Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of Retrieving Publications with Multiple Citation Networks

Academic papers in digital libraries are the main studying resource for most of researchers nowadays. While searching papers by keywords, a digital library generally responses the relevant papers having the same words with most frequent or recent retrieval. However, starting a new research topic and surveying important historical articles may not find the key papers effectively due to their long publication dates. In this paper, we introduce the idea of analyzing heterogeneous citation networks to improve the effectiveness of retrieving relevant research articles. The proposed model combines multiple citation networks include paper citation networks and author citation networks. We analyze and discuss the ranking results of applying different citation networks on the test collection of documents. The experiment results also can be the indication of developing effective paper recommendation systems.
Temporal models for personalized grade prediction in massive open online courses

With the booming of massive open online courses (MOOCs) in recent years, online educational pattern, both inside and outside of the campus community, have being become more popular. However, high attrition rate and low completion rate remain major impediments. How to reduce the dropout rate and improve the learning effect, with providing the personalized feedback to teaching and learning by forecasting the personal grade, has become the focus of the research of MOOCs. In this paper, a personalized grade and learning trend prediction method is proposed. A supervised classification method is applied to analyze the online learning behavior. According to the characteristics of online learning behavior changing with time, a long short-term memory recurrent neural network is employed to construct a learning trend prediction model, which predicts whether the learner will be online in the subsequent weeks (the possibility of returning class) and the final grade. The evaluation of the method was based on the data from five multi-disciplinary courses on icourse163 platform. The test results showed that the average AUC of the dropout prediction was 0.863 and the average AUC for predicting the final grade was 0.748 respectively. Compared with the baseline methods (Logistic Regression and SVM), this presented method increases the average AUC of the dropout prediction by 3.2% and 3.5% as well as the average AUC of the final grade prediction by 6.1% and 7.7%. Early warning and timely feedback would be valuable on MOOC, which is a promising way to optimize the design and organization of a course and to improve the learning effectiveness.
Understanding regulatory mechanisms underlying stem cells helps to identify cancer biomarkers

Detection of biomarker genes play a crucial role in disease detection and treatment. Bioinformatics offers a variety of approaches for identification of biomarker genes which play key roles in complex diseases. These computational approaches enhance the insight derived from experiments and reduce the efforts of biologists and experimentalists. This is essentially achieved through prioritizing a set of genes with certain attributes. In this thesis, we show that understanding the regulatory mechanisms underlying stem cells helps to identify cancer biomarkers. We got inspired by the regulatory mechanisms of the pluripotency network in mouse embryonic stem cells and formulated the problem where a set of master regulatory genes in regulatory networks is identified with two combinatorial optimization problems namely as minimum dominating set and minimum connected dominating set in weakly and strongly connected components. Then we applied the developed methods to regulatory cancer networks to identify disease-associated genes and anti-cancer drug targets in breast cancer and hepatocellular carcinoma (chapter 3). As not all the nodes in the solutions are critical, we developed a prioritization method to rank a set of candidate genes which are related to a certain disease based on systematic analysis of the genes that are differentially expressed in tumor and normal conditions (chapter 5). Moreover, we demonstrated that the topological features in regulatory networks surrounding differentially expressed genes are highly consistent in terms of using the output of several analysis tools (chapter 6). We compared two randomization strategies for TF-miRNA co-regulatory networks to infer significant network motifs underlying cellular identity. We showed that the edge-type conserving method surpasses the nonconserving method in terms of biological relevance and centrality overlap (chapter 7). We presented several web servers and software packages that are publicly available at no cost. The Cytoscape plugin of minimum connected dominating set identifies a set of key regulatory genes in a user provided regulatory network based on a heuristic approach. The ILP formulations of minimum dominating set and minimum connected dominating set return the optimal solutions for the aforementioned problems. Our source code is publicly available (chapter 3). The web servers TFmiR and TFmiR2 construct disease-, tissue-, process-specific networks for the sets of deregulated genes and miRNAs provided by a user. They highlight topological hotspots and offer detection of threeand four-node FFL motifs as a separate web service for both organisms mouse and human (chapter 4,8).
Mesenchymal Stem Cell Expressing TRAIL as Targeted Therapy against Sensitised Tumour

Tapping into the ability of engineered mesenchymal stem cells (MSCs) to mobilise into the tumour has expanded the scope of cancer treatment. Engineered MSCs expressing tumour necrosis factor (TNF)-related apoptosis inducing ligand (MSC-TRAIL) could serve as a platform for an efficient and targeted form of therapy. However, the presence of cancer stem cells (CSCs) that are resistant to TRAIL and apoptosis may represent a challenge for effective treatment. Nonetheless, with the discovery of small molecular inhibitors that could target CSCs and tumour signalling pathways, a higher efficacy of MSC-TRAIL mediated tumour inhibition can be achieved. This might pave the way for a more effective form of combined therapy, which leads to a better treatment outcome. In this review, we first discuss the tumour-homing capacity of MSCs, its effect in tumour tropism, the different approach behind genetically-engineered MSCs, and the efficacy and safety of each agent delivered by these MSCs. Then, we focus on how sensitisation of CSCs and tumours using small molecular inhibitors can increase the effect of these cells to either TRAIL or MSC-TRAIL mediated inhibition. In the conclusion, we address a few questions and safety concerns regarding the utilization of engineered MSCs for future treatment in patients.
A Parameter Space Framework for Online Outlier Detection Over High-Volume Data Streams

In diverse applications ranging from social networks to location-based online services to traffic monitoring, data streams are continuously monitored by multiple outlier analysts customized with different parameter settings. Real-time response to such complex outlier analytics in high-speed streaming data has been recognized as critical for many domains. In this paper, we propose a parameter space framework, called PSOD, for online outlier detection over sliding window streams to support a large variety of query requests in parameter space with both diverse pattern and window parameter settings. First, we design an ingenious neighbor table that records the neighbors for each point in different distance intervals and different slides, which enables us to maximally reuse the already acquired neighbor information across the entire parameter space. In addition, we propose a series of shared strategies in sliding window environment to minimize processing cost by eliminating the redundant query requests. Moreover, the PSOD effectively transforms the query group in 4-D parameter space into a periodic query group in 3-D parameter space to minimize the number of queries. Our experimental study on three real-world steaming data demonstrates that our PSOD successfully drives down the CPU costs by more than 100 folds compared with the state-of-the-art method.
Visualizing Expanded Query Results

When performing queries in web search engines, users often face difficulties choosing appropriate query terms. Search engines therefore usually suggest a list of expanded versions of the user query to disambiguate it or to resolve potential term mismatches. However, it has been shown that users find it difficult to choose an expanded query from such a list. In this paper, we describe the adoption of set‐based text visualization techniques to visualize how query expansions enrich the result space of a given user query and how the result sets relate to each other. Our system uses a linguistic approach to expand queries and topic modeling to extract the most informative terms from the results of these queries. In a user study, we compare a common text list of query expansion suggestions to three set‐based text visualization techniques adopted for visualizing expanded query results – namely, Compact Euler Diagrams, Parallel Tag Clouds, and a List View – to resolve ambiguous queries using interactive query expansion. Our results show that text visualization techniques do not increase retrieval efficiency, precision, or recall. Overall, users rate Parallel Tag Clouds visualizing key terms of the expanded query space lowest. Based on the results, we derive recommendations for visualizations of query expansion results, text visualization techniques in general, and discuss alternative use cases of set‐based text visualization techniques in the context of web search.
TerseCades: Efficient Data Compression in Stream Processing

This work is the first systematic investigation of stream processing with data compression: we have not only identified a set of factors that influence the benefits and overheads of compression, but have also demonstrated that compression can be effective for stream processing, both in the ability to process in larger windows and in throughput. This is done through a series of (i) optimizations on a stream engine itself to remove major sources of inefficiency, which leads to an order-of-magnitude improvement in throughput (ii) optimizations to reduce the cost of (de)compression, including hardware acceleration, and (iii) a new technique that allows direct execution on compressed data, that leads to a further 50% improvement in throughout. Our evaluation is performed on several real-world scenarios in cloud analytics and troubleshooting, with both microbenchmarks and production stream processing systems.
Gender differences in the mental health of unaccompanied refugee minors in Europe: a systematic review

Objectives Our aim was to summarise the current evidence regarding gender differences in the mental health of unaccompanied refugee minors (URM) and to identify gaps in research. Setting We focused on quantitative studies presenting primary data from Organisation for Economic Co-Operation and Development(OECD)countries. Language was restricted to English or German. Participants To be eligible, a study had to involve (former) URM who immigrated to an OECD country. Design We conducted a systematic review in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The databases MEDLINE, CINAHL, LIVIVO, PSYNDEX and PsycINFO were searched from 1990 to 2017. Studies were judged for eligibility by two independent reviewers each. We narratively summarised our results. Results 9 primary studies, all from Europe, examined gender differences in the mental health of URM. The majority of the included studies found female URM to be more often affected by post-traumatic or depressive symptoms than their male counterparts. There is only weak evidence regarding other mental health outcomes. Two studies each conducted gender-specific analyses on anxiety and externalising behaviour, but no statistically significant differences between female and male URM were detected. Conclusions Female gender is associated with a higher vulnerability towards certain mental health problems among URM residing in Europe. However, the lack of representative studies using reliable diagnostic methods indicates that the findings so far should be treated with caution. Further research is needed to clarify the role of gender for mental health in URM and to examine underlying mechanisms.
Feature Oriented Software Development Framework for Stock Exchange Systems

Many software companies use the popular method of layered architecture to develop their software products resulting in code to be more generic. This generalization introduces a lot of unnecessary elements which make the system architecture to be heavy and less elegant. To overcome this challenge, a concept of feature oriented software development (FOSD) is introduced. That way, the software system will be delivered as a set of features and the feature set could be decoupled at any time, according to the latest set of requirements without harming to any existing functionality. In this research the final outcome has been a feature oriented domain specific language (DSL). The objective of this developed feature oriented DSL is to make it effective even for business analysts to introduce new features without getting help from the development team of the project. A code generator and a core controller have been developed as part of this research exhibiting the above mentioned FOSD & DSL principles. The evaluation results indicate the generated feature oriented code identically behaves as its manually written counterpart code.
HARMONIC: A multimodal dataset of assistive human–robot collaboration

We present the Human And Robot Multimodal Observations of Natural Interactive Collaboration (HARMONIC) dataset. This is a large multimodal dataset of human interactions with a robotic arm in a shared autonomy setting designed to imitate assistive eating. The dataset provides human, robot, and environmental data views of 24 different people engaged in an assistive eating task with a 6-degree-of-freedom (6-DOF) robot arm. From each participant, we recorded video of both eyes, egocentric video from a head-mounted camera, joystick commands, electromyography from the forearm used to operate the joystick, third-person stereo video, and the joint positions of the 6-DOF robot arm. Also included are several features that come as a direct result of these recordings, such as eye gaze projected onto the egocentric video, body pose, hand pose, and facial keypoints. These data streams were collected specifically because they have been shown to be closely related to human mental states and intention. This dataset could be of interest to researchers studying intention prediction, human mental state modeling, and shared autonomy. Data streams are provided in a variety of formats such as video and human-readable CSV and YAML files.
High Gain and Wideband High Dense Dielectric Patch Antenna Using FSS Superstrate for Millimeter-Wave Applications

Gain and bandwidth enhancement of low profile, linearly polarized square dense dielectric patch antennas using a frequency selective surface (FSS) superstrate layer is proposed. A high dense dielectric patch antenna is utilized as a radiating element instead of a metallic patch in order to gain several significant advantages, including low profile, wide bandwidth, and high radiation efficiency. The implemented antenna is excited by an aperture-coupled feeding technique. The antenna gain is enhanced by using a highly reflective FSS superstrate layer, realizing an antenna gain enhancement of 11 dBi. The implemented antenna acquired a measured gain of about 17.78 dBi at 28 GHz with a 9% bandwidth and radiation efficiency of 90%. The bandwidth of the proposed antenna is improved by using a unit cell printed on two sides, as it provides a positive phase gradient over the desired frequency range. The antenna impedance bandwidth is broadened and the measured impedance matching $S{11}$ exhibited a 15.54% instead of 9% bandwidth while maintaining a high-gain characteristic of about 15.4 dBi. The implemented antenna presents a solid radiation performance with good agreement between the measured and simulation results. For some attractive advantages such as low profile, low cost, lightweight, small size, and ease of implementation, the proposed antenna is a very good candidate for millimeter-wave wireless communications.
A Posteriori Error Analysis for Evolution Nonlinear Schrödinger Equations up to the Critical Exponent

We provide a posteriori error estimates in the $L^\infty(L^2)-$norm for relaxation time discrete and fully discrete schemes for a class of evolution nonlinear Schr\"odinger equations up to the critical exponent. In particular for the discretization in time we use the relaxation Crank-Nicolson-type scheme introduced by Besse in \cite{Besse}. For the discretization in space we use finite element spaces that are allowed to change between time steps. The estimates are obtained using the reconstruction technique. Through this technique the problem is converted to a perturbation of the original partial differential equation and this makes it possible to use nonlinear stability arguments as in the continuous problem. In particular, main ingredients we use in our analysis are the Gagliardo-Nirenberg inequality and the two conservation laws (mass and energy conservation) of the continuous problem. Numerical results illustrate that the estimates are indeed of optimal order of convergence.
Challenges and Perspectives of Interacting with Hierarchy Visualizations on Large-Scale Displays

In this paper we take a look into typical interaction scenarios when it comes to visualizing hierarchical data while we particularly focus on one specific candidate that is the visual depiction by generalized Pythagoras trees. Those are visualizations that benefit from their aesthetically appealing appearance since they come close to a visual model of trees growing in nature. Traditionally, those generalized Pythagoras trees are visualized on typical computer screens, clearly indicating the hierarchy, but negatively, the finer substructures are oftentimes not visible anymore due to the display space restriction and the low resolution. Hence, in this paper we describe the generalized Pythagoras trees displayed on largescale high-resolution displays and illustrate the challenges and perspectives when we have to interact with the visual representation by using the technique individually, but also in collaboration. To explore possible interactions we experimented with tablet computers combined with an optical tracking system. We evaluate the effectiveness and aesthetics by means of several modifiable visual parameters and by interacting with the visualization.
Are there advances in pharmacotherapy for panic disorder? A systematic review of the past five years

ABSTRACT Introduction: Several effective medications are available for treating panic disorder (PD). However, outcomes are unsatisfactory in a number of patients, suggesting the usefulness of expanding the array of antipanic drugs and improving the quality of response to current recommended treatments. Areas covered: The authors have performed an updated systematic review of pharmacological studies (phase III onwards) to examine whether advances have been made in the last five years. Only four studies were included. D-cycloserine no longer seemed promising as a cognitive-behavioral therapy (CBT) enhancer. Some preliminary findings concerning the optimization of recommended medications deserved consideration, including: the possibility that SSRIs are more effective than CBT alone in treating panic attacks, combined therapy is preferable when agoraphobia is present, and clonazepam is more potent than paroxetine in decreasing panic relapse. Expert opinion: Given the lack of novel treatments, expanding a personalized approach to the existing medications seems to be the most feasible strategy to improve pharmacotherapy outcomes regarding PD. Recent technological progress, including wearable devices collecting real-time data, ‘big data’ platforms, and application of machine learning techniques might help make outcome prediction more reliable. Further research on previously promising novel treatments is also recommended.
Can We Achieve Open Category Detection with Guarantees?

Open category detection is the problem of detecting “alien” test instances that belong to categories/classes that were not present in the training data. In many applications, reliably detecting such aliens is central to ensuring safety and/or quality of test data analysis. Unfortunately, to the best of our knowledge, there are no algorithms that provide theoretical guarantees on their ability to detect aliens under general assumptions. Further, while there are algorithms for open category detection, there are few empirical results that directly report alien-detection rates. Thus, there are signiﬁcant theoretical and empirical gaps in our understanding of open category detection. In this paper, we take a step toward addressing this gap by studying a simpliﬁed, but practically relevant, variant of open category detection. In our setting, we are provided with a “clean” training set that contains only the target categories of interest. However, at test time, some fraction α of the test examples are aliens. Under the assumption that we know an upper bound on α , we develop an algorithm with PAC-style guarantees on the alien detection rate, while aiming to minimize false alarms. Our empirical results on synthetic and benchmark datasets demonstrate the regimes in which the algorithm can be effective and provide a baseline for further advancements.
Secondary Encrypted Secure Transmission in Cognitive Radio Networks

In order to secure the primary privacy information and provide quality-of-service provisioning for the secondary system, we propose a secondary encryption secure transmission scheme. In the proposed scheme, the primary system utilizes the secure secondary messages to encrypt the primary confidential messages and the secondary system can acquire some spectrum opportunities. Specifically, when the primary system is secure, the primary information can be directly transmitted; when the primary system is insecure while the secondary messages can be securely transmitted, the primary system utilizes the secure secondary messages to encrypt the primary information; otherwise, the spectrum will be utilized for secondary transmission. For the proposed scheme, we investigate the performances of the primary ergodic secrecy rate and the average secondary throughput. Numerical results have demonstrated that the secondary encryption secure transmission scheme can secure the primary privacy messages and improve the secondary transmission throughput.
ADMM and Accelerated ADMM as Continuous Dynamical Systems

Recently, there has been an increasing interest in using tools from dynamical systems to analyze the behavior of simple optimization algorithms such as gradient descent and accelerated variants. This paper strengthens such connections by deriving the differential equations that model the continuous limit of the sequence of iterates generated by the alternating direction method of multipliers, as well as an accelerated variant. We employ the direct method of Lyapunov to analyze the stability of critical points of the dynamical systems and to obtain associated convergence rates.