nils-herrmann/clean_scirepeval huggingface dataset:
Survey on Detection of Metal Illnesses by Analysing Twitter Data

Mental illnesses are serious problems that places a burden on individuals, their families and on society in general. Although their symptoms have been known for several years, accurate and quick diagnoses remain a challenge. Inaccurate or delayed diagnoses results in increased frequency and severity of mood episodes, and reduces the benefits of treatment. In this survey paper, we review papers that leverage data from social media and design predictive models. These models utilize patterns of speech and life features of various subjects to determine the onset period of bipolar disorder. This is done by studying the patients, their behaviour, moods and sleeping patterns, and then effectively mapping these features to detect whether they are currently in a prodromal phase before a mood episode or not.
1 Access to Finance

Expanding access to financial services holds the promise to help reduce poverty and spur economic development. But, as a practical matter, commercial banks have faced challenges expanding access to poor and low-income households in developing economies, and nonprofits have had limited reach. We review recent innovations that are improving the quantity and quality of financial access. They are taking possibilities well beyond early models centered on providing “microcredit” for small business investment. We focus on new credit mechanisms and devices that help households manage cash flows, save, and cope with risk. Our eye is on contract designs, product innovations, regulatory policy, and ultimately economic and social impacts. We relate the innovations and empirical evidence to theoretical ideas, drawing links in particular to new work in behavioral economics and to randomized evaluation methods.
Microgrids in Active Network Management- Part I: Hierarchical Control, Energy Storage, Virtual Power Plants, and Market Participation

The microgrid concept has been closely investigated and implemented by numerous experts worldwide. The first part of this paper describes the principles of microgrid design, considering the operational concepts and requirements arising from participation in active network management. Over the last several years, efforts to standardize microgrids have been made, and it is in terms of these advances that the current paper proposes the application of IEC/ISO 62264 standards to microgrids and Virtual Power Plants, along with a comprehensive review of microgrids, including advanced control techniques, energy storage systems, and market participation in both island and grid-connection operation. Finally, control techniques and the principles of energy-storage systems are summarized in a comprehensive flowchart.
FlowSampler: Visual Analysis of Urban Flows in Geolocated Social Media Data

Analysis of flows such as human movement can help spatial planners better understand territorial patterns in urban environments. In this paper, we describe FlowSampler, an interactive visual interface designed for spatial planners to gather, extract and analyse human flows in geolocated social media data. Our system adopts a graph-based approach to infer movement pathways from spatial point type data and expresses the resulting information through multiple linked multiple visualisations to support data exploration. We describe two use cases to demonstrate the functionality of our system and characterise how spatial planners utilise it to address analytical task.
Confronting depression and suicide in physicians: a consensus statement.

To encourage treatment of depression and prevention of suicide in physicians by calling for a shift in professional attitudes and institutional policies to support physicians seeking help.



An American Foundation for Suicide Prevention planning group invited 15 experts on the subject to evaluate the state of knowledge about physician depression and suicide and barriers to treatment. The group assembled for a workshop held October 6-7, 2002, in Philadelphia, 
The planning group worked with each participant on a preworkshop literature review in an assigned area. Abstracts of presentations and key publications were distributed to participants before the workshop. After workshop presentations, participants were assigned to 1 of 2 breakout groups: (1) physicians in their role as patients and (2) medical institutions and professional organizations. The groups identified areas that required further research, barriers to treatment, and recommendations for reform.


CONSENSUS PROCESS
This consensus statement emerged from a plenary session during which each work group presented its recommendations. The consensus statement was circulated to and approved by all participants.



The culture of medicine accords low priority to physician mental health despite evidence of untreated mood disorders and an increased burden of suicide. Barriers to physicians' seeking help are often punitive, including discrimination in medical licensing, hospital privileges, and professional advancement. This consensus statement recommends transforming professional attitudes and changing institutional policies to encourage physicians to seek help. As barriers are removed and physicians confront depression and suicidality in their peers, they are more likely to recognize and treat these conditions in patients, including colleagues and medical students.
BotFinder: finding bots in network traffic without deep packet inspection

Bots are the root cause of many security problems on the Internet, as they send spam, steal information from infected machines, and perform distributed denial-of-service attacks. Many approaches to bot detection have been proposed, but they either rely on end-host installations, or, if they operate on network traffic, require deep packet inspection for signature matching.
 In this paper, we present BotFinder, a novel system that detects infected hosts in a network using only high-level properties of the bot's network traffic. BotFinder does not rely on content analysis. Instead, it uses machine learning to identify the key features of command-and-control communication, based on observing traffic that bots produce in a controlled environment. Using these features, BotFinder creates models that can be deployed at network egress points to identify infected hosts. We trained our system on a number of representative bot families, and we evaluated BotFinder on real-world traffic datasets -- most notably, the NetFlow information of a large ISP that contains more than 25 billion flows. Our results show that BotFinder is able to detect bots in network traffic without the need of deep packet inspection, while still achieving high detection rates with very few false positives.
Selection of optimal flocculant for effective harvesting of the fucoxanthin-rich marine microalga Isochrysis galbana

Flocculation harvesting of the fucoxanthin-rich marine microalga Isochrysis galbana has received little attention. Therefore, we attempted to screen for an optimal chemical flocculant and optimize flocculation conditions from five chemical flocculants—ferric chloride (FC), aluminum sulfate (AS), polyaluminum chloride (PAC), aluminum potassium sulfate (APS), and zinc sulfate (ZS)—for effective flocculation of I. galbana. The growth rate, photosynthetic performance, and fucoxanthin content were determined in re-suspended flocculated algal cells and in the flocculation supernatant cultured algal cells. The results showed that high growth rate and fucoxanthin accumulation were observed when FC was used as the flocculant in I. galbana cultures, which indicated that FC may cause less harm to I. galbana than the other aluminum-based flocculants. Furthermore, satisfactory flocculation efficiency was also observed when FC was used to flocculate I. galbana, and the FC dosage was less than that required for flocculation of I. galbana using PAC, APS, and AS. Thus, we selected FC as the optimal flocculant for harvesting I. galbana based on its flocculation efficiency together with algal physiological performance, growth rate, and fucoxanthin content.
A wide dynamic range low power 2× time amplifier using current subtraction scheme

The most challenging issue of conventional Time Amplifiers (TAs) is their limited Dynamic Range (DR). This paper presents a mathematical analysis to clarify principle of operation of conventional 2× TA's. The mathematical derivations release strength reduction of the current sources of the TA is the simplest way to increase DR. Besides, a new technique is presented to expand the Dynamic Range (DR) of conventional 2× TAs. Proposed technique employs current subtraction in place of changing strength of current sources using conventional gain compensation methods, which results in more stable gain over a wider DR. The TA is simulated using Spectre-rf in TSMC 0.18um COMS technology. DR of the 2× TA is expanded to 300ps only with 9% gain error while it consumes only 28uW from a 1.2V supply voltage.
Defining humidity test duration for microinverter reliability assessment: A physics-of-failure approach

Microinverters are module-level power electronic (MLPE) systems that are expected to have a service life more than 25 years. The general practice for providing assurance in long-term reliability under humid climatic conditions is to subject the microinverters to ‘damp heat test’ at 85°C/85%RH for 1000hrs as recommended in lEC 61215 standard. However, there is limited understanding on the correlation between the said ‘damp heat’ test and field conditions for microinverters. In this paper, a physics-of-failure (PoF)-based approach is used to correlate damp heat test to field conditions. Results of the PoF approach indicates that even 3000hrs at 85°C/85%RH may not be sufficient to guarantee 25-years' service life in certain places in the world. Furthermore, we also demonstrate that use of Miami, FL weathering data as benchmark for defining damp heat test durations will not be sufficient to guarantee 25 years' service life. Finally, when tests were conducted at 85°C/85%RH for more than 3000hrs, it was found that the PV connectors are likely to fail before the actual power electronics could fail.
Towards Practical Visual Search Engine within Elasticsearch

In this paper, we describe our end-to-end content-based image retrieval system built upon Elasticsearch, a well-known and popular textual search engine. As far as we know, this is the first time such a system has been implemented in eCommerce, and our efforts have turned out to be highly worthwhile. We end up with a novel and exciting visual search solution that is extremely easy to be deployed, distributed, scaled and monitored in a cost-friendly manner. Moreover, our platform is intrinsically flexible in supporting multimodal searches, where visual and textual information can be jointly leveraged in retrieval. The core idea is to encode image feature vectors into a collection of string tokens in a way such that closer vectors will share more string tokens in common. By doing that, we can utilize Elasticsearch to efficiently retrieve similar images based on similarities within encoded sting tokens. As part of the development, we propose a novel vector to string encoding method, which is shown to substantially outperform the previous ones in terms of both precision and latency. First-hand experiences in implementing this Elasticsearchbased platform are extensively addressed, which should be valuable to practitioners also interested in building visual search engine on top of Elasticsearch.
FingerNet: Deep learning-based robust finger joint detection from radiographs

Radiographic image assessment is the most common method used to measure physical maturity and diagnose growth disorders, hereditary diseases and rheumatoid arthritis, with hand radiography being one of the most frequently used techniques due to its simplicity and minimal exposure to radiation. Finger joints are considered as especially important factors in hand skeleton examination. Although several automation methods for finger joint detection have been proposed, low accuracy and reliability are hindering full-scale adoption into clinical fields. In this paper, we propose FingerNet, a novel approach for the detection of all finger joints from hand radiograph images based on convolutional neural networks, which requires little user intervention. The system achieved 98.02% average detection accuracy for 130 test data sets containing over 1,950 joints. Further analysis was performed to verify the system robustness against factors such as epiphysis and metaphysis in different age groups.
SSENet-2014 Dataset: A Dataset for Detection of Multiconnection Attacks

Multiconnection attacks such as DoS, probe, flooding, etc., have become common and attackers have come out with sophisticated techniques as well as tools to launch variants of such attacks. This growing amount of attack and sophistication has given rise to the increasing need of efficient detection algorithm. To test and compare the performances of the proposed detection algorithms, benchmark datasets are required to represent the dynamic nature of the network. Though certain benchmark datasets are available, most datasets are either synthetic or contains suppressed information. In this paper, we introduce SSENet-2014 dataset which is generated in a real network environment. The attacks were generated using attack tools while carrying out normal activities. The description of the SSENet-2014 dataset is given. Then, a comparison is carried out with the most popular intrusion detection dataset, 10% KDD Cup 99. Two clustering approaches of K Means and Self Organizing Map (SOM) have been used in our experiments. Box plot is used to analyze the attributes of the two datasets. The results confirm the variability existing in the attribute values of 10% KDD Cup 99 and SSENet-2014 dataset. Also, it can be seen that SSENet-2014 dataset generated from a real network varies considerably from 10% KDD Cup 99 which is generated from simulated traffic.
Hybrid sampling for imbalanced data

Decision tree learning in the presence of imbalanced data is an issue of great practical importance, as such data is ubiquitous in a wide variety of application domains. We propose hybrid data sampling, which uses a combination of two sampling techniques such as random oversampling and random undersampling, to create a balanced dataset for use in the construction of decision tree classification models. The results demonstrate that our methodology is often able to improve the performance of a C4.5 decision tree learner in the context of imbalanced data.
Design and analysis of a linear oscillatory single-phase permanent magnet generator for free-piston stirling engine systems

This paper presents the design and analysis of a linear oscillatory single-phase permanent magnet generator (LOG) for free-piston stirling engine systems (FPSEs). In order to implement the design of LOG for suitable FPSEs, we performed a characteristic analysis of various types of LOGs having different design parameters. To improve the efficiency, we performed a characteristic analysis of the eddy-current loss based on the permanent magnet division and influence of core lamination. A dynamic characteristic analysis of LOG was performed for selected design parameters, and the analysis results of the designed LOG were compared with the measured results.
When Does Model-Based Control Pay Off?

Many accounts of decision making and reinforcement learning posit the existence of two distinct systems that control choice: a fast, automatic system and a slow, deliberative system. Recent research formalizes this distinction by mapping these systems to "model-free" and "model-based" strategies in reinforcement learning. Model-free strategies are computationally cheap, but sometimes inaccurate, because action values can be accessed by inspecting a look-up table constructed through trial-and-error. In contrast, model-based strategies compute action values through planning in a causal model of the environment, which is more accurate but also more cognitively demanding. It is assumed that this trade-off between accuracy and computational demand plays an important role in the arbitration between the two strategies, but we show that the hallmark task for dissociating model-free and model-based strategies, as well as several related variants, do not embody such a trade-off. We describe five factors that reduce the effectiveness of the model-based strategy on these tasks by reducing its accuracy in estimating reward outcomes and decreasing the importance of its choices. Based on these observations, we describe a version of the task that formally and empirically obtains an accuracy-demand trade-off between model-free and model-based strategies. Moreover, we show that human participants spontaneously increase their reliance on model-based control on this task, compared to the original paradigm. Our novel task and our computational analyses may prove important in subsequent empirical investigations of how humans balance accuracy and demand.
Adolescent onset of the gender difference in lifetime rates of major depression: a theoretical model.

Prepubescent boys are, if anything, more likely than girls to be depressed. During adolescence, however, a dramatic shift occurs: between the ages of 11 and 13 years, this trend in depression rates is reversed. By 15 years of age, females are approximately twice as likely as males to have experienced an episode of depression, and this gender gap persists for the next 35 to 40 years. We offer a theoretical framework that addresses the timing of this phenomenon. First, we discuss the social and hormonal mechanisms that stimulate affiliative needs for females at puberty. Next, we describe how heightened affiliative need can interact with adolescent transition difficulties to create a depressogenic diathesis as at-risk females reach puberty. This gender-linked vulnerability explains why adolescent females are more likely than males to become depressed when faced with negative life events and, particularly, life events with interpersonal consequences.
Microservices: Granularity vs. Performance

Microservice Architectures (MA) have the potential to increase the agility of software development. In an era where businesses require software applications to evolve to support emerging software requirements, particularly for Internet of Things (IoT) applications, we examine the issue of microservice granularity and explore its effect upon application latency. Two approaches to microservice deployment are simulated; the first with microservices in a single container, and the second with microservices partitioned across separate containers. We observed a negligible increase in service latency for the multiple container deployment over a single container.
Automatic Gap-fill Question Generation from Text Books

In this paper, we present an automatic question generation system that can generate gap-fill questions for content in a document. Gap-fill questions are fill-in-the-blank questions with multiple choices (one correct answer and three distractors) provided. The system finds the informative sentences from the document and generates gap-fill questions from them by first blanking keys from the sentences and then determining the distractors for these keys. Syntactic and lexical features are used in this process without relying on any external resource apart from the information in the document. We evaluated our system on two chapters of a standard biology textbook and presented the results.
Analyzing policy dependencies using historical information

Autonomic computing is central to the success of IT infrastructure deployment as its complexity and pervasiveness grows. This paper addresses one aspect of policy-based autonomic computing - the issue of identifying dependencies between policies, knowledge of which is useful to the policymaker while defining or updating policies. These dependencies are determined via assesment of the impact of a policy on the sensors (measurable entities at runtime). Our approach uses a simple pragmatic model over the measured runtime information from the recent past. Both static and runtime information is combined to provide effective feedback.
A GENERIC RECONFIGURABLE NEURAL NETWORK ARCHITECTURE IMPLEMENTED AS A NETWORK ON CHIP

Neural Networks are widely used in pattern recognition, security applications and data manipulation. We propose a novel hardware architecture for a generic neural network, using Network on Chip (NoC) interconnect. The proposed architecture allows for expandability, mapping of more than one logical unit onto a single physical unit, and dynamic reconfiguration based on application-specific demands. Simulation results show that this architecture has significant performance benefits over existing architectures.
Motivating factors influencing college students' choice of academic major.

To assess the factors, motivations, and nonacademic influences that affected the choice of major among pharmacy and nonpharmacy undergraduate students.



A survey was administered to 618 pharmacy and nonpharmacy majors to assess background and motivational factors that may have influenced their choice of major. The sample consisted of freshman and sophomore students enrolled in a required speech course.



African-American and Hispanic students were less likely to choose pharmacy as a major than Caucasians, whereas Asian-Americans were more likely to choose pharmacy as a major. Pharmacy students were more likely to be interested in science and math than nonpharmacy students.



Students' self-reported racial/ethnic backgrounds influence their decision of whether to choose pharmacy as their academic major. Results of this survey provide further insight into developing effective recruiting strategies and enhancing the marketing efforts of academic institutions.
A COMPACT, PLANAR PLATE-TYPE ANTENNA FOR 2.4/5.2/5.8-GHz TRI-BAND WLAN OPERATION

A novel, tri-band, planar plate-type antenna made of a compact metal plate for wireless local area network (WLAN) applications in the 2.4GHz (2400–2484MHz), 5.2GHz (5150– 5350MHz), and 5.8 GHz (5725–5825 MHz) bands is presented. The antenna was designed in a way that the operating principle includes dipole and loop resonant modes to cover the 2.4/5.2 and 5.8 GHz bands, respectively. The antenna comprises a larger radiating arm and a smaller loop radiating arm, which are connected to each other at the signal ground point. The antenna can easily be fed by using a 50 Ω mini-coaxial cable and shows good radiation performance. Details of the design are described and discussed in the article.
Auditory event-related dynamics of the EEG spectrum and effects of exposure to tones.

A new measure of event-related brain dynamics, the event-related spectral perturbation (ERSP), is introduced to study event-related dynamics of the EEG spectrum induced by, but not phase-locked to, the onset of the auditory stimuli. The ERSP reveals aspects of event-related brain dynamics not contained in the ERP average of the same response epochs. Twenty-eight subjects participated in daily auditory evoked response experiments during a 4 day study of the effects of 24 h free-field exposure to intermittent trains of 89 dB low frequency tones. During evoked response testing, the same tones were presented through headphones in random order at 5 sec intervals. No significant changes in behavioral thresholds occurred during or after free-field exposure. ERSPs induced by target pips presented in some inter-tone intervals were larger than, but shared common features with, ERSPs induced by the tones, most prominently a ridge of augmented EEG amplitude from 11 to 18 Hz, peaking 1-1.5 sec after stimulus onset. Following 3-11 h of free-field exposure, this feature was significantly smaller in tone-induced ERSPs; target-induced ERSPs were not similarly affected. These results, therefore, document systematic effects of exposure to intermittent tones on EEG brain dynamics even in the absence of changes in auditory thresholds.