random sound energy is presented here. Piezoelectric transducers are used for conversion of sounds into electric energy. The produced electric energy from multiple piezoelectric transducers are stored in multiple supercapacitors which are then summed up and amplified through adder and voltage multiplier circuits. The resultant electric power was used to charge a rechargeable DC battery so as to store this energy. A small 9 volt DC battery was found to be fully recharged within half an hour from fully discharged stage using medium sound source through the proposed conversion circuit. In this way, random sound energy from numerous sources around us can be stored as electric energy which can be used later to deliver electric power to drive compatible small loads. The proposed idea can give a new source of green energy and can contribute in global search for renewable energy.
A Roadmap for Natural Language Processing Research in Information Systems

Natural Language Processing (NLP) is now widely integrated into web and mobile applications, enabling natural interactions between human and computers. Although many NLP studies have been published, none have comprehensively reviewed or synthesized tasks most commonly addressed in NLP research. We conduct a thorough review of IS literature to assess the current state of NLP research, and identify 12 prototypical tasks that are widely researched. Our analysis of 238 articles in Information Systems (IS) journals between 2004 and 2015 shows an increasing trend in NLP research, especially since 2011. Based on our analysis, we propose a roadmap for NLP research, and detail how it may be useful to guide future NLP research in IS. In addition, we employ Association Rules (AR) mining for data analysis to investigate co-occurrence of prototypical tasks and discuss insights from the findings.
Cyber Security in the Energy World

The advancement of technology reshapes our energy landscape over the past few decades. More sensors are deployed; more processes are being automated and more information is being exchanged between systems. Technology advancement brings enormous opportunities; For instance, optimizing operation, reducing outage time, turning raw data into useful business information etc. However, as we move deeper into digitalization, cyber threats become imminent. Cyber-attack has evolved from petty crime to state-sponsored cyber terrorism. From Stuxnet that attacked the Iranian Nuclear Power Plant in 2010 to the Black Energy 3 that caused massive blackout in Ukraine in 2015, cyber-attacks on critical infrastructure has become a national security issue. Cyber-attack on critical infrastructure requires a combination of hacking and engineering knowledge to inflict maximum damages. Thus, protecting critical infrastructure requires both information technology (IT) and operational technology (OT) expertise. Cyber security approach must be holistic.
Computing the shortest path: A search meets graph theory

We propose shortest path algorithms that use A* search in combination with a new graph-theoretic lower-bounding technique based on landmarks and the triangle inequality. Our algorithms compute optimal shortest paths and work on any directed graph. We give experimental results showing that the most efficient of our new algorithms outperforms previous algorithms, in particular A* search with Euclidean bounds, by a wide margin on road networks and on some synthetic problem families.
Policy Improvement Methods: Between Black-Box Optimization and Episodic Reinforcement Learning

Policy improvement methods seek to optimize the parameters of a policy with respect to a utility function. There are two main approaches to performing this optimization: reinforcement learning (RL) and black-box optimization (BBO). Whereas BBO algorithms are generic optimization methods that, due to there generality, may also be applied to optimizing policy parameters, RL algorithms are specifically tailored to leveraging the structure of policy improvement problems. In recent years, benchmark comparisons between RL and BBO have been made, and there has been several attempts to specify which approach works best for which types of problem classes. In this article, we make several contributions to this line of research: 1) We define four algorithmic properties that further clarify the relationship between RL and BBO: action-perturbation vs. parameter-perturbation, gradient estimation vs. rewardweighted averaging, use of only rewards vs. use of rewards and state information, actor-critic vs. direct policy search. 2) We show how the chronology of the derivation of ever more powerful algorithms displays a trend towards algorithms based on parameter-perturbation and reward-weighted averaging. A striking feature of this trend is that it has moved RL methods closer and closer to BBO. 3) We continue this trend by applying two modifications to the state-of-the-art “Policy Improvement with Path Integrals” (PI), which yields an algorithm we denote PI. We show that PI is a BBO algorithm, and, more specifically, that it is a special case of the “Covariance Matrix Adaptation – Evolutionary Strategy” algorithm. Our empirical evaluation demonstrates that the simpler PI outperforms PI on simple evaluation tasks in terms of convergence speed and final cost. 4) Although our evaluation implies that, for these five tasks, BBO outperforms RL, we do not hold this to be a general statement, and provide an analysis of why these tasks are particularly well-suited for BBO. Thus, rather than making the case for BBO or RL, one of the main contributions of this article is rather to provide an algorithmic framework in which such cases may be made, as PI and PI use identical perturbation and parameter update methods, and differ only in being BBO and RL approaches respectively.
Query-Reduction Networks for Question Answering

In this paper, we study the problem of question answering when reasoning over multiple facts is required. We propose Query-Reduction Network (QRN), a variant of Recurrent Neural Network (RNN) that effectively handles both short-term (local) and long-term (global) sequential dependencies to reason over multiple facts. QRN considers the context sentences as a sequence of state-changing triggers, and reduces the original query to a more informed query as it observes each trigger (context sentence) through time. Our experiments show that QRN produces the state-of-the-art results in bAbI QA and dialog tasks, and in a real goal-oriented dialog dataset. In addition, QRN formulation allows parallelization on RNN’s time axis, saving an order of magnitude in time complexity for training and inference.
Succinct: Enabling Queries on Compressed Data

Succinct is a data store that enables efficient queries directly on a compressed representation of the input data. Succinct uses a compression technique that allows random access into the input, thus enabling efficient storage and retrieval of data. In addition, Succinct natively supports a wide range of queries including count and search of arbitrary strings, range and wildcard queries. What differentiates Succinct from previous techniques is that Succinct supports these queries without storing indexes — all the required information is embedded within the compressed representation. Evaluation on real-world datasets show that Succinct requires an order of magnitude lower memory than systems with similar functionality. Succinct thus pushes more data in memory, and provides low query latency for a larger range of input sizes than existing systems.
The gut-brain axis: interactions between enteric microbiota, central and enteric nervous systems

The gut-brain axis (GBA) consists of bidirectional communication between the central and the enteric nervous system, linking emotional and cognitive centers of the brain with peripheral intestinal functions. Recent advances in research have described the importance of gut microbiota in influencing these interactions. This interaction between microbiota and GBA appears to be bidirectional, namely through signaling from gut-microbiota to brain and from brain to gut-microbiota by means of neural, endocrine, immune, and humoral links. In this review we summarize the available evidence supporting the existence of these interactions, as well as the possible pathophysiological mechanisms involved. Most of the data have been acquired using technical strategies consisting in germ-free animal models, probiotics, antibiotics, and infection studies. In clinical practice, evidence of microbiota-GBA interactions comes from the association of dysbiosis with central nervous disorders (i.e. autism, anxiety-depressive behaviors) and functional gastrointestinal disorders. In particular, irritable bowel syndrome can be considered an example of the disruption of these complex relationships, and a better understanding of these alterations might provide new targeted therapies.
Can language restructure cognition? The case for space

Frames of reference are coordinate systems used to compute and specify the location of objects with respect to other objects. These have long been thought of as innate concepts, built into our neurocognition. However, recent work shows that the use of such frames in language, cognition and gesture varies cross-culturally, and that children can acquire different systems with comparable ease. We argue that language can play a significant role in structuring, or restructuring, a domain as fundamental as spatial cognition. This suggests we need to rethink the relation between the neurocognitive underpinnings of spatial cognition and the concepts we use in everyday thinking, and, more generally, to work out how to account for cross-cultural cognitive diversity in core cognitive domains.
Using altmetrics for assessing research impact in the humanities

The prospects of altmetrics are especially encouraging for research fields in the humanities that currently are difficult to study using established bibliometric methods. Yet, little is known about the altmetric impact of research fields in the humanities. Consequently, this paper analyses the altmetric coverage and impact of humanities-oriented articles and books published by Swedish universities during 2012. Some of the most common altmetric sources are examined using a sample of 310 journal articles and 54 books. Mendeley has the highest coverage of journal articles (61 %) followed by Twitter (21 %) while very few of the publications are mentioned in blogs or on Facebook. Books, on the other hand, are quite often tweeted while both Mendeley’s and the novel data source Library Thing’s coverage is low. Many of the problems of applying bibliometrics to the humanities are also relevant for altmetric approaches; the importance of non-journal publications, the reliance on print as well the limited coverage of non-English language publications. However, the continuing development and diversification of methods suggests that altmetrics could evolve into a valuable tool for assessing research in the humanities.
A Reconfigurable Fabric for Accelerating Large-Scale Datacenter Services

Datacenter workloads demand high computational capabilities, flexibility, power efficiency, and low cost. It is challenging to improve all of these factors simultaneously. To advance datacenter capabilities beyond what commodity server designs can provide, we designed and built a composable, reconfigurable hardware fabric based on field programmable gate arrays (FPGA). Each server in the fabric contains one FPGA, and all FPGAs within a 48-server rack are interconnected over a low-latency, high-bandwidth network.
 We describe a medium-scale deployment of this fabric on a bed of 1632 servers, and measure its effectiveness in accelerating the ranking component of the Bing web search engine. We describe the requirements and architecture of the system, detail the critical engineering challenges and solutions needed to make the system robust in the presence of failures, and measure the performance, power, and resilience of the system. Under high load, the large-scale reconfigurable fabric improves the ranking throughput of each server by 95% at a desirable latency distribution or reduces tail latency by 29% at a fixed throughput. In other words, the reconfigurable fabric enables the same throughput using only half the number of servers.
A Statistical Measure of a Population's Propensity to Engage in Post-Purchase Online Word-of-Mouth

The emergence of online communities has enabled firms to monitor consumer-generated online word-of-mouth (WOM) in realtime by mining publicly available information from the Internet. A prerequisite for harnessing this new ability is the development of appropriate WOM metrics and the identification of relationships between such metrics and consumer behavior. Along these lines this paper introduces a metric of a purchasing population’s propensity to rate a product online. Using data from a popular movie website we find that our metric exhibits several relationships that have been previously found to exist between aspects of a product and consumers’ propensity to engage in offline WOM about it. Our study, thus, provides positive evidence for the validity of our metric as a proxy of a population’s propensity to engage in post-purchase online WOM. Our results also suggest that the antecedents of offline and online WOM exhibit important similarities.
Hardware, Design and Implementation Issues on a Fpga-Based Smart Camera

Processing images to extract useful information in real-time is a complex task, dealing with large amounts of iconic data and requiring intensive computation. Smart cameras use embedded processing to save the host system from the low-level processing load and to reduce communication flows and overheads. Field programmable devices present special interest for smart cameras design: flexibility, reconfigurability and parallel processing skills are some specially important features. In this paper we present a FPGA-based smart camera research platform. The hardware architecture is described, and some design issues are discussed. Our goal is to use the possibility to reconfigure the FPGA device in order to adapt the system architecture to a given application. To that, a design methodology, based on pre-programmed processing elements, is proposed and sketched. Some implementation issues are discussed and a template tracking application is given as example, with its experimental results.
An Investigation of Information Sharing and seeking Behaviors in Virtual Communities

User participations in virtual communities include both information seeking and sharing behaviors to varying degrees. The intentions to share and to seek information may have different motivations. . However, existing studies rarely analyze the intentions for both types of behaviors. This study explicates the antecedents of the two types of usage intentions – intentions to share and intentions to seek – using a model that integrates social, community, and individual factors. The model is validated using a survey of 502 online investors of the most popular online stock message board in South Korea. The results show that the sense of belonging, entertainment value, and perceived usefulness influence both the intentions to share and seek. In addition, reputation seeking enhances the intention to post while perceived knowledge reduces the intention to seek.
TechMiner: Extracting Technologies from Academic Publications

In recent years we have seen the emergence of a variety of scholarly datasets. Typically these capture ‘standard’ scholarly entities and their connections, such as authors, affiliations, venues, publications, citations, and others. However, as the repositories grow and the technology improves, researchers are adding new entities to these repositories to develop a richer model of the scholarly domain. In this paper, we introduce TechMiner, a new approach, which combines NLP, machine learning and semantic technologies, for mining technologies from research publications and generating an OWL ontology describing their relationships with other research entities. The resulting knowledge base can support a number of tasks, such as: richer semantic search, which can exploit the technology dimension to support better retrieval of publications; richer expert search; monitoring the emergence and impact of new technologies, both within and across scientific fields; studying the scholarly dynamics associated with the emergence of new technologies; and others. TechMiner was evaluated on a manually annotated gold standard and the results indicate that it significantly outperforms alternative NLP approaches and that its semantic features improve performance significantly with respect to both recall and precision.
Non-sentential Question Resolution using Sequence to Sequence Learning

An interactive Question Answering (QA) system frequently encounters non-sentential (incomplete) questions. These non-sentential questions may not make sense to the system when a user asks them without the context of conversation. The system thus needs to take into account the conversation context to process the incomplete question. In this work, we present a recurrent neural network (RNN) based encoder decoder network that can generate a complete (intended) question, given an incomplete question and conversation context. RNN encoder decoder networks have been show to work well when trained on a parallel corpus with millions of sentences, however it is extremely hard to obtain conversation data of this magnitude. We therefore propose to decompose the original problem into two separate simplified problems where each problem focuses on an abstraction. Specifically, we train a semantic sequence model to learn semantic patterns, and a syntactic sequence model to learn linguistic patterns. We further combine syntactic and semantic sequence models to generate an ensemble model. Our model achieves a BLEU score of 30.15 as compared to 18.54 using a standard RNN encoder decoder model.
A Robust, Simple Genotyping-by-Sequencing (GBS) Approach for High Diversity Species

Advances in next generation technologies have driven the costs of DNA sequencing down to the point that genotyping-by-sequencing (GBS) is now feasible for high diversity, large genome species. Here, we report a procedure for constructing GBS libraries based on reducing genome complexity with restriction enzymes (REs). This approach is simple, quick, extremely specific, highly reproducible, and may reach important regions of the genome that are inaccessible to sequence capture approaches. By using methylation-sensitive REs, repetitive regions of genomes can be avoided and lower copy regions targeted with two to three fold higher efficiency. This tremendously simplifies computationally challenging alignment problems in species with high levels of genetic diversity. The GBS procedure is demonstrated with maize (IBM) and barley (Oregon Wolfe Barley) recombinant inbred populations where roughly 200,000 and 25,000 sequence tags were mapped, respectively. An advantage in species like barley that lack a complete genome sequence is that a reference map need only be developed around the restriction sites, and this can be done in the process of sample genotyping. In such cases, the consensus of the read clusters across the sequence tagged sites becomes the reference. Alternatively, for kinship analyses in the absence of a reference genome, the sequence tags can simply be treated as dominant markers. Future application of GBS to breeding, conservation, and global species and population surveys may allow plant breeders to conduct genomic selection on a novel germplasm or species without first having to develop any prior molecular tools, or conservation biologists to determine population structure without prior knowledge of the genome or diversity in the species.
Do hospital fall prevention programs work? A systematic review.

To analyze published hospital fall prevention programs to determine whether there is any effect on fall rates. To review the methodological quality of those programs and the range of interventions used. To provide directions for further research.



Systematic review of published hospital fall prevention programs. Meta-analysis.



Keyword searches of Medline, CINAHL, monographs, and secondary references. All papers were included that described fall rates before and during intervention. Risk ratios and 95% Confidence Intervals (95% CI) were estimated and random effects meta-analysis employed. Begg's test was applied to detect possible publication bias. Separate meta-analysis regressions were performed to determine whether individual components of multifaceted interventions were effective.



A total of 21 papers met the criteria (18 from North America), although only 10 contained sufficient data to allow calculation of confidence intervals. A rate ratio of <1 indicates a reduction in the fall rate, resulting from an intervention. Three were randomized controlled trials (pooled rate ratio 1.0 (CI 0.60, 1.68)), seven prospective studies with historical control (0.76 (CI 0.65, 0.88)). Pooled effect rate ratio from these 10 studies was 0.79 (CI 0.69, 0.89). The remaining 11 studies were prospective studies with historical control describing fall rates only. Individual components of interventions showed no significant benefit.



The pooled effect of about 25% reduction in the fall rate may be a result of intervention but may also be biased by studies that used historical controls not allowing for historical trends in the fall rate before and during the intervention. The randomized controlled trials apparent lack of effect might be due to a change in practice when patients and controls were in the same unit at the same time during a study. Studies did not analyze compliance with the intervention or opportunity costs resulting from the intervention. Research and clinical programs in hospital fall prevention should pay more attention to study design and the nature of interventions.