nils-herrmann/clean_scirepeval huggingface dataset:
Regression Conformal Prediction with Nearest Neighbours

In this paper we apply Conformal Prediction (CP) to the k -Nearest Neighbours Regression (k -NNR) algorithm and propose ways of extending the typical nonconformity measure used for regression so far. Unlike traditional regression methods which produce point predictions, Conformal Predictors output predictive regions that satisfy a given confidence level. The regions produced by any Conformal Predictor are automatically valid, however their tightness and therefore usefulness depends on the nonconformity measure used by each CP. In effect a nonconformity measure evaluates how strange a given example is compared to a set of other examples based on some traditional machine learning algorithm. We define six novel nonconformity measures based on the k -Nearest Neighbours Regression algorithm and develop the corresponding CPs following both the original (transductive) and the inductive CP approaches. A comparison of the predictive regions produced by our measures with those of the typical regression measure suggests that a major improvement in terms of predictive region tightness is achieved by the new measures.
Health is Wealth: Methods to Improve Attendance in a Lifestyle Intervention for a Largely Immigrant Filipino-American Sample

Diabetes prevention requires lifestyle changes, and traditional educational programs for lifestyle changes have had low attendance rates in ethnic populations. This article describes the development and implementation of an educational program, emphasizing retention strategies, cultural tailoring and community participation. Community-based participatory research approaches were used to adapt and test the feasibility of a culturally tailored lifestyle intervention (named Health is Wealth) for Filipino-American adults at risk for diabetes (n = 40) in order to increase program attendance. A unique feature of this program was the flexibility of scheduling the eight classes, and inclusion of activities, foods and proverbs consistent with Filipino culture. We found that with this approach, overall program attendance for the experimental and wait-listed control groups was 88% and participant satisfaction was high with 93% very satisfied. Flexible scheduling, a bilingual facilitator for the classes, and the community-academic partnership contributed to the high attendance for this lifestyle intervention.
Automatic Fruit Grading and Classification System Using Computer Vision: A Review

Automation in agriculture comes into play to increase productivity, quality and economic growth of the country. Fruit grading is an important process for producers which affects the fruits quality evaluation and export market. Although the grading and sorting can be done by the human, but it is slow, labor intensive, error prone and tedious. Hence, there is a need of an intelligent fruit grading system. In recent years, researchers had developed numerous algorithms for fruit sorting using computer vision. Color, textural and morphological features are the most commonly used to identify the diseases, maturity and class of the fruits. Subsequently, these features are used to train soft computing technique network. In this paper, use of image processing in agriculture has been reviewed so as to provide an insight to the use of vision based systems highlighting their advantages and disadvantages.
ChaLearn Looking at People: Events and Resources

This paper reviews the historic of ChaLearn Looking at People (LAP) events. We started in 2011 (with the release of the first Kinect device) to run challenges related to human action/activity and gesture recognition. Since then we have regularly organized events in a series of competitions covering all aspects of visual analysis of humans. So far we have organized more than 10 international challenges and events in this field. This paper reviews associated events, and introduces the ChaLearn LAP platform where public resources (including code, data and preprints of papers) related to the organized events are available. We also provide a discussion on perspectives of ChaLearn LAP activities.
Runoff and erosional responses to a drought ‐ induced shift in a desert grassland community composition

This study investigates how drought‐induced change in semiarid grassland community affected runoff and sediment yield in a small watershed in southeast Arizona, USA. Three distinct periods in ecosystem composition and associated runoff and sediment yield were identified according to dominant species: native bunchgrass (1974–2005), forbs (2006), and the invasive grass, Eragrostis lehmanniana (2007–2009). Precipitation, runoff, and sediment yield for each period were analyzed and compared at watershed and plot scales. Average watershed annual sediment yield was 0.16 t ha yr. Despite similarities in precipitation characteristics, decline in plant canopy cover during the transition period of 2006 caused watershed sediment yield to increase 23‐fold to 1.64 t ha yr comparing with preceding period under native bunchgrasses (0.06 t ha yr) or succeeding period under E. lehmanniana (0.06 t ha yr). In contrast, measurements on small runoff plots on the hillslopes of the same watershed showed a significant increase in sediment discharge that continued after E. lehmanniana replaced native grasses. Together, these findings suggest alteration in plant community increased sediment yield but that hydrological responses to this event differ at watershed and plot scales, highlighting the geomorphological controls at the watershed scale that determine sediment transport efficiency and storage. Resolving these scalar issues will help identify critical landform features needed to preserve watershed integrity under changing climate conditions.
Integrated Wireless Technologies for Smart Homes Applications

Due to the rapid advances in wireless communication and information technologies it is now possible to embed various levels of smartness in the home. These smart homes are ones that can interact intelligently with their inhibitors to provide comfort and safe living. This interaction may range from simple control of ambient temperature to context-aware and mobile agent based services. An example of that is delivery of particular information content based on the smart home inhibitor location inside the home and the activities that he or she is engaged with. Wireless networks and sensors are seen to play an increasingly important role as key enablers in emerging pervasive computing technologies that are required for the realization of smart homes. The wide spread of wireless networks in our daily life is enabled by the communication standards such as WiFi, Bluetooth, Zigbee, RFID, and cellular technologies. A combination of these standards is envisaged to be used to construct the smart home. Effectively all wireless technologies that can support some form of remote data transfer, sensing and control are candidates for inclusion in the smart home portfolio. A top level architecture of a smart home is illustrated in Fig. 1 (Al-Qutayri & Jeedella, 2010). It includes a server/gateway/router that can be used as the central point of connectivity for devices within the home as well as allowing connectivity to the outside world. The setup also includes smart sensors as well as appliances that have either wired or wireless connectivity. Communicating with the smart home from the outside can be done using one or a combination of the following external networks such as phone lines, xDSL lines, cable TV, GSM and power line networks. Following this introduction, Section 2 reviews some of the major wireless communication technologies that can be used to realize a smart home. Section 3 describes briefly some of the smart sensors that can be used. Section 4 describes the major computing technologies, including ubiquitous and pervasive, context-aware, and agents computing which introduce smartness aspects to the home. Section 5 details some of the application that can be realized with smart homes. Section 6 describes the design and implementation of some smart home systems. In particular, the end-to-end realization of a secure system that enables monitoring and control of various devices with multiple levels of settings is described and the results 2
Oscillations, phase-of-firing coding, and spike timing-dependent plasticity: an efficient learning scheme.

Recent experiments have established that information can be encoded in the spike times of neurons relative to the phase of a background oscillation in the local field potential-a phenomenon referred to as "phase-of-firing coding" (PoFC). These firing phase preferences could result from combining an oscillation in the input current with a stimulus-dependent static component that would produce the variations in preferred phase, but it remains unclear whether these phases are an epiphenomenon or really affect neuronal interactions-only then could they have a functional role. Here we show that PoFC has a major impact on downstream learning and decoding with the now well established spike timing-dependent plasticity (STDP). To be precise, we demonstrate with simulations how a single neuron equipped with STDP robustly detects a pattern of input currents automatically encoded in the phases of a subset of its afferents, and repeating at random intervals. Remarkably, learning is possible even when only a small fraction of the afferents ( approximately 10%) exhibits PoFC. The ability of STDP to detect repeating patterns had been noted before in continuous activity, but it turns out that oscillations greatly facilitate learning. A benchmark with more conventional rate-based codes demonstrates the superiority of oscillations and PoFC for both STDP-based learning and the speed of decoding: the oscillation partially formats the input spike times, so that they mainly depend on the current input currents, and can be efficiently learned by STDP and then recognized in just one oscillation cycle. This suggests a major functional role for oscillatory brain activity that has been widely reported experimentally.
A Unified Knowledge Representation and Context-aware Recommender System in Internet of Things

Within the rapidly developing Internet of Things (IoT), numerous and diverse physical devices, Edge devices, Cloud infrastructure, and their quality of service requirements (QoS), need to be represented within a unified specification in order to enable rapid IoT application development, monitoring, and dynamic reconfiguration. But heterogeneities among different configuration knowledge representation models pose limitations for acquisition, discovery and curation of configuration knowledge for coordinated IoT applications. This paper proposes a unified data model to represent IoT resource configuration knowledge artifacts. It also proposes IoT-CANE (Context-Aware recommendatioN systEm) to facilitate incremental knowledge acquisition and declarative context driven knowledge recommendation.
A Review on Challenging Issues in Arabic Sentiment Analysis

Ali Hamdi Faculty of Computer Science and Information Systems, Universiti Teknologi Malaysia, Johor, Malaysia Email: ali@alihamdi.com  Understanding what people think about an idea or how they evaluate a product, a service or a policy is important for individuals, companies and governments. Sentiment analysis is the process of automatically identifying opinions expressed in text on certain subjects. The accuracy of sentiment analysis has a direct effect on decision making in both business and government. Working with the Arabic language is very important because of the growing number of online contents in Arabic and the existing resources are limited and the accuracy of existing methods is low. In this study, we do a survey to highlight Arabic sentiment analysis challenging issues based on two main perspectives: Arabic-specific and general linguistic issues. The Arabic-specific challenges are mainly caused by Arabic morphological complexity, limited resources and dialects, while the general linguistic issues include polarity fuzziness, polarity strength, implicit sentiment, sarcasm, spam, review quality and domain dependence.
Development of the mammalian lymphatic vasculature.

The two vascular systems of our body are the blood and lymphatic vasculature. Our understanding of the cellular and molecular processes controlling the development of the lymphatic vasculature has progressed significantly in the last decade. In mammals, this is a stepwise process that starts in the embryonic veins, where lymphatic EC (LEC) progenitors are initially specified. The differentiation and maturation of these progenitors continues as they bud from the veins to produce scattered primitive lymph sacs, from which most of the lymphatic vasculature is derived. Here, we summarize our current understanding of the key steps leading to the formation of a functional lymphatic vasculature.
The Production of Food and Fiber : An Adaptation of CoP Features for Sustainable Water Use in Agribusiness

Fresh water and arable land are essential for agricultural production and food processing. However, managing conflicting demands over water and land can be challenging for business leaders, environmentalists and other stakeholders. This paper characterizes these challenges as wicked problems. Wicked problems are ill-formed, fuzzy, and messy, because they involve many clients and decisions makers with conflicting values. They are also not solvable, but rather must be managed. How can agribusiness leaders effectively manage wicked problems, especially if they have little practice in doing so? This paper argues that a Community of Practice (CoP) and its tripartite elements of domain, community and practice can be effective in helping businesses manage wicked problems by focusing on the positive links between environmental stewardship and economic performance. Empirically, the paper examines three agribusinesses to assess the extent in which CoP is used as a strategy for sustainable water management.
A Bayesian network-based framework for semantic image understanding

Current research in content-based semantic image understanding is largely confined to exemplar-based approaches built on low-level feature extraction and classification. The ability to extract both low-level and semantic features and perform knowledge integration of different types of features is expected to raise semantic image understanding to a new level. Belief networks, or Bayesian networks (BN), have proven to be an effective knowledge representation and inference engine in artificial intelligence and expert systems research. Their effectiveness is due to the ability to explicitly integrate domain knowledge in the network structure and to reduce a joint probability distribution to conditional independence relationships. In this paper, we present a general-purpose knowledge integration framework that employs BN in integrating both low-level and semantic features. The efficacy of this framework is demonstrated via three applications involving semantic understanding of pictorial images. The first application aims at detecting main photographic subjects in an image, the second aims at selecting the most appealing image in an event, and the third aims at classifying images into indoor or outdoor scenes. With these diverse examples, we demonstrate that effective inference engines can be built within this powerful and flexible framework according to specific domain knowledge and available training data to solve inherently uncertain vision problems.
Application of Neural Networks in Financial Data Mining

This paper deals with the application of a well-known neural network technique, multi-layer back-propagation (BP) neural network, in financial data mining. A modified neural network forecasting model is presented, and an intelligent mining system is developed. The system can forecast the buying and selling signs according to the prediction of future trends to stock market, and provide decision-making for stock investors. The simulation result of seven years to Shanghai Composite Index shows that the return achieved by this mining sys-tem is about three times as large as that achieved by the buy and hold strategy, so it is advantageous to apply neural networks to forecast financial time series, the different investors could benefit from it. data mining, neural network, stock forecasting.
Abortion and subsequent depressive symptoms: an analysis of the National Longitudinal Study of Adolescent Health.

Twenty states currently require that women seeking abortion be counseled on possible psychological responses, with six states stressing negative responses. The majority of research finds that women whose unwanted pregnancies end in abortion do not subsequently have adverse mental health outcomes; scant research examines this relationship for young women.



Four waves of data from the National Longitudinal Study of Adolescent Health were analyzed. Population-averaged lagged logistic and linear regression models were employed to test the relationship between pregnancy resolution outcome and subsequent depressive symptoms, adjusting for prior depressive symptoms, history of traumatic experiences, and sociodemographic covariates. Depressive symptoms were measured using a nine-item version of the Center for Epidemiologic Studies Depression scale. Analyses were conducted among two subsamples of women whose unwanted first pregnancies were resolved in either abortion or live birth: (1) 856 women with an unwanted first pregnancy between Waves 2 and 3; and (2) 438 women with an unwanted first pregnancy between Waves 3 and 4 (unweighted n's).



In unadjusted and adjusted linear and logistic regression analyses for both subsamples, there was no association between having an abortion after an unwanted first pregnancy and subsequent depressive symptoms. In fully adjusted models, the most recent measure of prior depressive symptoms was consistently associated with subsequent depressive symptoms.



In a nationally representative, longitudinal dataset, there was no evidence that young women who had abortions were at increased risk of subsequent depressive symptoms compared with those who give birth after an unwanted first pregnancy.
How (not) to Train your Generative Model: Scheduled Sampling, Likelihood, Adversary?

Modern applications and progress in deep learning research have created renewed interest for generative models of text and of images. However, even today it is unclear what objective functions one should use to train and evaluate these models. In this paper we present two contributions. Firstly, we present a critique of scheduled sampling, a state-of-the-art training method that contributed to the winning entry to the MSCOCO image captioning benchmark in 2015. Here we show that despite this impressive empirical performance, the objective function underlying scheduled sampling is improper and leads to an inconsistent learning algorithm. Secondly, we revisit the problems that scheduled sampling was meant to address, and present an alternative interpretation. We argue that maximum likelihood is an inappropriate training objective when the end-goal is to generate natural-looking samples. We go on to derive an ideal objective function to use in this situation instead. We introduce a generalisation of adversarial training, and show how such method can interpolate between maximum likelihood training and our ideal training objective. To our knowledge this is the first theoretical analysis that explains why adversarial training tends to produce samples with higher perceived quality.
Environmental contamination by canine geohelminths

Intestinal nematodes affecting dogs, i.e. roundworms, hookworms and whipworms, have a relevant health-risk impact for animals and, for most of them, for human beings. Both dogs and humans are typically infected by ingesting infective stages, (i.e. larvated eggs or larvae) present in the environment. The existence of a high rate of soil and grass contamination with infective parasitic elements has been demonstrated worldwide in leisure, recreational, public and urban areas, i.e. parks, green areas, bicycle paths, city squares, playgrounds, sandpits, beaches. This review discusses the epidemiological and sanitary importance of faecal pollution with canine intestinal parasites in urban environments and the integrated approaches useful to minimize the risk of infection in different settings.
Dietary flaxseed lignan or oil combined with tamoxifen treatment affects MCF-7 tumor growth through estrogen receptor- and growth factor-signaling pathways.

This study aimed to elucidate which component of flaxseed, i.e. secoisolariciresinol diglucoside (SDG) lignan or flaxseed oil (FO), makes tamoxifen (TAM) more effective in reducing growth of established estrogen receptor positive breast tumors (MCF-7) at low circulating estrogen levels, and potential mechanisms of action. In a 2 x 2 factorial design, ovariectomized athymic mice with established tumors were treated for 8 wk with TAM together with basal diet (control), or basal diet supplemented with SDG (1 g/kg diet), FO (38.5 g/kg diet), or combined SDG and FO. SDG and FO were at levels in 10% flaxseed diet. Palpable tumors were monitored and after animal sacrifice, analyzed for cell proliferation, apoptosis, ER-mediated (ER-alpha, ER-beta, trefoil factor 1, cyclin D1, progesterone receptor, AIBI), growth factor-mediated (epidermal growth factor receptor, human epidermal growth factor receptor-2, insulin-like growth factor receptor-1, phosphorylated mitogen activated protein kinase, PAKT, BCL2) signaling pathways and angiogenesis (vascular endothelial growth factor). All treatments reduced the growth of TAM-treated tumors by reducing cell proliferation, expression of genes, and proteins involved in the ER- and growth factor-mediated signaling pathways with FO having the greatest effect in increasing apoptosis compared with TAM treatment alone. SDG and FO reduced the growth of TAM-treated tumors but FO was more effective. The mechanisms involve both the ER- and growth factor-signaling pathways.
A joint solution for the hidden and exposed terminal problems in CSMA/CA wireless networks

1389-1286/$ see front matter 2012 Elsevier B.V http://dx.doi.org/10.1016/j.comnet.2012.06.008 ⇑ Corresponding author. E-mail addresses: cshuang@umac.mo (C. Huan (C.-T. Lea), eealbert@ust.hk (A.K.-S. Wong). It is well-known that the Carrier Sense Multiple Access with Collision Avoidance (CSMA/ CA)-based wireless networks suffer seriously from the hidden terminal problem and the exposed terminal problem. So far, no satisfactory solutions that can resolve both problems simultaneously have been found. In this paper, we present a joint solution to the two problems. Our approach avoids the drawback of lessening one problem but aggregating the other. It is compatible with the IEEE 802.11 MAC and requires no protocol change. Analysis and simulations show that the proposed scheme can significantly reduce the hidden and exposed terminal problems. Not only it can significantly improve the throughput of the network and the fairness among different flows, it can also provide a much more stable link layer. In simulated scenarios under heavy traffic conditions, compared to the conventional IEEE 802.11 MAC, the new method can achieve up to 1.8 times gain in network throughput for single-hop flows and up to 2.6 times gain for multihop flows.