Miracle-dz/UniFAR-FTUs huggingface dataset:
Sex Offender Supervision: Communication, Training, and Mutual Respect Are Necessary for Effective Collaboration Between Probation Officers and Therapists

Developed with the goal of preventing recidivism, contemporary sex offender supervision models focus on collaboration between probation officers and therapists. This exploratory study used focus groups to examine the working relationships between probation officers and therapists from two large U.S. urban probation departments. Overall, both probation officers and therapists were quite positive about their working relationships; they valued each others’ roles and agreed that regular, accurate, and timely communication occurred frequently. Not all relationships, however, were effective. Several probation officers and therapists expressed dissatisfaction with poor communication, conflicts between the goals of therapy and probation, a lack of resources, and deficits in the policies they needed to adequately implement components of their supervision model (the containment model). Our findings suggest ways to structure sexual offender supervision that integrate the distinct orientations of probation officers and therapists into a collaboration that promotes public safety and work well for all.
Novel approaches to study coronary vasculature development in mice

Background: Coronary artery development is an intensely studied field. Mice are a popular genetic model for developmental studies, but there is no widely accepted protocol for high‐throughput, high‐resolution imaging of their developmental and adult coronary artery anatomy. Results: Using tissue‐clearing protocols and confocal microscopy, we have analyzed embryonic and juvenile mouse hearts in Cx40:GFP knock‐in models with a special focus on septal artery development. We found that the septal artery, which supplies the interventricular septum, was initially formed as an arterial plexus that connected to both the left and right coronary arteries. During development, the plexus was remodeled into a single tube, which then remained connected only to the right coronary artery. Since optical imaging became limited at postnatal stages, it was supplemented with injection techniques using India ink and Microfil; the latter was subsequently analyzed with micro‐CT to visualize the anatomy of coronary vessels in 3D. Conclusions: The techniques described here enable us to study the finer details of coronary artery development in mice and can, therefore, be implemented to study the pathogenesis of coronary malformations in various mouse models. Developmental Dynamics 247:1018‐1027, 2018. © 2018 Wiley Periodicals, Inc.
A Convolutional Siamese Network for Developing Similarity Knowledge in the SelfBACK Dataset

The Siamese Neural Network (SNN) is a neural network architecture capable of learning similarity knowledge between cases in a case base by receiving pairs of cases and analysing the differences between their features to map them to a multi-dimensional feature space. This paper demonstrates the development of a Convolutional Siamese Network (CSN) for the purpose of case similarity knowledge generation on the SelfBACK dataset. We also demonstrate a CSN is capable of performing classification on the SelfBACK dataset to an accuracy which is comparable with a standard Convolutional Neural Network.
Antisocial Personality Disorder Conceptualization and Treatment

Antisocial personality disorder exacts a costly toll on society and poses many unique challenges for treatment. This chapter reviews historic conceptions of antisocial personality disorder and the related but distinct condition of psychopathy. The systematic co-occurrence of antisocial personality disorder with other diagnostic conditions (e.g., alcoholism, other forms of drug dependence) is discussed, and research is reviewed indicating that disorders of these types (termed “externalizing” disorders) share a common underlying dispositional vulnerability. This is followed by a review of current diagnostic criteria for antisocial personality disorder and changes to the diagnosis that have been proposed for the DSM-5, highlighting parallels to the literature (including changes proposed for the DSM-5) on conduct disorder, the childhood precursor to adult antisocial personality disorder. The chapter concludes with a discussion of currently available methods of treatment for antisocial populations, emphasizing the best-supported cognitive behavioral approaches, and future directions for treatment based on recent developments in the literature reviewed. Particular emphasis is placed on ways in which interventions could be tailored to meet the unique treatment needs of phenotypically distinct subgroups of antisocial individuals, and how emerging knowledge of the neurobiological underpinnings of antisocial personality disorder and psychopathy might be applied to developing alternative methods of treatment such as pharmacologically based or neuro-reprogramming (e.g., brain-process oriented training, or direct brain biofeedback) approaches that directly target cognitive and affective processing deficits common in these populations.
Transferring digit classifier's features to a traffic sign detector

Traffic sign detection system is one of the most important part of self-driving cars. It is hard to correctly detect and classify the traffic signs because of the small scale property and the complexity of road environments. In this work, we propose a novel framework of feature transferring for traffic sign detection. We improve the traffic sign detection performance in the wild by transferring digit classifier's features to a detector. Specifically, we train a convolutional neural network(CNN) classifier on a digit training set, in which each image is cropped from the traffic sign detection dataset, and then use the classifier's high-level features as an additional supervision to the detector. With the help of the additional supervision, the detector can learn a better representation of traffic sign. Extensive experiments validate the effectiveness of our approach. Our method achieves state-of-the-art performance in traffic sign detection task on the largest traffic sign detection dataset, Tsinghua-Tencent 100K.
Digital Commons @ Trinity Digital Commons @ Trinity

Background: Although health care organizations such as hospitals and clinics have widely embraced social media as a means to educate the community on health topics and increase patient loyalty and satisfaction, little is known about the content these organizations actually share when using social media channels. Objective: This study aimed to explore the types of content US hospitals post on their Facebook pages and how hospitals’ Facebook activities differ with regard to content types. Methods: We collected and thematically analyzed more than 1700 Facebook posts made over a 3-month period by 17 US hospitals. During the first phase, the 2 researchers coded a set of 159 posts and created an initial thematic web of content. During the second phase, the researchers coded the remaining posts and then revised, refined, and validated the initial web of content accordingly. Coding consensus was achieved on 1184 of the 1548 analyzable sampled posts (76.49%). Results: We identified a list of 13 unique health social media post themes and classified those themes into 3 thematic groups that included announcing , sharing , and recognizing activities. The most frequently used theme was sharing health information , which appeared in 35.81% (424/1184) of the posts analyzed. Such posts sought to provide health tips and advice to community members. Recognizing special days and recognizing employees were the second and third most frequently used themes, respectively, with 14.95% (177/1184) and 11.82% (140/1184) of the posts containing those themes. The frequency of these themes was surprising as the content was geared more toward stakeholders internal to the organization, although most previous literature has focused on social media as a tool to connect with external stakeholders. In addition, we found many of the posts involved more than one theme, and selected sets of themes co-occurred frequently. For example, 25.4% (45/177) of the posts recognizing special days also included content to share health information, and approximately 38% (32/85) of the posts announcing research activities also included content to share health information . Finally, we found similarities and differences between the sampled hospitals in terms of the types of content they posted more frequently on their Facebook pages. Conclusions: Hospitals use Facebook as an inexpensive way to educate people on health and wellness topics and to communicate different types of information and news to the public audience. Hospitals and clinics that are expanding their social media activities or are starting to embark on social media strategies can use the results of this study to better formulate their activities on Facebook. (J Med Internet Res 2018;20(5):e190) doi: 10.2196/jmir.9549
Analysis of One Dynamic Multi-Keyword Ranked Search Scheme over Encrypted Cloud Data

In 2016, Xia et al. have proposed a scheme for privacy-preserving multi-keyword ranked search over encrypted cloud data [IEEE TPDS, 2016, 340-352]. In this note we show that Xia et al.’s scheme is ﬂawed because the introduced relevance scores do not indicate the true Euclidean distances between the index vectors and the query vector. The scheme has not developed a proper procedure for distance comparison which should be compatible with the technique of Scalar-Product-Preserving Encryption. In the scheme the returned documents are not indeed re-lated to the queried keywords. We also present an improvement using the technique developed by Wong et al. ’s work [ACM SIGMOD 2009].
Length Aware Hybrid Approach to Sentiment Analysis

Sentiment analysis is a popular research problem to find out within the natural language processing that is dealing with identifying the sentiments or mood of people’s towards elements such as product, text, services and the technology. However, there are few researches conducted on the sentiment analysis of technical article review, so to overcome this deficiency conducts the sentiment analysis over the technical article review and classifying the sentence by overall sentiments that is representing the review is positive or negative. The paper presents the combination of SVM and KNN and find out how much given article sound technically good. The proposed technique is compared with other existing techniques and results shows that the proposed technique is better as compared to the other technique.
Mood Prediction On Tweets Using Classification Algorithm

: Data mining is a technique which offers the computer algorithm to compute patterns and find the category of data using classification and clustering. In data mining classification is performed with supervised learning and unsupervised learning. Selection of algorithm depends upon the type and behavior of data. The data can be as structured and unstructured. Structured data is that which reside in fixed field. It is first depends on creating data model. Unstructured data refers to information that does not have a predefined data model or not organized in a predefined manner. In data mining text mining has become an important research area. Text mining is a discovery of new, previously unknown information by automatically extracting information from different resources [5]. The various applications in text mining are information retrieval, machine learning, data mining, and statics and computation semantics. In form of text data most of the information is stored. Now a days in a direction of multiple language support most of the research is progressing. This system is capable to group the similar data from different kinds of language source according to their original semantic and also being able to gain information across language [2]. In the presented work the identified twitter data set is used to perform text analysis. Therefore the entire input data samples are required to classify in two classes namely positive and negative. Therefore a binary classifier namely ID3 decision tree and their improved variant is utilized for analysis and performing the classification task. Before classification of text data there is need to improve the quality of data. Therefore the raw text data is first pre-processed then tagged according to the lexical means. After tagging on the original text data the classification algorithms are trained and make use to classify the text according to their sentiments. The implementation of the improved ID3 text classification technique and their performance is evaluated in terms of their accuracy and the error rate. These parameters show how accurately the text patterns are identified using the data mining technique. Additionally for finding their performance in terms of their efficiency the time and space complexity is also measured that shows the effective classification with less consumption.
What to Choose? The Value of Label Claims to Produce Consumers

This paper addresses three key empirical questions related to health, nutrient, and process claims on front-label packaging; namely, 1) How do consumers value alternative claims on product and process-based attributes for fresh produce; 2) Are these values additively separable; and 3) To what degree is there heterogeneity between consumers on these values? We use a hypothetical choice experiment on red leaf lettuce attribute bundles, and estimate several logit models (MNL and ML) that provide estimates of marginal utilities (and with the inclusion of varying prices, marginal values) of various attributes related to general health claims, specific nutrition and health claims, certification logos related to health and nutrition currently found in the marketplace, as well as certified organic claims (relative to the conventional reference group). The results showed that consumers do distinguish between competing claims and logos, though the impacts are not always as expected, likely due to the information set used at the time of the choice. We found some evidence of attribute bundling between the health claims and the familiar Five-a-day program logo, and between organic production and a claim regarding vitamin C content. Finally, we found that use of the unconditional distributions (relative to the conditional)in a ML model overstates the degree of preference heterogeneity across the sample and overstates the magnitude of the marginal effects of the random parameters. This may create misleading impressions regarding the existence and size of specialized niche markets, the response of consumers to varying health, nutrition, or process claims, and/or the response of consumers to the introduction of new products with these (or similar) claims.
Does National Identity Collide with Economic Interest? Public Support for Northeast Asian Economic Integration in South Korea

What are the determinants of individual preferences for regional economic integration? This paper explores how national identity and economic interest are intertwined in forming preferences for regional economic integration. We argue that national identity tends to reinforce support for regional economic integration. However, national identity influences the unskilled more than the skilled, leading to the seemingly irrational formation of preferences among unskilled labor, even when those preferences are against their economic interest. Strong national identity often prompts unskilled labor to support regional economic integration when it may result in more costs than benefits for them. We empirically test these arguments using the Korean sample from the 2006 Chicago Council on Global Affairs-East Asia Institute public opinion survey on international relations. Our statistical analysis finds supporting evidence for our arguments about the relationship between national identity, economic interest, and preferences for regional economic integration.
The UIR Uncertainty Corpus for Chinese: Annotating Chinese Microblog Corpus for Uncertainty Identification from Social Media

Uncertainty identification is an important semantic processing task, which is critical to the quality of information in terms of factuality in many NLP techniques and applications, such as question answering, information extraction, and so on. Especially in social media, the factuality becomes a primary concern, because the social media texts are usually written wildly. The lack of open uncertainty corpus for Chinese social media contexts bring limitations for many social media oriented applications. In this work, we present the first open uncertainty corpus of microblogs in Chinese, namely, the UIR Uncertainty Corpus (UUC). At current stage, we annotated 40,168 Chinese microblogs from Sina Microblog. The schema of CoNLL 2010 have been adapted, where the corpus contains annotations at each microblog level for uncertainty and 6 sub-classes with 11,071 microblogs under uncertainty. To adapt to the characteristics of social media, we identify the uncertainty based on the contextual uncertain semantics rather than the traditional cue-phrases, and the sub-class could provide more information for research on handing uncertainty in social media texts. The Kappa value indicated that our annotation results were substantially reliable.
Lingmotif-lex: a Wide-coverage, State-of-the-art Lexicon for Sentiment Analysis

We present Lingmotif-lex , a new, wide-coverage, domain-neutral lexicon for sentiment analysis in English. We describe the creation process of this resource, its assumptions, format, and valence system. Unlike most sentiment lexicons currently available, Lingmotif-lex places strong emphasis on multi-word expressions, and has been manually curated to be as accurate, unambiguous, and comprehensive as possible. Also unlike existing available resources, Lingmotif-lex comprises a comprehensive set of contextual valence shifters (CVS) that account for valence modiﬁcation by context. Formal evaluation is provided by testing it on two publicly available sentiment analysis datasets, and comparing it with other English sentiment lexicons available, which we adapted to make this comparison as fair as possible. We show how Lingmotif-lex achieves signiﬁcantly better performance than these lexicons across both datasets.
Integrated? Sustainable Communication and the Dominant Social Paradigm: Can They Be

Abstract. The relationship between marketing and the environment has been examined since the early 1970s but began to focus on advertising in the early 1990s.Environmentally related ads were frequently analyzed for content, and then a frame-work for green advertising was developed, expanding the domain of inquiry to thelevel of paradigms. This article expands on both of these areas of research by examin-ing both green advertising and sustainable communication and positioning themwithin the dominant social paradigm of western industrial society. It is argued thatachieving sustainable consumption is problematic for both areas because their goalcontradicts the basic elements of the paradigm. For meaningful change to occur overtime, it is necessary to develop the theory of sustainable communication further andexpand knowledge of the functioning of the dominant social paradigm of westernindustrial societies. Key Words • capitalism • dominant social paradigm • greenadvertising • liberalism •
Dynamically Adjusting the Mining Capacity in Cryptocurrency with Binary Blockchain

Many cryptocurrencies rely on Blockchain for its operation. Blockchain serves as a public ledger where all the completed transactions can be looked up. To place transactions in the Blockchain, a mining operation must be performed. However, due to a limited mining capacity, the transaction confirmation time is increasing. To mitigate this problem many ideas have been proposed, but they all come with own challenges. We propose a novel parallel mining method that can adjust the mining capacity dynamically depending on the congestion level. It does not require an increase in the block size or a reduction of the block confirmation time. The proposed scheme can increase the number of parallel blockchains when the mining congestion is experienced, which is especially effective under DDoS attack situation. We describe how and when the Blockchain is split or merged, how to solve the imbalanced mining problem, and how to adjust the difficulty levels and rewards. We then show the simulation results comparing the performance of binary blockchain and the traditional single blockchain.
The trends and risk factors to predict adverse outcomes in gestational diabetes mellitus: a 10-year experience from 2006 to 2015 in a single tertiary center

Objective To investigate the change of maternal characteristics, delivery and neonatal outcomes in gestational diabetes mellitus (GDM) over recent 10 years and to identify the risk factors associated with adverse outcome. Methods Consecutive GDM patients (n=947) delivered in our institution were included. Research period was arbitrarily divided into 2 periods (period 1: from 2006 to 2010, period 2: from 2011 to 2015). Multiple pregnancies or preexisting diabetes were excluded. Maternal baseline characteristics, delivery and neonatal outcomes were reviewed. Fetal biometric findings by prenatal ultrasonography were collected. Adverse pregnancy outcome (APO) was defined by the presence of one of the followings; shoulder dystocia, neonatal macrosomia (>4 kg), neonatal hypoglycemia (<35 mg/dL), respiratory distress syndrome (RDS), and admission to the neonatal intensive care unit (NICU) in term pregnancy. Results Period 2 was associated with older maternal age (34 vs. 33, P<0.001) and higher proportion of GDM A2 compared to period 1 (30.9% vs. 23.0%, P=0.009). By univariate analysis, APO was associated with increased body mass index (BMI) at pre-pregnancy (23.4 kg/m2 vs. 21.8 kg/m2, P=0.001) or delivery (27.9 kg/m2 vs. 25.8 kg/m2, P<0.001), higher HbA1c at diagnosis (5.6% vs. 5.3%, P<0.001) or delivery (5.8% vs. 5.5%, P=0.044), and larger fetal biometric findings (abdominal circumference [AC] and estimated fetal weight, P=0.029 and P=0.007, respectively). Multivariate analysis showed pre-pregnancy BMI (odds ratio [OR], 1.101; 90% confidence interval [CI], 1.028–1.180) and fetal AC (OR, 1.218; 90% CI, 1.012–1.466) were independently associated with adverse outcomes. Conclusion Our study demonstrated the trends and relevant factors associated with the adverse outcomes.
Collecting Code-Switched Data from Social Media

We address the problem of mining code-switched data from the web, where code-switching is deﬁned as the tendency of bilinguals to switch between their multiple languages both across and within utterances. We propose a method that identiﬁes data as code-switched in languages L 1 and L 2 when a language classiﬁer labels the document as language L 1 but the document also contains words that can only belong to L 2 . We apply our method to Twitter data and collect a set of more than 43,000 tweets. We obtain language identiﬁers for a subset of 8,000 tweets using crowd-sourcing with high inter-annotator agreement and accuracy. We validate our Twitter corpus by comparing it to the Spanish-English corpus of code-switched tweets collected for the EMNLP 2016 Shared Task for Language Identiﬁcation, in terms of code-switching rates, language composition and amount of code-switch types found in both datasets. We then trained language taggers on both corpora and show that a tagger trained on the EMNLP corpus exhibits a considerable drop in accuracy when tested on the new corpus and a tagger trained on our new corpus achieves very high accuracy when tested on both corpora.