nils-herrmann/clean_scirepeval huggingface dataset:
Evaluation of a Behavioral Mobile Phone App Intervention for the Self-Management of Type 2 Diabetes: Randomized Controlled Trial Protocol

Patients with type 2 diabetes mellitus (T2DM) struggle with the management of their condition due to difficulty relating lifestyle behaviors with glycemic control. While self-monitoring of blood glucose (SMBG) has proven to be effective for those treated with insulin, it has been shown to be less beneficial for those only treated with oral medications or lifestyle modification. We hypothesized that the effective self-management of non-insulin treated T2DM requires a behavioral intervention that empowers patients with the ability to self-monitor, understand the impact of lifestyle behaviors on glycemic control, and adjust their self-care based on contextualized SMBG data.



The primary objective of this randomized controlled trial (RCT) is to determine the impact of bant2, an evidence-based, patient-centered, behavioral mobile app intervention, on the self-management of T2DM. Our second postulation is that automated feedback delivered through the mobile app will be as effective, less resource intensive, and more scalable than interventions involving additional health care provider feedback.



This study is a 12-month, prospective, multicenter RCT in which 150 participants will be randomly assigned to one of two groups: the control group will receive current standard of care, and the intervention group will receive the mobile phone app system in addition to standard of care. The primary outcome measure is change in glycated hemoglobin A1c from baseline to 12 months.



The first patient was enrolled on July 28, 2015, and we anticipate completing this study by September, 2018.



This RCT is one of the first to evaluate an evidence-based mobile app that focuses on facilitating lifestyle behavior change driven by contextualized and structured SMBG. The results of this trial will provide insights regarding the usage of mobile tools and consumer-grade devices for diabetes self-care, the economic model of using incentives to motivate behavior change, and the consumption of test strips when following a rigorously structured approach for SMBG.
On the Security and Scalability of Bitcoin's Blockchain

The blockchain emerges as an innovative tool which proves to be useful in a number of application scenarios. A number of large industrial players, such as IBM, Microsoft, Intel, and NEC, are currently investing in exploiting the blockchain in order to enrich their portfolio of products. A number of researchers and practitioners speculate that the blockchain technology can change the way we see a number of online applications today. Although it is still early to tell for sure, it is expected that the blockchain will stimulate considerable changes to a large number of products and will positively impact the digital experience of many individuals around the globe. In this tutorial, we overview, detail, and analyze the security provisions of Bitcoin and its underlying blockchain-effectively capturing recently reported attacks and threats in the system. Our contributions go beyond the mere analysis of reported vulnerabilities of Bitcoin; namely, we describe and evaluate a number of countermeasures to deter threats on the system-some of which have already been incorporated in the system. Recall that Bitcoin has been forked multiple times in order to fine-tune the consensus (i.e., the block generation time and the hash function), and the network parameters (e.g., the size of blocks). As such, the results reported in this tutorial are not only restricted to Bitcoin, but equally apply to a number of "altcoins" which are basically clones/forks of the Bitcoin source code. Given the increasing number of alternative blockchain proposals, this tutorial extracts the basic security lessons learnt from the Bitcoin system with the aim to foster better designs and analysis of next-generation secure blockchain currencies and technologies.
Oxytocin enhances brain reward system responses in men viewing the face of their female partner.

The biological mechanisms underlying long-term partner bonds in humans are unclear. The evolutionarily conserved neuropeptide oxytocin (OXT) is associated with the formation of partner bonds in some species via interactions with brain dopamine reward systems. However, whether it plays a similar role in humans has as yet not been established. Here, we report the results of a discovery and a replication study, each involving a double-blind, placebo-controlled, within-subject, pharmaco-functional MRI experiment with 20 heterosexual pair-bonded male volunteers. In both experiments, intranasal OXT treatment (24 IU) made subjects perceive their female partner's face as more attractive compared with unfamiliar women but had no effect on the attractiveness of other familiar women. This enhanced positive partner bias was paralleled by an increased response to partner stimuli compared with unfamiliar women in brain reward regions including the ventral tegmental area and the nucleus accumbens (NAcc). In the left NAcc, OXT even augmented the neural response to the partner compared with a familiar woman, indicating that this finding is partner-bond specific rather than due to familiarity. Taken together, our results suggest that OXT could contribute to romantic bonds in men by enhancing their partner's attractiveness and reward value compared with other women.
Purchase occasion influence on the role of music in advertising

The role of background music in audience responses to commercials (and other marketing elements) has received increasing attention in recent years. This article extends the discussion of music’s influence in two ways: (1) by using music theory to analyze and investigate the effects of music’s structural profiles on consumers’ moods and emotions and (2) by examining the relationship between music’s evoked moods that are congruent versus incongruent with the purchase occasion and the resulting effect on purchase intentions. The study reported provides empirical support for the notion that when music is used to evoke emotions congruent with the symbolic meaning of product purchase, the likelihood of purchasing is enhanced. D 2003 Elsevier Inc. All rights reserved.
Case Studies and Organisational Sustainability Modelling Presented by Cloud Computing Business Framework

Cloud Computing Business Framework (CCBF) is proposed to help organisations achieve good Cloud design, deployment, migration and services. While organisations adopt Cloud Computing for Web Services, technical and business challenges emerge and one of these includes the measurement of Cloud business performance. Organisational Sustainability Modelling (OSM) is a new way to measure Cloud business performance quantitatively and accurately. It combines statistical computation and 3D Visualisation to present the Return on Investment arising from the adoption of Cloud Computing by organisations. 3D visualisation simplifies the review process and is an innovative way for Return of Investment (ROI) valuation. Two detailed case studies with SAP and Vodafone have been presented, where OSM has analysed the business performance and explained how CCBF offers insights, which are relatively helpful for WS and Grid businesses. Comparisons and discussions between CCBF and other approaches related to WS are presented, where lessons learned are useful for Web Services, Cloud and Grid communities.
Epistemological Problems of Artificial Intelligence

In (McCarthy and Hayes 1969), we proposed dividing the artificial intelligence problem into two parts—an epistemological part and a heuristic part. This lecture further explains this division, explains some of the epistemological problems, and presents some new results and approaches. The epistemological part of AI studies what kinds of facts about the world are available to an observer with given opportunities to observe, how these facts can be represented in the memory of a computer, and what rules permit legitimate conclusions to be drawn from these facts. It leaves aside the heuristic problems of how to search spaces of possibilities and how to match patterns. Considering epistemological problems separately has the following advantages:
SEISMIC PERFORMANCE OF RC HIGH-RISE BUILDINGS – A CASE STUDY OF 44 STOREY STRUCTURE IN SKOPJE ( MACEDONIA )

Preliminary notes High-rise buildings are designed and constructed by use of modern materials and integral structural systems which are not usual for typical buildings. The existing seismic regulations act as a limiting factor and cannot cover specific behaviour of these buildings. Considering the increasing trend in their construction worldwide, additional investigations are necessary, particularly for structures in seismically active areas. It is necessary to elaborate official codes which will clearly prescribe methods, procedures and criteria for analysis and design of such type of structures. The main goal of the paper is to present a review of the existing structural systems, design recommendations and guidelines for high-rises worldwide, as well as selected results from seismic performance of 44 stories RC high-rise building which is a unique experience coming from design and construction of the four high-rise buildings in Skopje (Macedonia).
THE ORGANIZED / DISORGANIZED TYPOLOGY OF SERIAL MURDER Myth or Model ?

Despite weaknesses in the organized/disorganized classification of serial killers, it is drawn on for “offender profiles,” theories of offending, and in murder trials. This dichotomy was therefore tested by the multidimensional scaling of the co-occurrence of 39 aspects of serial killings derived 100 murders committed by 100 U.S. serial killers. Results revealed no distinct subsets of offense characteristics reflecting the dichotomy. They showed a subset of organized features typical of most serial killings. Disorganized features are much rarer and do not form a distinct type. These results have implications for testing typologies supporting expert opinion or to help understand variations in criminal acts, as well as the development of a science of investigative psychology that goes beyond offender profiling.
Results of m-polar Fuzzy Graphs with Application

An m-polar fuzzy model is useful for multi-polar information, multi-agent, multi-attribute and multiobject network models which gives more precision, flexibility, and comparability to the system as compared to the classical, fuzzy and bipolar fuzzy models. In this paper, m-polar fuzzy sets are used to introduce the notion of m-polar psi-morphism on product m-polar fuzzy graph (mFG). The action of this morphism is studied and established some results on weak and co-weak isomorphism. d2-degree and total d2-degree of a vertex in product mFG are defined and studied their properties. A real life situation has been modeled as an application of product mFG.
Churn Identification in Microblogs using Convolutional Neural Networks with Structured Logical Knowledge

For brands, gaining new customer is more expensive than keeping an existing one. Therefore, the ability to keep customers in a brand is becoming more challenging these days. Churn happens when a customer leaves a brand to another competitor. Most of the previous work considers the problem of churn prediction using CDRs. In this paper, we use micro-posts to classify customers into churny or nonchurny. We explore the power of CNNs since they achieved state-of-the-art in various computer vision and NLP applications. However, the robustness of end-toend models has some limitations such as the availability of a large amount of labeled data and uninterpretability of these models. We investigate the use of CNNs augmented with structured logic rules to overcome or reduce this issue. We developed our system called Churn teacher by using an iterative distillation method that transfers the knowledge, extracted using just the combination of three logic rules, directly into the weight of Deep Neural Networks (DNNs). Furthermore, we used weight normalization to speed up training our convolutional neural networks. Experimental results showed that with just these three rules, we were able to get state-of-the-art on publicly available Twitter dataset about three Telecom brands.
The Extended Contact Effect : Knowledge of Cross-Group Friendships and Prejudice

The extended contact hypothesis proposes that knowledge that an in-group member has a close relationship with an out-group member can lead to more positive intergroup attitudes. Proposed mechanisms are the in-group or out-group member serving as positive exemplars and the inclusion of the out-group member's group membership in the self. In Studies I and 2, respondents knowing an in-group member with an out-group friend had less negative attitudes toward that out-group, even controlling for disposition.il variables and direct out-group friendships. Study 3, with constructed intergroup-conflict situations (on the robbers cave model). found reduced negative out-group attitudes after participants learned of cross-group friendships. Study 4, a minimal group experiment, showed less negative out-group attitudes for participants observing an apparent in-group-out-group friendship.
Chapter Two THE NETWORKING OF TERROR IN THE INFORMATION AGE

Editors’ abstract. Middle East Arab terrorists are on the cutting edge of organizational networking and stand to gain significantly from the information revolution. They can harness information technology to enable less hierarchical, more networked designs—enhancing their flexibility, responsiveness, and resilience. In turn, information technology can enhance their offensive operational capabilities for the war of ideas as well as for the war of violent acts. Zanini and Edwards (both at RAND) focus their analysis primarily on Middle East terrorism but also discuss other groups around the world. They conclude with a series of recommendations for policymakers. This chapter draws on RAND research originally reported in Ian Lesser et al., Countering the New Terrorism (1999).
Micro Controller Based Automatic Plant Irrigation System *

The main aim of this paper is to provide automatic irrigation to the plants which helps in saving money and water. The entire system is controlled using 8051 micro controller which is programmed as giving the interrupt signal to the sprinkler.Temperature sensor and humidity sensor are connected to internal ports of micro controller via comparator,When ever there is a change in temperature and humidity of the surroundings these sensors senses the change in temperature and humidity and gives an interrupt signal to the micro-controller and thus the sprinkler is activated.
A Chatbot Based On AIML Rules Extracted From Twitter Dialogues

A chat dialogue system, a chatbot, or a conversational agent is a computer program designed to hold a conversation using natural language. Many popular chat dialogue systems are based on handcrafted rules, written in Artificial Intelligence Markup Language (AIML). However, a manual design of rules requires significant efforts, as in practice most chatbots require hundreds if not thousands of rules. This paper presents the method of automated extraction of AIML rules from real Twitter conversation data. Our preliminary experimental results show the possibility of obtaining natural-language conversation between the user and a dialogue system without the necessity of handcrafting its knowledgebase.
Unsupervised Object Discovery and Segmentation of RGBD-images

In this paper we introduce a system for unsupervised object discovery and segmentation of RGBD-images. The system models the sensor noise directly from data, allowing accurate segmentation without sensor specific hand tuning of measurement noise models making use of the recently introduced Statistical Inlier Estimation (SIE) method . Through a fully probabilistic formulation, the system is able to apply probabilistic inference, enabling reliable segmentation in previously challenging scenarios. In addition, we introduce new methods for filtering out false positives, significantly improving the signal to noise ratio. We show that the system significantly outperform state-of-the-art in on a challenging real-world dataset.
Relationship between generic strategies , competitive advantage and organizational performance : an empirical analysis

In recent years, there has been a growing intensity of competition in virtually all areas of business in both markets upstream for raw materials such as components, supplies, capital and technology and markets downstream for consumer goods and services. This paper examines the relationships among generic strategy, competitive advantage, and organizational performance. Firstly, the nature of generic strategies, competitive advantage, and organizational performance is examined. Secondly, the relationship between generic strategies and competitive advantage is analyzed. Finally, the implications of generic strategies, organizational performance, performance measures and competitive advantage are studied. This study focuses on: (i) the relationship of generic strategy and organisational performance in Australian manufacturing companies participating in the “Best Practice Program in Australia”, (ii) the relationship between generic strategies and competitive advantage, and (iii) the relationship among generic strategies, competitive advantage and organisational performance.
Hybrid Optimization Algorithm for Large-Scale QoS-Aware Service Composition

In this paper we present a hybrid approach for automatic composition of web services that generates semantic input-output based compositions with optimal end-to-end QoS, minimizing the number of services of the resulting composition. The proposed approach has four main steps: 1) generation of the composition graph for a request; 2) computation of the optimal composition that minimizes a single objective QoS function; 3) multi-step optimizations to reduce the search space by identifying equivalent and dominated services; and 4) hybrid local-global search to extract the optimal QoS with the minimum number of services. An extensive validation with the datasets of the Web Service Challenge 2009-2010 and randomly generated datasets shows that: 1) the combination of local and global optimization is a general and powerful technique to extract optimal compositions in diverse scenarios; and 2) the hybrid strategy performs better than the state-of-the-art, obtaining solutions with less services and optimal QoS.
BlitzNet: A Real-Time Deep Network for Scene Understanding

Real-time scene understanding has become crucial in many applications such as autonomous driving. In this paper, we propose a deep architecture, called BlitzNet, that jointly performs object detection and semantic segmentation in one forward pass, allowing real-time computations. Besides the computational gain of having a single network to perform several tasks, we show that object detection and semantic segmentation benefit from each other in terms of accuracy. Experimental results for VOC and COCO datasets show state-of-the-art performance for object detection and segmentation among real time systems.
Generating Contradictory, Neutral, and Entailing Sentences

Learning distributed sentence representations remains an interesting problem in the field of Natural Language Processing (NLP). We want to learn a model that approximates the conditional latent space over the representations of a logical antecedent of the given statement. In our paper, we propose an approach to generating sentences, conditioned on an input sentence and a logical inference label. We do this by modeling the different possibilities for the output sentence as a distribution over the latent representation, which we train using an adversarial objective. We evaluate the model using two state-of-the-art models for the Recognizing Textual Entailment (RTE) task, and measure the BLEU scores against the actual sentences as a probe for the diversity of sentences produced by our model. The experiment results show that, given our framework, we have clear ways to improve the quality and diversity of generated sentences.
A Public-Key Cryptosystem Based On Stochastic Petri Net

In this paper, we present a new method to build public-key Cryptosystem. The method is based on the state explosion problem occurred in the computing of average number of tokens in the places of Stochastic Petri Net (SPN). The reachable markings in the coverability tree of SPN are used as the encryption keys. Accordingly, multiple encryption keys can be generated, thus we can perform multiple encryption to get as strong security as we expect. The decryption is realized through solving a group of ordinary differential equations from Continuous Petri Net (CPN), which has the same underlying Petri net as that of SPN. The decipherment difficulty for attackers is in exponential order. The contribution of this paper is that we can use continuous mathematics to design cryptosystems besides discrete mathematics.
SoundNet: Learning Sound Representations from Unlabeled Video

We learn rich natural sound representations by capitalizing on large amounts of unlabeled sound data collected in the wild. We leverage the natural synchronization between vision and sound to learn an acoustic representation using two-million unlabeled videos. Unlabeled video has the advantage that it can be economically acquired at massive scales, yet contains useful signals about natural sound. We propose a student-teacher training procedure which transfers discriminative visual knowledge from well established visual recognition models into the sound modality using unlabeled video as a bridge. Our sound representation yields significant performance improvements over the state-of-the-art results on standard benchmarks for acoustic scene/object classification. Visualizations suggest some high-level semantics automatically emerge in the sound network, even though it is trained without ground truth labels.
Design and Implementation of a Smart IoT Gateway

With rapid development of Internet of things (IoT), there exists an ever-growing demand for ubiquitous connectivity to integrate multiple heterogeneous networks, such as Zigbee Ad hoc network, wireless LAN, cable network, etc. Some typical applications including environmental monitoring, intelligent transportation, medical care, smart home, industry control, safety defense, etc. require a smart gateway to provide high data-rate, end-to-end connectivity utilizing the higher bandwidth of multi-hop networks among those heterogeneous networks. This paper proposes a novel configurable smart IoT gateway which has three important benefits. Firstly, the gateway has plug gable architecture, whose modules with different communication protocols can be customized and plugged in according to different networks. Secondly, it has unified external interfaces which are fit for flexible software development. Finally, it has flexible protocol to translate different sensor data into a uniform format. Compared with similar research, the gateway has better scalability, flexibility and lower cost.