nils-herrmann/clean_scirepeval huggingface dataset:
XX The Rise of Social Bots

The Turing test aimed to recognize the behavior of a human from that of a computer algorithm. Such challenge is more relevant than ever in today’s social media context, where limited attention and technology constrain the expressive power of humans, while incentives abound to develop software agents mimicking humans. These social bots interact, often unnoticed, with real people in social media ecosystems, but their abundance is uncertain. While many bots are benign, one can design harmful bots with the goals of persuading, smearing, or deceiving. Here we discuss the characteristics of modern, sophisticated social bots, and how their presence can endanger online ecosystems and our society. We then review current efforts to detect social bots on Twitter. Features related to content, network, sentiment, and temporal patterns of activity are imitated by bots but at the same time can help discriminate synthetic behaviors from human ones, yielding signatures of engineered social tampering.
On converting community detection algorithms for fuzzy graphs in Neo4j

An essential feature of large scale free graphs, such as the Web, protein-to-protein interaction, brain connectivity, and social media graphs, is that they tend to form recursive communities. The latter are densely connected vertex clusters exhibiting quick local information dissemination and processing. Under the fuzzy graph model vertices are fixed while each edge exists with a given probability according to a membership function. This paper presents Fuzzy Walktrap and Fuzzy Newman-Girvan, fuzzy versions of two established community discovery algorithms. The proposed algorithms have been applied to a synthetic graph generated by the Kronecker model with different termination criteria and the results are discussed. Fuzzy graphs; Membership function; Community detection; Termination criteria; Walktrap algorithm; NewmanGirvan algorithm; Edge density; Kronecker model; Large graph analytics; Higher order data
Measuring the Popularity of Job Skills in Recruitment Market: A Multi-Criteria Approach

To cope with the accelerating pace of technological changes, talents are urged to add and refresh their skills for staying in active and gainful employment. This raises a natural question: what are the right skills to learn? Indeed, it is a nontrivial task to measure the popularity of job skills due to the diversified criteria of jobs and the complicated connections within job skills. To that end, in this paper, we propose a data driven approach for modeling the popularity of job skills based on the analysis of large-scale recruitment data. Specifically, we first build a job skill network by exploring a large corpus of job postings. Then, we develop a novel Skill Popularity based Topic Model (SPTM) for modeling the generation of the skill network. In particular, SPTM can integrate different criteria of jobs (e.g., salary levels, company size) as well as the latent connections within skills, thus we can effectively rank the job skills based on their multi-faceted popularity. Extensive experiments on real-world recruitment data validate the effectiveness of SPTM for measuring the popularity of job skills, and also reveal some interesting rules, such as the popular job skills which lead to high-paid employment.
Using mobile phone text messages to improve insulin injection technique and glycaemic control in patients with diabetes mellitus: a multi-centre study in Turkey.

To improve the knowledge and skills of diabetic patients on insulin injections using mobile phone short message services and to evaluate the association of this intervention with metabolic outcomes.



Mobile communication technologies are widely used in Turkey, which maintains a diabetic population of more than 6·5 million. However, there are a limited number of studies using mobile technologies in the challenging and complicated management of diabetes.



A one group pretest-posttest design was used in this study.



The study sample consisted of 221 people with type 1 and type 2 Diabetes Mellitus from eight outpatient clinics in six cities in Turkey. The 'Demographic and diabetes-related information Form' and 'Insulin Injection Technique and Knowledge Form' were used in the initial interview. Subsequently, 12 short messages related to insulin administration were sent to patients twice a week for six months. Each patient's level of knowledge and skills regarding both the insulin injection technique and glycaemic control (glycated haemoglobin A1c) levels were measured at three months and six months during the text messaging period and six months later (12 months total) when text messaging was stopped.



The mean age of the patients with diabetes was 39·8 ± 16·2 years (min: 18; max: 75). More than half of the patients were females with a mean duration of diabetes of 11·01 ± 7·22 years (min 1; max: 32). Following the text message reminders, the patients' level of knowledge and skills regarding the insulin injection technique improved at month 3 and 6 (p < 0·05). The patients' A1c levels statistically significantly decreased at the end of month 3, 6 and 12 compared to the baseline values (p < 0·05). The number of insulin injection sites and the frequency of rotation of skin sites for insulin injections also increased.



This study demonstrated that a short message services-based information and reminder system on insulin injection administration provided to insulin-dependent patients with diabetes by nurses resulted in improved self-administration of insulin and metabolic control.


 TO CLINICAL PRACTICE
Today, with the increased use of mobile communication technologies, it is possible for nurses to facilitate diabetes management by using these technologies. We believe that mobile technologies, which are not only easy to use and to follow-up with by healthcare providers, are associated with positive clinical outcomes for patients and should be more commonly used in the daily practice of diabetes management.
Personality's Influence on the Relationship between Online Word-of-mouth and Consumers' Trust in Shopping Website

This paper did a survey on 162 college students, with an aim to find out the role of consumers’ personality in their trust in the website as far as the online word-of-mouth is concerned. All the data collected are analyzed by SPSS 15.0 and LISREL. It shows that there is a significant difference between the introverted consumers and the extroverted consumers viewing their attitude towards online word-of-mouth. When the online word-of-mouth is affecting the consumers’ trust in shopping websites, the introverted and extroverted influence exist difference.
Mindfulness Mechanisms in Sports: Mediating Effects of Rumination and Emotion Regulation on Sport-Specific Coping

The main objective of the project was to examine a proposed theoretical model of mindfulness mechanisms in sports. We conducted two studies (the first study using a cross-sectional design and the second a longitudinal design) to investigate if rumination and emotion regulation mediate the relation between dispositional mindfulness and sport-specific coping. Two hundred and forty-two young elite athletes, drawn from various sports, were recruited for the cross-sectional study. For the longitudinal study, 65 elite athletes were recruited. All analyses were performed using Bayesian statistics. The path analyses showed credible indirect effects of dispositional mindfulness on coping via rumination and emotion regulation in both the cross-sectional study and the longitudinal study. Additionally, the results in both studies showed credible direct effects of dispositional mindfulness on rumination and emotion regulation. Further, credible direct effects of emotion regulation as well as rumination on coping were also found in both studies. Our findings support the theoretical model, indicating that rumination and emotion regulation function as essential mechanisms in the relation between dispositional mindfulness and sport-specific coping skills. Increased dispositional mindfulness in competitive athletes (i.e. by practicing mindfulness) may lead to reductions in rumination, as well as an improved capacity to regulate negative emotions. By doing so, athletes may improve their sport-related coping skills, and thereby enhance athletic performance.
Policy Optimization as Wasserstein Gradient Flows

Policy optimization is a core component of reinforcement learning (RL), and most existing RL methods directly optimize parameters of a policy based on maximizing the expected total reward, or its surrogate. Though often achieving encouraging empirical success, its underlying mathematical principle on policy-distribution optimization is unclear. We place policy optimization into the space of probability measures, and interpret it as Wasserstein gradient flows. On the probabilitymeasure space, under specified circumstances, policy optimization becomes a convex problem in terms of distribution optimization. To make optimization feasible, we develop efficient algorithms by numerically solving the corresponding discrete gradient flows. Our technique is applicable to several RL settings, and is related to many state-ofthe-art policy-optimization algorithms. Empirical results verify the effectiveness of our framework, often obtaining better performance compared to related algorithms.
Research Approaches to Mobile Use in the Developing World: A Review of the Literature

at Columbia University for support during the writing of the early draft of paper, and to numerous readers—particularly the three anonymous reviewers—for their suggestions. Opinions and analysis are the author's, and not necessarily those of Microsoft Corporation.  The paper reviews roughly 200 recent studies of mobile (cellular) phone use in the developing world, and identifies major concentrations of research. It categorizes studies along two dimensions. One dimension distinguishes studies of the determinants of mobile adoption from those that assess the impacts of mobile use, and from those focused on the interrelationships between mobile technologies and users. A secondary dimension identifies a subset of studies with a strong economic development perspective. The discussion considers the implications of the resulting review and typology for future research.
Security system using raspberry Pi

In recent times we tend to use a number of surveillance systems for monitoring the targeted area. This requires an enormous amount of storage space along with a lot of human power in order to implement and monitor the area under surveillance. This is supposed to be costly and not a reliable process. In this paper we propose an intelligent surveillance system that continuously monitors the targeted area and detects motion in each and every frame. If the system detects motion in the targeted area then a notification is automatically sent to the user by sms and the video starts getting recorded till the motion is stopped. Using this method the required memory space for storing the video is reduced since it doesn't store the entire video but stores the video only when a motion is detected. This is achieved by using real time video processing using open CV (computer vision / machine vision) technology and raspberry pi system.
GENDER ISSUES IN YOUNG CHILDREN ' S LITERATURE

In recent decades, extensive studies from diverse disciplines have focused on children's developmental awareness of different gender roles and the relationships between genders. Among these studies, researchers agree that children's picture books have an increasingly significant place in children's development because these books are a widely available cultural resource, offering young children a multitude of opportunities to gain information, become familiar with the printed pictures, be entertained, and experience perspectives other than their own. In such books, males are habitually described as active and domineering, while females rarely reveal their identities and very frequently are represented as meek and mild. This valuable venue for children's gender development thus unfortunately reflects engrained societal attitudes and biases in the available choices and expectations assigned to different genders. This discriminatory portrayal in many children's picture books also runs the risk of leading children toward a misrepresented and misguided realization of their true potential in their expanding world.
Pick and place mobile robot for the disabled through voice commands

A mobile robot is designed to pick and place the objects through voice commands. This work would be practically useful to wheelchair bound persons. The pick and place robot is designed in a way that it is able to help the user to pick up an item that is placed at two different levels using an extendable arm. The robot would move around to pick up an item and then pass it back to the user or to a desired location as told by the user. The robot control is achieved through voice commands such as left, right, straight, etc. in order to help the robot to navigate around. Raspberry Pi 2 controls the overall design with 5 DOF servo motor arm. The webcam is used to navigate around which provides live streaming using a mobile application for the user to look into. Results show the ability of the robot to pick and place the objects up to a height of 23.5cm through proper voice commands.
Difficulties in maintenance of clubfoot abduction brace and solutions - maintenance of clubfoot abduction brace, locks and keys.

To determine the frequency of early relapse after achieving good initial correction in children who were on clubfoot abduction brace.



The cross-sectional study was conducted at the Jinnah Postgraduate Medical Centre, Karachi, and included parents of children of either gender in the age range of 6 months to 3years with idiopathic clubfoot deformities who had undergone Ponseti treatment between September 2012 and June 2013, and who were on maintenance brace when the data was collected from December 2013 to March 2014. Parents of patients with follow-up duration in brace less than six months and those with syndromic clubfoot deformity were excluded. The interviews were taken through a purposive designed questionnaire. SPSS 16 was used for data analysis.



The study included parents of 120 patients. Of them, 95(79.2%) behaved with good compliance on Denis Browne Splint, 10(8.3%) were fair and 15(12.5%)showed poor compliance. Major reason for poor and non-compliance was unaffordability of time and cost for regular follow-up. Besides, 20(16.67%) had inconsistent use due to delay inre-procurement of Foot Abduction Braceonce the child had outgrown the shoe. Only 4(3.33%) talked of cultural barriers and conflict of interest between the parents. Early relapse was observed in 23(19.16%) patients and 6(5%) of them responded to additional treatment and were put back on brace treatment; 13(10.83%) had minor relapse with forefoot varus, without functional disability, and the remaining 4(3.33%) had major relapse requiring extensive surgery. Overall success was recorded in 116(96.67%) cases.



The positioning of shoes on abduction brace bar, comfort in shoes, affordability, initial and subsequent delay in procurement of new shoes once the child's feet overgrew the shoe, were the four containable factors on the part of Ponseti practitioner.
Unicorn: Continual Learning with a Universal, Off-policy Agent

Some real-world domains are best characterized as a single task, but for others this perspective is limiting. Instead, some tasks continually grow in complexity, in tandem with the agent’s competence. In continual learning, also referred to as lifelong learning, there are no explicit task boundaries or curricula. As learning agents have become more powerful, continual learning remains one of the frontiers that has resisted quick progress. To test continual learning capabilities we consider a challenging 3D domain with an implicit sequence of tasks and sparse rewards. We propose a novel agent architecture called Unicorn, which demonstrates strong continual learning and outperforms several baseline agents on the proposed domain. The agent achieves this by jointly representing and learning multiple policies efficiently, using a parallel off-policy learning setup.
The Appeal of MOBA Games: What Makes People Start, Stay, and Stop

Online multiplayer games are often rich sources of complex social interactions. In this paper, we focus on the unique player experiences (PX) created by Multiplayer Online Battle Arena (MOBA) games. We examine key phases of players' engagement with the genre and investigate why players start, stay, and stop playing MOBAs. Our study identifies how team interactions during play with friends or strangers affect PX during these phases. Results indicate the ability to play with friends is salient when beginning play and during periods of engagement. Teams that include friends support a wider range of play possibilities - socially and competitively -- than teams of strangers. However, social factors appear less relevant to those choosing to stop playing, who do so for a variety of reasons. This study contributes to the field by identifying a strategy to improve the wellbeing of players.
A big-picture look at enterprise architectures

I t’s rare that a business stays just as it began year after year.So if we agree that businesses evolve, it follows that information systems must evolve to keep pace.So far,so good.The disconnect occurs when an enterprise’s management knows that the information systems must evolve,but keeps patching and whipping the legacy systems to meet one more requirement. If you put the problem in its simplest terms,management has a choice about how it will grow its information systems.If there is a clear strategic vision for the enterprise, it seems logical to have an equally broad vision for the systems that support that strategy. Managers can thus choose to plan evolution,or they can react when reality hits and “evolve”parts of the information system according to the latest crisis. It’s a bit of a no-brainer as to which is the better choice. But it’s also easy to understand why few enterprises pick it. Conceiving, planning, and monitoring systems that support a long-range strategic vision is not trivial. Enterprise-wide information systems typically start from a base of legacy systems.And not just any legacy systems.They are typically unwieldy systems of systems with a staggering array of hardware,software, design strategies, and implementation platforms. To make the job even more difficult, “enterprise-wide” often means city to city, state to state, or even country to country. Getting these pieces to seamlessly interact and evolve according to long-range strategic business objectives may seem like mission impossible; for a large distributed organization,however, it is mission critical.
Acceptance and mindfulness-based therapy: new wave or old hat?

Some contemporary theorists and clinicians champion acceptance and mindfulness-based interventions, such as Acceptance and Commitment Therapy (ACT), over cognitive-behavioral therapy (CBT) for the treatment of emotional disorders. The objective of this article is to juxtapose these two treatment approaches, synthesize, and clarify the differences between them. The two treatment modalities can be placed within a larger context of the emotion regulation literature. Accordingly, emotions can be regulated either by manipulating the evaluation of the external or internal emotion cues (antecedent-focused emotion regulation) or by manipulating the emotional responses (response-focused emotion regulation). CBT and ACT both encourage adaptive emotion regulation strategies but target different stages of the generative emotion process: CBT promotes adaptive antecedent-focused emotion regulation strategies, whereas acceptance strategies of ACT counteract maladaptive response-focused emotion regulation strategies, such as suppression. Although there are fundamental differences in the philosophical foundation, ACT techniques are fully compatible with CBT and may lead to improved interventions for some disorders. Areas of future treatment research are discussed.
Applying Software Patterns to Address Interoperability in Blockchain-based Healthcare Apps

Since the inception of the Bitcoin technology, its underlying data structure–the blockchain–has garnered much attention due to properties such as decentralization, transparency, and immutability. These properties make blockchains suitable for apps that require disintermediation through trustless exchange, consistent and incorruptible transaction records, and operational models beyond cryptocurrency. In particular, blockchain and its smart contract capabilities have the potential to address healthcare interoperability issues, such as enabling effective interactions between users and medical applications, delivering patient data securely to a variety of organizations and devices, and improving the overall efficiency of medical practice workflow. Despite the interest in using blockchain for healthcare interoperability, however, little information is available on the concrete architectural styles and patterns for applying blockchain to healthcare apps. This paper provides an initial step in filling this gap by showing: (1) the features and implementation challenges in healthcare interoperability, (2) an end-to-end case study of a blockchain-based healthcare app we are developing, and (3) how applying foundational software patterns can help address common interoperability challenges faced by blockchain-based healthcare apps.
Rule-based machine learning classification and knowledge discovery for complex problems

Learning classifier systems (LCSs) are an advantageous, powerful, and flexible class of algorithms that have, to date, been underutilized largely due to the perception that they are difficult to apply, evaluate, and interpret. . It offers an accessible, user friendly LCS platform for supervised rule-based machine learning, classification, data mining, prediction, and knowledge discovery. ExSTraCS seeks to make no assumptions about the data, and is therefore model free and particularly well suited to complex problems that are multi-factorial, interacting (non-linear), heterogeneous, noisy, class imbalanced, multi-class, or larger-scale. ExSTraCS is written in Python, open source, well documented, and freely available at sourceforge.net.
How software engineers use documentation: the state of the practice

Software engineering is a human task, and as such we must study what software engineers do and think. Understanding the normative practice of software engineering is the first step toward developing realistic solutions to better facilitate the engineering process. We conducted three studies using several data-gathering approaches to elucidate the patterns by which software engineers (SEs) use and update documentation. Our objective is to more accurately comprehend and model documentation use, usefulness, and maintenance, thus enabling better decision making and tool design by developers and project managers. Our results confirm the widely held belief that SEs typically do not update documentation as timely or completely as software process personnel and managers advocate. However, the results also reveal that out-of-date software documentation remains useful in many circumstances.
DistributedLog: A High Performance Replicated Log Service

DistributedLog is a high performance, strictly ordered, durably replicated log. It is multi-tenant, designed with a layered architecture that allows reads and writes to be scaled independently and supports OLTP, stream processing and batch workloads. It also supports a globally synchronous consistent replicated log spanning multiple geographically separated regions. This paper describes how DistributedLog is structured, its components and the rationale underlying various design decisions. We have been using DistributedLog in production for several years, supporting applications ranging from transactional database journaling, real-time data ingestion, and analytics to general publish-subscribe messaging.