Miracle-dz/UniFAR-FTUs huggingface dataset:
Inferring Advertiser Sentiment in Online Articles using Wikipedia Footnotes

Online advertising platforms in partnerships with media companies typically have access to an online user’s history of viewed articles. If a concerned brand (advertiser) plans to run advertisement campaigns on users exposed to negative articles, it is essential to first identify articles with negative sentiment about the brand. For an advertising platform, scalable identification of such articles with little human-annotation effort is necessary for launching campaigns soon after an advertiser signs up. In this context, generic sentiment analysis tools suffer from the lack of contextual world knowledge associated with the advertiser. Human annotation of articles for supervised approaches is laborious and painstaking. To address these problems, we propose the use of publicly available Wikipedia footnote references for an advertiser, and propagate their sentiment to several articles related to the advertiser. In particular, our proposed approach has three components: (i) automatically find Wikipedia references which have negative sentiment about an advertiser, (ii) learn distributed representations (doc2vec) of article texts referred in footnotes and other unlabeled articles, and (iii) inferring sentiment in unlabeled articles using label propagation (from references) in the doc2vec space. Our experiments spanning three real brands, and data from a major advertising platform (Yahoo Gemini) show significant lifts in sentiment inference compared to existing baselines. In addition, we share valuable insights on how article sentiment influences the online activities of a user with respect to a brand.
Implicit Bias in Crowdsourced Knowledge Graphs

Collaborative creation of knowledge is an approach which has been successfully demonstrated by crowdsourcing project like Wikipedia. Similar techniques have recently been adopted for the creation of collaboratively generated Knowledge Graphs like, for example, Wikidata. While such an approach enables the creation of high quality structured content, it also comes with the challenge of introducing contributors’ implicit bias in the generated Knowledge Graph. In this paper, we investigate how paid crowdsourcing can be used to understand contributor bias for controversial facts to be included into collaborative Knowledge Graphs. We propose methods to trace the provenance of crowdsourced fact checking thus enabling bias transparency rather than aiming at eliminating bias from the Knowledge Graph.
Recent opioid use and fall-related injury among older patients with trauma

BACKGROUND: Evidence for an association between opioid use and risk of falls or fractures in older adults is inconsistent. We examine the association between recent opioid use and the risk, as well as the clinical outcomes, of fall-related injuries in a large trauma population of older adults. METHODS: In a retrospective, observational, multicentre cohort study conducted on registry data, we included all patients aged 65 years and older who were admitted (hospital stay > 2 d) for injury in 57 trauma centres in the province of Quebec, Canada, between 2004 and 2014. We looked at opioid prescriptions filled in the 2 weeks preceding the trauma in patients who sustained a fall, compared with those who sustained an injury through another mechanism. RESULTS: A total of 67 929 patients were retained for analysis. Mean age was 80.9 (± 8.0) years and 69% were women. The percentage of patients who had filled an opioid prescription in the 2 weeks preceding an injury was 4.9% (95% confidence interval [CI] 4.7%–5.1%) for patients who had had a fall, compared with 1.5% (95% CI 1.2%–1.8%) for those who had had an injury through another mechanism. After we controlled for confounding variables, patients who had filled an opioid prescription within 2 weeks before injury were 2.4 times more likely to have a fall rather than any other type of injury. For patients who had a fall-related injury, those who used opioids were at increased risk of in-hospital death (odds ratio 1.58; 95% CI 1.34–1.86). INTERPRETATION: Recent opioid use is associated with an increased risk of fall and an increased likelihood of death in older adults.
TIM: A Tool for Gaining Insights into Psychotherapy

We introduce and demonstrate the usefulness of a tool that automatically annotates therapist utterances in real-time according to the therapeutic role that they perform in an evidence-based psychological dialogue. This is implemented within the context of an on-line service that supports the delivery of one-to-one therapy. When combined with patient outcome measures, this tool allows us to discover the active ingredients in psychotherapy. In particular, we show that particular measures of therapy content are more strongly correlated with patient improvement than others, suggesting that they are a critical part of psychotherapy. As this tool gives us interpretable measures of therapy content, it can enable services to quality control the therapy delivered. Furthermore, we show how specific insights can be presented to the therapist so they can reflect on and improve their practice.
Evaluation of One-Class Support Vector Classification for Mapping the Paddy Rice Planting Area in Jiangsu Province of China from Landsat 8 OLI Imagery

Identification of paddy fields is essential for monitoring the rice cultivated area and predicting rice productivity. Timely and accurate extraction of rice distribution can bring vital information for national food security, agricultural policy formulation, and regional environmental sustainability. Conventional classification methods usually suffered from low accuracy, multi-class training samples, or demanding imagery requirements. This paper proposes to use one-class support vector classification (OCSVC) to extract rice cultivated area with Landsat Optical Land Imager (OLI) imagery. Instead of sampling and training all land cover types as performed by multi-class classification methods, OCSVC only used the training samples of target class (rice) for rice mapping. The performance of OCSVC was evaluated in terms of the classification accuracy of rice mapping and rice acreage estimation based on high-resolution imagery, field survey data and rice acreage data from government reports for Jiangsu Province, China. At the county-level, OCSVC was also compared with the commonly used multi-class support vector classification (MCSVC), decision tree classification (DTC), and vegetation index-based thresholding (VIT). Our results demonstrated that OCSVC produced a comparable overall accuracy to DTC and outperformed MCSVC and VIT. The computational efficiency of OCSVC increased approximately ten times as compared to MCSVC. The OCSVC produced the best correlation between its classified area and reported area among the four classification methods evaluated. When applied to the provincial level, the classification overall accuracy for OCSVC was 88.54%. The detected rice planting area for Jiangsu Province was 22,602 km2, which was consistent with the statistics from the National Bureau of Statistics (22,948 km2). This OCSVC-based mapping strategy provides a practical and efficient way to detect the rice planting extent with Landsat imagery at a large scale.
Hiding Vulnerabilities of Internet of Things Software Using Anti-Tamper Technique

With the advancement of Field Programmable Gate Array (FPGA) technology, the Internet of Things (IoT) devices are cheaply equipped with FPGA. In this work, we study a FPGA-based hiding technique of security vulnerabilities which are inevitably residing in the IoT software. When software engineers develop software code and perform static security testing, they could find vulnerabilities which cannot be removed from source code. For example, an IoT device needs to have hard-coded password in the source code in order to communicate with server. The proposed method isolates the parts of the source code that contain the security vulnerability, and then converts them to bitstream of specific FPGA circuit. It is difficult for attackers to find vulnerabilities that are implemented as a hardware circuit such as FPGA.
Privacy Enhancement for Delivery Route Optimization through Occupancy Prediction

Delivery route optimization for logistic industry is one of applications proposed on smart meter infrastructure. It's expected to drastically reduce absent-delivery which amounts to 20% of total delivery in Japan, which estimated to save $billions a year. But as previous works pointed out, the concern on user privacy is the biggest hurdle yet to be addressed. In this research, we proposed a new approach to improve user privacy by converting electricity data into route data and optimize it before providing to service provider. Then, we tested pragmatic privacy improvement and route optimization through actual delivery experiment. Results showed that the information leakage rate (# of absence detection per delivery) decreased from 23% to 4% by this system and decreased to 2% with additional operational change. Also, the experiment validated decrease of absent-delivery rate from 23% to 2% and travel distance by 5% while improving privacy. Applying adequate method to "delivery optimization through occupancy prediction" enabled achieving both user privacy and absent-delivery reduction significantly.
Pressure Vessel Design Simulation: Implementing of Multi-Swarm Particle Swarm Optimization

The new era knowledge of optimization algorithm is massively boosted recently. Among several optimization models, multi-swarm approach has been proposed most recently for balancing the exploration and exploitation capability through the Particle Swarm Optimization (PSO) algorithm. The proposed multi-swarm model which is called Meeting Room Approach (MRA), is tested and evaluated based on solving normal and large-scale problems. In the current research, the feasibility of the proposed Multi-Swarm Particle Swarm Optimization (MPSO) is adopted to simulate mechanical engineering problem namely pressure vessel design (PVD). The results indicated the potential of the proposed MPSO model on simulating the PVD problem with optimum solution over the standalone PSO. Further, the current study results authenticated against other famous meta-heuristics models. Overall, MPSO reported an excellent optimization solution with fast convergence learning process.
The WDC Training Dataset and Gold Standard for Large-Scale Product Matching

A current research question in the area of entity resolution (also called link discovery or duplicate detection) is whether and in which cases embeddings and deep neural network based matching methods outperform traditional symbolic matching methods. The problem with answering this question is that deep learning based matchers need large amounts of training data. The entity resolution benchmark datasets that are currently available to the public are too small to properly evaluate this new family of matching methods. The WDC Training Dataset for Large-Scale Product Matching fills this gap. The English language subset of the training dataset consists of 20 million pairs of offers referring to the same products. The offers were extracted from 43 thousand e-shops which provide schema.org annotations including some form of product ID such as a GTIN or MPN. We also created a gold standard by manually verifying 2200 pairs of offers belonging to four product categories. Using a subset of our training dataset together with this gold standard, we are able to publicly replicate the recent result of Mudgal et al. that embeddings and deep neural network based matching methods outperform traditional symbolic matching methods on less structured data.
Energy-Efficient Compressive State Recovery From Sparsely Noisy Measurements

The compressive sensing theory has been intensively inspired to develop new methods and applications. Energy conservation for every node and overall energy consumption in the network is one of the main design issues in such networks. In large-scale sensor networks, information is relatively sparse compared with the number of nodes. In such networks, the state recovery problem can be recast as a sparse signal recovery problem in the discrete spatial domain to be solved with a small number of linear measurements as an underdetermined linear system by an l1-norm minimization program. A series of signal processing methods is applied to recover the current state of sensory data, e.g., temperature, pressure, force, flow, humidity, position, or motion, from noisy measurements. We propose an energy-efficient state recovery method for sensor networks; then, the proposed method is employed to recover an air quality signal in an air-quality-monitoring system. The signal contains the air quality indexes for all monitoring sites in the system. To have a sparse representation of the signal, it is first transformed to frequency domain and then recovered and reconstructed from a small portion of coefficients. Different random matrices driven from Bernoulli and Gaussian distributions are investigated to find an energy-efficient sensing scheme for signal reconstruction. The results reveal more than 60% saving in power consumption with only 10% reconstruction and recovery error. The proposed method prolongs network lifetime with noticeable saving in deployment and maintenance cost, particularly in large-scale sensor networks with slowly varying phenomena.
TaskMate: A Mechanism to Improve the Quality of Instructions in Crowdsourcing

Developing instructions for microtask crowd workers requires time to ensure consistent interpretations by crowd workers. Even with substantial effort, workers may still misinterpret the instructions due to ambiguous language and structure in the task design. Prior work demonstrated methods for facilitating iterative improvement with help from the requester. However, any participation by the requester reduces the time saved by delegating the work—and hence the utility of using crowdsourcing. We present TaskMate, a system for facilitating worker-led refinement of task instructions with minimal involvement by the requester. Small teams of workers search for ambiguities and vote on the interpretation they believe the requester intended. This paper describes the workflow, our implementation, and our preliminary evaluation.
Applying Design Science Research in the Development of Human Resource Record Management System with Predictive Analysis through Pointing System

In Human Resource Record Management System developed not only providing an efficient and systematic tool in managing the record and keeping it securely but delivered an automatic computation of the seminars and training attended and service record of the employee using the NBC guidelines policy. The computed points will be utilized for promotion. Moreover, the system also generates an individual account for the employees for quick access of their record and easily update their Personal Data Sheet through Local Area Network or Wide Area Network. The system was designed and developed using MariaDB Database, PHP (Hypertext Pre-processor), Notepad++ and Web Browser. The developed system was utilized and tested following the test plan of Dilky Firdaus, integrating the Design Science Research Model (See Fig. 1). The said model is significantly applied for the success of the development process wherein it includes problem identification and motivation, definition of objectives as a solution, design and demonstration, evaluation and communication. With that, it is found out that the system was successfully complied all necessary requirements, satisfied the objectives, successfully tested and recommended for fully implementation this coming year.
Effect of Oxytocin on Hunger Discrimination

Centrally and peripherally administered oxytocin (OT) decreases food intake and activation of the endogenous OT systems, which is associated with termination of feeding. Evidence gathered thus far points to OT as a facilitator of early satiation, a peptide that reduces the need for a meal that has already begun. It is not known, however, whether OT can diminish a feeling of hunger, thereby decreasing a perceived need to seek calories. Therefore, in the current project, we first confirmed that intraperitoneal (i.p.) OT at 0.3–1 mg/kg reduces food intake in deprived and non-deprived rats. We then used those OT doses in a unique hunger discrimination protocol. First, rats were trained to discriminate between 22- and 2-h food deprivation (hungry vs. sated state) in a two-lever operant procedure. After rats acquired the discrimination, they were food-restricted for 22 h and given i.p. OT before a generalization test session. OT did not decrease 22-h deprivation-appropriate responding to match that following 2-h food deprivation, thus, it did not reduce the perceived level of hunger. In order to better understand the mechanisms behind this ineffectiveness of OT, we used c-Fos immunohistochemistry to determine whether i.p. OT activates a different subset of feeding-related brain sites under 22- vs. 2-h deprivation. We found that in sated animals, OT induces c-Fos changes in a broader network of hypothalamic and brain stem sites compared to those affected in the hungry state. Finally, by employing qPCR analysis, we asked whether food deprivation vs. sated state have an impact on OT receptor expression in the brain stem, a CNS “entry” region for peripheral OT. Fasted animals had significantly lower OT receptor mRNA levels than their ad libitum-fed counterparts. We conclude that OT does not diminish a feeling of hunger before a start of a meal. Instead OT's anorexigenic properties are manifested once consumption has already begun which is—at least to some extent—driven by changes in brain responsiveness to OT treatment in the hungry vs. fed state. OT should be viewed as a mediator of early satiation rather than as a molecule that diminishes perceived hunger.
Effectiveness of intravitreal ranibizumab for the treatment of neovascular age-related macular degeneration in a Canadian retina practice: a retrospective review.

OBJECTIVE To assess the effectiveness of intravitreal ranibizumab for neovascular age-related macular degeneration (AMD) in a tertiary care retina practice and compare these results with published efficacy data from randomized clinical trials. DESIGN Nonrandomized, consecutive, single-centre, retrospective chart review analysis. PARTICIPANTS Ninety-four patients (95 eyes) with neovascular AMD. METHODS All treatment-naïve patients with neovascular AMD who received ranibizumab and for whom 1 year of follow-up was available were included in the analysis. The following information was gathered from each patient's chart: age, sex, ocular history, treated eye, duration of symptoms at presentation, subtype of choroidal neovascular membrane, Snellen visual acuity at each visit, number of injections, visits, and optical coherence tomography measurements. RESULTS Subjects had a mean age of 81 (SD 7.11) years. The mean number of injections was 5.1 (SD 2.85) with a mean of 9.4 (SD 2.27) visits in the 12-month period. Overall, there was a gain of 2.88 (SD 24.6) letters in all eyes, and a loss of 2.5 (SD 23.1) letters in patients who met the visual acuity inclusion criteria for the clinical trials. Of the patients who met the inclusion criteria, 75% lost fewer than 15 letters and 11% gained more than 15 letters. CONCLUSIONS Visual outcomes in our study patients compared poorly with the clinical trials. Possibilities for the disparity include gaps in the number and frequency of follow-up visits, patient or doctor assessment fatigue, or gaps in optical coherence tomography utilization and the number of injections administered.
Summarizing Entity Temporal Evolution in Knowledge Graphs

Knowledge graphs are dynamic in nature, new facts about an entity are added or removed over time. Therefore, multiple versions of the same knowledge graph exist, each of which represents a snapshot of the knowledge graph at some point in time. Entities within the knowledge graph undergo evolution as new facts are added or removed. The problem of automatically generating a summary out of different versions of a knowledge graph is a long-studied problem. However, most of the existing approaches are limited to a pairwise version comparison. This limitation makes it difficult to capture a complete evolution out of several versions of the same knowledge graph. To overcome this limitation, we envision an approach to create a summary graph capturing temporal evolution of entities across different versions of a knowledge graph. The entity summary graphs may then be used for documentation generation, profiling or visualization purposes. First, we take different temporal versions of a knowledge graph and convert them into RDF molecules. Secondly, we perform Formal Concept Analysis on these molecules to generate summary information. Finally, we apply a summary fusion policy in order to generate a compact summary graph which captures the evolution of entities.
Gait-based Person Re-identiﬁcation: a Survey

The way people walk is a strong correlate of their identity. Several studies have shown that both humans and machines can recognize individuals just by their gait, given that proper measurements of the observed motion patterns are available. For surveillance applications, gait is also attractive because it does not require active collaboration from users and is hard to fake. However, the acquisition of good quality measures of a person’s motion patterns in unconstrained environments, ( e.g. in person re-identiﬁcation applications) has proved very challenging in practice. Existing technology (video cameras) suffer from changes in view-point, daylight, clothing, wear accessories, and other variations in the person’s appearance. Novel 3D sensors are bringing new promises to the ﬁeld, but still many research issues are open. This paper presents a survey of the work done in gait analysis for re-identiﬁcation in the last decade, looking at the main approaches, datasets and evaluation methodologies. We identify several relevant dimensions of the problem and provide a taxonomic analysis of the current state-of-the-art. Finally, we discuss the levels of performance achievable with the current technology and give a perspective of the most challenging and promising directions of research for the future.
CAPTCHA: Impact of Website Security on User Experience

As currently many people use the Internet to access websites, Internet security becomes an important topic. One popular security mechanism is Captcha or Completely Automated Public Turing Computer and Humans Apart, which determine whether or not the user is human. Nowadays there exist many forms of Captcha with different testing mechanisms. In this study, the researcher would like to determine a form of Captcha that improves user experience on websites. Captcha is divided into 5 types in this study: Invisible Captcha, reCaptcha by Google, Text-based Captcha, Game-based Captcha and Math Captcha, which were tested on 40 people aged 25-34 years as according to surveys people in this age group were the largest group of Internet users. After passing all 5 Captcha tests, the researcher was able to conclude that reCaptcha by Google was the fastest to pass (3.09 seconds), while Captcha that granted the best user experience was Math Captcha because of its entertainment and intuitiveness. Another interesting issue is Invisible Captcha, although being convenient, was not trusted by the majority of the sample users due to its perceived insecurity. Conversely, reCaptcha by Google was deemed the safest.