Query: Predicting task from eye movements: On the importance of spatial distribution, dynamics, and image features

Relevant passages:
Quantitative Analysis of Human-Model Agreement in Visual Saliency Modeling: A Comparative Study <=SEP=> Visual attention is a process that enables biological and machine vision systems to select the most relevant regions from a scene. Relevance is determined by two components: 1) top-down factors driven by task and 2) bottom-up factors that highlight image regions that are different from their surroundings. The latter are often referred to as “visual saliency.” Modeling bottom-up visual saliency has been the subject of numerous research efforts during the past 20 years, with many successful applications in computer vision and robotics. Available models have been tested with different datasets (e.g., synthetic psychological search arrays, natural images or videos) using different evaluation scores (e.g., search slopes, comparison to human eye tracking) and parameter settings. This has made direct comparison of models difficult. Here, we perform an exhaustive comparison of 35 state-of-the-art saliency models over 54 challenging synthetic patterns, three natural image datasets, and two video datasets, using three evaluation scores. We find that although model rankings vary, some models consistently perform better. Analysis of datasets reveals that existing datasets are highly center-biased, which influences some of the evaluation scores. Computational complexity analysis shows that some models are very fast, yet yield competitive eye movement prediction accuracy. Different models often have common easy/difficult stimuli. Furthermore, several concerns in visual saliency modeling, eye movement datasets, and evaluation scores are discussed and insights for future work are provided. Our study allows one to assess the state-of-the-art, helps to organizing this rapidly growing field, and sets a unified comparison framework for gauging future efforts, similar to the PASCAL VOC challenge in the object recognition and detection domains.

Task and context determine where you look. <=SEP=> The deployment of human gaze has been almost exclusively studied independent of any specific ongoing task and limited to two-dimensional picture viewing. This contrasts with its use in everyday life, which mostly consists of purposeful tasks where gaze is crucially involved. To better understand deployment of gaze under such circumstances, we devised a series of experiments, in which subjects navigated along a walkway in a virtual environment and executed combinations of approach and avoidance tasks. The position of the body and the gaze were monitored during the execution of the task combinations and dependence of gaze on the ongoing tasks as well as the visual features of the scene was analyzed. Gaze distributions were compared to a random gaze allocation strategy as well as a specific "saliency model." Gaze distributions showed high similarity across subjects. Moreover, the precise fixation locations on the objects depended on the ongoing task to the point that the specific tasks could be predicted from the subject's fixation data. By contrast, gaze allocation according to a random or a saliency model did not predict the executed fixations or the observed dependence of fixation locations on the specific task.

A Model of Saliency-Based Visual Attention for Rapid Scene Analysis <=SEP=> A visual attention system, inspired by the behavior and the neuronal architecture of the early primate visual system, is presented. Multiscale image features are combined into a single topographical saliency map. A dynamical neural network then selects attended locations in order of decreasing saliency. The system breaks down the complex problem of scene understanding by rapidly selecting, in a computationally efficient manner, conspicuous locations to be analyzed in detail.

Task constraints in visual working memory <=SEP=> This paper examines the nature of visual representations that direct ongoing performance in sensorimotor tasks. Performance of such natural tasks requires relating visual information from different gaze positions. To explore this we used the technique of making task relevant display changes during saccadic eye movements. Subjects copied a pattern of colored blocks on a computer monitor, using the mouse to drag the blocks across the screen. Eye position was monitored using a dual-purkinje eye tracker, and the color of blocks in the pattern was changed at different points in task performance. When the target of the saccade changed color during the saccade, the duration of fixations on the model pattern increased, depending on the point in the task that the change was made. Thus different fixations on the same visual stimulus served a different purpose. The results also indicated that the visual information that is retained across successive fixations depends on moment by moment task demands. This is consistent with previous suggestions that visual representations are limited and task dependent. Changes in blocks in addition to the saccade target led to greater increases in fixation duration. This indicated that some global aspect of the pattern was retained across different fixations. Fixation durations revealed effects of the display changes that were not revealed in perceptual report. This can be understood by distinguishing between processes that operate at different levels of description and different time scales. Our conscious experience of the world may reflect events over a longer time scale than those underlying the substructure of the perceptuo-motor machinery.

An Integrated Model of Top-Down and Bottom-Up Attention for Optimizing Detection Speed <=SEP=> Integration of goal-driven, top-down attention and image-driven, bottom-up attention is crucial for visual search. Yet, previous research has mostly focused on models that are purely top-down or bottom-up. Here, we propose a new model that combines both. The bottom-up component computes the visual salience of scene locations in different feature maps extracted at multiple spatial scales. The topdown component uses accumulated statistical knowledge of the visual features of the desired search target and background clutter, to optimally tune the bottom-up maps such that target detection speed is maximized. Testing on 750 artificial and natural scenes shows that the model’s predictions are consistent with a large body of available literature on human psychophysics of visual search. These results suggest that our model may provide good approximation of how humans combine bottom-up and top-down cues such as to optimize target detection speed.

Factors affecting the acceptance of information systems supporting emergency operations centres <=SEP=> Despite the recognition that information system acceptance is an important antecedent of effective emergency management, there has been comparatively very little research examining this aspect of technology acceptance. The current research responded to this gap in literature by adapting and integrating existing models of technology acceptance. This was done in order to examine how a range of technology acceptance factors could affect the acceptance of emergency operations centre information systems. Relationships between several of these factors were also examined. Questionnaire data from 383 end-users of four different emergency operations centre information systems were analysed using structural equation modelling. This analysis concluded that technology acceptance factors of performance expectancy, effort expectancy, social influence and information quality explained 65 percent of variance in symbolic adoption, which is a combination of mental acceptance and psychological attachment towards an information system. A number of moderating effects of age, gender, experience of use and domain experience were also identified. A mediating component, of performance expectancy, explained 49 percent of variance between facilitating conditions, information quality, effort expectancy, and resulting symbolic adoption. These findings highlight a need to re-focus technology acceptance research on both mediating and moderating effects and the importance of considering domain specific factors. Applied recommendations are also made, for successfully implementing relevant information

Interventions to address the academic impairment of children and adolescents with ADHD. <=SEP=> There exists a strong link between ADHD and academic underachievement. Both the core behavioral symptoms of ADHD and associated executive functioning deficits likely contribute to academic impairment. Current evidence-based approaches to the treatment of ADHD (i.e., stimulant medication, clinical behavior therapy and classroom behavioral interventions) have demonstrated a robust impact on behavioral variables such as attention and disruptive behavior within classroom analogue settings; however, their efficacy in improving academic outcomes is much less clear. Although surprisingly few treatment outcome studies of ADHD have attempted to incorporate interventions that specifically target academic outcomes, the studies that are available suggest that these interventions may be beneficial. The state of the treatment literature for addressing academic impairment in children and adolescents with ADHD will be reviewed herein, as well as limitations of current research, and directions for future research.

Intravesical stone formation on intrauterine contraceptive device <=SEP=> Since more than 30 years, intrauterine contraceptive devices (IUCD) have been used for a contraceptive opportunity. Although they are termed to be a safe and effective method for contraception, they also have some type of complications and uterine perforation, septic abortion, pelvic abscess are the serious complications of these devices. The incidence of uterine perforation is very low, but in the literature nearly 100 cases were reported about the extra uterine localization of IUCD. Migration may occur to the adjacent organs. We here in describe a case of a 31 year-old woman who had an IUCD with stone formation in the bladder. In the literature all of the cases were reported as IUCD migration, but although it seems technically impossible, IUCD placement into the bladder should also be considered in misplaced IUCDs.

Offset calibration technique for capacitive transimpedance amplifier used in uncooled infrared detection <=SEP=> This paper presents a novel readout circuit of uncooled, bolometer-based, focal plane arrays (FPAs). The offset and flicker noise are the design challenges of microbolometer readout circuits (ROCs). The ROC is not only required to apply careful noise cancellation techniques, but also to be insensitive to process and supply voltage variations. The proposed circuit involves a new offset cancellation technique which overcomes process variations, noise, random, and systematic offset. & 2016 Elsevier Ltd. All rights reserved.

Big data framework for students' academic performance prediction: A systematic literature review <=SEP=> Big Data is becoming an integral part of education system worldwide, bringing in so much of prediction potential and therefore opportunities to improve learning and teaching methodologies. In fact, it has become the digital policy instrument for policy makers to make strategic decisions supported by big data analytics. This paper uses Systematic Literature Review (SLR) to establish a general overview and background in establishing gaps that need to be addressed in big data analytics for education landscape. A total of 59 research papers from 473 papers were chosen and analyzed. There is an increased trend in incorporating big data in education however, it is not sufficient and need more research in this area particularly predictive models and frameworks for educational settings.

Seismic surveying with drone-mounted geophones <=SEP=> Seismic imaging is the primary technique for subsurface exploration. Traditional seismic imaging techniques rely heavily on manual labor to plant sensors, lay miles of cabling, and then recover the sensors. Often sites of resource or rescue interest may be difficult or hazardous to access. Thus, there is a substantial need for unmanned sensors that can be deployed by air and potentially in large numbers. This paper presents working prototypes of autonomous drones equipped with geophones (vibration sensors) that can fly to a site, land, listen for echoes and vibrations, store the information on-board, and subsequently return to home base. The design uses four geophone sensors (with spikes) in place of the landing gear. This provides a stable landing attitude, redundancy in sensing, and ensures the geophones are oriented perpendicular to the ground. The paper describes hardware experiments demonstrating the efficacy of this technique and a comparison with traditional manual techniques. The performance of the seismic drone was comparable to a well planted geophone, proving the drone mount system is a feasible alternative to traditional seismic sensors.

Prevalence of diabetes mellitus and impaired fasting glucose levels in the Eastern Province of Saudi Arabia: results of a screening campaign. <=SEP=> INTRODUCTION
This study aimed to estimate the prevalence of diagnosed and undiagnosed diabetes mellitus (DM) in the Eastern Province of Saudi Arabia, and to study its relationship with socioeconomic factors.


METHODS
The study targeted all Saudi subjects aged 30 years and above who resided in the Eastern Province in 2004. DM screening was conducted by taking the capillary fasting blood glucose (CFBG) after eight hours or more of fasting, or the casual capillary blood glucose (CCBG). A positive screening test for hyperglycaemia was defined as CFBG more than or equal to 100 mg/dl (5.6 mmol/l), or CCBG more than or equal to 140 mg/dl (7.8 mmol/l). A positive result was confirmed on another day through the measurement of fasting plasma glucose (FPG) levels from a venous sample. A diagnosis of DM was considered if FPG was more than or equal to 126 mg/dl (7.0 mmol/l), or when there was a history of a previous diagnosis.


RESULTS
Out of 197,681 participants, 35,929 (18.2 percent) had a positive history of DM or a positive screening test for hyperglycaemia. After confirmation by venous blood testing, the prevalence of DM dropped to 17.2 percent while the prevalence of newly diagnosed DM was 1.8 percent. The prevalence increased with age and was higher in women, widows, divorcees, those who had a low education level and the unemployed.


CONCLUSION
The prevalence of DM in Saudi Arabia is one of the highest reported in the world, and its yield of screening is high.

Articulation points guided redundancy elimination for betweenness centrality <=SEP=> Betweenness centrality (BC) is an important metrics in graph analysis which indicates critical vertices in large-scale networks based on shortest path enumeration. Typically, a BC algorithm constructs a shortest-path DAG for each vertex to calculate its BC score. However, for emerging real-world graphs, even the state-of-the-art BC algorithm will introduce a number of redundancies, as suggested by the existence of articulation points. Articulation points imply some common sub-DAGs in the DAGs for different vertices, but existing algorithms do not leverage such information and miss the optimization opportunity.
 We propose a redundancy elimination approach, which identifies the common sub-DAGs shared between the DAGs for different vertices. Our approach leverages the articulation points and reuses the results of the common sub-DAGs in calculating the BC scores, which eliminates redundant computations. We implemented the approach as an algorithm with two-level parallelism and evaluated it on a multicore platform. Compared to the state-of-the-art implementation using shared memory, our approach achieves an average speedup of 4.6x across a variety of real-world graphs, with the traversal rates up to 45 ~ 2400 MTEPS (Millions of Traversed Edges per Second).

Audio-Visual Person Recognition in Multimedia Data From the Iarpa Janus Program <=SEP=> Currently, datasets that support audio-visual recognition of people in videos are scarce and limited. In this paper, we introduce an expansion of video data from the IARPA Janus program to support this research area. We refer to the expanded set, which adds labels for voice to the already-existing face labels, as the Janus Multimedia dataset. We first describe the speaker labeling process, which involved a combination of automatic and manual criteria. We then discuss two evaluation settings for this data. In the core condition, the voice and face of the labeled individual are present in every video. In the full condition, no such guarantee is made. The power of audiovisual fusion is then shown using these publicly-available videos and labels, showing significant improvement over only recognizing voice or face alone. In addition to this work, several other possible paths for future research with this dataset are discussed.

Development of 24 GHz rectennas for Fixed Wireless Access <=SEP=> We need electricity to use wireless information. If we reduce amount of batteries or electrical wires with a wireless power transmission technology via microwave (MPT), it is a green communication system. We Kyoto University propose a Fixed Wireless Access (FWA) system with the MPT with NTT, Japan. In this paper, we show mainly development results of 24GHz rectennas, rectifying antenna, for FWA. We developed some types of the rectennas. Finally we achieve 65% of RF-DC conversion efficiency with output filter of harmonic balance.

A Weighted Dictionary Learning Model for Denoising Images Corrupted by Mixed Noise <=SEP=> This paper proposes a general weighted l2-l0 norms energy minimization model to remove mixed noise such as Gaussian-Gaussian mixture, impulse noise, and Gaussian-impulse noise from the images. The approach is built upon maximum likelihood estimation framework and sparse representations over a trained dictionary. Rather than optimizing the likelihood functional derived from a mixture distribution, we present a new weighting data fidelity function, which has the same minimizer as the original likelihood functional but is much easier to optimize. The weighting function in the model can be determined by the algorithm itself, and it plays a role of noise detection in terms of the different estimated noise parameters. By incorporating the sparse regularization of small image patches, the proposed method can efficiently remove a variety of mixed or single noise while preserving the image textures well. In addition, a modified K-SVD algorithm is designed to address the weighted rank-one approximation. The experimental results demonstrate its better performance compared with some existing methods.

Maladaptive and adaptive emotion regulation through music: a behavioral and neuroimaging study of males and females <=SEP=> Music therapists use guided affect regulation in the treatment of mood disorders. However, self-directed uses of music in affect regulation are not fully understood. Some uses of music may have negative effects on mental health, as can non-music regulation strategies, such as rumination. Psychological testing and functional magnetic resonance imaging (fMRI) were used explore music listening strategies in relation to mental health. Participants (n = 123) were assessed for depression, anxiety and Neuroticism, and uses of Music in Mood Regulation (MMR). Neural responses to music were measured in the medial prefrontal cortex (mPFC) in a subset of participants (n = 56). Discharge, using music to express negative emotions, related to increased anxiety and Neuroticism in all participants and particularly in males. Males high in Discharge showed decreased activity of mPFC during music listening compared with those using less Discharge. Females high in Diversion, using music to distract from negative emotions, showed more mPFC activity than females using less Diversion. These results suggest that the use of Discharge strategy can be associated with maladaptive patterns of emotional regulation, and may even have long-term negative effects on mental health. This finding has real-world applications in psychotherapy and particularly in clinical music therapy.

Guido: A Musical Score Recognition System <=SEP=> This paper presents an optical music recognition system Guido that can automatically recognize the main musical symbols of music scores that were scanned or taken by a digital camera. The application is based on object model of musical notation and uses linguistic approach for symbol interpretation and error correction. The system offers musical editor with a partially automatic error correction.

Batteries and Ultracapacitors for Electric, Hybrid, and Fuel Cell Vehicles <=SEP=> The application of batteries and ultracapacitors in electric energy storage units for battery powered (EV) and charge sustaining and plug-in hybrid-electric (HEV and PHEV) vehicles have been studied in detail. The use of IC engines and hydrogen fuel cells as the primary energy converters for the hybrid vehicles was considered. The study focused on the use of lithium-ion batteries and carbon/carbon ultracapacitors as the energy storage technologies most likely to be used in future vehicles. The key findings of the study are as follows. 1) The energy density and power density characteristics of both battery and ultracapacitor technologies are sufficient for the design of attractive EVs, HEVs, and PHEVs. 2) Charge sustaining, engine powered hybrid-electric vehicles (HEVs) can be designed using either batteries or ultracapacitors with fuel economy improvements of 50% and greater. 3) Plug-in hybrids (PHEVs) can be designed with effective all-electric ranges of 30-60 km using lithium-ion batteries that are relatively small. The effective fuel economy of the PHEVs can be very high (greater than 100 mpg) for long daily driving ranges (80-150 km) resulting in a large fraction (greater than 75%) of the energy to power the vehicle being grid electricity. 4) Mild hybrid-electric vehicles (MHEVs) can be designed using ultracapacitors having an energy storage capacity of 75-150 Wh. The fuel economy improvement with the ultracapacitors is 10%-15% higher than with the same weight of batteries due to the higher efficiency of the ultracapacitors and more efficient engine operation. 5) Hybrid-electric vehicles powered by hydrogen fuel cells can use either batteries or ultracapacitors for energy storage. Simulation results indicate the equivalent fuel economy of the fuel cell powered vehicles is 2-3 times higher than that of a gasoline fueled IC vehicle of the same weight and road load. Compared to an engine-powered HEV, the equivalent fuel economy of the hydrogen fuel cell vehicle would be 1.66-2.0 times higher

Multi-label Text Categorization with Model Combination based on F1-score Maximization <=SEP=> Text categorization is a fundamental task in natural language processing, and is generally defined as a multi-label categorization problem, where each text document is assigned to one or more categories. We focus on providing good statistical classifiers with a generalization ability for multi-label categorization and present a classifier design method based on model combination and F1-score maximization. In our formulation, we first design multiple models for binary classification per category. Then, we combine these models to maximize the F1-score of a training dataset. Our experimental results confirmed that our proposed method was useful especially for datasets where there were many combinations of category labels.

On accelerating pair-HMM computations in programmable hardware <=SEP=> This paper explores hardware acceleration to significantly improve the runtime of computing the forward algorithm on Pair-HMM models, a crucial step in analyzing mutations in sequenced genomes. We describe 1) the design and evaluation of a novel accelerator architecture that can efficiently process real sequence data without performing wasteful work; and 2) aggressive memoization techniques that can significantly reduce the number of invocations of, and the amount of data transferred to the accelerator. We describe our demonstration of the design on a Xilinx Virtex 7 FPGA in an IBM Power8 system. Our design achieves a 14.85× higher throughput than an 8-core CPU baseline (that uses SIMD and multi-threading) and a 147.49 × improvement in throughput per unit of energy expended on the NA12878 sample.

The human connectome: Origins and challenges <=SEP=> The human connectome refers to a map of the brain's structural connections, rendered as a connection matrix or network. This article attempts to trace some of the historical origins of the connectome, in the process clarifying its definition and scope, as well as its putative role in illuminating brain function. Current efforts to map the connectome face a number of significant challenges, including the issue of capturing network connectivity across multiple spatial scales, accounting for individual variability and structural plasticity, as well as clarifying the role of the connectome in shaping brain dynamics. Throughout, the article argues that these challenges require the development of new approaches for the statistical analysis and computational modeling of brain network data, and greater collaboration across disciplinary boundaries, especially with researchers in complex systems and network science.

NLTK: The Natural Language Toolkit <=SEP=> NLTK, the Natural Language Toolkit, is a suite of open source program modules, tutorials and problem sets, providing ready-to-use computational linguistics courseware. NLTK covers symbolic and statistical natural language processing, and is interfaced to annotated corpora. Students augment and replace existing components, learn structured programming by example, and manipulate sophisticated models from the outset.

Fully convolutional networks for multi-modality isointense infant brain image segmentation <=SEP=> The segmentation of infant brain tissue images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) plays an important role in studying early brain development. In the isointense phase (approximately 6–8 months of age), WM and GM exhibit similar levels of intensity in both T1 and T2 MR images, resulting in extremely low tissue contrast and thus making the tissue segmentation very challenging. The existing methods for tissue segmentation in this isointense phase usually employ patch-based sparse labeling on single T1, T2 or fractional anisotropy (FA) modality or their simply-stacked combinations without fully exploring the multi-modality information. To address the challenge, in this paper, we propose to use fully convolutional networks (FCNs) for the segmentation of isointense phase brain MR images. Instead of simply stacking the three modalities, we train one network for each modality image, and then fuse their high-layer features together for final segmentation. Specifically, we conduct a convolution-pooling stream for multimodality information from T1, T2, and FA images separately, and then combine them in high-layer for finally generating the segmentation maps as the outputs. We compared the performance of our approach with that of the commonly used segmentation methods on a set of manually segmented isointense phase brain images. Results showed that our proposed model significantly outperformed previous methods in terms of accuracy. In addition, our results also indicated a better way of integrating multi-modality images, which leads to performance improvement.

A 24-GHz high-gain Yagi-Uda antenna array <=SEP=> A compact 24-GHz Yagi-Uda antenna has been developed using standard design tables and simple scaling to take into account the added capacitance due to the supporting dielectric substrate. The antenna results in a directivity of 9.3 dB, a front-to-back ratio of 11 dB, and a bandwidth of 2.5-3%. The Yagi-Uda antenna has been implemented in an 11-beam system using a planar array and a 2-inch Teflon spherical lens. The measured patterns show a 22 dB gain beam, a cross-polarization level of -24 dB, and a crossover level of -6 dB. The design method presented in this paper is quite straightforward, and can be used to develop low-, medium-, and even high-gain endfire Yagi-Uda antennas.

A Decomposed Dual-Cross Generative Adversarial Network for Image Rain Removal <=SEP=> Rain removal is important for many computer vision applications, such as surveillance, autonomous car, etc. Traditionally, rain removal is regarded as a signal removal problem which usually causes over-smoothing by removing texture details in non-rain background regions. This paper considers the issue of rain removal from a completely different perspective, to treat rain removal as a signal decomposition problem. Specifically, we decompose the rain image into two components, namely non-rain background image and rain streaks image. Then, we introduce an adversarial training mechanism to synthesize non-rain background image and rain streaks image in a Dual-Cross manner, which makes the two adversarial branches interact with each other, archiving a win-win result ultimately. The proposed Decomposed Dual-Cross Generative Adversarial Network (DDC-GAN) shows significantly performance improvement compared with stateof-the-art methods on both synthetic and real-world images in terms of qualitative and quantitative measures (over 3dB gains in PSNR).

Content-based audio segmentation using support vector machines <=SEP=> Audio exists at everywhere, but is often out-of-order. It is necessary to arrange them into regularized classes in order to use them more easily. It is also useful, especially in video content analysis, to segment an audio stream according to audio types. In this paper, we present our work in applying support vector machines (SVMs) in audio segmentation and classification. Five audio classes are considered: silence, music, background sound, pure speech, and non-pure speech which includes speech over music and speech over noise. A SVM learns optimal class boundaries from training data to best separate between two classes. A sound clip is segmented by classifying each sub-clip of one second into one of these five classes. Experiments on a database composed of clips of 14870 seconds in total length show that the average accuracy rate for the SVM method is much better than that of the traditional Euclidean distance based (nearest neighbor) method.

Building contextual visual vocabulary for large-scale image applications <=SEP=> Not withstanding its great success and wide adoption in Bag-of-visual Words representation, visual vocabulary created from single image local features is often shown to be ineffective largely due to three reasons. First, many detected local features are not stable enough, resulting in many noisy and non-descriptive visual words in images. Second, single visual word discards the rich spatial contextual information among the local features, which has been proven to be valuable for visual matching. Third, the distance metric commonly used for generating visual vocabulary does not take the semantic context into consideration, which renders them to be prone to noise. To address these three confrontations, we propose an effective visual vocabulary generation framework containing three novel contributions: 1) we propose an effective unsupervised local feature refinement strategy; 2) we consider local features in groups to model their spatial contexts; 3) we further learn a discriminant distance metric between local feature groups, which we call discriminant group distance. This group distance is further leveraged to induce visual vocabulary from groups of local features. We name it contextual visual vocabulary, which captures both the spatial and semantic contexts. We evaluate the proposed local feature refinement strategy and the contextual visual vocabulary in two large-scale image applications: large-scale near-duplicate image retrieval on a dataset containing 1.5 million images and image search re-ranking tasks. Our experimental results show that the contextual visual vocabulary shows significant improvement over the classic visual vocabulary. Moreover, it outperforms the state-of-the-art Bundled Feature in the terms of retrieval precision, memory consumption and efficiency.

Paraphrase Identification Based on Weighted URAE, Unit Similarity and Context Correlation Feature <=SEP=> A deep learning model adaptive to both sentence-level and articlelevel paraphrase identification is proposed in this paper. It consists of pairwise unit similarity feature and semantic context correlation feature. In this model, sentences are represented by word and phrase embedding while articles are represented by sentence embedding. Those phrase and sentence embedding are learned from parse trees through Weighted Unfolding Recursive Autoencoders (WURAE), an unsupervised learning algorithm. Then, unit similarity matrix is calculated by matching the pairwise lists of embedding. It is used to extract the pairwise unit similarity feature through CNN and k-max pooling layers. In addition, semantic context correlation feature is taken into account, which is captured by the combination of CNN and LSTM. CNN layers learn collocation information between adjacent units while LSTM extracts the long-term dependency feature of the text based on the output of CNN. This model is experimented on a famous English sentence paraphrase corpus, MSRPC, and a Chinese article paraphrase corpus. The results show that the deep semantic feature of text could be extracted based on WURAE, unit similarity and context correlation feature. We release our code of WURAE, deep learning model for paraphrase identification and pre-trained phrase end sentence embedding data for use by the community.

The impact of cyberloafing towards Malaysia employees' productivity: A conceptual framework <=SEP=> The increasing use of the internet has changed the way we live and work. Moreover, in countries where there is a use of internet on a large scale, changes in the content and context of work have been observed. The use of Internet has become a tool to achieve competitive advantage by organizations. Also, it has been associated with many negative consequences such as reduced employees' productivity. Despite its importance, there is a lack of cyberloafing studies in Malaysia. The paper proposes a modified version of Triandis's theory of interpersonal behaviour by incorporating a new independent variable, ability to hide cyberloafing that directly predicts employees cyberloafing. This paper also recommends examining the relationship between cyberloafing and employees' productivity. Some studies believe that cyberloafing is beneficial for employees' productivity but other studies suggest that it is harmful to employees' productivity. By understanding what causes cyberloafing and its impacts, organizations can be more effective in managing the issue.