nils-herrmann/clean_scirepeval huggingface dataset:
A graph-based semi-supervised learning approach towards household energy disaggregation

Non-Intrusive Appliance Load Monitoring has drawn increasing attention in the last few years. Many existing studies that use machine learning for this problem assume that the analyst has access to the actual appliances states at every sample instant, whereas in fact collecting this information exposes consumers to severe privacy risks. It may, however, be possible to persuade consumers to provide brief samples of the operation of their home appliances as part of a “registration” process for smart metering (if appropriate financial incentives are offered). This labeled data would then be supplemented by a large volume of unlabeled data. Hence, we propose the use of semi-supervised learning for non-intrusive appliance load monitoring. Furthermore, based on our previous work, we model the simultaneous operation of multiple appliances via multi-label classification. Thus, our proposed approach employs semi-supervised multi-label classifiers for the monitoring task. Experiments on publicly-available dataset demonstrate our proposed method.
Semi-supervised learning approach for Indonesian Named Entity Recognition (NER) using co-training algorithm

The problem of utilizing machine learning approach in Indonesian Named Entity Recognition (NER) system is the limited amount of labelled data for training process. However, unlike the limited availability of labelled data, unlabelled data is widely available from many sources. This enables a semi-supervised learning approach to solve this NER system problem. This research aims to design a semi-supervised learning model to solve NER system problem. A semi-supervised co-training learning is used to utilize unlabelled data in NER learning process to produce new labelled data that can be applied to enhance a new NER classi□cation system. This research uses two kinds of data, Indonesian DBPedia data as labelled data and news article text from Indonesian news sites (kompas.com, cnnindonesia.com, tempo.co, merdeka.com and viva.co.id) as unlabelled data. The pre-processing steps applied to analyze unstructured text are sentence segmentation, tokenization, stemming, and PoS Tagging. The results of this pre-process are the NER and its context used as unlabelled data for the semi-supervised co-training process. The SVM algorithm is used as a classi□cation algorithm in this process. 10 Cross Fold Validation is used as the system testing approach. Based on the result of the NER testing system, the precision is 73.6%, the recall is 80.1% and f1 mean is 76.5%.
A Trust-Based Intrusion Detection System for Mobile RPL Based Networks

Successful deployment of Low power and Lossy Networks (LLNs) requires self-organising, self-configuring, security, and mobility support. However, these characteristics can be exploited to perform security attacks against the Routing Protocol for Low-Power and Lossy Networks (RPL). In this paper, we address the lack of strong identity and security mechanisms in RPL. We first demonstrate by simulation the impact of Sybil-Mobile attack, namely SybM, on RPL with respect to control overhead, packet delivery and energy consumption. Then, we introduce a new Intrusion Detection System (IDS) scheme for RPL, named Trust-based IDS (T-IDS). T-IDS is a distributed, cooperative and hierarchical trust-based IDS, which can detect novel intrusions by comparing network behavior deviations. In T-IDS, each node is considered as monitoring node and collaborates with his peers to detect intrusions and report them to a 6LoWPAN Border Router (6BR). In our solution, we introduced a new timer and minor extensions to RPL messages format to deal with mobility, identity and multicast issues. In addition, each node is equipped with a Trusted Platform Module co-processor to handle identification and off-load security related computation and storage.
Knowledge exchange processes in organizations and policy arenas: a narrative systematic review of the literature.

This article presents the main results from a large-scale analytical systematic review on knowledge exchange interventions at the organizational and policymaking levels. The review integrated two broad traditions, one roughly focused on the use of social science research results and the other focused on policymaking and lobbying processes.



Data collection was done using systematic snowball sampling. First, we used prospective snowballing to identify all documents citing any of a set of thirty-three seminal papers. This process identified 4,102 documents, 102 of which were retained for in-depth analysis. The bibliographies of these 102 documents were merged and used to identify retrospectively all articles cited five times or more and all books cited seven times or more. All together, 205 documents were analyzed. To develop an integrated model, the data were synthesized using an analytical approach.



This article developed integrated conceptualizations of the forms of collective knowledge exchange systems, the nature of the knowledge exchanged, and the definition of collective-level use. This literature synthesis is organized around three dimensions of context: level of polarization (politics), cost-sharing equilibrium (economics), and institutionalized structures of communication (social structuring).



The model developed here suggests that research is unlikely to provide context-independent evidence for the intrinsic efficacy of knowledge exchange strategies. To design a knowledge exchange intervention to maximize knowledge use, a detailed analysis of the context could use the kind of framework developed here.
A Survey on the State of the Practice in Video Game Software Development

Video Game Software Development is a promising area of empirical research because our first observations in industry environment identified a lack of a systematic process and method support and rarely conducted/documented studies. Nevertheless, video games specific types of software products focus strongly on user interface and game design. Thus, engineering processes, methods for game construction and verification/validation, and best-practices, derived from traditional software engineering, might be applicable in context of video game development. We selected the Austrian games industry as a manageable and promising starting point for systematically capturing the state-of-the practice in Video game development. In this paper we present the survey design and report on the first results of a national survey in the Austrian games industry. The results of the survey showed that the Austrian games industry is organized in a set of small and young studios with the trend to ad-hoc and flexible development processes and limitations in systematic method support.
Handling Cold-Start Problem in Review Spam Detection by Jointly Embedding Texts and Behaviors

Solving the cold-start problem in review spam detection is an urgent and significant task. It can help the on-line review websites to relieve the damage of spammers in time, but has never been investigated by previous work. This paper proposes a novel neural network model to detect review spam for the cold-start problem, by learning to represent the new reviewers’ review with jointly embedded textual and behavioral information. Experimental results prove the proposed model achieves an effective performance and possesses preferable domain-adaptability. It is also applicable to a large-scale dataset in an unsupervised way.
Trajectory tracking control law for a tractor-trailer wheeled mobile robot

Trajectory tracking problem is one of the most important subjects that has been focused with many researchers in past years. In this paper, a Tractor-Trailer type robot including two nonholonomic constraints is analyzed. First, robot kinematic equations are obtained and transformed to the chained-form equations. Next, controllability of robot on the reference trajectory is evaluated and then appropriate reference trajectories for the tractor-trailer robot are generated. Finally a controller based on feedback linearization method is investigated in order to stabilize tracking errors about the origin. Obtained results show that the designed controller performs quite effective.
Searching for the structure of coping: a review and critique of category systems for classifying ways of coping.

From analyzing 100 assessments of coping, the authors critiqued strategies and identified best practices for constructing category systems. From current systems, a list of 400 ways of coping was compiled. For constructing lower order categories, the authors concluded that confirmatory factor analysis should replace the 2 most common strategies (exploratory factor analysis and rational sorting). For higher order categories, they recommend that the 3 most common distinctions (problem- vs. emotion-focused, approach vs. avoidance, and cognitive vs. behavioral) no longer be used. Instead, the authors recommend hierarchical systems of action types (e.g., proximity seeking, accommodation). From analysis of 6 such systems, 13 potential core families of coping were identified. Future steps involve deciding how to organize these families, using their functional homogeneity and distinctiveness, and especially their links to adaptive processes.
Factors associated with institutional delivery service utilization in Ethiopia

Most obstetric complications occur unpredictably during the time of delivery, but they can be prevented with proper medical care in the health facilities. Despite the Ethiopian government's efforts to expand health service facilities and promote health institution-based delivery service in the country, an estimated 85% of births still take place at home.



The review was conducted with the aim of generating the best evidence on the determinants of institutional delivery service utilization in Ethiopia.



The reviewed studies were accessed through electronic web-based search strategy from PubMed, HINARI, Mendeley reference manager, Cochrane Library for Systematic Reviews, and Google Scholar. Review Manager V5.3 software was used for meta-analysis. Haenszel odds ratios (ORs) and their 95% confidence intervals (CIs) were calculated. Heterogeneity of the study was assessed using I (2) test.



People living in urban areas (OR =13.16, CI =1.24, 3.68), with primary and above educational level of the mother and husband (OR =4.95, CI =2.3, 4. 8, and OR =4.43, CI =1.14, 3.36, respectively), who encountered problems during pregnancy (OR =2.83, CI =4.54, 7.39), and living at a distance <5 km from nearby health facility (OR =2.6, CI =3.33, 6.57) showed significant association with institutional delivery service utilization. Women's autonomy was not significantly associated with institutional delivery service utilization.



Distance to health facility and problems during pregnancy were factors positively and significantly associated with institutional delivery service utilization. Promoting couples education beyond primary education regarding the danger signs of pregnancy and benefits of institutional delivery through available communication networks such as health development army and promotion of antenatal care visits and completion of four standard visits by pregnant women were recommended.
Towards Natural Language Understanding using Multimodal Deep Learning

This thesis describes how multimodal sensor data from a 3D sensor and microphone array can be processed with deep neural networks such that its fusion, the trained neural network, is a) more robust to noise, b) outperforms unimodal recognition and c) enhances unimodal recognition in absence of multimodal data. We built a framework for a complete workflow to experiment with multimodal sensor data ranging from recording (with Kinect 3D sensor), labeling, 3D signal processing, analysing and replaying. We also built three custom recognizers (automatic speech recognizer, 3D object recognizer and 3D gesture recognizer) to convert the raw sensor streams to decisions and feed this to the neural network using a late fusion strategy. We recorded 25 particpants performing 27 unique verbal and gestural interactions (intents) with objects and trained the neural network using a supervised strategy. We proved that the framework works by building a deep neural networks assisted speech recognizer that performs approximately 5% better with multimodal data at 20 dB SnR up to 61% better with multimodal data at -5 dB SnR while performing identical to the individual recognizer when fed a unimodal datastream. Analysis shows that performance gain in low acoustic noise is due to true fusion of classifer results while gain at high acoustic noise is due to absence of speech results as it cannot detect speech events anymore, while the gesture recognizer is not affected. The impact of this thesis is significant for computational linguists and computer vision researchers as it describes how practical issues with (real and) real-time data can be solved such as dealing with sensor noise, GPU offloading for computational performance, 3D object and hand tracking. The speech-, objectand gesture recognizers are not state-of-the-art and the small vocabulary with 27 unique phrases and 9 objects can be considered a preliminary experiment. The main contributions of this thesis project are a) validated multimodal fusion framework and workflow for embodied natural language understanding named MASU, b) 600GB, 2,5 hour labelled multimodal database with synchronous multi channel audio and 3D video, c) algorithm for 3D hand-object detection and tracking, d) recipe to train a deep neural network model for multimodal fusion and e) demontrate MASU in practical real-time scenario. Faculty: Faculty of Electrical Engineering, Mathematics and Computer Science Department: Intelligent Systems Committee members: Prof. M.A. Larson TU Delft Dr. Ir. E.A. Hendriks TU Delft Dr. M.J. Tax TU Delft
Exercise and chronic low back pain: what works?

The aim of this review was to investigate current evidence for the type and quality of exercise being offered to chronic low back pain (CLBP) patients, within randomised controlled trials (RCTs), and to assess how treatment outcomes are being measured. A two-fold methodological approach was adopted: a methodological assessment identified RCTs of 'medium' or 'high' methodological quality. Exercise quality was subsequently assessed according to the predominant exercise used. Outcome measures were analysed based on current recommendations. Fifty-four relevant RCTs were identified, of which 51 were scored for methodological quality. Sixteen RCTs involving 1730 patients qualified for inclusion in this review based upon their methodological quality, and chronicity of symptoms; exercise had a positive effect in all 16 trials. Twelve out of 16 programmes incorporated strengthening exercise, of which 10 maintained their positive results at follow-up. Supervision and adequate compliance were common aspects of trials. A wide variety of outcome measures were used. Outcome measures did not adequately represent the guidelines for impairment, activity and participation, and impairment measures were over-represented at the expense of others. Despite the variety offered, exercise has a positive effect on CLBP patients, and results are largely maintained at follow-up. Strengthening is a common component of exercise programmes, however, the role of exercise co-interventions must not be overlooked. More high quality trials are needed to accurately assess the role of supervision and follow-up, together with the use of more appropriate outcome measures.
Data Envelopment Analysis: a Management Science Tool for Scientific Marketing Research New Books in Review

Marketing has a tradition in conducting scientific research with cutting-edge techniques developed in management science, such as data envelopment analysis (DEA) (Charnes et al. 1985). Two decades ago, Kamakura, Ratchford, and Agrawal (1988) applied DEA to examine market efficiency and consumer welfare loss. In this review of three new books, my purpose is (1) to provide a background of DEA for marketing scholars and executives and (2) to motivate them with exciting DEA advances for marketing theory and practice. All three books provide brief descriptions of DEA’s history, origin, and basic models. For beginners, Ramanathan’s work, An Introduction to Data Envelopment Analysis, is a good source, offering basic concepts of DEA’s efficiency and programming formulations in a straightforward manner (with some illustrations) in the first three chapters. A unique feature of this book is that it dedicates a chapter to discussing as many as 11 DEA computer software programs and explaining some noncommercial DEA packages that are available free on the Internet for academic purposes. As Ramanathan states (p. 111), “[I]n this computer era, it is important that any management science technique has adequate software support so that potential users are encouraged to use it. Software harnesses the computing power of [personal computers] for use in practical decision-making situations. It can also expedite the implementation of a method. Naveen Donthu
Complementary perspectives on metaphor : Cognitive linguistics and relevance theory

Contemporary theories of metaphor differ in many dimensions, including the discipline they originate from (e.g., linguistics, psychology, philosophy), and whether they are developed primarily within a cognitive or pragmatic theoretical framework. This article evaluates two directions of metaphor research within linguistics, cognitive linguistics and relevance theory, which both aim to capture essential aspects of the reason for metaphor, and how people ordinarily use and understand metaphor in daily life. We argue, contrary to most received opinion, that cognitive linguistics and relevance theory provide complementary perspectives on metaphor. Both theories offer important insights into the role of metaphor in cognition and language use, and suggest detailed hypotheses on metaphor understanding that surely are part of a comprehensive theory of metaphor.
Wearable Intelligent Systems for E-Health

Due to the increasingly aging population, there is a rising demand for assistive living technologies for the elderly to ensure their health and well-being. The elderly are mostly chronic patients who require frequent check-ups of multiple vital signs, some of which (e.g., blood pressure and blood glucose) vary greatly according to the daily activities that the elderly are involved in. Therefore, the development of novel wearable intelligent systems to effectively monitor the vital signs continuously over a 24 hour period is in some cases crucial for understanding the progression of chronic symptoms in the elderly. In this paper, recent development of Wearable Intelligent Systems for e-Health (WISEs) is reviewed, including breakthrough technologies and technical challenges that remain to be solved. A novel application of wearable technologies for transient cardiovascular monitoring during water drinking is also reported. In particular, our latest results found that heart rate increased by 9 bpm (P < 0.001) and pulse transit time was reduced by 5 ms (P < 0.001), indicating a possible rise in blood pressure, during swallowing. In addition to monitoring physiological conditions during daily activities, it is anticipated that WISEs will have a number of other potentially viable applications, including the real-time risk prediction of sudden cardiovascular events and deaths.  Smart and intelligent computing
CANaLI : A System for Answering Controlled Natural Language Questions on RDF Knowledge Bases UCLA CSD Technical Report Number : 160004

The fast growth in number, size and availability of rdf knowledge bases (kbs) is creating a pressing need for research advances that will let people consult them without having to learn structured query languages, such as sparql, and the internal organization of the kbs. In this paper we present our Question Answering (QA) system, that accepts questions posed in a Controlled Natural Language. The questions entered by the user are annotated on the fly, and an ontology driven auto-completion system displays suggested patterns computed in real time from the partially completed sentence the person is typing. By following these patterns, users can enter only semantically correct questions which are unambiguously interpreted by the system. This approach assures high levels of usability and generality. Experiments conducted on well-known QA benchmarks, including questions on the encyclopedic DBpedia and specialized domains, such as music and medicine, show a better accuracy and precision than previous systems.
Multi-domain learning and generalization in dialog state tracking

Statistical approaches to dialog state tracking synthesize information across multiple turns in the dialog, overcoming some speech recognition errors. When training a dialog state tracker, there is typically only a small corpus of well-matched dialog data available. However, often there is a large corpus of mis-matched but related data – perhaps pertaining to different semantic concepts, or from a different dialog system. It would be desirable to use this related dialog data to supplement the small corpus of well-matched dialog data. This paper addresses this task as multi-domain learning, presenting 3 methods which synthesize data from different slots and different dialog systems. Since deploying a new dialog state tracker often changes the resulting dialogs in ways that are difficult to predict, we study how well each method generalizes to unseen distributions of dialog data. Our main result is the finding that a simple method for multi-domain learning substantially improves performance in highly mis-matched conditions.
Strategic integration of knowledge management and customer relationship management

The purpose of this paper is to introduce the concept of strategic integration of knowledge management (KM ) and customer relationship management (CRM). The integration is a strategic issue that has strong ramifications in the long-term competitiveness of organizations. It is not limited to CRM; the concept can also be applied to supply chain management (SCM), product development management (PDM), eterprise resource planning (ERP) and retail network management (RNM) that offer different perspectives into knowledge management adoption.  Through literature review and establishing new perspectives with examples, the components of knowledge management, customer relationship management, and strategic planning are amalgamated.  Findings include crucial details in the various components of knowledge management, customer relationship management, and strategic planning, i.e. strategic planning process, value formula, intellectual capital measure, different levels of CRM and their core competencies.  Although the strategic integration of knowledge management and customer relationship management is highly conceptual, a case example has been provided where the concept is applied. The same concept could also be applied to other industries that focus on customer service.  The concept of strategic integration of knowledge management and customer relationship management is new. There are other areas, yet to be explored in terms of additional integration such as SCM, PDM, ERP, and RNM. The concept of integration would be useful for future research as well as for KM and CRM practitioners.