Miracle-dz/UniFAR-FTUs huggingface dataset:
Real-Time Bidding: A New Frontier of Computational Advertising Research

In display and mobile advertising, the most significant development in recent years is the Real-Time Bidding (RTB), which allows selling and buying in real-time one ad impression at a time. Since then, RTB has fundamentally changed the landscape of the digital marketing by scaling the buying process across a large number of available inventories. The demand for automation, integration and optimisation in RTB brings new research opportunities in the IR/DM/ML fields. However, despite its rapid growth and huge potential, many aspects of RTB remain unknown to the research community for many reasons. In this tutorial, together with invited distinguished speakers from online advertising industry, we aim to bring the insightful knowledge from the real-world systems to bridge the gaps and provide an overview of the fundamental infrastructure, algorithms, and technical and research challenges of this new frontier of computational advertising. We will also introduce to researchers the datasets, tools, and platforms which are publicly available thus they can get hands-on quickly.
Toward context-aware just-in-time information: micro-activity recognition of everyday objects

Transferring computational tasks from user-worn devices to everyday objects allows users to freely focus on their regular non-computing tasks. Identifying micro-activities (short-repetitive-activities that compose the macro-level behavior) enables the understanding of subtle behavioral changes and providing just-in-time information without explicit user input. In this paper, we propose the concept of micro-activity recognition of augmented-everyday-objects and evaluate the applicability of machine learning algorithms that are previously used for macro-level activity recognition. We outline a few proof-of-concept application scenarios that provide micro-activity-aware just-in-time information.
Comparison of paper and computer displays in reading including frequent movement between pages

This paper describes experiments to compare reading with paper versus electronic media when reading with frequently moving back and forth between pages. In the first experiment, eighteen participants read aloud multi-page documents with endnotes in three conditions: paper, a large display, and a small display. Results revealed that reading from paper was 6.8% faster than reading from a large computer display and 11.4% faster than reading from a small computer display. Detailed analyses of the reading process showed that participants performed both reading and page-turning simultaneously in the paper condition. However, when using computer displays, reading and turning pages were performed separately. In the second experiment, 12 participants read documents using electronic system with more effective features for page-turning such as an overview of pages and links from reference symbols to notes. However, paper remained the most efficient medium to support reading with endnotes. This indicates that electronic media should be improved to support between-page navigation.
Manufacturing cheap, resilient, and stealthy opaque constructs

It has become common to distribute software in forms that are isomorphic to the original source code. An important example is Java bytecode. Since such codes are easy to decompile, they increase the risk of malicious reverse engineering attacks.In this paper we describe the design of a Java code obfuscator, a tool which - through the application of code transformations - converts a Java program into an equivalent one that is more difficult to reverse engineer.We describe a number of transformations which obfuscate control-flow. Transformations are evaluated with respect to potency (To what degree is a human reader confused? ), resilience (How well are automatic deobfuscation attacks resisted? ), cost (How much time/space overhead is added? ), and stealth (How well does obfuscated code blend in with the original code? ). The resilience of many control-altering transformations rely on the resilience of opaque predicates. These are boolean valued expressions whose values are known to the obfuscator but difficult to determine for an automatic deobfuscator. We show how to construct resilient, cheap, and stealthy opaque predicates based on the intractability of certain static analysis problems such as alias analysis.
Page Placement Strategies for GPUs within Heterogeneous Memory Systems

Systems from smartphones to supercomputers are increasingly heterogeneous, being composed of both CPUs and GPUs. To maximize cost and energy efficiency, these systems will increasingly use globally-addressable heterogeneous memory systems, making choices about memory page placement critical to performance. In this work we show that current page placement policies are not sufficient to maximize GPU performance in these heterogeneous memory systems. We propose two new page placement policies that improve GPU performance: one application agnostic and one using application profile information. Our application agnostic policy, bandwidth-aware (BW-AWARE) placement, maximizes GPU throughput by balancing page placement across the memories based on the aggregate memory bandwidth available in a system. Our simulation-based results show that BW-AWARE placement outperforms the existing Linux INTERLEAVE and LOCAL policies by 35% and 18% on average for GPU compute workloads. We build upon BW-AWARE placement by developing a compiler-based profiling mechanism that provides programmers with information about GPU application data structure access patterns. Combining this information with simple program-annotated hints about memory placement, our hint-based page placement approach performs within 90% of oracular page placement on average, largely mitigating the need for costly dynamic page tracking and migration.
A multi-scale modeling approach for software architecture deployment

For large component-based applications, identifying a valid deployment architecture has emerged as a major challenge. Actually, this deployment architecture (i.e., allocation of software components to its hardware hosts) should satisfy various constraints related to the software components and the target environment such as the hierarchical description of components, their connections and the resource constraints. The numerous constraints make hard to construct manually the correct deployment architecture. In this work, we propose a formal method based on a formal language called BRS (Bigraphical Reactive System) in order to guarantee the correctness of the deployment architecture. Furthermore, in order to support its automatic construction, our proposed method follows a multi-scale modeling. In fact, the designer starts by modeling the first scale architecture which is refined automatically by successively adding smaller scale components until obtaining the deployment architecture at the last scale. This refinement is ensured by applying a set of rules. In this paper, we address communicating systems as a study domain.
Internet-based psychological treatments for depression

Major depression is highly prevalent, and is associated with high societal costs and individual suffering. Evidence-based psychological treatments obtain good results, but access to these treatments is limited. One way to solve this problem is to provide internet-based psychological treatments, for example, with therapist support via email. During the last decade, internet-delivered cognitive-behavioral therapy (ICBT) has been tested in a series of controlled trials. However, the ICBT interventions are delivered with different levels of contact with a clinician, ranging from nonexisting to a thorough pretreatment assessment in addition to continuous support during treatment. In this review, the authors have found an evidence for a strong correlation between the degree of support and outcome. The authors have also reviewed how treatment content in ICBT varies among treatments, and how various therapist factors may influence outcome. Future possible applications of ICBT for depression and future research needs are also discussed.
The deployment of shared data objects among handheld and wearable devices

With the great success on making phones smarter, vendors now plan on replicating the same idea on wearable accessories. Accordingly, applications on these devices are full of new possibilities to interact with users. However, in order to provide consistent user experience, it poses a major challenge on how to efficiently deploy shared application states among the devices. In this paper, we consider to minimize the data transmission latencies between the processes and the shared data objects on a set of mobile devices with distributed shared memory. The problem is proved to be NP-hard. Nevertheless, efficient solutions can still be obtained when special cases are considered. On one hand, we propose a polynomial-time optimal algorithm when the memory of each mobile device is segmented into blocks and each of the shared data objects is of single block. On the other hand, in order to provide a practical way to address the problem, we then propose a (1, ∈) asymptotic approximation algorithm, where ∈ > 0 and can be arbitrarily small, with a 2-augmentation-bound of memory size. In the end, a series of simulations was conducted, and the results were very encouraging.
The borderland between care and self-care.

The aim of this thesis was to examine different approaches to support the self-care of persons with Type 2 diabetes, with special reference to practical, social, and sexual aspects of women's self-management. The methods to elucidate this comprised: evaluating a new model for diabetes patient education; designing a model to analyse the role of social networks in women's diabetes; conducting individual and focus group interviews for deeper understanding of the social and sexual aspects of diabetes; and collecting questionnaire data as a complement to the above.The experience-based educational program led by pharmacists was found to improve participants' subjective control over diabetes and to provide important emotional support and encouragement to continue self-care. Metabolic control as measured by HbA1c temporarily improved. The social network model elucidated potential mechanism leading to conflict of disease and social demands in women's diabetes. Qualitative analysis of the focus group interviews pointed to the role of guilt, shame, and social taboo in connection with the women's diabetes and sexuality.Borderland is the metaphor I have chosen to describe the space between the traditional health care system and the everyday self-care of people with chronic disease. Using Borderland as a framework, a future model for diabetes management, anchored in our own and other's findings, is outlined and the concept of "Disease Manager Role" is introduced. The vision of a self-care support center in Borderland addresses such issues as accessibility, continuity, equitable provider-user relations, shared care plans, and strengthening social support.
Sequence mining under multiple constraints

In this paper, we address the problem of mining sequential patterns under multiple constraints. Unlike classical algorithms, our approach handles various types of constraints which are not only numeric but also symbolic and syntactic. These multiple constraints enable us to express a large scope of knowledge to focus on interesting patterns. We illustrate our approach with the detection of gene--rare disease relationships from biomedical texts for the documentation of rare diseases.
Multi-stage seed selection for viral marketing

In this paper, a multi-stage seed selection approach is proposed, that performs seed selection in several stages to optimize viral marketing in online social network. In each stage, seeds are selected among the central nodes that have not yet been activated. Thus, the proposed approach exploits the information that is acquired during the previous stages in order to improve its performance in the forthcoming stages. Experimental results using data from a real-world social network show that the proposed approach brings significant improvement in performance (up to 30% compared to the single-stage approach).
Developing high performing employment services for people with mental illness

Aims This analysis examined employment outcomes achieved by a supported employment service in Hawke's Bay, New Zealand. The evaluation aimed to identify opportunities and challenges for ongoing service development, and to demonstrate how other employment services can use these methods and benchmarks in service development. Methods Service performance over 4 years was examined on multiple levels, and compared with outcomes recently reported by published international controlled trials with high fidelity to evidence-based practice in supported employment. Findings As a segregated supported employment service, there were no formal arrangements to coordinate employment services with local mental health services. Despite this limitation, the service was high performing on a range of employment outcome variables. Conclusions With challenging economic times ahead, this analysis provides a timely framework for allied health professionals, supported employment and vocational rehabilitation service providers, to re.
Indoor Person Identification through Footstep Induced Structural Vibration

Person identification is crucial in various smart building applications, including customer behavior analysis, patient monitoring, etc. Prior works on person identification mainly focused on access control related applications. They achieve identification by sensing certain biometrics with specific sensors. However, these methods and apparatuses can be intrusive and not scalable because of instrumentation and sensing limitations. In this paper, we introduce our indoor person identification system that utilizes footstep induced structural vibration. Because structural vibration can be measured without interrupting human activities, our system is suitable for many ubiquitous sensing applications. Our system senses floor vibration and detects the signal induced by footsteps. Then the system extracts features from the signals that represent characteristics of each person's gait pattern. With the extracted features, the system conducts hierarchical classification at an individual step level and then at a trace (i.e., collection of consecutive steps) level. Our system achieves over 83% identification accuracy on average. Furthermore, when the application requires different levels of accuracy, our system can adjust confidence level threshold to discard uncertain traces. For example, at a threshold that allows only most certain 50% traces for classification, the identification accuracy increases to 96.5%.
Disability and Active Life Expectancy Among Older Cambodians

Older adults in Cambodia are survivors of harsh living conditions, including poverty and periods of extreme violence. Although these experiences may affect health outcomes, little data has existed to monitor Cambodia's older population. The current paper uses data from the 2004 Study of the Elderly in Cambodia (SEC) and focuses on disability status. The data is the first of its kind for Cambodia—a comprehensive survey employing a representative sample of adults 60 and older living across the country. Disability prevalence by age and sex, Active Life Expectancy (ALE) and common correlates of disability, using Activity of Daily Living (ADL) items, are examined. In addition, disability prevalence rates are compared to those for older adults living in other countries in South-east Asia. The results indicate that older Cambodians are more disabled than their counterparts living elsewhere. Possible reasons include long-term influence of social disruption and poverty. Women live longer than men, but spend a greater proportion of their remaining life in states of disability and severe disability. Correlates of disability show that younger age, being male, having higher income satisfaction, being married and living in urban areas associate with lower probabilities of reporting disability. A contribution of the analysis is the examination of a basic measure of health among a population of which little is known.
Dynamic User Modeling in Social Media Systems

Social media provides valuable resources to analyze user behaviors and capture user preferences. This article focuses on analyzing user behaviors in social media systems and designing a latent class statistical mixture model, named temporal context-aware mixture model (TCAM), to account for the intentions and preferences behind user behaviors. Based on the observation that the behaviors of a user in social media systems are generally influenced by intrinsic interest as well as the temporal context (e.g., the public's attention at that time), TCAM simultaneously models the topics related to users' intrinsic interests and the topics related to temporal context and then combines the influences from the two factors to model user behaviors in a unified way. Considering that users' interests are not always stable and may change over time, we extend TCAM to a dynamic temporal context-aware mixture model (DTCAM) to capture users' changing interests. To alleviate the problem of data sparsity, we exploit the social and temporal correlation information by integrating a social-temporal regularization framework into the DTCAM model. To further improve the performance of our proposed models (TCAM and DTCAM), an item-weighting scheme is proposed to enable them to favor items that better represent topics related to user interests and topics related to temporal context, respectively. Based on our proposed models, we design a temporal context-aware recommender system (TCARS). To speed up the process of producing the top-k recommendations from large-scale social media data, we develop an efficient query-processing technique to support TCARS. Extensive experiments have been conducted to evaluate the performance of our models on four real-world datasets crawled from different social media sites. The experimental results demonstrate the superiority of our models, compared with the state-of-the-art competitor methods, by modeling user behaviors more precisely and making more effective and efficient recommendations.
Media Bias in German Online Newspapers

Online newspapers have been established as a crucial information source, at least partially replacing traditional media like television or print media. As all other media, online newspapers are potentially affected by media bias.This describes non-neutral reporting of journalists and other news producers, e.g. with respect to specific opinions or political parties. Analysis of media bias has a long tradition in political science. However, traditional techniques rely heavily on manual annotation and are thus often limited to the analysis of small sets of articles. In this paper, we investigate a dataset that covers all political and economical news from four leading German online newspapers over a timespan of four years. In order to analyze this large document set and compare the political orientation of different newspapers, we propose a variety of automatically computable measures that can indicate media bias. As a result, statistically significant differences in the reporting about specific parties can be detected between the analyzed online newspapers.
Road Sensing: Personal Sensing and Machine Learning for Development of Large Scale Accessibility Map

This paper proposes a methodology for developing large scale accessibility map with personal sensing by using smart phone and machine learning technologies. The strength of the proposed method is its low cost data collection, which is a key to break through stagnations of accessibility map that currently applied to limited areas. This paper developed and evaluated a prototype system that estimates types of ground surfaces by applying supervised learning techniques to activity sensing data of wheelchair users recorded by a three-axis accelerometer, focusing on knowledge extraction and visualization. As a result of evaluation using nine wheelchair users' data with Support Vector Machine, three ground surface types, curb, tactile indicator, and slope, were detected with f-score (and accuracy) of 0.63 (0.92), 0.65 (0.85), and 0.54 (0.97) respectively.
Justifiability of amniocentesis on the basis of positive findings of triple test, ultrasound scan and advanced maternal age

Objective. To assess the effectiveness of antenatal screening for chromosomalabnormalities based on maternal age (≥35 years), positiveultrasound findings or a positive triple test. Materials and methods.Retrospective six-year study. The pregnant women routinely underwentestablished clinical and laboratory practice at the Departmentof Medical Genetics between 1997 and 2003. The women’s case noteswere examined to identify indications for karyotyping, gestation periodand the outcome of karyotyping and pregnancy. Results. Invasiveantenatal tests were performed on 1440 cases, 1168 (81.11%) age 35(a),72 (5.00%) positive triple test (b), 24 (1.67%) positive ultrasound scanning(c) and 176 (12.2%) other (psychological, personal reasons, etc)(d). The overall positive predictive value was 1.67% (1.6%(a), 1.4% (b),12.5% (c), 0.0% (d). The constructed model of logistic regression gavean odds-ratio of 8.647 for the “positive ultrasound result vs. maternalage ≥35” indication, while the odds-ratio for the triple test vs. maternalage ≥35 was 0.854. Conclusions. Amniocentesis and cytogeneticanalysis of foetal karyotype should be presented as a diagnosticpossibility to all women over 35 years. The application of biochemicalmarkers was far from the expected results. If we compare results forindication positive ultrasound scanning vs. maternal age, an oddsratioof ~9 was obtained. These results demonstrate that the likelihoodof obtaining positive results (i.e. the presence of chromosome alterations)from an amniocentesis having this indication is almost 9 timeshigher than from having an amniocentesis performed solely for advancedmaternal age.
CLEF 15th Birthday

2014 marks the 15th birthday for CLEF, an evaluation campaign activity which has applied the Cranfield evaluation paradigm to the testing of multilingual and multimodal information access systems in Europe. This paper provides a summary of the motivations which led to the establishment of CLEF, and a description of how it has evolved over the years, the major achievements, and what we see as the next challenges.
Myopoint: Pointing and Clicking Using Forearm Mounted Electromyography and Inertial Motion Sensors

We describe a mid-air, barehand pointing and clicking interaction technique using electromyographic (EMG) and inertial measurement unit (IMU) input from a consumer armband device. The technique uses enhanced pointer feedback to convey state, a custom pointer acceleration function tuned for angular inertial motion, and correction and filtering techniques to minimize side-effects when combining EMG and IMU input. By replicating a previous large display study using a motion capture pointing technique, we show the EMG and IMU technique is only 430 to 790 ms slower and has acceptable error rates for targets greater than 48 mm. Our work demonstrates that consumer-level EMG and IMU sensing is practical for distant pointing and clicking on large displays.