Miracle-dz/UniFAR-FTUs huggingface dataset:
Proactive Information Retrieval by Capturing Search Intent from Primary Task Context

A significant fraction of information searches are motivated by the user’s primary task. An ideal search engine would be able to use information captured from the primary task to proactively retrieve useful information. Previous work has shown that many information retrieval activities depend on the primary task in which the retrieved information is to be used, but fairly little research has been focusing on methods that automatically learn the informational intents from the primary task context. We study how the implicit primary task context can be used to model the user’s search intent and to proactively retrieve relevant and useful information. Data comprising of logs from a user study, in which users are writing an essay, demonstrate that users’ search intents can be captured from the task and relevant and useful information can be proactively retrieved. Data from simulations with several datasets of different complexity show that the proposed approach of using primary task context generalizes to a variety of data. Our findings have implications for the design of proactive search systems that can infer users’ search intent implicitly by monitoring users’ primary task activities.
Fabrication of calixarene-grafted bio-polymeric magnetic composites for magnetic solid phase extraction of non-steroidal anti-inflammatory drugs in water samples

Calixarene framework functionalized bio-polymeric magnetic composites (MSp-TDI-calix) were synthesized and utilized as magnetic solid-phase extraction (MSPE) sorbent for the extraction of non-steroidal anti-inflammatory drugs (NSAIDs), namely indoprofen (INP), ketoprofen (KTP), ibuprofen (IBP) and fenoprofen (FNP), from environmental water samples. MSp-TDI-calix was characterized by FT-IR, XRD, FESEM, EDX, VSM and BET analysis, and the results were compared with Sp-TDI and Sp-TDI-calix. To maximize the extraction performance of MSp-TDI-calix decisive MSPE affective parameters such as sorbent amount, extraction time, sample volume, type of organic eluent, volume of organic eluent, desorption time and pH were comprehensively optimized prior to HPLC-DAD determination. The analytical validity of the proposed MSPE method was evaluated under optimized conditions and the following figures of merit were acquired: linearity with good determination coefficient (R2 ≥ 0.991) over the concentration range of 0.5–500 µg/L, limits of detection (LODs) ranged from 0.06–0.26 µg/L and limits of quantitation (LOQ) between 0.20–0.89 µg/L. Excellent reproducibility and repeatability under harsh environment with inter-day and intra-day relative standard deviations were obtained in the range of 2.5–3.2% and 2.4–3.9% respectively. The proposed method was successfully applied for analysis of NSAIDs in tap water, drinking water and river water with recovery efficiency ranging from 88.1–115.8% with %RSD of 1.6–4.6%.
StoryLens: A Multiple Views Corpus for Location and Event Detection

The news media landscape tends to focus on long-running narratives. Correctly processing new information, therefore, requires considering multiple lenses when analyzing media content. Traditionally it would have been considered sufficient to extract the topics or entities contained in a text in order to classify it, but today it is important to also look at more sophisticated annotations related to fine-grained geolocation, events, stories and the relations between them. In order to leverage such lenses we propose a new corpus that offers a diverse set of annotations over texts collected from multiple media sources. We also showcase the framework used for creating the corpus, as well as how the information from the various lenses can be used in order to support different use cases in the EU project InVID for verifying the veracity of online video.
Progressive gradient walk for neural network fitness landscape analysis

Understanding the properties of neural network error landscapes is an important problem faced by the neural network research community. A few attempts have been made in the past to gather insight about neural network error landscapes using fitness landscape analysis techniques. However, most fitness landscape metrics rely on the analysis of random samples, which may not represent the high-dimensional neural network search spaces well. If the random samples do not include areas of good fitness, then the presence of local optima and/or saddle points cannot be quantified. This paper proposes a progressive gradient walk as an alternative sampling algorithm for neural network error landscape analysis. Experiments show that the proposed walk captures areas of good fitness significantly better than the random walks.
Time Series Classification and its Applications

Time-series classification is one of the most important machine learning tasks related to time series. It is the common denominator in various recognition tasks, such as signature verification, person identification based on keystroke dynamics, detection of cardiovascular diseases and brain disorders (e.g. early stage of Alzheimer disease or dementia). This tutorial aims to give an introduction to the most prominent challenges, methods, evaluation protocols and biomedical applications related to time series classification. Besides the "conventional" time series classification task, early classification and semi-supervised classification will be considered. Both preprocessing techniques -- FFT, SAX, etc. -- and wide-spread classifiers -- such as similarity-based, feature-based, motif/shaplet-based classifiers and convolutional neural networks -- will be covered. As dynamic time warping (DTW) is the one of the key components of many time series classifiers, including recent ones based on deep learning, we will describe this technique in detail. Slides: http://www.biointelligence. hu/pdf/timeseriestutorial.pdf
A Survey on Different Means of Personalized Dialog Output for an Adaptive Personal Assistant

Using Personal Assistants (PAs) for tasks in our everyday life is quite common these days. The PAs (Apple Siri, Amazon Alexa, Google Now, Microsoft Cortana etc.) help us to set reminders, find our way through traffic, or send messages to friends and colleagues. While being always available to serve our needs, they collect our personal data to improve their Services and tune their behavior. In order to find out which features (e.g., proactivity, storing of data) are accepted or even desired by potential users to model an adaptive PA with respective spoken output, we conducted an online survey with 1,051 German speaking and 499 US participants. We compared the participants' information on their interaction with different systems, including in-vehicle Spoken Dialog Systems (SDSs), smartphone voice control, and smart home devices (e.g., Amazon Echo, Google Home). We had a look both into output features of spoken dialog by showing dialog examples, as well as posing direct questions on potential system features and its interaction behavior. The results show that especially proactivity is desired by the participants while none of the participants has a positive attitude towards the systems' data usage when asked explicitly.
Data-Driven Audio Feature Selection for Audio Quality Recognition in Broadcast News

In1 this paper, we describe automatic audio quality recognition architecture for radio broadcast news based on audio feature selection, using the discrimination ability of the audio descriptors as a criterion of selection. Specifically, we labeled streams of broadcast news transmissions according to their audio quality based on the human auditory perception. Parameterization algorithms extract a large set of audio descriptors and an algorithm of data-driven criteria rank the descriptors' relevance. After that, the feature subsets fed machine learning algorithms for classification. This methodology showed that the k-nearest neighbor classifier provides significantly good results, considering the achieved accuracy. Moreover, the experimental framework verifies the assumption that discarding irrelevant audio descriptors before the classification stage works in favor to the overall identification performance.
Review of Chirped Fiber Bragg Grating (CFBG) Fiber-Optic Sensors and Their Applications

Fiber Bragg Gratings (FBGs) are one of the most popular technology within fiber-optic sensors, and they allow the measurement of mechanical, thermal, and physical parameters. In recent years, a strong emphasis has been placed on the fabrication and application of chirped FBGs (CFBGs), which are characterized by a non-uniform modulation of the refractive index within the core of an optical fiber. A CFBG behaves as a cascade of FBGs, each one reflecting a narrow spectrum that depends on temperature and/or strain. The key characteristic of CFBGs is that their reflection spectrum depends on the strain/temperature observed in each section of the grating; thus, they enable a short-length distributed sensing, whereas it is possible to detect spatially resolved variations of temperature or strain with resolution on the order of a millimeter over the grating length. Based on this premise, CFBGs have found important applications in healthcare, mechanical engineering, and shock waves analysis, among others. This work reviews the present and emerging trends in CFBG sensors, focusing on all aspects of the sensing element and outlining the application case scenarios for which CFBG sensors have been demonstrated.
Optimizing clustering to promote data diversity when generating an ensemble classifier

In this paper, we propose a method to generate an optimized ensemble classifier. In the proposed method, a diverse input space is created by clustering training data incrementally within a cycle. A cycle is one complete round that includes clustering, training, and error calculation. In each cycle, a random upper bound of clustering is chosen and data clusters are generated. A set of heterogeneous classifiers are trained on all generated clusters to promote structural diversity. An ensemble classifier is formed in each cycle and generalization error of that ensemble is calculated. This process is optimized to find the set of classifiers which can have the lowest generalization error. The process of optimization terminates when generalization error can no longer be minimized. The cycle with the lowest error is then selected and all trained classifiers of that particular cycle are passed to the next stage. Any classifier having lower accuracy than the average accuracy of the pool is discarded, and the remaining classifiers form the proposed ensemble classifier. The proposed ensemble classifier is tested on classification benchmark datasets from UCI repository. The results are compared with existing state-of-the-art ensemble classifier methods including Bagging and Boosting. It is demonstrated that the proposed ensemble classifier performs better than the existing ensemble methods.
Thoracoscopic approach in the treatment of ectopic thymic parathyroid adenoma

Primary hyperparathyroidism is overproduction of parathyroid hormone, causing hypercalcemia. Parathyroid adenomas have been found to be the etiology for 80% of cases of primary hyperparathyroidism, while almost a quarter of them are ectopic. We present a case of ectopic thymic parathyroid adenoma, treated by the thoracoscopic approach. Preoperative computed tomogram and technetium-99-sestamibi imaging showed an adenoma, located in the front mid-mediastinum, approximately 8 × 10 mm in size. Resection of the tumor was indicated according to the persistent hypercalcemia after unsuccessful surgical treatment performed 3 years ago. It was decided to perform a parathyroidectomy during thoracoscopy. The diagnosis of parathyroid adenoma was established on postoperative histopathology staining. The patient was discharged from hospital 5 days after the surgery. We found this minimally invasive operation to be an effective and well-tolerated treatment option, determined by the experience of the surgeon.
Recommending Teammates with Deep Neural Networks

The effects of team collaboration on performance have been explored in a variety of settings. Online games enable people with significantly different skills to cooperate and compete within a shared context. Players can affect teammates' performance either via direct communication or by influencing teammates' actions. Understanding such effects can help us provide insights into human behavior as well as make team recommendations. In this work, we aim at recommending teammates to each individual player for maximal skill growth. We study the effect of collaboration in online games using a large dataset from Dota 2, a popular Multiplayer Online Battle Arena game. To this aim, we construct an online co-play teammate network of players, whose links are weighted based on the gain in skill achieved due to team collaboration. We then use the performance network to devise a recommendation system based on a modified deep neural network autoencoder method.
A Two-Stage Auction Mechanism for Cloud Resource Allocation

The contemporary literature on cloud resource allocation is mostly focused on studying the interactions between customers and cloud managers. Nevertheless, the recent growth in the customers’ demands and the emergence of private cloud providers (CPs) entice the cloud managers to rent extra resources from the CPs so as to handle their backlogged tasks and attract more customers. This also renders the interactions between the cloud managers and the CPs an important problem to study. In this paper, we investigate both interactions through a two-stage auction mechanism. For the interactions between customers and cloud managers, we adopt the options-based sequential auctions (OBSAs) to design the cloud resource allocation paradigm. As compared to existing works, our framework can handle customers with heterogeneous demands, provide truthfulness as the dominant strategy, enjoy a simple winner determination procedure, and preclude the delayed entrance issue. We also provide the performance analysis of the OBSAs, which is among the first in literature. Regarding the interactions between cloud managers and CPs, we propose two parallel markets for resource gathering, and capture the selfishness of the CPs by their offered prices. We conduct a comprehensive analysis of the two markets and identify the bidding strategies of the cloud managers.
Voice Disguise in Automatic Speaker Recognition

Humans are able to identify other people’s voices even in voice disguise conditions. However, we are not immune to all voice changes when trying to identify people from voice. Likewise, automatic speaker recognition systems can also be deceived by voice imitation and other types of disguise. Taking into account the voice disguise classification into the combination of two different categories (deliberate/non-deliberate and electronic/non-electronic), this survey provides a literature review on the influence of voice disguise in the automatic speaker recognition task and the robustness of these systems to such voice changes. Additionally, the survey addresses existing applications dealing with voice disguise and analyzes some issues for future research.
Outlier Detection in Graphs: On the Impact of Multiple Graph Models

Various previous works proposed techniques to detect outliers in graph data. Usually, some complex dataset is modeled as a graph and a technique for detecting outliers in graphs is applied. The impact of the graph model on the outlier detection capabilities of any method has been ignored. Here we assess the impact of the graph model on the outlier detection performance and the gains that may be achieved by using multiple graph models and combining the results obtained by these models. We show that assessing the similarity between graphs may be a guidance to determine effective combinations, as less similar graphs are complementary with respect to outlier information they provide and lead to better outlier detection.
The epistemic innocence of clinical memory distortions

In some neuropsychological disorders, distorted reports seem to fill gaps in people's memory of their past, where people's self‐image, history, and prospects are often enhanced. False beliefs about the past compromise both people's capacity to construct a reliable autobiography and their trustworthiness as communicators. However, such beliefs contribute to people's sense of competence and self‐confidence, increasing psychological well‐being. Here, we consider both the psychological benefits and epistemic costs and argue that distorting the past is likely to also have epistemic benefits that cannot be obtained otherwise, such as enabling people to exchange information, receive feedback, and retain key beliefs about themselves.
Toward Father‐friendly Parenting Interventions: A Qualitative Study

Levels of father participation in parenting interventions are often very low, yet little is known about the factors which influence father engagement. We aimed to qualitatively explore perceived barriers to, and preferences for, parenting interventions in a community sample of fathers. Forty‐one fathers across nine focus groups were interviewed using a semi‐structured interview. Data were analysed using inductive thematic analysis. Key barriers to father participation identified included: the perception that interventions are mother‐focused; beliefs about gender roles regarding parenting and help‐seeking; mothers’ role as ‘gatekeeper’; lack of knowledge and awareness of parenting interventions; and lack of relevance of interventions. Fathers reported preferences for specific content and intervention features, facilitator characteristics, practical factors, and highlighted the need for father‐targeted recruitment and advertising. Many of the barriers and preferences identified are consistent with previous research; however, fathers’ beliefs and attitudes around gender roles and help‐seeking, as well as the perception that interventions are predominantly mother‐focused, may be key barriers for community fathers. Strategies to overcome these barriers and better meet the needs of fathers in promoting and delivering parenting interventions are discussed.
A sentiment analysis-based machine learning approach for financial market prediction via news disclosures

Stock market prediction plays an important role in financial decision-making for investors. Many of them rely on news disclosures to make their decisions in buying or selling stocks. However, accurate modelling of stock market trends via news disclosures is a challenging task, considering the complexity and ambiguity of natural languages used. Unlike previous work along this line of research, which typically applies bag-of-words to extract tens of thousands of features to build a prediction model, we propose a sentiment analysis-based approach for financial market prediction using news disclosures. Specifically, sentiment analysis is carried out in the pre-processing phase to extract sentiment-related features from financial news. Historical stock market data from the perspective of time series analysis is also included as an input feature. With the extracted features, we use a support vector machine (SVM) to build the prediction model, with its parameters optimised through particle swarm optimisation (PSO). Experimental results show that our proposed SVM and PSO-based model is able to obtain better results than a deep learning model in terms of time and accuracy. The results presented here are to date the best in the literature based on the financial news dataset tested. This excellent performance is attributed to the sentiment analysis done during the pre-processing stage, as it reduces the feature dimensions significantly.
Towards practical federation of BGPs in the presence of blank nodes

Distributed query processing, when blank nodes might occur in sources and only the signature of sources are known, may in the worst case require that all possible query partitions be evaluated in order to ensure soundness and completeness of answers. The work presented in this paper attempts to push the boundary as to what query sizes and structures lies within practical feasibility. The presented approach utilizes semantic information obtained by probing sources for occurrences of blank nodes, interleaved in a recursive algorithm for calculating restricted growth strings, in order to detect and abort unfruitful branches in the partition generating process. The approach is evaluated against a well-known SPARQL benchmark, modified for the distributed case, and tentative conclusions regarding the effectiveness are drawn.
Effects of Individual Traits on Diversity-Aware Music Recommender User Interfaces

When recommendations become increasingly personalized, users are often presented with a narrower range of content. To mitigate this issue, diversity-enhanced user interfaces for recommender systems have in the past found to be effective in increasing overall user satisfaction with recommendations. However, users may have different requirements for diversity, and consequently different visualization requirements. In this paper, we evaluate two visual user interfaces, SimBub and ComBub, to present the diversity of a music recommender system from different perspectives. SimBub is a baseline bubble chart that shows music genres and popularity by color and size, respectively. In addition, ComBub visualizes selected audio features along the X and Y axis in a more advanced and complex visualization. Our goal is to investigate how individual traits such as musical sophistication (MS) and visual memory (VM) influence the satisfaction of the visualization for perceived music diversity, overall usability, and support to identify blind-spots. We hypothesize that music experts, or people with better visual memory, will perceive higher diversity in ComBub than SimBub. A within-subjects user study (N=83) is conducted to compare these two visualizations. Results of our study show that participants with high MS and VM tend to perceive significantly higher diversity from ComBub compared to SimBub. In contrast, participants with low MS perceived significantly higher diversity from SimBub than ComBub; however, no significant result is found for the participants with low VM. Our research findings show the necessity of considering individual traits while designing diversity-aware interfaces.
Rethinking the Future of Wireless Emergency Alerts

The Wireless Emergency Alerting (WEA) service is a standards-based transport and presentation channel used nationwide in the United States. The service can deliver short text warnings to wireless subscribers through a cell broadcast mechanism. For emergency situations in which a broadcast modality and a single, short text message are sufficient to convey information, the WEA service can be efficient and effective. However, the content to be delivered may necessitate more than a single, unchanging short message. In this research, we first examine the WEA service from the perspective of alert originators. We then use the insights gained to explore the efficacy of a range of potential extensions to the service. The extensions mainly address the importance of user context and the ability to create awareness through careful attention to the integrity of the vital information. We evaluated these extensions using a WEA emulation testbed in two public usability trials. We present an analysis of the broad range of improvements as a basis for further research into improving the service. We conclude that (1) precise geo-targeting augmented with location information and maps is an important aspect of capturing users' context, and (2) presenting information in a digested form can markedly improve the actionability and the accuracy of interpretation.