Miracle-dz/UniFAR-FTUs huggingface dataset:
Discrimination-Aware Projected Matrix Factorization

<italic>Non-negative Matrix Factorization</italic> (NMF) has been one of the most popular clustering techniques in machine leaning, and involves various real-world applications. Most existing works perform matrix factorization on high-dimensional data directly. However, the intrinsic data structure is always hidden within the low-dimensional subspace. And, the redundant features within the input space may affect the final result adversely. In this paper, a new unsupervised matrix factorization method, <italic>Discrimination-aware Projected Matrix Factorization</italic> (DPMF), is proposed for data clustering. The main contributions are threefold: (1) The linear discriminant analysis is jointly incorporated into the unsupervised matrix factorization framework, so the clustering can be accomplished in the discriminant subspace. (2) The manifold regularization is introduced to perceive the geometric information, and the <inline-formula><tex-math notation="LaTeX">${\ell _{2,1}}$</tex-math><alternatives><mml:math><mml:msub><mml:mi>ℓ</mml:mi><mml:mrow><mml:mn>2</mml:mn><mml:mo>,</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msub></mml:math><inline-graphic xlink:href="wang-ieq1-2936855.gif"/></alternatives></inline-formula>-norm is utilized to improve the robustness. (3) An efficient optimization algorithm is designed to solve the proposed problem with proved convergence. Experimental results on one toy dataset and eight real-world benchmarks show the effectiveness of the proposed method.
Effective Applications of Automated Writing Feedback in Process-based Writing Instruction

Koh, Won-Young. (2017). Effective applications of automated writing feedback in process-based writing instruction. English Teaching, 72(3), 91-118. This study investigated the appropriate ways in offering immediate automated writing feedback within the framework of process-based writing pedagogy by comparing relative effects of two different automated writing evaluation (AWE) system application types on improving writing performance. The experiment took an initiative step in elucidating at what point in the process-based writing stages AWE feedback is best to be served. The research is conducted to confirm whether providing an instant language-related feedback whenever EFL students call for will either interfere the development of content indeed – as it has been expected by process-based writing approach, or bring improvement in students’ writing. Two application types, namely non-continuous feedback (NCF) and continuous feedback (CF) group are differentiated in terms of in which point of the writing stages students are enabled to get access to the AWE system. With the purpose of the study, a total of 20 students participated. The findings revealed that CF group did not receive language-related AWE feedback to the point of distracting the development of their content. Furthermore, CF group significantly outperformed NCF group in overall writing product, especially on the dimension of grammar and content. Students also expressed a positive attitude toward receiving instant language-related feedback via AWE system.
pCRM1exportome: database of predicted CRM1-dependent Nuclear Export Signal (NES) motifs in cancer-related genes

MOTIVATION The consensus pattern of Nuclear Export Signal (NES) is a short sequence motif that is commonly identified in protein sequences, whether the motif acts as an NES (true positive) or not (false positive). Finding more plausible NES functioning regions among the vast array of consensus-matching segments would provide an interesting resource for further experimental validation. Better defined NES should also allow meaningful mapping of cancer-related mutation positions, leading to plausible explanations for the relationship between nuclear export and disease. RESULTS Possible NES candidate regions are extracted from the cancer-related human reference proteome. Extracted NES are scored for reliability by combining sequence-based and structure-based approaches. The confidently identified NES candidate motifs were checked for overlap with cancer-related mutation positions annotated in the COSMIC database. Among the ∼700 cancer-related sequences in the COSMIC Cancer Gene Census, 178 sequences are predicted to have possible NES motifs containing cancer-related mutations at their key positions. These lists are organized into our database (pCRM1exportome), and other protein sequences in the human reference proteome can also be retrieved by their UniProt IDs. AVAILABILITY AND IMPLEMENTATION The database is freely available at http://prodata.swmed. edu/pCRM1exportome. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Expression and Composition of Optimization-Based Applications for Software-Defined Networking

Victor Heorhiadi: Expression and Composition of Optimization-based Applications for Software-Defined Networking (Under the direction of Michael K. Reiter) Motivated by the adoption of the Software Defined Networking and its increasing focus on applications for resource management, we propose a novel framework for expressing network optimization applications. Named the SDN Optimization Layer (SOL), the framework and its extensions alleviate the burden of constructing optimization applications by abstracting the low-level details of mathematical optimization techniques such as linear programming. SOL utilizes the path abstraction to express a wide variety of network constraints and resource-management logic. We show that the framework is general and efficient enough to support various classes of applications. We extend SOL to support composition of multiple applications in a fair and resource-efficient way. We demonstrate that SOL’s composition produces better resource efficiency than previously available composition approaches and is tolerant to network variations. Finally, as a case study, we develop a new application for load balancing network intrusion prevention systems, called SNIPS. We highlight the challenges in developing the SNIPS optimization from the ground up, show SOL’s (conceptually) simplified version, and verify that both produce nearly identical solutions.
Characterization and Perturbation of Functional Networks That Support Human Memory

Episodic memory is essential to our daily lives, as it attaches meaning to the constant stream of sensory inputs to the brain. However, episodic memory often fails in a number of common neurocognitive disorders. Effective therapies remain elusive, owing to the complexity of brain networks and neural processes that support episodic encoding and retrieval. In particular, it is not understood how inter-regional communication within the brain supports memory function, though such communication may be critical to the highly integrative nature of episodic memory. To uncover the patterns of memory-related functional connectivity, we asked a large cohort of neurosurgical patients with indwelling electrodes to perform a verbal free-recall task, in which patients viewed lists of simple nouns and recalled them a short time later. As patients performed this task, we collected intracranial EEG (iEEG) from electrodes placed on the cortical surface and within the medial temporal lobe (MTL). First, we examined whole-brain functional networks that emerged during the encoding and retrieval phases of this task, using spectral methods to correlate frequency-specific signals between brain regions. We identified a dynamic network of regions that exhibited enhanced theta (3-8 Hz) connectivity during successful memory processing, whereas regions tended to desynchronize at high frequencies (30-100 Hz). Next, using only electrodes placed within the MTL, we asked whether functional coupling was also observed among this mesoscale subnetwork of highly specialized regions that play an outsize role in memory. Recapitulating our earlier findings, we noted broadly enhanced theta connectivity within the MTL, centering on the left entorhinal cortex during successful encoding operations. Finally, to determine whether such low-frequency functional connections reflect correlative or causal relations in the brain, we applied direct electrical stimulation via electrodes placed within the MTL. We found that lowfrequency connections (5-13 Hz) predicted the emergence of theta activity at distant regions in the brain – particularly when stimulation occurred near white matter – indicating the potential causal relevance of iEEGbased functional connections. Taken together, these studies underscore the importance of low-frequency functional coupling to memory across spatial scales, and suggest this form of coupling indicates a causal relation between brain regions. Degree Type Dissertation Degree Name Doctor of Philosophy (PhD) Graduate Group Bioengineering First Advisor Michael J. Kahana
Energy Efficiency Optimization Based on eICIC for Wireless Heterogeneous Networks

To solve the problem of load imbalance and system interference in heterogeneous networks (HetNets), an energy efficiency (EE) optimized strategy based on enhanced intercell interference coordination (eICIC) technology is proposed. The optimization of the EE of the system is established by taking into account four components which are the connection between the users and base stations (BSs), almost blank subframe (ABS) ratio, resource allocation in the frequency domain, and the power transmission of the macro BSs (MBSs). In our optimized strategy, the user’s optimal BS connection status are determined according to the overall EE change of the system, and the allocation of subframes and resource blocks of the MBSs both in the time domain and frequency domain are optimized, respectively. We use the Gauss–Seidel method to iteratively solve the optimization problem. Simulation results show that the optimized strategy outperforms other solutions in terms of EE and user fairness in average. The proposed optimization strategy increases the system EE by 24.2% and 19.1%, respectively, when compared with the maximal reference signal received power (MAX-RSRP) and the low power-ABS (LP-ABS) strategies. The Jain fairness index of our proposed optimization strategy is averagely increased by 33.8% and 23.4%, respectively. In addition, the proposed strategy can further improve the user satisfaction and the performance in the system.
Spatial Coordinate Transformations with Noisy Data

The parametric transformation of spatial coordinates between different reference frames is traditionally dealt with a stepwise approach which imposes a suboptimal treatment in the presence of noisy data. The chapter explains briefly the drawbacks of this approach and then presents an alternative scheme for spatial coordinate transformations that improves the classic stepwise solution when using noisy coordinates of known stochastic structure. The proposed methodology is simple in principle, although its numerical implementation with nonlinear parametric models is a bit more involved and it relies on the joint least squares adjustment of the observed coordinates using their full stochastic model over all points of interest. The mathematical framework and the related properties of this “ stacking ” approach are presented in detail, along with a numerical example that demonstrates its feasibility for practical problems in geospatial applications.
Korean Intonational Phrases Do Not Distinguish Dependent Clauses from Within-Clause Units

This study examined how adult native speakers of Korean prosodically structure their speech during spontaneous story telling. It asked participants to produce a spontaneous story from a picture book in L1 Korean. Participants’ phonological phrasing (via pause) was analyzed with reference to eight hierarchical syntactic structures defined in the study. The results suggested three-level hierarchy in phonological structure in spoken Korean: between sentences > between independent clauses > between a dependent clause and the matrix clause = any within-clause boundaries. Korean speakers paused the longest prior to starting a new sentence. They paused statistically significantly shorter between two independent clauses (e.g., coordinate clauses and adverbial clauses), and even shorter between a dependent clause (e.g., complement clauses and relative clauses) and the matrix clause. Interestingly, however, the last type of clausal boundaries, i.e., between interdependent but separate clauses, did not differ from any of the observed within-clause boundaries with respect to intonation structure marked by pauses. These results are consistent with the literature as to across-clause boundaries, but contra the traditional prediction that syntactic hierarchical structure may be mapped onto phonological structure: clausal boundaries are prosodically treated the same as within-clausal boundaries. Crosslinguistic and educational implications are discussed.
Heart Rate Monitoring and Alert System Using Smartphone

Heart disease like arrhythmia need continual long-term monitoring. For example, in emergency at home, where the patient is unable help themselves or seek help, there is a need for long distance health monitoring for early and faster assessment for treatment. This project presents a remote monitoring system for monitoring the irregularity in heart rate, which enables real-time monitoring for the cardiovascular diseases (CVDs) patient. The system utilizes Photoplethysmography (PPG) sensor to obtain the pulse reading. The sensor is non-invasive where pulse reading is taken from the finger. The microcontroller is used to receive and process the signal. When irregularity in heart rate is detected, the microcontroller will send data to a smartphone using Bluetooth. A mobile application is developed to receive the data and to send out an alert in the form of a text message to another mobile phone. The alert is successfully sent to the specified recipient such as medical doctors or next of kin in the emergency which contain the details such as heart rate information and GPS coordinate. Evaluation on the functionality of the device shows that the developed device can reach accuracy of 97.50%, precision of 96.55%, sensitivity of 99.29% and specificity of 91.67%.
Gestaltung von In-Car Multi-Device Cross-Platform-Umgebungen

Die Gestaltung von graphischen Bedienoberflächen für Applikationen in Umgebungen mit mehreren Geräten unterschiedlicher Hersteller wird immer bedeutender – auch in der Automobilbranche. Um dieser Herausforderung zu begegnen, muss verstanden werden, wie Design-Entscheidungen für solche Applikationen getroffen werden. Als Vorstudie einer Untersuchung dieser Thematik wurden neun User Experience Designer befragt. Der Fokus liegt auf Anwendungen für in-car Entertainmentund Komfortfunktionen auf integrierten und mitgebrachten Geräten. Konkret erkannt als die größten Probleme für übergreifende Konzepte sind: Das Aufeinandertreffen unterschiedlicher Konsistenzen durch plattformspezifische Gestaltungsparadigmen und Prämissen, das Fehlen von spezifischen Prozessen, Methoden und Werkzeugen um Zielkonflikte aufzulösen. Das Kernproblem ist das Fehlen einer wissenschaftlich ermittelten Grundlage, wie sich User Experience in Mehrgeräteumgebungen unterschiedlicher Hersteller verhält.
An Efficient Energy Utilization based Cluster Head Selection for Mobile Sensor Network

The sensor nodes in WSN (Wireless Sensor Network) are energy dependent and each and every functioning is only possible to exhaust some amount of power or energy. The WSN is decentralized network and nodes are free to move in limited radio range for communication. The routing protocol is performing important role for communication and better routing strategy is reduces the energy consumption and enhances the utilization of energy. LEACH is the energy efficient protocol and this protocol is only designed for energy based routing. In this paper we proposed a new multipath routing strategy with higher energy node selection scheme for selection of Cluster Head (CH) and route establishment. The performance of proposed LEACH protocol is compare with normal LEACH protocol in WSN. The energy utilization of nodes is improves if the possibility of link breakage is reduced and the proposed energy approach is established strong link in dynamic network. The proposed scheme ignores the shortest path routing procedure but established the reliable path in between source to destination. The packet retransmission is consumes the lot of extra energy for sending or forwarding the same data and also not sure, in next time data is successful deliver to destination. The multipath routing is improves the reliability of link in terms of mobility. If the node/s is the part of link and goes to out of range then instantly the alternative route is present for data delivery in dynamic network. The network performance is measure through performance metrics like throughput, routing overhead and number of survive nodes in dynamic network.
Detecting deceptive behaviour in the wild: text mining for online child protection in the presence of noisy and adversarial social media communications

A real-life application of text mining research “in the wild”, i.e. in online social media, differs from more general applications in that its defining characteristics are both domain and process dependent. This gives rise to a number of challenges of which contemporary research has only scratched the surface. More specifically, a text mining approach applied in the wild typically has no control over the dataset size. Hence, the system has to be robust towards limited data availability, a variable number of samples across users and a highly skewed dataset. Additionally, the quality of the data cannot be guaranteed. As a result, the approach needs to be tolerant to a certain degree of linguistic noise. Finally, it has to be robust towards deceptive behaviour or adversaries. This thesis examines the viability of a text mining approach for supporting cybercrime investigations pertaining to online child protection. The main contributions of this dissertation are as follows. A systematic study of different aspects of methodological design of a state-ofthe- art text mining approach is presented to assess its scalability towards a large, imbalanced and linguistically noisy social media dataset. In this framework, three key automatic text categorisation tasks are examined, namely the feasibility to (i) identify a social network user’s age group and gender based on textual information found in only one single message; (ii) aggregate predictions on the message level to the user level without neglecting potential clues of deception and detect false user profiles on social networks and (iii) identify child sexual abuse media among thousands of legal other media, including adult pornography, based on their filename. Finally, a novel approach is presented that combines age group predictions with advanced text clustering techniques and unsupervised learning to identify online child sex offenders’ grooming behaviour. The methodology presented in this thesis was extensively discussed with law enforcement to assess its forensic readiness. Additionally, each component was evaluated on actual child sex offender data. Despite the challenging characteristics of these text types, the results show high degrees of accuracy for false profile detection, identifying grooming behaviour and child sexual abuse media identification.
Using Machine Learning to Optimize Web Interactions on Heterogeneous Mobile Systems

The web has become a ubiquitous application development platform for mobile systems. Yet, web access on mobile devices remains an energy-hungry activity. Prior work in the field mainly focuses on the initial page loading stage, but fails to exploit the opportunities for energy-efficiency optimization while the user is interacting with a loaded page. This paper presents a novel approach for performing energy optimization for interactive mobile web browsing. At the heart of our approach is a set of machine learning models, which estimate at runtime the frames per second for a given user interaction input by running the computation-intensive web render engine on a specific processor core under a given clock speed. We use the learned predictive models as a utility function to quickly search for the optimal processor setting to carefully trade responsive time for reduced energy consumption. We integrate our techniques to the open-source Chromium browser and apply it to two representative mobile user events: scrolling and pinching (i.e., zoom in and out). We evaluate the developed system on the landing pages of the top-100 hottest websites and two big.LITTLE heterogeneous mobile platforms. Our extensive experiments show that the proposed approach reduces the system-wide energy consumption by over 36% on average and up to 70%. This translates to an over 17% improvement on energy-efficiency over a state-of-the-art event-based web browser scheduler, but with significantly fewer violations on the quality of service.
Influences of Cardiorespiratory Fitness and Other Precursors on Cardiovascular Disease and All-Cause Mortality in Men and Women

Objective. —To quantify the relation of cardiorespiratory fitness to cardiovascular disease (CVD) mortality and to all-cause mortality within strata of other personal characteristics that predispose to early mortality. Design. —Observational cohort study. We calculated CVD and all-cause death rates for low (least fit 20%), moderate (next 40%), and high (most fit 40%) fitness categories by strata of smoking habit, cholesterol level, blood pressure, and health status. Setting. —Preventive medicine clinic. Study Participants. —Participants were 25 341 men and 7080 women who completed preventive medical examinations, including a maximal exercise test. Main Outcome Measures. —Cardiovascular disease and all-cause mortality. Results. —There were 601 deaths during 211 996 man-years of follow-up, and 89 deaths during 52 982 woman-years of follow-up. Independent predictors of mortality among men, with adjusted relative risks (RRs) and 95% confidence intervals (Cls), were low fitness (RR, 1.52; 95% CI, 1.28-1.82), smoking (RR, 1.65; 95% CI, 1.39-1.97), abnormal electrocardiogram (RR, 1.64; 95% CI, 1.34-2.01), chronic illness (RR, 1.63; 95% CI, 1.37-1.95), increased cholesterol level (RR, 1.34; 95% CI, 1.13-1.59), and elevated systolic blood pressure (RR, 1.34; 95% CI, 1.13-1.59). The only statistically significant independent predictors of mortality in women were low fitness (RR, 2.10; 95% CI, 1.36-3.21) and smoking (RR, 1.99; 95% CI, 1.25-3.17). Inverse gradients were seen for mortality across fitness categories within strata of other mortality predictors for both sexes. Fit persons with any combination of smoking, elevated blood pressure, or elevated cholesterol level had lower adjusted death rates than low-fit persons with none of these characteristics. Conclusions. —Low fitness is an important precursor of mortality. The protective effect of fitness held for smokers and nonsmokers, those with and without elevated cholesterol levels or elevated blood pressure, and unhealthy and healthy persons. Moderate fitness seems to protect against the influence of these other predictors on mortality. Physicians should encourage sedentary patients to become physically active and thereby reduce the risk of premature mortality.
3D pose estimation using CNN and 3D depth map probabilistic model.

We have integrated the techniques of 3D pose estimation and action recognition from 2D images. By using Convolutional Neural network and training the network by providing a set of images from the pretrained data for detection of pose from the images. This estimation of the pose will further be used for the human computer interface for other projects. With this method we intend to reduce the challenges between 2D joint detection and 3D pose/model reconstruction.