nils-herrmann/clean_scirepeval huggingface dataset:
Predicting Twitter User Socioeconomic Attributes with Network and Language Information

Inferring socioeconomic attributes of social media users such as occupation and income is an important problem in computational social science. Automated inference of such characteristics has applications in personalised recommender systems, targeted computational advertising and online political campaigning. While previous work has shown that language features can reliably predict socioeconomic attributes on Twitter, employing information coming from users' social networks has not yet been explored for such complex user characteristics. In this paper, we describe a method for predicting the occupational class and the income of Twitter users given information extracted from their extended networks by learning a low-dimensional vector representation of users, i.e. graph embeddings. We use this representation to train predictive models for occupational class and income. Results on two publicly available datasets show that our method consistently outperforms the state-of-the-art methods in both tasks. We also obtain further significant improvements when we combine graph embeddings with textual features, demonstrating that social network and language information are complementary.
Improvasher: A Real-Time Mashup System for Live Musical Input

In this paper we present Improvasher a real-time musical accompaniment system which creates an automatic mashup to accompany live musical input. Improvasher is built around two music processing modules, the first, a performance following technique, makes beat-synchronous predictions of chroma features from a live musical input. The second, a music mashup system, determines the compatibility between beat-synchronous chromagrams from different pieces of music. Through the combination of these two techniques, a real-time predictive mashup can be generated towards a new form of automatic accompaniment for interactive musical performance.
Sentential Representations in Distributional Semantics

This thesis is about the problem of representing sentential meaning in distributional semantics. Distributional semantics obtains the meanings of words through their usage, based on the hypothesis that words occurring in similar contexts will have similar meanings. In this framework, words are modeled as distributions over contexts and are represented as vectors in high dimensional space. Compositional distributional semantics attempts to extend this approach to higher linguistics structures. Some basic composition models proposed in literature to obtain the meaning of phrases or possibly sentences show promising results in modeling simple phrases. The goal of the thesis is to further extend these composition models to obtain sentence meaning representations. The thesis puts more focus on unsupervised methods which make use of the context of phrases and sentences to optimize the parameters of a model. Three different methods are presented. The first model is the PLF model, a practical composition and linguistically motivated model which is based on the lexical function model introduced by Baroni and Zamparelli (2010) and Coecke et al. (2010). The second model is the Chunk-based Smoothed Tree Kernels (CSTKs) model, extending Smoothed Tree Kernels (Mehdad et al., 2010) by utilizing vector representations of chunks. The final model is the C-PHRASE model, a neural network-based approach, which jointly optimizes the vector representations of words and phrases using a context predicting objective. The thesis makes three principal contributions to the field of compositional distributional semantics. The first is to propose a general framework to estimate the parameters and evaluate the basic composition models. This provides a fair way to comparing the models using a set of phrasal datasets. The second is to extend these basic models to the sentence level, using syntactic information to build up the sentence vectors. The third contribution is to evaluate all the proposed models, showing that they perform on par with or outperform competing models presented in the literature.  Marco Baroni Title: Associate Professor
Locally Normalized Filter Banks Applied to Deep Neural-Network-Based Robust Speech Recognition

This letter describes modifications to locally normalized filter banks (LNFB), which substantially improve their performance on the Aurora-4 robust speech recognition task using a Deep Neural Network-Hidden Markov Model (DNN-HMM)-based speech recognition system. The modified coefficients, referred to as LNFB features, are a filter-bank version of locally normalized cepstral coefficients (LNCC), which have been described previously. The ability of the LNFB features is enhanced through the use of newly proposed dynamic versions of them, which are developed using an approach that differs somewhat from the traditional development of delta and delta–delta features. Further enhancements are obtained through the use of mean normalization and mean–variance normalization, which is evaluated both on a per-speaker and a per-utterance basis. The best performing feature combination (typically LNFB combined with LNFB delta and delta–delta features and mean–variance normalization) provides an average relative reduction in word error rate of 11.4% and 9.4%, respectively, compared to comparable features derived from Mel filter banks when clean and multinoise training are used for the Aurora-4 evaluation. The results presented here suggest that the proposed technique is more robust to channel mismatches between training and testing data than MFCC-derived features and is more effective in dealing with channel diversity.
A zigbee-based home automation system

In recent years, the home environment has seen a rapid introduction of network enabled digital technology. This technology offers new and exciting opportunities to increase the connectivity of devices within the home for the purpose of home automation. Moreover, with the rapid expansion of the Internet, there is the added potential for the remote control and monitoring of such network enabled devices. However, the adoption of home automation systems has been slow. This paper identifies the reasons for this slow adoption and evaluates the potential of ZigBee for addressing these problems through the design and implementation of a flexible home automation architecture. A ZigBee based home automation system and Wi-Fi network are integrated through a common home gateway. The home gateway provides network interoperability, a simple and flexible user interface, and remote access to the system. A dedicated virtual home is implemented to cater for the system's security and safety needs. To demonstrate the feasibility and effectiveness of the proposed system, four devices, a light switch, radiator valve, safety sensor and ZigBee remote control have been developed and evaluated with the home automation system.
Speaker Change Detection in Broadcast TV Using Bidirectional Long Short-Term Memory Networks

Speaker change detection is an important step in a speaker diarization system. It aims at finding speaker change points in the audio stream. In this paper, it is treated as a sequence labeling task and addressed by Bidirectional long short term memory networks (Bi-LSTM). The system is trained and evaluated on the Broadcast TV subset from ETAPE database. The result shows that the proposed model brings good improvement over conventional methods based on BIC and Gaussian Divergence. For instance, in comparison to Gaussian divergence, it produces speech turns that are 19.5% longer on average, with the same level of purity.
Spiral-STC: An On-Line Coverage Algorithm of Grid Environments by a Mobile Robot

W e describe an on-line sensor based algorithm for covering pla.nar areas by a squareshaped tool attached to a mobile robot. Let D be the tool size. The algorithm, called Spiral-STC, incrementally subdivides the planar work-area into disjoint D-size cells, while following a spanning tree of the resulting grid. The algorithm covers general grid environments using a path whose length is at most (n+m)D, where n is the number of D-size cells and m 5 n is the number of boundary cells, defined as cells that share at least one point with the grid boundary. W e also report that any on-line coverage algorithm generates a covering path whose length is at least (2-e)lOpt in worst case, where lopt is the length of the optimal covering path. Since (n+m)D I 2lOpt, Spiral-STC is worst-case optimal. Moreover, m << n in practical environments, and the algorithm generates close-to-optimal covering paths in such environments. Simulation results demonstrate the spiral-like covering patterns typical to the algorithm.
Warpage Tuning Study for Multi-chip Last Fan Out Wafer Level Package

In recent years, the IoT popularity pushes the package development of 3C products into a more functional and thinner target. For high I/O density and low cost considered package, the promising Fan-out Wafer Level Packaging (FOWLP) provides a solution to match OSAT existing capability, besides, the chip last process in FOWLP can further enhance the total yield by selectable known-good dies (KGDs). However, under processing, the large portion of molding compound induces high warpage to challenge fabrication limitation. The additional leveling process is usually applied to lower the warpage that caused by the mismatch of coefficient of thermal expansion and Young's modulus from carriers, dies, and molding compound. This process results in the increase of package cost and even induce internal damages that affect device reliability. In order to avoid leveling process and improve warpage trend, in this paper, we simulated several models with different design of molding compound and dies, and then developed a multi-chip last FOWLP test vehicle by package dimension of 12x15 mm2 with 8x9 and 4x9 mm2 multiple dies, respectively. The test vehicle performed three redistribution layers (RDLs) including one fine pitch RDL of line width/line spacing 2um/2um, which is also the advantage of multi-chip last FOWLP, and also exhibited ball on trace structure for another low cost option. For the wafer warpage discussion, the results showed that tuning the thickness of molding compound can improve warpage trend, especially in the application of high modulus carrier, which improved wafer warpage within 1mm, for package warpage discussion, the thinner die can lower the warpage of package. Through well warpage controlling, the multi-chip last FOWLP package with ball on trace design was successfully presented in this paper, and also passed the package level reliability of TCB 1000 cycles, HTSL 1000 hrs, and uHAST 96 hrs, and drop test by board level reliability.
Applications of chlorophyll fluorescence can improve crop production strategies: an examination of future possibilities.

Chlorophyll fluorescence has been routinely used for many years to monitor the photosynthetic performance of plants non-invasively. The relationships between chlorophyll fluorescence parameters and leaf photosynthetic performance are reviewed in the context of applications of fluorescence measurements to screening programmes which seek to identify improved plant performance. The potential role of chlorophyll fluorescence imaging in increasing both the sensitivity and throughput of plant screening programmes is examined. Finally, consideration is given to possible specific applications of chlorophyll fluorescence for screening of plants for tolerance to environmental stresses and for improvements in glasshouse production and post-harvest handling of crops.
IoT security: challenges and solutions for mining

The Internet of Things (IoT) paradigm with its vast range of heterogeneous connecting technologies heralds a new era for internet research, especially given that this explosion in connectivity for devices or `things' is not without risk. Scholars recognise that IoT security concerns persist and that evidence highlighting increasing cyber-security vulnerabilities requires attention. Currently IoT security literature confirms: industry confusion, lack of clear standards, interoperability fears and security problems (with reference to identity, authentication, access control, protocol and network security, privacy, and trust and governance difficulties all within the IoT technology realm). In short, there is urgent need for governance in IoT to avoid unstructured fragmentation of architectures, protocols and identification systems, and responsibilities. To address anticipated future numbers of IoT devices, a secure, scalable, yet flexible solution is needed to work across a range of technologies and dynamic environments. Specific industries such as the Western Australian (WA) Mining and Resource sector, whilst recognised as an early adopter of technology/ IoT applications, is also currently seeking security solutions that provide competitive advantage. To this end, the research being conducted here is utilising qualitative methodologies (alongside document analyses), and specific real-world/live case-studies towards relevant organisations' IoT cyber-security decision making, with a view to developing best-practice cross-party guidance(s). Work here is at its early stages; ultimately variables identified and subsequently validated shall go towards a new developed design guide for the deployment of Information Technology/ Operational Technology and IoT environments, to address IoT security concerns, applicable to (major WA mining and resources companies and) the energy and resources generally.
Analysis of the Impact of Negative Sampling on Link Prediction in Knowledge Graphs

Knowledge graphs are large, useful, but incomplete knowledge repositories. ey encode knowledge through entities and relations which dene each other through the connective structure of the graph. is has inspired methods for the joint embedding of entities and relations in continuous low-dimensional vector spaces, that can be used to induce new edges in the graph, i.e., link prediction in knowledge graphs. Learning these representations relies on contrasting positive instances with negative ones. Knowledge graphs include only positive relation instances, leaving the door open for a variety of methods for selecting negative examples. We present an empirical study on the impact of negative sampling on the learned embeddings, assessed through the task of link prediction. We use state-of-the-art knowledge graph embedding methods – Rescal , TransE, DistMult and ComplEX – and evaluate on benchmark datasets – FB15k and WN18. We compare well known methods for negative sampling and propose two new embedding based sampling methods. We note a marked dierence in the impact of these sampling methods on the two datasets, with the ”traditional” corrupting positives method leading to best results on WN18, while embedding based methods benet FB15k.
Online social networking addiction among college students in Singapore: Comorbidity with behavioral addiction and affective disorder.

This study aimed to determine the prevalence of addiction to social networking sites/platforms (SNS) and its comorbidity with other behavioral addiction and affective disorder among college students in Singapore. 1110 college students (age: M=21.46, SD=1.80) in Singapore completed measures assessing online social networking, unhealthy food intake and shopping addiction as well as depression, anxiety and mania. Descriptive analyses were conducted to investigate the prevalence and comorbidity of behavioral addiction and affective disorder. Chi-square tests were used to examine gender differences.


THE PRINCIPAL RESULTS
The prevalence rates of SNS, food and shopping addiction were 29.5%, 4.7% and 9.3% respectively for the total sample. SNS addiction was found to co-occur with food addiction (3%), shopping addiction (5%), and both food and shopping addiction (1%). The comorbidity rates of SNS addiction and affective disorder were 21% for depression, 27.7% for anxiety, and 26.1% for mania. Compared with the total sample, students with SNS addiction reported higher comorbidity rates with other behavioral addiction and affective disorder. In general, females as compared to males reported higher comorbidity rates of SNS addiction and affective disorder.



SNS addiction has a high prevalence rate among college students in Singapore. Students with SNS addiction were vulnerable to experience other behavior addiction as well as affective disorder, especially among females.
Neural-Network-Based Adaptive Leader-Following Control for Multiagent Systems With Uncertainties

A neural-network-based adaptive approach is proposed for the leader-following control of multiagent systems. The neural network is used to approximate the agent's uncertain dynamics, and the approximation error and external disturbances are counteracted by employing the robust signal. When there is no control input constraint, it can be proved that all the following agents can track the leader's time-varying state with the tracking error as small as desired. Compared with the related work in the literature, the uncertainty in the agent's dynamics is taken into account; the leader's state could be time-varying; and the proposed algorithm for each following agent is only dependent on the information of its neighbor agents. Finally, the satisfactory performance of the proposed method is illustrated by simulation examples.
Design and Sensitivity Analysis of Highly Compact Comparator for Ku-Band Monopulse Radar

This paper gives the design of a highly compact comparator at a Ku-band frequency and presents analysis results of the comparator for the fabrication inaccuracies. First an unconventional magic-t using a nonstandard waveguide is designed at 15.50 GHz. To reduce the volume occupied by the magic-t, its E-arm (or difference port) is kept parallel to the plane of two inputs of the magic-t instead of perpendicular to them as is done in a convention magic-t. The sum and the difference ports of the above folded magic-t are then matched using inductive windows at 15.50 GHz. Keeping the required location of the outputs of the comparator in mind, four of these matched folded magic-ts are suitably interconnected to design a highly compact comparator. The effects of the fabrication errors in the waveguide and matching elements dimensions on the centre frequency, magnitude and phase response of the comparator are also analyzed and presented.
Audit and Analysis of Impostors: An experimental approach to detect fake profile in online social network

In the present generation, the social life of every person has become associated with online social networks (OSN). These sites have made drastic changes in the way we socialize. Making friends and keeping in contact with them as well as being updated of their activities, has become easier. But with their rapid growth, problems like fake profiles, online impersonation have also increased. The risk lies in the fact that anybody can create a profile to impersonate a real person on the OSN. The fake profile could be exploited to build online relationship with a targeted person purely through online interactions with the friends of victim.
 In present work, we have proposed experimental framework with which detection of fake profile is feasible within the friend list, however this framework is restricted to a specific online social networking site namely Facebook. This framework extracts data from the friend list and uses it to classify them as real or fake by using unsupervised and supervised machine learning.
Flexible capacitive sensors for high resolution pressure measurement

Thin, flexible, robust capacitive pressure sensors have been the subject of research in many fields where axial strain sensing with high spatial resolution and pressure resolution is desirable for small loads, such as tactile robotics and biomechanics. Simple capacitive pressure sensors have been designed and implemented on flexible substrates in general agreement with performance predicted by an analytical model. Two designs are demonstrated for comparison. The first design uses standard flex circuit technology, and the second design uses photolithography techniques to fabricate capacitive sensors with higher spatial and higher pressure resolution. Sensor arrays of varying sensor size and spacing are tested with applied loads from 0 to 1 MPa. Pressure resolution and linearity of the sensors are significantly improved with the miniaturized, custom fabricated sensor array compared to standard flexible circuit technology.
Clinic Overbooking to Improve Patient Access and Increase Provider Productivity

The problem of patient no-shows (patients who do not arrive for scheduled appointments) is significant in many health care settings, where no-show rates can vary widely. No-shows reduce provider productivity and clinic efficiency, increase health care costs, and limit the ability of a clinic to serve its client population by reducing its effective capacity. In this article, we examine the problem of no-shows and propose appointment overbooking as one means of reducing the negative impact of no-shows. We find that patient access and provider productivity are significantly improved with overbooking, but that overbooking causes increases in both patient wait times and provider overtime. We develop a new clinic utility function to capture the trade-offs between these benefits and costs, and we show that the relative values that a clinic assigns to serving additional patients, minimizing patient waiting times, and minimizing clinic overtime will determine whether overbooking is warranted. From the results of a series of simulation experiments, we determine that overbooking provides greater utility when clinics serve larger numbers of patients, no-show rates are higher, and service variability is lower. Even with highly variable service times, many clinics will achieve positive net results with overbooking. Our analysis provides valuable guidance to clinic administrators about the use of appointment overbooking to improve patient access, provider productivity, and overall clinic performance.  Scheduling Policies and Systems, Simulation, and Utility Assessment.
E-GOVERNMENT EVALUATION FACTORS: CITIZEN’S PERSPECTIVE

The e-government field is growing to a considerable size, both in its contents and position with respect to other research fields. The government to citizen segment of egovernment is taking the lead in terms of its importance and size. Like the evaluation of all other information systems initiatives, the evaluation of egovernments in both theory and practice has proved to be important but complex. The complexity of evaluation is mostly due to the multiple perspectives involved, the difficulties of quantifying benefits, and the social and technical context of use. The importance of e-government evaluation is due to the enormous investment of governments on delivering e-government services, and to the considerable pace of growing in the e-government field. However, despite the importance of the evaluation of e-government services, literature shows that e-government evaluation is still an immature area in terms of development and management. This work is part of a research effort that aims to develop a holistic evaluation framework for e-government systems. The main aim of this paper is to investigate the citizen’ perspective in evaluating e-government services, and present a set of evaluating factors that influence citizens’ utilization of e-government services. These evaluation factors can serve as part of an e-government evaluation framework. Moreover, the evaluation factors can also be used as means of providing valuable feedback for the planning of future egovernment initiatives.