Miracle-dz/UniFAR-FTUs huggingface dataset:
CogBeacon: A Multi-Modal Dataset and Data-Collection Platform for Modeling Cognitive Fatigue

In this work, we present CogBeacon, a multi-modal dataset designed to target the effects of cognitive fatigue in human performance. The dataset consists of 76 sessions collected from 19 male and female users performing different versions of a cognitive task inspired by the principles of the Wisconsin Card Sorting Test (WCST), a popular cognitive test in experimental and clinical psychology designed to assess cognitive flexibility, reasoning, and specific aspects of cognitive functioning. During each session, we record and fully annotate user EEG functionality, facial keypoints, real-time self-reports on cognitive fatigue, as well as detailed information of the performance metrics achieved during the cognitive task (success rate, response time, number of errors, etc.). Along with the dataset we provide free access to the CogBeacon data-collection software to provide a standardized mechanism to the community for collecting and annotating physiological and behavioral data for cognitive fatigue analysis. Our goal is to provide other researchers with the tools to expand or modify the functionalities of the CogBeacon data-collection framework in a hardware-independent way. As a proof of concept we show some preliminary machine learning-based experiments on cognitive fatigue detection using the EEG information and the subjective user reports as ground truth. Our experiments highlight the meaningfulness of the current dataset, and encourage our efforts towards expanding the CogBeacon platform. To our knowledge, this is the first multi-modal dataset specifically designed to assess cognitive fatigue and the only free software available to allow experiment reproducibility for multi-modal cognitive fatigue analysis.
Bayesian Inference as a Problem Solving Tool in Proactive Routing Protocols

This paper rationalizes nine proactive protocols in terms of their efficiencies, inefficiencies and convergence properties. This is done in order to pinpoint challenges associated with these protocols, so that novel algorithms may be proposed for coping with the discovered issues. The methodology followed to put forward the said solutions is that of Bayesian inference. Bayesian inference is a statistical technique having wide applications in the decision theory. The paper highlights salient features of Bayesian inference and, in the meantime, scrutinizes its applicability as a problem solving mechanism in the decision making of proactive routing protocols.
MoNoise: A Multi-lingual and Easy-to-use Lexical Normalization Tool

In this paper, we introduce and demonstrate the online demo as well as the command line interface of a lexical normalization system (MoNoise) for a variety of languages. We further improve this model by using features from the original word for every normalization candidate. For comparison with future work, we propose the bundling of seven datasets in six languages to form a new benchmark, together with a novel evaluation metric which is particularly suitable for cross-dataset comparisons. MoNoise reaches a new state-of-art performance for six out of seven of these datasets. Furthermore, we allow the user to tune the ‘aggressiveness’ of the normalization, and show how the model can be made more efficient with only a small loss in performance. The online demo can be found on: http://www.robvandergoot. com/monoise and the corresponding code on: https://bitbucket.org/robvanderg/monoise/
Practical Erase Suspension for Modern Low-latency SSDs

As NAND flash technology continues to scale, flash-based SSDs have become key components in data-center servers. One of the main design goals for data-center SSDs is low read tail latency, which is crucial for interactive online services as a single query can generate thousands of disk accesses. Towards this goal, many prior works have focused on minimizing the effect of garbage collection on read tail latency. Such advances have made the other, less explored source of long read tails, block erase operation, more important. Prior work on erase suspension addresses this problem by allowing a read operation to interrupt an ongoing erase operation, to minimize its effect on read latency. Unfortunately, the erase suspension technique attempts to suspend/resume an erase pulse at an arbitrary point, which incurs additional hardware cost for NAND peripherals and reduces the lifetime of the device. Furthermore, we demonstrate this technique suffers a write starvation problem, using a real, production-grade SSD. To overcome these limitations, we propose alternative practical erase suspension mechanisms, leveraging the iterative erase mechanism used in modern SSDs, to suspend/resume erase operation at well-aligned safe points. The resulting design achieves a sub-200µs 99.999th percentile read tail latency for 4KB random I/O workload at queue depth 16 (70% reads and 30% writes). Furthermore, it reduces the read tail latency by about 5× over the baseline for the two data-center workloads that we evaluated with.
Worst deterministic pattern allocation: a viable approach to attain statistical gain in ATM

The paper deals with a novel strategy for connection admission control in ATM networks. This strategy, called worst deterministic pattern allocation (WDPA), aims at reaping statistical gain of ATM, through a simple and controllable bandwidth allocation. WDPA is based on the concept of constraining information sources to emit their cells according to a superimposed deterministic pattern. This forced regularity is exploited in order to easily control the cell transfer within the network. Practically, WDPA establishes the worst emission pattern of the source. Resource allocation is performed taking into account only the parameters of the declared deterministic pattern. The same allocation rule is utilized in every network section. Application of WDPA to a large class of switching structures with output buffers is also discussed and its compatibility with this architecture is demonstrated. The presented performance study aims at evaluating the impact of the shaping on the original source cell stream and at determining general criteria for the choice of the deterministic pattern parameters. Finally, the authors compare the efficiency resulting from application of WDPA with that relevant to peak rate, mean rate and an ideal statistical allocation strategies. <<ETX>>
Automatic Inference of Specifications in the K Framework

This work has been partially supported by the EU (FEDER) and Spanish MINECO under grants TIN2015-69175-C4-1-R and TIN2013-45732-C4-1-P, and by Generalitat Valenciana ref. PROMETEOII/2015/013.
Mobile Time-Based Targeting: Matching Product-Value Appeal to Time of Day

Abstract Mobile technologies, with their seamless integration into people’s everyday life, are said to enable “perpetual contact” with the users always accompanied by their mobile devices. This creates unprecedented opportunity to better understand the relationship between time and information technologies (ITs), in particular, how time of day may influence users’ IT behavior. With this understanding, companies may also be able to promote IT products by highlighting their value that appeals to users in a particular time segment of a day. Building primarily on the ego depletion theory and through conducting a combination of field experiment and survey, we show that matching IT product value appeal to people’s psychological state during different times of day can lead to more optimal outcome (higher adoption). The findings advance our understanding of how IT behavior, as a behavior embedded in time, is affected by time of day, and provide actionable guidance to practitioners in performing mobile time-based targeting.
Estimulação elétrica nervosa transcutânea em mulheres disfônicas

BACKGROUND: studies indicate correlation between dysphonia and muscle tension. AIM: to evaluate bilaterally the electrical activity of the suprahyoid muscles (SH), sternocleidomastoid (SCM), and trapezius (T), the presence of pain and the voice, after applying transcutaneous electrical nerve stimulation (TENS). METHOD: ten (10) women with nodules or bilateral mucus thickening, and phonation fissure. Volunteers were submitted to 10 TENS sessions (200µs and 10Hz) for 30 minutes. Pain was evaluated using an analogical visual scale; the voice was evaluated through laryngoscopy and through a perceptive-auditory and acoustic analysis; and the myoelectric signal was converted using the Root Media Square (RMS). Voice and EMG data gathering was performed during the production of the E/vowel and during spontaneous speech (SS). STATISTICAL ANALYSIS: Shapiro-Wilk Test followed by the Wilcoxon Test, or t Student, or Friedman Test (p < 0.05). RESULTS: It was observed that the TENS decreased the RMS readings, pre and pos treatment, for the Right T (RT) (2.80 ± 1.36 to 1.77 ± 0.93), the Left T (LT) (3.62 ± 2.10 to 2.10 ± 1.06), the Left SCM (LSCM) (2.64 ± 0.69 to 1.94 ± 0.95), and the SH (11.59 ± 7.72 to 7.82 ± 5.95) during the production of the E/vowel; and for the RT (3.56 ± 2.77 to 1.93 ± 1.13), the LT (4.68 ± 2.56 to 3.09 ± 2.31), the Right SCM (RSCM) (3.94 ± 2.04 to 2.51 ± 1.87), and the LSCM (3.54 ± 1.04 to 3.12 ± 3.00) during SS. A relieve in pain was also observed. Regarding the voice analysis, there was a decrease in level of laryngeal injuries; no difference was observed during the production of the E/vowel in the perceptive-auditory analysis; there was a decrease in the level of dysphonia and hoarseness during SS. CONCLUSION: TENS is effective in improving the clinical and functional signs of dysphonic women.
Language Modeling with Shared Grammar

Sequential recurrent neural networks have achieved superior performance on language modeling, but overlook the structure information in natural language. Recent works on structure-aware models have shown promising results on language modeling. However, how to incorporate structure knowledge on corpus without syntactic annotations remains an open problem. In this work, we propose neural variational language model (NVLM), which enables the sharing of grammar knowledge among different corpora. Experimental results demonstrate the effectiveness of our framework on two popular benchmark datasets. With the help of shared grammar, our language model converges significantly faster to a lower perplexity on new training corpus.
Free vibration of laminated composite plates by the various shear deformation theories

In this paper, the analysis of various higher-order shear deformation theories for the free vibration of laminated composite plates is presented. A Navier-type analytical method was used to solve the governing differential equations. Natural frequencies of simply supported laminated composite plates are calculated. The present results are compared with the available published results.
Web Site Usability with Remote Users

ABSTRACT Incorporating usability into any Web site creation or redesign is essential. Capturing the perspective of the user makes the site more efficient and effective for the people who will actually be using it. There are a number of usability techniques and several can be incorporated in a study of remote users. The purpose of this paper is to provide an overview of how the traditional usability techniques of focus groups and formal usability studies can be extended to studies involving off-campus users.
Sequential piecewise PCE approximation of likelihood functions in Bayesian inference

A method for sequentially constructing polynomial chaos expansions, with the aim of approximating likelihood functions that occur in Bayesian inference problems is presented. The proposed approach is called piecewise polynomial chaos expansion (P-PCE) and is based on sequentially constructing PCEs in refined domains on the residuals of previously constructed PCEs. The obtained local spectral representation allows the computation of posterior expectations by post-processing the PCE coefficients based on the recently developed concept of spectral likelihood expansion (SLE). This paper presents a summary of the proposed theory and showcases the solution of two Bayesian inference problems using the presented approach.
A comparison of Bayesian synthesis approaches for studies comparing two means: A tutorial

Researchers often seek to synthesize results of multiple studies on the same topic to draw statistical or substantive conclusions and to estimate effect sizes that will inform power analyses for future research. The most popular synthesis approach is meta‐analysis. There have been few discussions and applications of other synthesis approaches. This tutorial illustrates and compares multiple Bayesian synthesis approaches (i.e., integrative data analyses, meta‐analyses, data fusion using augmented data‐dependent priors, and data fusion using aggregated data‐dependent priors) and discusses when and how to use these Bayesian synthesis approaches to combine studies that compare two independent group means or two matched group means. For each approach, fixed‐, random‐, and mixed‐effects models with other variants are illustrated with real data. R code is provided to facilitate the implementation of each method and each model. On the basis of these analyses, we summarize the strengths and limitations of each approach and provide recommendations to guide future synthesis efforts.
How to Kill Them All An Exploratory Study on the Impact of Code Observability on Mutation Testing

Mutation testing is well-known for its efficacy in assessing test quality, and starting to be applied in the industry. However, what should a developer do when confronted with a low mutation score? Should the test suite be plainly reinforced to increase the mutation score, or should the production code be improved as well, to make the creation of better tests possible? In this paper, we aim to provide a new perspective to developers that enables them to understand and reason about the mutation score in the light of testability and observability . First, we investigate whether testability and observability metrics are correlated with the mutation score on six open-source Java projects. We observe a correlation between observability metrics and the mutation score, e.g., test directness , which measures the extent to which the production code is tested directly, seems to be an essential factor. Based on our insights from the correlation study, we propose a number of "mutation score anti-patterns’’, enabling software engineers to refactor their existing code or add tests to improve the mutation score. In doing so, we observe that relatively simple refactoring operations enable an improvement or increase in the mutation score.
Dynamic Contactless Power Transfer On Go-karts

Wireless power transfer is getting more attention than ever before but seems to be lagging in the go-kart industry. Manufacturers are designing more electric powered go-karts, but haven’t found a proper solution for most of its major drawbacks. The added weight from the battery pack and the required time to recharge are just two of these major drawbacks. These drawbacks make electric go-karts less desired in the rental industry, where turn-around-time is required to be as short as possible; in order to maximize profit. An Inductive Power Transfer System can be used to charge the battery pack of an electric go-kart statically in the pit-lane, but also dynamically on the track. Both of which are shown to reduce the required size of the battery-pack of electric cars and busses. This study determined the feasibility of an IPTS on go-karts, considering the current state of the art, in order to solve these drawbacks. The design of the charger was optimized for a go-kart while keeping its most important features the same. A safety analysis was performed using a FEM-program to see whether the system can be used by the public. The go-kart with the charger was modeled and simulations were used to determine the required size of the battery pack in a case-study. The results of this case-study were used to evaluate the financial viability of an IPTS on go-karts.
Perceptions of undergraduate Graphic Design students on the educational potential of Facebook

The popularity of Facebook among university students inevitably raises questions on the educational potential of this Social Networking Site for Higher Education. From the limited literature on the instructional uses of Facebook, one can draw conflicting conclusions. Benefits were identified through the communicative potential, student participation in study groups and through informal learning, i.e. learning that takes place outside the formal structures of the learning environment. In contrast, it is also argued that the instructional benefits of Facebook are not straightforward. This phenomenographic investigation examines the perceptions of undergraduate Graphic Design students in a higher education institution in Cyprus, on the use of Facebook for teaching and learning. Characteristic of Art and Design education is the centrality of the studio and student self-reflection. Despite some literature that considers Facebook provides a viable alternative to the physical studio, the participants in this investigation expressed a preference for face-to-face instruction and consider Facebook as complimenting rather than replacing studio practices. Some participants benefited from the use of Facebook by joining support groups and exploring information relevant to their studies. Further research can investigate how Facebook can be embedded in studio-based teaching and learning. Keywords: Facebook; Graphic Design; teaching and learning; informal learning; studio (Published: 24 September 2012) Citation: Research in Learning Technology 2012, 20 : 17490 - http://dx.doi. org/10.3402/rlt.v20i0. 17490
GCN-MF: Disease-Gene Association Identification By Graph Convolutional Networks and Matrix Factorization

Discovering disease-gene association is a fundamental and critical biomedical task, which assists biologists and physicians to discover pathogenic mechanism of syndromes. With various clinical biomarkers measuring the similarities among genes and disease phenotypes, network-based semi-supervised learning (NSSL) has been commonly utilized by these studies to address this class-imbalanced large-scale data issue. However, most existing NSSL approaches are based on linear models and suffer from two major limitations: 1) They implicitly consider a local-structure representation for each candidate; 2) They are unable to capture nonlinear associations between diseases and genes. In this paper, we propose a new framework for disease-gene association task by combining Graph Convolutional Network (GCN) and matrix factorization, named GCN-MF. With the help of GCN, we could capture non-linear interactions and exploit measured similarities. Moreover, we define a margin control loss function to reduce the effect of sparsity. Empirical results demonstrate that the proposed deep learning algorithm outperforms all other state-of-the-art methods on most of metrics.
To Study The Efficacy Of Reciprocating Single File System And Multifile Rotary Systems In Elimination Of Root Canal Bacteria And Endotoxins

AIM: To study the efficacy of reciprocating single file system and multifile rotary systems in elimination of root canal bacteria and endotoxins. MATERIAL AND METHOD: Forty permanent single rooted mandibular premolars with straight canals inoculated with Escherichia Coli suspension for 21 days were selected for the study. Teeth were randomly divided into four groups (n=10) according to instrumentation system: Grp. A–Reciproc (VDW); Grp. B–WaveOne (Dentsply Maillefer); Grp. C–MTwo (VDW); and Grp. D–K3( Sybron Endo); Bacterial and endotoxin samples were collected with a sterile paper point before instrumentation and after instrumentation. Culture methods estimated the colony-forming units (CFU) and the Limulus Amebocyte Lysate test was used for quantification of endotoxins. Results so obtained were calculated and statistically analysed. RESULT: Results at S1 concluded that bacteria and endotoxins were found in all of the investigated root canals. After instrumentation all systems were associated with the significant reduction of the bacterial load and endotoxins respectively: Grp. A– Reciproc (88.25% and 89.10%); Grp. B– WaveOne (83.53% and 80.59%); Grp. C– MTwo(79.41% and 75.84%) and Grp. D– K3 (67% and 74.4%). Statistically no difference was found amongst the instrumentation systems regarding bacteria and endotoxin elimination (P <0.05). CONCLUSION: The reciprocating single file, Reciproc and WaveOne were as effective as the multifile rotary systems for the eradication of bacteria and endotoxins from root canals.
Green Service Level Agreement Compliance for Optical WDM Networks

In this paper we consider Green Service Level Agreement (SLA) as a constraint in finding a route for a dynamically received connection request in Wavelength Division Multiplexing (WDM) networks. We show that it is possible to save energy and reduce Greenhouse Gas (GHG) emission while satisfying optical network SLAs. The Integer Linear Programming (ILP) method introduced in this paper has been engineered to be simple and be solved in a timely manner needed for serving a dynamic connection request in control plane of the optical network. The ILP method introduced in this paper for the control plane of Optical WDM networks could also be used for the control plane of Software Defined Networks (SDN) and Software Defined Wide Area Networks.
Multi-Modal Sarcasm Detection in Twitter with Hierarchical Fusion Model

Sarcasm is a subtle form of language in which people express the opposite of what is implied. Previous works of sarcasm detection focused on texts. However, more and more social media platforms like Twitter allow users to create multi-modal messages, including texts, images, and videos. It is insufficient to detect sarcasm from multi-model messages based only on texts. In this paper, we focus on multi-modal sarcasm detection for tweets consisting of texts and images in Twitter. We treat text features, image features and image attributes as three modalities and propose a multi-modal hierarchical fusion model to address this task. Our model first extracts image features and attribute features, and then leverages attribute features and bidirectional LSTM network to extract text features. Features of three modalities are then reconstructed and fused into one feature vector for prediction. We create a multi-modal sarcasm detection dataset based on Twitter. Evaluation results on the dataset demonstrate the efficacy of our proposed model and the usefulness of the three modalities.
Simple and Effective Paraphrastic Similarity from Parallel Translations

We present a model and methodology for learning paraphrastic sentence embeddings directly from bitext, removing the time-consuming intermediate step of creating para-phrase corpora. Further, we show that the resulting model can be applied to cross lingual tasks where it both outperforms and is orders of magnitude faster than more complex state-of-the-art baselines.
TCP and UDP Based Performance Analysis of AODV, DSR and DSDV Routing Protocols Under Different Traffic Conditions in Mobile AdHoc Networks

Routing protocol is the primary strategy to design any wireless network. In Mobile AdHoc Network many routing protocols are present for sending packets from source to destination. Routing protocols in Mobile AdHoc Networks (MANETs) are reactive routing protocols, proactive routing protocols, hybrid routing protocols. Performance analysis of different routing protocols is the major step before selecting the routing protocol. Routing protocols perform different under different traffic conditions in different environments. In this paper, the TCP and UDP based performance analysis is carried out in reactive routing protocols and proactive routing protocols with different packet size using NS2 simulator under different traffic conditions. The delay, throughput and packet delivery ratio are common measures parameters used for the comparison of performance of reactive protocols and proactive protocols.