Miracle-dz/UniFAR-FTUs huggingface dataset:
Interviewing and Diagnostic Exercises for Clinical and Counseling Skills Building

Contents: Preface for Instructors/Supervisors. Preface for Students/Trainees. Types of Interviewing Skills Indexed by Chapter: Table 1. Diagnosis Indexed by Chapter: Table 2. Conceptual Issues Indexed by Chapter: Table 3. Part I: Introduction. Interviewing Skills Hightlighted in the Text. Highlighted Diagnostic Practice. Part II: Adult Profiles for Use in Individual Sessions. Preface to Part II. Case of Monisha: Presenting Issues--College Adjustment, Academic Pressure. Case of Jie: Presenting Issues--School Performance, Culture. Case of Brenda: Issues--Parenting Young Children, Identity Shift. Case of Aaron: Presenting Issues--Hallucinations, Substance Abuse. Case of Mary: Presenting Issues--Depression, Anxiety. Case of Mark: Issues--Survival Guilt, Career Confusion. Case of Sarah: Issues--Husband With Alzheimer's Disease, Family Pressure. Case of David: Presenting Issues--Substance Abuse, Employment. Case of Lisa: Presenting Issues--Marital Difficulties, Life Changes. Case of Gary: Presenting Issues--Aggression, Substance Abuse. Part III: Child and Teen Profiles for Use in Individual Sessions. Preface to Part III. Case of Cynthia: Issues--Eating Disorder, Emerging Sexuality. Case of Jeffrey: Issues--Social Alienation, School Failure. Case of Melissa: Presenting Issues--Divorce, Shared Custody. Case of Edward: Presenting Issues--Single-Parent Family, Acculturation. Case of Raoul: Presenting Issues--Racial Prejudice, Substance Use. Case of Erica: Presenting Issues--Bereavement, Behavior Problems. Case of Joseph: Presenting Issues--Abandonment, Agression. Case of Sabina: Presenting Issues--Acculturation Conflicts, Emancipation. Case of Alex: Presenting Issues--Neglect, Behavior Problems. Case of Cathy: Presenting Issues--Sexual Abuse, Abandonment. Appendix: Interviewing Skills Worksheets.
Collaborative Filtering with the Trace Norm: Learning, Bounding, and Transducing

Trace-norm regularization is a widely-used and successful approach for collaborative ltering and matrix completion. However, its theoretical understanding is surprisingly weak, and despite previous attempts, there are no distribution-free, non-trivial learning guarantees currently known. In this paper, we bridge this gap by providing such guarantees, under mild assumptions which correspond to collaborative ltering as performed in practice. In fact, we claim that previous diculties partially stemmed from a mismatch between the standard learning-theoretic modeling of collaborative ltering, and its practical application. Our results also shed some light on the issue of collaborative ltering with bounded models, which enforce predictions to lie within a certain range. In particular, we provide experimental and theoretical evidence that such models lead to a modest yet signicant improvement.
Mining Twitter Data for Influenza Detection and Surveillance

Twitter — a social media platform — has gained phenomenal popularity among researchers who have explored its massive volumes of data to offer meaningful insights into many aspects of modern life. Twitter has also drawn great interest from public health community to answer many health-related questions regarding the detection and spread of certain diseases. However, despite the growing popularity of Twitter as an influenza detection source among researchers, healthcare officials do not seem to be as intrigued by the opportunities that social media offers for detecting and monitoring diseases. In this paper, we demonstrate that 1) Twitter messages (tweets) can be reliably classified based on influenza related keywords; 2) the spread of influenza can be predicted with high accuracy; and, 3) there is a way to monitor the spread of influenza in selected cities in real-time. We propose an approach to efficiently mine and extract data from Twitter streams, reliably classify tweets based on their sentiment, and visualize data via a real-time interactive map. Our study benefits not only aspiring researchers who are interested in conducting a study involving the analysis of Twitter data but also health sectors officials who are encouraged to incorporate the analysis of vast information from social media data sources, in particular, Twitter.
Characteristic Pattern of Genetic Aberrations in Ovarian Granulosa Cell Tumors

The cytogenetic abnormalities of granulosa cell tumors (GCT) of the ovary are only partially known. Up to now, mainly numerical chromosomal aberrations have been described. Therefore we performed a comprehensive study on paraffin-embedded material of 20 GCT (17 adult, 3 juvenile; patient age between 16 and 78 y) combining comparative genomic hybridization (CGH); fluorescence in situ hybridization (FISH) using DNA-specific probes for chromosome 12, 17, 22, and X; DNA cytometry; and immunohistochemistry (inhibin, p53, Ki67). By DNA cytometry, 16 of 20 tumors (80%) were diploid. However, 6 of 16 diploid tumors (37%) showed aberrations by FISH. FISH revealed monosomy 22 in 8/18 cases (40%); trisomy 12 in 5/20 (25%); monosomy X in 2/20 (10%); and loss of chromosome 17 in one case (5%). The main findings by CGH were gains of chromosomes 12 (6 cases, 33%) and 14 (6 cases, 33%) and losses of chromosomes 22 (7 cases, 35%) and X (1 case, 5%), mostly comprising whole chromosomes or chromosome arms. Inhibin and p53 were expressed in 100% and 95% of the tumors, respectively. The Ki67 index ranged from 0% to 61%. Neither immunohistochemistry, nor DNA cytometry and molecular genetic analysis, provided statistically significant prognostic information.In summary, our study reveals a distinctive pattern of cytogenetic alterations in GCT. Our observations confirm earlier reports that trisomy 12 and 14 are frequent aberrations; however, monosomy 22 seemingly is even more prevalent.
Mid-level Representation for Visual Recognition

Visual Recognition is one of the fundamental challenges in AI, where the goal is to understand the semantics of visual data. Employing mid-level representation, in particular, shifted the paradigm in visual recognition. The mid-level image/video representation involves discovering and training a set of mid-level visual patterns (e.g., parts and attributes) and represent a given image/video utilizing them. The mid-level patterns can be extracted from images and videos using the motion and appearance information of visual phenomenas. This thesis targets employing mid-level representations for different high-level visual recognition tasks, namely (i)image understanding and (ii)video understanding. In the case of image understanding, we focus on object detection/recognition task. We investigate on discovering and learning a set of mid-level patches to be used for representing the images of an object category. We specifically employ the discriminative patches in a subcategory-aware webly-supervised fashion. We, additionally, study the outcomes provided by employing the subcategory-based models for undoing dataset bias.
Geometric verification using semi-2D constraints for 3D object retrieval

Geometric verification with epipolar geometry often results in a high score for an incorrect image pair due to ambiguity in its geometric constraints. The ambiguity is caused by a high degree of freedom in the epipolar geometry and a weak constraint from the fitting between a point and a line. In order to mitigate the ambiguity, we propose to filter geometrically inconsistent components, namely correspondences, a sample, a model, and inliers in a RANSAC-based geometric verification. For the filtering, we introduce novel semi-2D constraints whose geometric constraint is weaker than full-2D constraint, but stronger than pure-epipolar constraint. Additionally, an advantage of the proposed approach is that it requires only an image pair instead of neither additional information nor prior learning. Experiments on the public dataset containing 3D object images show that the proposed approach improves the true positive rate when the false positive rate is low, and greatly reduces computational time for the geometric verification of both a correct image pair and an incorrect image pair.
The graphlet spectrum

Current graph kernels suffer from two limitations: graph kernels based on counting particular types of subgraphs ignore the relative position of these subgraphs to each other, while graph kernels based on algebraic methods are limited to graphs without node labels. In this paper we present the graphlet spectrum, a system of graph invariants derived by means of group representation theory that capture information about the number as well as the position of labeled subgraphs in a given graph. In our experimental evaluation the graphlet spectrum outperforms state-of-the-art graph kernels.
Charity and Social Change: The Impact of Individual Preferences on Service-Learning Outcomes.

Students from seven institutions of higher education reported their preferences for different paradigms of service at the beginning of their service-learning courses. At the end of the courses, they described the associated service activities in terms of the same paradigms and also completed scales describing their learning outcomes and attitudes toward civic issues. Students who expressed positive preferences for Charity or Social Change activities or both kinds of activity showed more positive learning outcomes and attitude change when there was a match between preference and service than when they experienced a mismatch. For a group of students with limited enthusiasm for either Charity or Social Change activities, the most facilitative service involved both Charity and Social Change experiences. The implications of these findings for service-learning practice and for future research are discussed.
Retrieval and Clustering of Medicines Within Healthcare Data Records

Electronic Health Records (EHRs) are typically designed to electronically document all information that is administratively and clinically relevant in a patient's use of a healthcare facility. This paper intends to improve discoverability of medications which may exist within the narrative-based free-text notes of patients' health care data records. This led us to introduce a context sensitive approach to retrieve candidate pharmaceuticals. Additionally, a combination of contraction promotion and clustering based upon edit distance will be utilised to increase the precision of this process.
Design of surface electrode array for electromyography in the genioglossus muscle

The patency of the upper airway is dependent on the sustained activity of upper airway muscles, in particular the genioglossus (GG) muscles which act to protrude the tongue. Obstructive sleep apnea syndrome (OSAS) is a common and serious disorder in which this mechanism fails, resulting in frequent collapse of the airway during sleep. The properties of the upper airway muscles are therefore of interest in both normal and OSAS subjects, so that their role in the pathogenesis of OSAS can be better understood. Electromyography (EMG) is an important tool for assessing muscle activity and has been used in many GG studies. We have designed a new appliance incorporating a surface electrode array for GG EMG recording. The electrode configuration enables estimation of muscle fibre conduction velocity (CV), an important parameter for monitoring muscle fatigue, which has not been studied before in the GG. The appliance is also designed to provide accurate and repeatable placement of electrodes. Preliminary results presented demonstrate the performance of our appliance, which will now be used to measure GG CV in a range of fatiguing conditions.
On Investigating EMD parameters to Search for Gravitational Waves

The Hilbert–Huang transform (HHT) is a novel, adaptive approach to time series analysis. It does not impose a basis set on the data or otherwise make assumptions about the data form, and so the time-frequency decomposition is not limited by spreading due to uncertainty. Because of the high resolution of the time-frequency, we investigate the possibility of the application of the HHT to the search for gravitational waves. It is necessary to determine some parameters in the empirical mode decomposition (EMD), which is a component of the HHT, and in this paper we propose and demonstrate a method to determine the optimal values of the parameters to use in the search for gravitational waves.
Pervasive trusted computing

This paper analyses the role trusted computing can play in a pervasive/ubiquitous environment. Traditionally, the ideology of Weiser (1991) and others when considering the pervasive computing vista, dictated that computers would invisibly integrate themselves into the background environment, providing useful services to users, as well as contextual information as to their surroundings. While mindful of this invisibility property, we examine trusted computing as a component of devices that themselves hold the promise of ubiquity. In this respect, we examine a contrived application scenario in order to abstract a number of useful properties trusted computing can provide in a pervasive setting
Approximate Bayesian Computation for epidemiological models: Application to the Cuban HIV-AIDS epidemic with contact-tracing and unobserved infectious population

Statistical inference with missing data is a recurrent issue in epidemiology where the infection process is only partially observable. In this paper, Approximate Bayesian Computation, an alternative to data imputation methods such as Monte Carlo Markov chain integration, is proposed for making inference in epidemiological models. This method of inference is not based on the likelihood function and relies exclusively on numerical simulations of the model. We apply the Approximate Bayesian Computation framework to calibrate an epidemiological model dedicated to the analysis of the HIV contact-tracing program in Cuba. We first evaluate numerically, using synthetic data sets, the statistical properties of the estimated posterior distributions obtained with different variants of Approximate Bayesian Computation. Then, once the epidemiological model has been calibrated with the Cuban VIH database, we make predictions concerning the efficiency of the detection system, and the evolution of the disease in the forthcoming years.
A Social Network Analysis of Jemaah Islamiyah: The Applications to Counter-Terrorism and Intelligence

This article attempts to contribute to and advance the growing literature on social network analysis and terrorism studies, through a social network analysis of the Jemaah Islamiyah cell that was responsible for the Bali bombings in 2002. In doing so, the article endeavors to provide a potential framework for the intelligence analysis of terrorist cells. Such a framework will assist in (1) understanding the communication and structure of such cells and (2) assist in predicting the likely outcomes of terrorist cells when employed in real-time intelligence analysis.
Spectrum-based fault diagnosis for service-oriented software systems

Due to the loosely coupled and highly dynamic nature of service-oriented systems, the actual configuration of such system only fully materializes at runtime, rendering many of the traditional quality assurance approaches useless. In order to enable service systems to recover from and adapt to runtime failures, an important step is to detect failures and diagnose problematic services automatically. This paper presents a lightweight, fully automated, spectrum-based diagnosis technique for service-oriented software systems that is combined with a framework-based online monitor. An experiment with a case system is set up to validate the feasibility of pinpointing problematic service operations. The results indicate that this approach is able to identify problematic service operations correctly in 72% of the cases.
Scalable Clustering of Documents with Multiple Membership

Document clustering has recently garnered a large amount of attention from the IR, data mining, and machine learning research communities as an effective way of not only organizing textual information, but also for discovering interesting patterns in that information. Most existing methods, however, suffer from two main drawbacks. First, most clustering algorithms are very restrictive, as documents are only allowed to participate in a single cluster. Allowing documents to participate in more than one cluster is important in the context of document clustering, since a document can often span more than two topics or concepts. Second, most existing methods cannot scale to very large document collections. Highly scalable document clustering algorithms are becoming increasingly relevant, as clustering algorithms are being applied not to hundreds or thousands of documents, but to millions or even billions of documents. In this paper we aim to address both of these weaknesses. We extend a data compression technique called data bubbles to include the notion of degree of membership; when coupled with the OPTICS clustering algorithm, the result is a method for hierarchically clustering documents with varying degrees of cluster membership that scales linearly in the number of documents. We present empirical results demonstrating the scalability and effectiveness of this approach.
Comparing ASP, CP, ILP on two challenging applications: wire routing and haplotype inference

We study three declarative programming paradigms, Answer Set Programming (ASP), Constraint Programming (CP), and Integer Linear Programming (ILP), on two challenging applications: wire routing and haplotype inference. We represent these problems in each formalism in a systematic way, compare the formulations both from the point of view of knowledge representation (e.g., how tolerant they are to elaborations) and from the point of view of computational efficiency (in terms of computation time and program size). We discuss possible ways of improving the computational efficiency, and other reformulations of the problems based on different mathematical models.
Website fingerprinting: attacking popular privacy enhancing technologies with the multinomial naïve-bayes classifier

Privacy enhancing technologies like OpenSSL, OpenVPN or Tor establish an encrypted tunnel that enables users to hide content and addresses of requested websites from external observers This protection is endangered by local traffic analysis attacks that allow an external, passive attacker between the PET system and the user to uncover the identity of the requested sites. However, existing proposals for such attacks are not practicable yet. We present a novel method that applies common text mining techniques to the normalised frequency distribution of observable IP packet sizes. Our classifier correctly identifies up to 97% of requests on a sample of 775 sites and over 300,000 real-world traffic dumps recorded over a two-month period. It outperforms previously known methods like Jaccard's classifier and Naïve Bayes that neglect packet frequencies altogether or rely on absolute frequency values, respectively. Our method is system-agnostic: it can be used against any PET without alteration. Closed-world results indicate that many popular single-hop and even multi-hop systems like Tor and JonDonym are vulnerable against this general fingerprinting attack. Furthermore, we discuss important real-world issues, namely false alarms and the influence of the browser cache on accuracy.
Observation of atoms laser cooled below the Doppler limit.

By measuring the ballistic motion of sodium atoms released from optical molasses1 we determine their temperature to be as low as 43 ± 20 μK. Four different measurement techniques are used, giving temperatures ranging from 43 to 80 μK. These observations are inconsistent with the accepted theory of laser cooling,2 which sets a lower limit on the temperature of kBT = ħγ/2. This Doppler cooling limit is achieved for a detuning of Δ = – γ/2. For sodium cooled on the D2 line, γ = 2π × 10 MHz, and the Doppler cooling limit is 240 μK. We have measured the detuning dependence of the temperature and found it to be in strong disagreement with theoretical predictions. The temperature is minimized for |δ| much larger than γ/2 and is always below the cooling limit except at small detunings. These results are remarkable and unexpected; they raise important questions about the adequacy of present theories of laser cooling.
Multimedia Topic Models Considering Burstiness of Local Features

SUMMARY A number of studies have been conducted on topic modeling for various types of data, including text and image data. We focus particularly on the burstiness of the local features in modeling topics within video data in this paper. Burstiness is a phenomenon that is often discussed for text data. The idea is that if a word is used once in a document, it is more likely to be used again within the document. It is also observed in video data; for example, an object or visual word in video data is more likely to appear repeatedly within the same video data. Based on the idea mentioned above, we propose a new topic model, the Correspondence Dirichlet Compound Multinomial LDA (Corr-DCMLDA), which takes into account the burstiness of the local features in video data. The unknown parameters and latent variables in the model are estimated by conducting a collapsed Gibbs sampling and the hyperparameters are estimated by focusing on the fixed-point iterations. We demonstrate through experimentation on the genre classification of social video data that our model works more effectively than several baselines.
Interactive Temporal Association Analytics

Traditional temporal association mining systems, once supplied with a specific parameter setting such as time periods of interest, minimum support and confidence, generate the rule set from scratch. This one-at-a-time paradigm forces the analysts to perform successive trial-and-error iterations to finally discover interesting temporal patterns. This process is not only prohibitively time and resource consuming, but also ineffective in providing meaningful feedback for improving the desired rule outcome. In this work, we introduce the first solution to interactively explore temporal associations from evolving data at multiple levels of abstraction, henceforth referred to as temporal association rule analytics (TARA). The offline rule preparation phase of the TARA infrastructure extracts the temporal associations from the raw data and compresses them into a knowledge-rich yet compact evolving parameter space (EPS) structure. The online exploration phase of TARA leverages this EPS structure to offer rich classes of novel exploration operations from parameter recommendations and time-travel queries to the discovery of hidden insights of associations with near instantaneous responsiveness. As demonstrated by our extensive experiments on realworld data sets, TARA accomplishes three to five orders of magnitude improvement over state-of-the art approaches while offering a rich interactive exploration experience.