Miracle-dz/UniFAR-FTUs huggingface dataset:
2018). Peer-Based Social Media Features in Behavior Change Interventions: Systematic Review. Journal of Medical Internet Research, 20(2),

Background: Incorporating social media features into digital behavior change interventions (DBCIs) has the potential to contribute positively to their success. However, the lack of clear design principles to describe and guide the use of these features in behavioral interventions limits cross-study comparisons of their uses and effects. Objective: The aim of this study was to provide a systematic review of DBCIs targeting modifiable behavioral risk factors that have included social media features as part of their intervention infrastructure. A taxonomy of social media features is presented to inform the development, description, and evaluation of behavioral interventions. Methods: Search terms were used in 8 databases to identify DBCIs that incorporated social media features and targeted tobacco smoking, diet and nutrition, physical activities, or alcohol consumption. The screening and review process was performed by 2 independent researchers. Results: A total of 5264 articles were screened, and 143 articles describing a total of 134 studies were retained for full review. The majority of studies (70%) reported positive outcomes, followed by 28% finding no effects with regard to their respective objectives and hypothesis, and 2% of the studies found that their interventions had negative outcomes. Few studies reported on the association between the inclusion of social media features and intervention effect. A taxonomy of social media features used in behavioral interventions has been presented with 36 social media features organized under 7 high-level categories. The taxonomy has been used to guide the analysis of this review. Conclusions: Although social media features are commonly included in DBCIs, there is an acute lack of information with respect to their effect on outcomes and a lack of clear guidance to inform the selection process based on the features’ suitability for the different behaviors. The proposed taxonomy along with the set of recommendations included in this review will support future research aimed at isolating and reporting the effects of social media features on DBCIs, cross-study comparisons, and evaluations.
: Energy efficient Peer Selection and Time switching Ratio Allocation

—This paper considers device-to-device (D2D) network with Simultaneous Wireless Information and Power Transfer (SWIPT) enabled devices to ensure self-sustained communication in situations like disasters. Such direct link networks can ensure connectivity with devices having drained back-up, when trapped in collapsed infrastructure, through mutual sharing of energy on RF link. To guarantee successful execution of SWIPT session for an isolated device in wake of disasters, it is pertinent to select a reliable peer with ultimate aim to maximize link Energy Efﬁciency (EE). In practice, Energy Harvesting (EH) is not achievable after Information Decoding (ID), however, it has been made possible through splitting the signal in the time domain. Selection of D2D peer for self-sustained communication with an objective to maximize EE through optimum time based splitting of signal has not been extensively studied . In this paper to manifest the aforesaid goal, we worked out a joint problem of peer association and time switching ratio allocation with an objective to maximize the EE for a device contained under collapsed infrastructure. We propose an Energy efﬁcient Peer Selection and Time switching Ratio Allocation (EPS-TRA) algorithm to solve the proposed mixed integer problem. Numerical results validate our proposed approach in acquiring better EE when compared with Uniform Allocation Scheme of time slots for EH & ID. Furthermore, results explain how EE of the link varies with the choice of constrained variables i.e. data rate and harvested energy.
Predicting Highway Stormwater Pollutographs Using a Hybrid Evolutionary Data Driven Modeling Framework

Urban and non-urban stormwater runoff is considered as a source of pollution to many receiving water bodies. To reduce the impact of pollution on receiving waters, knowledge of input runoff concentration and hydrograph is needed in order to design and optimize best management practices (BMPs). Also, to include the first flush impact on BMP design and risk assessment, it is essential to measure the pollutograph (pollutant concentration vs. storm duration) of desired chemical constituents. Since monitoring of pollutographs can be extremely expensive, its prediction for specific highway watersheds is advantages. This paper is prepared to describe the application of a hybrid evolutionary data driven model identification procedure for modeling stormwater pollutographs. The method works on the basis of 1) using a multiobjective genetic algorithm (GA) called NSGA-II for model search, and 2) employing a hybrid GA for model calibration. The use of the model for successful prediction of TSS pollutograph is demonstrated. The obtained model suggests that the reattachment or deposition of particles during the storm event is negligible and the release rate of particles is related to rain intensity through an exponential function.
Language Program Evaluation: Theory and Practice

Although much has been written about the testing and evaluation of language students, few books have dealt with the much broader issue of measuring the success of language programs. Filling a critical gap in the literature of applied linguistics, Language Program Evaluation provides teachers, researchers, and administrators with a complete introduction to both qualitative and quantitative approaches to evaluating the effectiveness of their programs. Through a careful blend of theory, research, and practice, the author presents a testing model that is specifically tailored to the concerns of language education programs and which can be adapted to different contexts and updated as teaching and learning situations evolve. He proposes a shift from purely quantitative studies which consider only end-of-program achievement gains to ones that include an investigation of program process using qualitative, naturalistic methods.
SHAREXP: AN ECLIPSE PLUG-IN FOR EXPERTISE SHARING AMONG DEVELOPERS

We nowadays live in a world of tailorable systems in which end-users are able to transform their working environment while achieving their tasks, day to day and over the time. Tailorability is most of the time achieved through dynamic component integration thanks to a huge number of components available over the Internet. In the context of software development, one of the main environments that developers are using worldwide for component integration is Eclipse. In this context, the main issue for developers is not anymore the integration of new components, but how to find the most interesting set of components that will fulfil their needs. Solving this issue could definitely increase their productivity and efficiency. We offer ShareXP, an Eclipse plug-in that allows members of a group to share their expertise, this expertise being embodied in the “assemblages” each of them has built.
Bank of Finland Discussion Papers Banks' Option to Lend, Interest Rate Sensitivity, and Credit Availability Suomen Pankin Keskustelualoitteita Finlands Banks Diskussionsunderlag Bank of Finland Discussion Papers Banks' Option to Lend, Interest Rate Sensitivity, and Credit Availability

Banks' option to lend, interest rate sensitivity, and credit availability The views expressed are those of the authors and do not necessarily reflect the views of the Bank of Finland. Abstract Interest rate risk is a major concern for banks because of the nominal nature of their assets and the asset-liability maturity mismatch. This paper proposes a new way to derive a bank's interest rate sensitivity, by examining separately the effects of interest rate changes on existing loans (loans-in-place) and potential loans (loans-in-process). A potential loan is shown to be equivalent to an American option to lend, and is valued using option theory. An increase in interest rates usually has a negative effect on existing loans. However, if both deposit and lending rates rise by the same amount, the value of a potential loan generally increases. Hence a bank's lending slack (ratio of loans-in-process to loans-in-place) will determine its overall interest rate risk. Empirical evidence indicates that low-slack banks indeed have significantly more interest rate risk than high-slack banks. The model also makes predictions regarding the effect of deposit and lending rate parameters on bank credit availability. Empirical tests with quarterly data are generally supportive of these predictions.
Sedimentary patterns across the Paleocene-Eocene boundary in the Atlantic and Gulf Coastal Plains of the United States

Fossiliferous clay and sand belonging to calcareous nannofossil Zones NP 9 (latest Paleocene) and NP 10 (earliest Eocene) are widespread in the U.S. Gulf and Atlantic Coastal Plains. Although the thickness of Zone NP 9-NP 10 strata is several times greater in the eastern Gulf Coastal Plain than in the Atlantic Coastal Plain, the hiatus that usually is present between these two zones in inner neritic strata is of longer duration in the Gulf Coast. The eastern Gulf Coastal Plain contains 2 thin and 2 thick, uppermost Paleocene (Zone NP 9) depositional sequences and 6 thin, lower Eocene (Zones NP 10-NP 11) sequences. Individual sequences are more difficult to distinguish in Atlantic Coastal Plain deposits, presumably because of a lower sedimentation rate, but 2 thin, uppermost Paleocene (Zone NP 9) and 6 thin, lower Eocene (Zones NP 10-NP 11)sequences are recognized. Although 6 sequences are found in Zones NP 10-NP 11 in both the Atlantic and Gulf Coastal Plains, the number of sequences in each NP zone differs in the 2 areas, which suggests that erosion has removed entire sequences in both areas. There is continuous deposition across the Zone NP 9-NP 10 boundary only in the northern Atlantic Coastal Plain, where middle to outer neritic deposits record the presence of several latest Paleocene (upper Zone NP 9) events that occurred at approximately 55.2 Ma. These events include 1) a change from glauconitic sand to non-glauconitic clay, 2) a change in dominant clay mineralogy from smectite and illite to kaolinite, 3) a sharp decrease in the benthic foraminiferal species diversity along with a change from more normal marine assemblages to lower oxygen assemblages, 4) a large increase in the abundance of planktonic foraminifers, and 5) a significant increase in the number of first and last appearances of both calcareous nannofossil and sporomorph assemblages. These foraminiferal and sediment changes persist into the earliest Eocene (lowermost Zone NP 10) and there are no significant changes precisely at the Paleocene-Eocene (Zone NP 9-NP 10 boundary).
Does professor-student trust improve motivation and perceived performance? La relación de confianza entre profesor y alumno, ¿mejora la motivación y los resultados?

Our study takes into account the way in which both academic and personal trust has an effect on the motivation of students and their perceived performance. To do this we sent an online survey in which we asked higher education students to evaluate their professors as a whole using a Likert scale. The independent variables were academic trust and personal trust, whilst the dependent variables were motivation and perceived performance. The results showed a positive correlation between academic trust and motivation, as well as with perceived performance. The same result was achieved when studying personal trust in regards to motivation and perceived performance. This implies that there is an important positive relationship between trust and the general performance of students in class. The ramifications of our study on future research push for the idea that trust must be seen from differing perspectives, and that it is an implicit part of teaching skills.
Fire risk in Amazonia due to climate change in the HadCM3 climate model: Potential interactions with deforestation

Deforestation in Amazonia seems likely to continue in the 21st century, and fire risk could increase as a result of global climate change. With deforestation activities providing a source of ignition, the combination of this with increased fire risk could lead to widespread impacts on Amazonian ecosystems. This would also contribute further to climate change through feedbacks on the global carbon cycle. This study uses the McArthur Forest Fire Danger Index and an ensemble of variants of the HadCM3 climate model to simulate potential changes to fire risk in Amazonia over the 21st century. Significant increases in central and eastern Amazonia are simulated by 2020, and high fire danger for over 50% of the forest is simulated in all model runs by 2080. By 2080, as much as 25% of Amazonia shows high levels of agreement in projections of combined fire risk and deforestation. The areas simulated with high fire danger are also those projected to be impacted most significantly by deforestation. If the regional climate changes simulated for Amazonia by HadCM3 are realistic, the risk of significant forest damage could become high, and deforestation activities could, therefore, have a greater potential for impacting on wider areas of forest.
Filtering : Multimedia Recommendation with Item-and Component-Level A ention

Multimedia content is dominating today’s Web information. e nature of multimedia user-item interactions is 1/0 binary implicit feedback (e.g., photo likes, video views, song downloads, etc.), which can be collected at a larger scale with a much lower cost than explicit feedback (e.g., product ratings). However, the majority of existing collaborative ltering (CF) systems are not well-designed for multimedia recommendation, since they ignore the implicitness in users’ interactions with multimedia content. We argue that, in multimedia recommendation, there exists itemand component-level implicitness which blurs the underlying users’ preferences. e item-level implicitness means that users’ preferences on items (e.g., photos, videos, songs, etc.) are unknown, while the componentlevel implicitness means that inside each item users’ preferences on dierent components (e.g., regions in an image, frames of a video, etc.) are unknown. For example, a “view” on a video does not provide any specic information about how the user likes the video (i.e., item-level) and which parts of the video the user is interested in (i.e., component-level). In this paper, we introduce a novel aention mechanism in CF to address the challenging itemand component-level implicit feedback in multimedia recommendation, dubbed Aentive Collaborative Filtering (ACF). Specically, our aention model is a neural network that consists of two aention modules: the component-level aention module, starting from any content feature extraction network (e.g., CNN for images/videos), which learns to select informative components of multimedia items, and the item-level aention module, which learns to score the item preferences. ACF can be seamlessly incorporated into classic CF models with implicit feedback, such as BPR and SVD++, and eciently trained using SGD. rough extensive experiments on two real-world multimedia Web services: Vine and Pinterest, we show that ACF signicantly outperforms state-of-the-art CF methods. ∗Xiangnan He is the corresponding author. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for prot or commercial advantage and that copies bear this notice and the full citation on the rst page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permied. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specic permission and/or a fee. Request permissions from permissions@acm.org. SIGIR’17, August 7–11, 2017, Shinjuku, Tokyo, Japan. © 2017 ACM. 978-1-4503-5022-8/17/08. $15.00 DOI: hp://dx.doi. org/10.1145/3077136.3080797 CCS CONCEPTS •Information systems →Multimedia information systems;
Adapting Social Neuroscience Measures for Schizophrenia Clinical Trials,

The psychometric properties of 4 paradigms adapted from the social neuroscience literature were evaluated to determine their suitability for use in clinical trials of schizophrenia. This 2-site study (University of California, Los Angeles and University of North Carolina) included 173 clinically stable schizophrenia outpatients and 88 healthy controls. The social cognition battery was administered twice to the schizophrenia group (baseline, 4-week retest) and once to the control group. The 4 paradigms included 2 that assess perception of nonverbal social and action cues (basic biological motion and emotion in biological motion) and 2 that involve higher level inferences about self and others’ mental states (self-referential memory and empathic accuracy). Each paradigm was evaluated on (1) patient vs healthy control group differences, (2) test-retest reliability, (3) utility as a repeated measure, and (4) tolerability. Of the 4 paradigms, empathic accuracy demonstrated the strongest characteristics, including large between-group differences, adequate test-retest reliability (.72), negligible practice effects, and good tolerability ratings. The other paradigms showed weaker psychometric characteristics in their current forms. These findings highlight challenges in adapting social neuroscience paradigms for use in clinical trials.
Edinburgh Research Explorer Causal Machine Learning for Healthcare and Precision Medicine

Causal machine learning (CML) has experienced increasing popularity in health-care. Beyond the inherent capabilities of adding domain knowledge into learning systems, CML provides a complete toolset for investigating how a system would react to an intervention (e.g. outcome given a treatment). Quantifying effects of interventions allows actionable decisions to be made whilst maintaining robustness in the presence of confounders. Here, we explore how causal inference can be incorporated into different aspects of clinical decision support (CDS) systems by using recent advances in machine learning. Throughout this paper, we use Alzheimer’s disease (AD) to create examples for illustrating how CML can be advantageous in clinical scenarios. Furthermore, we discuss important challenges present in healthcare applications such as processing high-dimensional and un-structured data, generalisation to out-of-distribution samples, and temporal relationships, that despite the great effort from the research community remain to be solved. Finally, we review lines of research within causal representation learning, causal discovery and causal reasoning which offer the potential towards addressing the aforementioned challenges.
Tradinnova-ACO : Ant Colony Optimization Metaheuristic applied to Stock-Market Investment

A stock market investor buys and sells stocks in order to obtain the best possible profit. This dealing can be depicted on a graph, on which each node represents the stocks purchased. In this way, this paper proposes the use of ant colony optimization for calculating the best sequence when buying and selling, thereby helping when deciding how to invest.
Implementation of the DMV-based stereo object tracking system

In this paper, a new stereo object tracking system using the disparity motion vector(DMV) is proposed. Here, the DMV is defined as a disparity difference between two time-sequential disparity maps of T-1 and T frames and it can be sequentially estimated from the disparity maps which are extracted from the input stereo sequence. Here, the disparity maps are obtained by using the feature-based matching algorithm, in which the block size and the search range for the feature-based matching are given by Nx×Ny=1×1 and Sx=±32, respectively. And then, the DMV can be estimated by calculating the motion difference between two consecutive disparity maps. Basically, the DMV has a relatively large change of disparity values in the target region because of the target’s movement, whereas it has very low difference values in the background region; there are almost no changes by comparing with the target region. As a result, the DMV can provide us with the motional information of a moving target by showing a large disparity difference in the target area. That is, the dynamic relationship between these disparity vectors of T-1 and T frames seems to be very similar to that between the motion vectors of T-1 and T frames in the sequence of the conventional 2D video images. Accordingly, the target area and its location coordinates can be detected by using these DMV maps. Basing on these locational data of a moving target, the pan/tilt embedded in the stereo camera system can be controlled and as a result, real-time stereo tracking of a moving target can be achieved. From some experiments with 9 frames of the stereo sequence having 256×256 pixels, it is shown that the proposed DMV-based stereo object tracking system can track the moving target with a relatively low error ratio of about 3.02% on average. This good experimental result finally suggests a possibility of implementing the DMV-based stereo object tracking system.
Fault-tolerant Routing based on Fault Infor m ation of Cross-edges in Dual-cubes

In this study, we aim to improve the faulttolerant routing algorithm in dual-cubes. The dual-cube is a topology developed by extending the hypercube. The dualcube can interconnect the same number of nodes as the hypercube with almost the half number of edges. The faulttolerant routing methods in dual-cubes proposed by previous works give priority to the preferred neighbor nodes, and forward the message to one of them. Therefore, in case where it is necessary to forward the message to one of the spare nodes to establish a fault-free path to the destination node, the routing fails. Hence, we tried to improve the performance of the fault-tolerant routing method by modifying it. In our method, each node calculates additional information if its cross-edge is faulty or not, and exchanges the information with its neighbor nodes. We also modified the fault-tolerant routing algorithm so that it can make use of the information regarding the cross-edges of the neighbor nodes. To evaluate our method, we conducted a computer experiment in which we compared it with the methods by previous work from the viewpoints of the ratio of successful routings and the ratio of the detours. As a result, our method showed better performance than the methods by the previous works.
Quantitative Identification of Deformed Coals Based on Logging Response

The contrast between coal core observation and the corresponding interval logging curve has been done,and the logging response characteristics of different coal structure has been researched.It shows that;in south Qinshui area,the logging response of granulated coal and mylonite coal(collectively referred to as tectonic coal) in No.3 coal seam of Shanxi formation and No.15 coal seam of Taiyuan formation all performance characteristics that small compensation density,big compensated sonic,big caliper,which are obviously different from primary structure coal obvious,and cataclastic coal is fall in between.According to the research results,the paper concludes the logging response of coal body structure to the quantitative general form,puts forward the use of coal body structure index as coal body structure to quantitative general index,and illustrates the coal body structure index n of the calculating method and the feasibility of quantitative sentenced to knowledge,and a based on logging response of coal body structure to quantitative general method.