Miracle-dz/UniFAR-FTUs huggingface dataset:
OpenAlerts: A Software System to Evaluate Smart Emergency Alerts and Notifications

Alerts and notifications are increasingly being used in emergency situations. However, notifying users when an alert is not contextually useful results in those users opting out of this mode of alerting. There is a need to extensively evaluate such notifications and understand their utility before adopting them in real-world alert systems. In this paper, we describe OpenAlerts, an open-source software that allows creating and evaluating the impact of simulated alerts and notifications. The proposed system is an extension to a testbed that was used by over two-hundred users across two university campuses to evaluate potential improvements to the US-based Wireless Emergency Alerts. The system allows A/B-type user testing of many potential enhancements including embedding maps, fine geo-targeting of alerts, embedding URLS, and speech-to-text.
Space–Time Sampling for Network Observability

Designing sparse sampling strategies is crucial in having resilient estimation and control in networked systems as they make sampling less expensive and networks more resilient w.r. t where and when samples are collected. It is shown under what conditions taking coarse samples from a network will contain the same amount of information as a finer set of samples. Our goal is to estimate the initial condition of linear time-invariant networks using a set of noisy measurements. The observability condition is reformulated as the frame condition, where one can easily trace the location and time stamps of each sample. We compare the estimation quality of various sampling strategies using estimation measures, which depend on the spectrum of the corresponding frame operators. Using properties of the minimal polynomial of the state matrix, deterministic and randomized methods are suggested to construct observability frames. Intrinsic tradeoffs assert that collecting samples from fewer subsystems dictates taking more samples (in average) per subsystem. Three scalable algorithms are developed to generate sparse space–time sampling strategies with explicit error bounds.
Value-based Search in Execution Space for Mapping Instructions to Programs

Training models to map natural language instructions to programs, given target world supervision only, requires searching for good programs at training time. Search is commonly done using beam search in the space of partial programs or program trees, but as the length of the instructions grows finding a good program becomes difficult. In this work, we propose a search algorithm that uses the target world state, known at training time, to train a critic network that predicts the expected reward of every search state. We then score search states on the beam by interpolating their expected reward with the likelihood of programs represented by the search state. Moreover, we search not in the space of programs but in a more compressed state of program executions, augmented with recent entities and actions. On the SCONE dataset, we show that our algorithm dramatically improves performance on all three domains compared to standard beam search and other baselines.
Traditional Chinese Medicine Injections in the Treatment of Diabetic Foot: A Systematic Review and Meta-Analysis

Context The role of traditional Chinese medicine injections (TCMIs) in diabetic foot (DF) has not been well estimated. Objective To evaluate the clinical effective rate, safety, and the financial cost of TCMIs in treating DF and ulcer wound healing. Methods We searched PubMed, Embase, CENTRAL, China National Knowledge Infrastructure (CNKI), VIP database, and Wanfang database from inception to May 2018 to find all randomized control trials (RCTs) related to TCMIs in DF treatment. The search items were “Traditional Chinese Medicine Injection” AND “Diabetic foot or Diabetic foot ulcer” AND “random”. Study Selection and Synthesis Only RCTs of TCMIs combined conventional therapies versus conventional therapies and that can be quantitatively synthesized were included. Finally, 17 studies and 1294 participants were included after extraction. Two investigators independently extracted and analyzed the data using RevMan5.3 software. Results The overall clinical effective rate of TCMI groups is higher than that of control groups [RR=1.27, 95CI % (1.20, 1.34), P<0.00001] based on fixed effect model analysis. Regarding motor nerve conduction velocity of median nerve and peroneal nerve, TCMI group showed a significant improvement (MD=3.84[2.28, 5.41], P<0.00001; MD=2.89[0.63, 5.15], P=0.01). Regarding plasma viscosity TCMI group showed a statistically difference (MD=0.27[0.04, 0.49], P=0.02). In terms of blood viscosity at high shear rate, there was an improvement of TCMI group (MD=0.36[0.05, 0.67], P=0.02). However, sensory nerve conduction velocity of peroneal nerve and median nerve showed a contradiction to motor nerve conduction velocity, respectively (MD=2.59[-1.69, 6.87], p=0.24; MD=2.73[-0.96, 6.43], P=0.15). Conclusion The data of this study shows that TCMIs can bring benefits to patients with diabetic foot. However, due to low methodological quality of included RCTs, more rigorous designed RCTs with large sample size are recommended to provide more high-quality evidence.
On Relating Technical, Social Factors, and the Introduction of Bugs

As collaborative coding environments make it easier to contribute to software projects, the number of developers involved in these projects keeps increasing. This increase makes it more difficult for code reviewers to deal with buggy contributions. Collaborative environments like GitHub provide a rich source of data on developers' contributions. Such data can be used to extract information about developers regarding technical (e.g., their experience) and social (e.g., their interactions) factors. Recent studies analyzed the influence of these factors on different activities of software development. However, there is still room for improvement on the relation between these factors and the introduction of bugs. We present a broader study, including 8 projects from different domains and 6,537 bug reports, on relating five technical, three social factors, and the introduction of bugs. The results indicate that technical and social factors can discriminate between buggy and clean commits. But, the technical factors are more determining than social ones. Particularly, the developers' habits of not following technical contribution norms and the developer's commit bugginess are associated with an increase on commit bugginess. On the other hand, project's establishment, ownership level of developers' commit, and social influence are related to a lower chance of introducing bugs.
Prestasi Akademik Siswa Ditinjau Dari Prokrastinasi Dan Persepsi Anak Pada Pola Asuh Orang Tua Di Smk Muhammadiyah 3 YOGYAKARTA

ABSTRACT The objective of the study is to determinethe influence of procrastination and childperceptionon parenting to the student achievementat 3thSMK Muhammadiyah Yogyakarta. As additional, the syle of parenting was assessed in improving student academic achievement. The subjects of the research are the students in the grade eleven at 3thSMK Muhammadiyah Yogyakarta. The method used in this research is regression analysis. The results of this research shows the relation of procrastination and the perception of the child on parenting to the student achivement was not significant in the meanwhile the influence of procrastination (p: 0, 000) and child perception on democratist parenting (p:0,031) to the student achivement showed significant result. The determination coefficient (R2) of this research was 0.622. As concludedon the procrastination and child perception on democratic parenting contributed amoung 62,2% of the influence to the academic achievement. Keyword : Procrastination,perception of the child on parenting, academic achievement.
Reconstruction of Travel Movies Using Emotional Arcs

An increasing number of travelers edit and share movies while traveling, owing to the widespread use of smartphones. Whereas short movies have garnered much attention, manually concatenating highlights and commentaries is often considered complicated for producing good movies. Thus, we propose a method for creating impressive short movies by reconstructing travel shorts based on their emotional arcs, ostensibly representing the emotional trajectories of stories in a time series. Our method extracts adjectives from travel movies utterances, calculates emotional scores using a sentiment dictionary, and rearranges scenes based on emotional arc templates. The templates consist of 194 popular cinema screenplays. We conduct a user study for which travelers evaluate short travel movies reconstructed by our method. Their impressions were changed according to templates of the emotional arcs.
MARTO: Dynamic Control of Learning Materials Based on Learners State

E-learning using digital learning materials is widespread due to freedom in choosing time and space for learning. Often, these learning materials are provided uniformly regardless of the learners' knowledge and the attention. However, to maximize the power of digitized materials, the presented materials can be dynamically adapted to the learner's state of learning, in particular, the degree of concentration. Based on the background, we propose MARTO, a system to change the content of learning based on the concentration level of the learner using an EEG. We have also examined how properly the concentration level is obtained with EEG by comparing it with the result obtained with eye gaze movement.
Boosted Sparse and Low-Rank Tensor Regression

We propose a sparse and low-rank tensor regression model to relate a univariate outcome to a feature tensor, in which each unit-rank tensor from the CP decomposition of the coefficient tensor is assumed to be sparse. This structure is both parsimonious and highly interpretable, as it implies that the outcome is related to the features through a few distinct pathways, each of which may only involve subsets of feature dimensions. We take a divide-and-conquer strategy to simplify the task into a set of sparse unit-rank tensor regression problems. To make the computation efficient and scalable, for the unit-rank tensor regression, we propose a stagewise estimation procedure to efficiently trace out its entire solution path. We show that as the step size goes to zero, the stagewise solution paths converge exactly to those of the corresponding regularized regression. The superior performance of our approach is demonstrated on various real-world and synthetic examples.
Book Section: Essay and Review: Signature Killers

In the past 20 years there has been a plethora of books on forensic psychiatry/psychology, most attempting to explain how different mental disorders relate to various legal tests and standards. The psychiatric diagnosis per se, however, rarely explains the criminal act, except in cases where the behavior was a direct outgrowth of a major mental illness, significant retardation, or the relatively infrequent cases having an organic etiology (Revitch & Schlesinger, 1981). Thus the forensic practitioner needs broad knowledge not only of major mental illnesses but also of the psychopathology and psychodynamics of crime, particularly when asked to address predictive, investigative and dispositional issues (Sadoff, 1975).
Drinking Risk Level Reductions Associated with Improvements in Physical Health and Quality of Life Among Individuals with Alcohol Use Disorder

BACKGROUND Abstinence and no heavy drinking days are currently the only Food and Drug Administration-approved end points in clinical trials for alcohol use disorder (AUD). Many individuals who fail to meet these criteria may substantially reduce their drinking during treatment, and most individuals with AUD prefer drinking reduction goals. One- and two-level reductions in World Health Organization (WHO) drinking risk levels have been proposed as alternative end points that reflect reduced drinking and are associated with reductions in drinking consequences, improvements in mental health, and reduced risk of developing alcohol dependence. The current study examined the association between WHO drinking risk level reductions and improvements in physical health and quality of life in a sample of individuals with alcohol dependence. METHODS Secondary data analysis of individuals with alcohol dependence (n = 1,142) enrolled in the longitudinal, prospective COMBINE study, a multi site randomized placebo-controlled clinical trial, examining the association between reductions in WHO drinking risk levels and change in blood pressure, liver enzyme levels, and self-reported quality of life following treatment for alcohol dependence. RESULTS One- and two-level reductions in WHO drinking risk level during treatment were associated with significant reductions in systolic blood pressure (p < 0.001), improvements in liver enzyme levels (all p < 0.01), and significantly better quality of life (p < 0.001). CONCLUSIONS One- and two-level reductions in WHO drinking risk levels predicted significant improvements in markers of physical health and quality of life, suggesting that the WHO drinking risk level reduction could be a meaningful surrogate marker of improvements in how a person "feels and functions" following treatment for alcohol dependence. The WHO drinking risk levels could be useful in medical practice for identifying drinking reduction targets that correspond with clinically significant improvements in health and quality of life.
Odanacatib Restores Trabecular Bone of Skeletally Mature Female Rabbits With Osteopenia but Induces Brittleness of Cortical Bone: A Comparative Study of the Investigational Drug With PTH, Estrogen, and Alendronate

A reader of the JBMR informed the editors about concern regarding a possible duplicate image in Fig. 7E in Khan and colleagues. The editors sought an explanation from the authors for this possible panel duplication. The authors acknowledge an error made in assembling the data from the apoptosis experiment in Fig. 7 in which one panel was erroneously included twice and the bright field images were omitted. This erratum corrects wording in the Materials and Methods section and in Fig. 7. The authors regret the error in the 10nM representative panel.
Resolving “orphaned” non-specific structures using machine learning and natural language processing methods

Abstract Scholarly publications of biodiversity literature contain a vast amount of information in human readable format. The detailed morphological descriptions in these publications contain rich information that can be extracted to facilitate analysis and computational biology research. However, the idiosyncrasies of morphological descriptions still pose a number of challenges to machines. In this work, we demonstrate the use of two different approaches to resolve meronym (i.e. part-of) relations between anatomical parts and their anchor organs, including a syntactic rule-based approach and a SVM-based (support vector machine) method. Both methods made use of domain ontologies. We compared the two approaches with two other baseline methods and the evaluation results show the syntactic methods (92.1% F1 score) outperformed the SVM methods (80.7% F1 score) and the part-of ontologies were valuable knowledge sources for the task. It is notable that the mistakes made by the two approaches rarely overlapped. Additional tests will be conducted on the development version of the Explorer of Taxon Concepts toolkit before we make the functionality publicly available. Meanwhile, we will further investigate and leverage the complementary nature of the two approaches to further drive down the error rate, as in practical application, even a 1% error rate could lead to hundreds of errors.
The Structure of Small Beta Barrels

The small beta barrel is a protein structural domain, highly conserved throughout evolution and hence exhibits a broad diversity of functions. Here we undertake a comprehensive review of the structural features of this domain. We begin with what characterizes the structure and the variable nomenclature that has been used to describe it. We then go on to explore the anatomy of the structure and how functional diversity is achieved, including through establishing a variety of multimeric states, which, if misformed, contribute to disease states. We conclude with work following from such a comprehensive structural study.
Hybrid and Convolutional Neural Networks for Locomotion Recognition

This paper explores the relevance of an approach based exclusively on deep neural networks for locomotion recognition. This work is done within the Sussex-Huawei Locomotion-Transportation (SHL) recognition challenge as team Power of Things. Provided data used during the experiments is part of the SHL dataset for which we emphasize the adaptability to different applications of the ubiquitous computing. This quality emerges from the broad spectrum of modalities that this dataset encompasses, they are 16 in total. More than 500 different convolutional and hybrid architectures are evaluated, and a Bayesian optimization procedure is used for hyper-parameters space exploration. The influence of these hyper-parameters on performances is analyzed using the fANOVA framework. Best models achieve a recognition rate of about 92% measured by the f1 score.
Structure, subunit organization and behavior of the asymmetric Type IIT restriction endonuclease BbvCI

Abstract BbvCI, a Type IIT restriction endonuclease, recognizes and cleaves the seven base pair sequence 5′-CCTCAGC-3′, generating 3-base, 5′-overhangs. BbvCI is composed of two protein subunits, each containing one catalytic site. Either site can be inactivated by mutation resulting in enzyme variants that nick DNA in a strand-specific manner. Here we demonstrate that the holoenzyme is labile, with the R1 subunit dissociating at low pH. Crystallization of the R2 subunit under such conditions revealed an elongated dimer with the two catalytic sites located on opposite sides. Subsequent crystallization at physiological pH revealed a tetramer comprising two copies of each subunit, with a pair of deep clefts each containing two catalytic sites appropriately positioned and oriented for DNA cleavage. This domain organization was further validated with single-chain protein constructs in which the two enzyme subunits were tethered via peptide linkers of variable length. We were unable to crystallize a DNA-bound complex; however, structural similarity to previously crystallized restriction endonucleases facilitated creation of an energy-minimized model bound to DNA, and identification of candidate residues responsible for target recognition. Mutation of residues predicted to recognize the central C:G base pair resulted in an altered enzyme that recognizes and cleaves CCTNAGC (N = any base).
The relationship between burnout, personality traits, and medical specialty. A national study among Dutch residents

Abstract Purpose: To examine the associations between residents’ personality traits, type of specialty, and symptoms of burnout. Method: A cross-sectional online survey among Dutch residents was conducted (see Supplementary Material). The 20-item Dutch translation of the Maslach Burnout Inventory was used to ascertain burnout. Personality traits were assessed with the 44-item Dutch Big Five Inventory. Logistic regression analyses, including all five personality traits, were used to assess associations with burnout. Analyses were stratified by specialties. Results: One thousand two hundred thirty one residents participated, 185 (15.0%) of whom met the criteria for burnout. Neuroticism was significantly associated with resident burnout in all specialties, more strongly in supportive (odds ratio (OR) 6.19, 95% CI 2.12–18.12) and surgical (OR 4.37, 95% CI 1.76–10.86) than in medical residents (OR 1.99, 95% CI 1.22–3.24). Extraversion was significantly associated with less burnout in surgical residents (OR 0.26, 95% CI 0.13–0.58). These findings remained highly significant after controlling for gender, overtime, autonomy at work, satisfaction between work and private life, and the perceived quality of the learning environment. Conclusions: Burnout risk was associated with personality traits in residents. Consistently, residents scoring high on neuroticism reported more burnout. Extraverted surgical residents were less susceptible to burnout. Residents scoring high on neuroticism may require more intense monitoring during their training years.
Deep Convolutional Bidirectional LSTM Based Transportation Mode Recognition

Traditional machine learning approaches for recognizing modes of transportation rely heavily on hand-crafted feature extraction methods which require domain knowledge. So, we propose a hybrid deep learning model: Deep Convolutional Bidirectional-LSTM (DCBL) which combines convolutional and bidirectional LSTM layers and is trained directly on raw sensor data to predict the transportation modes. We compare our model to the traditional machine learning approaches of training Support Vector Machines and Multilayer Perceptron models on extracted features. In our experiments, DCBL performs better than the feature selection methods in terms of accuracy and simplifies the data processing pipeline. The models are trained on the Sussex-Huawei Locomotion-Transportation (SHL) dataset. The submission of our team, Vahan, to SHL recognition challenge uses an ensemble of DCBL models trained on raw data using the different combination of sensors and window sizes and achieved an F1-score of 0.96 on our test data.
Virtual energy storage from TCLs using QoS preserving local randomized control

We propose a control architecture for distributed coordination of a collection of on/off TCLs (thermostatically controlled loads), such as residential air conditioners, to provide the same service to the power grid as a large battery. This involves a collection of loads to coordinate their on/off decisions so that the aggregate power consumption profile tracks a grid-supplied reference. A key constraint is to maintain each consumer's quality of service (QoS). Recent works have proposed randomization at the loads. Thermostats at the loads are replaced by a randomized controller, and the grid broadcasts a scalar to all loads, which tunes the probability of turning on or off at each load depending on its state. In this paper we propose a modification of a previous design by Meyn and Bušić. The previous design by Meyn and Bušić ensures that the indoor temperature remains within a pre-specified bound, but other QoS metrics, especially the frequency of turning on and off was not limited. The controller we propose can be tuned to reduce the cycling rate of a TCL to any desired degree. The proposed design is compared against the design by Meyn and Bušić and another well cited design in the literature on control of TCL populations, by Mathieu et al. We show through simulations that the proposed controller is able to reduce the cycling of individual ACs compared to the previous designs with little loss in tracking of the grid-supplied reference signal.