title: Beyond the Connectome: The Dynome

abstract: The human connectome will provide a detailed mapping of the brain's connectivity, with fundamental insights for health and disease. However, further understanding of brain function and dysfunction will require an integrated framework that links brain connectivity with brain dynamics, as well as the biological details that relate this connectivity more directly to function. In this Perspective, we describe such a framework for studying the brain's "dynome" and its relationship to cognition.


title: Inhibitory Phosphorylation of Separase Is Essential for Genome Stability and Viability of Murine Embryonic Germ Cells

abstract: Activity of separase, a cysteine protease that cleaves sister chromatid cohesin at the onset of anaphase, is tightly regulated to ensure faithful chromosome segregation and genome stability. Two mechanisms negatively regulate separase: inhibition by securin and phosphorylation on serine 1121. To gauge the physiological significance of the inhibitory phosphorylation, we created a mouse strain in which Ser1121 was mutated to Ala (S1121A). Here we report that this S1121A point mutation causes infertility in mice. We show that germ cells in the mutants are depleted during development. We further demonstrate that S1121A causes chromosome misalignment during proliferation of the postmigratory primordial germ cells, resulting in mitotic arrest, aneuploidy, and eventual cell death. Our results indicate that inhibitory phosphorylation of separase plays a critical role in the maintenance of sister chromatid cohesion and genome stability in proliferating postmigratory primordial germ cells.


Iterative Hough Transform for Line Detection in 3D Point Clouds


title: Bankruptcy visualization and prediction using neural networks: A study of U.S. commercial banks

abstract: Abstract We develop a model of neural networks to study the bankruptcy of U.S. banks, taking into account the specific features of the recent financial crisis. We combine multilayer perceptrons and self-organizing maps to provide a tool that displays the probability of distress up to three years before bankruptcy occurs. Based on data from the Federal Deposit Insurance Corporation between 2002 and 2012, our results show that failed banks are more concentrated in real estate loans and have more provisions. Their situation is partially due to risky expansion, which results in less equity and interest income. After drawing the profile of distressed banks, we develop a model to detect failures and a tool to assess bank risk in the short, medium and long term using bankruptcies that occurred from May 2012 to December 2013 in U.S. banks. The model can detect 96.15% of the failures in this period and outperforms traditional models of bankruptcy prediction.


title: A feature-based method for traffic anomaly detection

abstract: The wide spread use of GPS-enabled devices facilitates us to sense the movement of vehicles. Detecting anomalous movement behavior on road segments can benefit both drivers and transportation authorities. An important challenge behind this is how to detect these anomalies effectively and timely under large scale of raw GPS trajectories. In this light, we propose a Feature-Based method for Traffic Anomaly Detection (FBTAD). A key observation is that road segments, where these incidents turns up, tend to have their vehicle flow features changed in a short period of time. For example, a traffic accident may immediately and significantly slow down the travel speed on a road segment. In this paper, we first map-match raw trajectories. Then we calculate the road segments' traffic features in each time slot, e.g., 10 minutes, and introduce an offline spatial-temporal index for speeding up the anomaly detection process. Finally, we retrieve anomaly candidates by checking the road segment's traffic flow acceleration based on the index built above, and examine candidates' density change ratio or moving objects' outflow ratio to further infer traffic anomalies. The effectiveness and efficiency of our approach is validated by extensive experimentation. Our evaluations show that the method proposed in this paper is able to detect traffic anomalies more efficiently as well as earlier than the baseline method.


title: A 100–170MHz fully-differential Sallen-Key 6th-order low-pass filter for wideband wireless communication

abstract: An integrated fully-differential 6th-order continuous time low-pass filter for wideband wireless communication is proposed in this paper. The filter is designed with the Sallen-Key biquadratic cells in which fully-differential operational amplifiers with two pairs of inputs are used. Stability optimization technology is discussed for the operational amplifier in order to improve its stability condition when it is closed loop to constitute unit gain Sallen-Key biquadratic cells. The filter is implemented in TSMC 0.13pm CMOS process, and has a core area of 0.5×0.22 mm2 excluding bonding pads. The simulated results show that the filter can operate successfully over a cutoff frequency range of 100MHz to 170MHz, and achieve more than 43dB adjacent channel rejection and more than 8.6dBm in-band IIP3. The power consumption is 15.5mA from a 1.2V supply voltage including 5mA of the output buffers.


Techniques , Applications and Challenging Issue in Text Mining


title: The Semantics and Pragmatics of Presupposition

abstract: In this paper, we offer a novel analysis of presuppositions, paying particular attention to the interaction between the knowledge resources that are required to interpret them. The analysis has two main features. First, we capture an analogy between presuppositions, anaphora and scope ambiguity (cf. van der Sandt I992), by utilizing semantic underspecification (cf. Reyle I993). Second, resolving this underspecification requires reasoning about how the presupposition is rhetorically connected to the discourse context. This has several consequences. First, since pragmatic information plays a role in computing the rhetorical relation, it also constrains the interpretation of presuppositions. Our account therefore provides a formal framework for analysing problematic data, which require pragmatic reasoning. Second, binding presuppositions to the context via rhetorical links replaces accommodating them, in the sense of adding them to the context (cf. Lewis I979). The treatment of presupposition is thus generalized and integrated into the discourse update procedure. We formalize this approach in SDRT (Asher I993; Lascarides & Asher I993), and demonstrate that it provides a rich framework for interpreting presuppositions, where semantic and pragmatic constraints are integrated


title: Interactive image search by 2D semantic map

abstract: In this demo, we present a novel interactive image search system, image search by 2D semantic map. This system enables users to indicate what semantic concepts are expected to appear and even how these concepts are spatially distributed in the desired images. To this end, we design an intuitive interface for users to formulate a query in the form of 2D semantic map, called concept map, by typing textual queries in a blank canvas. In the ranking process, by interpreting each textual concept as a set of representative visual instances, the concept map query is translated into a visual instance map, which is then used for comparison with the images in the database. Besides, in this demo, we also show an image search system with a simplest semantic map, a 2D color map, where the concepts are limited from the colors.


title: ${\cal H}_{\infty}$ Estimation for Uncertain Systems With Limited Communication Capacity

abstract: This paper investigates the problem of robust Hinfin estimation for uncertain systems subject to limited communication capacity. The parameter uncertainty belongs to a given convex polytope and the communication limitations include measurement quantization, signal transmission delay, and data packet dropout, which appear typically in a network environment. The problem of Hinfin filter design is first solved for a nominal system subject to the aforementioned information limitations, which is then extended to the uncertain case based on the notion of quadratic stability. To further reduce the overdesign in the quadratic framework, this paper also proposes a parameter-dependent filter design procedure, which is much less conservative than the quadratic approach. The quadratic and parameter-dependent approaches provide alternatives for designing robust Hinfin filters with different degrees of conservativeness and computational complexity. Two examples, including a mass-spring system, are utilized to illustrate the design procedures proposed in this paper.


title: Coordinated UAV Path Planning Using Differential Evolution

abstract: A wobble plate type compressor is disclosed which includes a compressor housing having a crank chamber and a cylinder block which is provided with an axial center bore and a plurality of cylinders. A plurality of pistons are slidably fitted within each of the cylinders. A cam rotor is disposed within the crank chamber to rotate together with a drive shaft. A wobble plate is coupled to the pistons through each connecting rod. The wobble plate is disposed on the inclined surface of the cam rotor to convert rotating motion of the cam rotor into reciprocating motion to be imparted to the pistons while preventing the rotational motion of the wobble plate. A first bevel gear is non-rotatably disposed within the center bore of the cylinder block. A second bevel gear is fixed on the wobble plate and engaged with the first bevel gear through spherical member to prevent rotating of the wobble plate. The drive shaft extends within the first and second bevel gears and spherical member, and is supported on the first bevel gear. Therefore, the drive shaft is rotated without deflection and offset, and vibration and noise is reduced.


title: Modeling MOOC Student Behavior With Two-Layer Hidden Markov Models

abstract: Massive open online courses (MOOCs) provide educators with an abundance of data describing how students interact with the platform, but this data is highly underutilized today. This is in part due to the lack of sophisticated tools to provide interpretable and actionable summaries of huge amounts of MOOC activity present in log data. In this paper, we propose a method for automatically discovering student behavior patterns by leveraging the click log data that can be obtained from the MOOC platform itself in a completely unsupervised manner.


title: A Simple Algorithm for Nearest Neighbor Search in High Dimensions

abstract: The problem of finding the closest point in high-dimensional spaces is common in pattern recognition. Unfortunately, the complexity of most existing search algorithms, such as k-d tree and R-tree, grows exponentially with dimension, making them impractical for dimensionality above 15. In nearly all applications, the closest point is of interest only if it lies within a user-specified distance /spl epsiv/. We present a simple and practical algorithm to efficiently search for the nearest neighbor within Euclidean distance /spl epsiv/. The use of projection search combined with a novel data structure dramatically improves performance in high dimensions. A complexity analysis is presented which helps to automatically determine /spl epsiv/ in structured problems. A comprehensive set of benchmarks clearly shows the superiority of the proposed algorithm for a variety of structured and unstructured search problems. Object recognition is demonstrated as an example application. The simplicity of the algorithm makes it possible to construct an inexpensive hardware search engine which can be 100 times faster than its software equivalent. A C++ implementation of our algorithm is available.


title: A semantic matching energy function for learning with multi-relational data

abstract: Large-scale relational learning becomes crucial for handling the huge amounts of structured data generated daily in many application domains ranging from computational biology or information retrieval, to natural language processing. In this paper, we present a new neural network architecture designed to embed multi-relational graphs into a flexible continuous vector space in which the original data is kept and enhanced. The network is trained to encode the semantics of these graphs in order to assign high probabilities to plausible components. We empirically show that it reaches competitive performance in link prediction on standard datasets from the literature as well as on data from a real-world knowledge base (WordNet). In addition, we present how our method can be applied to perform word-sense disambiguation in a context of open-text semantic parsing, where the goal is to learn to assign a structured meaning representation to almost any sentence of free text, demonstrating that it can scale up to tens of thousands of nodes and thousands of types of relation.


title: Robotic Forceps Manipulator With a Novel Bending Mechanism

abstract: This paper proposes a new bending technique with a screwdrive mechanism that allows for omnidirectional bending motion by rotating two linkages, each consisting of a right-handed screw, a universal joint, and a left-handed screw. The new screwdrive mechanism, termed double-screw-drive (DSD) mechanism, is utilized in a robotic forceps manipulator for laparoscopic surgery. A robotic forceps manipulator incorporating the DSD mechanism (DSD forceps) can bend without using wires. Without wires, it has high rigidity, and can bend at 90° in any arbitrary direction. In addition, the gripper of the DSD forceps can perform rotational motion, which is achieved by rotating a third linkage in the DSD mechanism. Opening and closing motions of the gripper are attained by wire actuation. Fundamental experiments examining the bending force and the accuracy of the DSD forceps were conducted, and an analysis of the accuracy was performed. Control of the DSD forceps through a teleoperation system was achieved via a joystick-type manipulator. A servo system was constructed for each linkage and the wire actuation mechanism, and tracking control experiments as well as a suturing experiment were conducted. The results of the experiments showed that the required design specifications were fulfilled. Thus, the validity of the DSD forceps was demonstrated.


Bayesian updating in causal probabilistic networks by local computations


title: A probabilistic method for identifying start codons in bacterial genomes

abstract: As the pace of genome sequencing has accelerated, the need for highly accurate gene prediction systems has grown. Computational systems for identifying genes in prokaryotic genomes have sensitivities of 98-99% or higher (Delcher et al., Nucleic Acids Res., 27, 4636-4641, 1999). These accuracy figures are calculated by comparing the locations of verified stop codons to the predictions. Determining the accuracy of start codon prediction is more problematic, however, due to the relatively small number of start sites that have been confirmed by independent, non-computational methods. Nonetheless, the accuracy of gene finders at predicting the exact gene boundaries at both the 5' and 3' ends of genes is of critical importance for microbial genome annotation, especially in light of the important signaling information that is sometimes found on the 5' end of a protein coding region. In this paper we propose a probabilistic method to improve the accuracy of gene identification systems at finding precise translation start sites. The new system, RBSfinder, is tested on a validated set of genes from Escherichia coli, for which it improves the accuracy of start site locations predicted by computational gene finding systems from the range 67-77% to 90% correct.


title: Object Detection in Videos by High Quality Object Linking

abstract: Compared with object detection in static images, object detection in videos is more challenging due to degraded image qualities. An effective way to address this problem is to exploit temporal contexts by linking the same object across video to form tubelets and aggregating classification scores in the tubelets. In this paper, we focus on obtaining high quality object linking results for better classification. Unlike previous methods that link objects by checking boxes between neighboring frames, we propose to link in the same frame. To achieve this goal, we extend prior methods in following aspects: (1) a cuboid proposal network that extracts spatio-temporal candidate cuboids which bound the movement of objects; (2) a short tubelet detection network that detects short tubelets in short video segments; (3) a short tubelet linking algorithm that links temporally-overlapping short tubelets to form long tubelets. Experiments on the ImageNet VID dataset show that our method outperforms both the static image detector and the previous state of the art. In particular, our method improves results by 8.8 percent over the static image detector for fast moving objects.


title: VisLoiter+: An Entropy Model-Based Loiterer Retrieval System with User-Friendly Interfaces

abstract: It is very difficult to fully automate the detection of loitering behavior in video surveillance, therefore humans are often required for monitoring. Alternatively, we could provide a list of potential loiterer candidates for a final yes/no judgment of a human operator. Our system, VisLoiter+, realizes this idea with a unique, user-friendly interface and by employing an entropy model for improved loitering analysis. Rather than using only frequency of appearance, we expand the loiter analysis with new methods measuring the amount of person movements across multiple camera views. The interface gives an overview of loiterer candidates to show their behavior at a glance, complemented by a lightweight video playback for further details about why a candidate was selected. We demonstrate that our system outperforms state-of-the-art solutions using real-life data sets.


title: Joint Learning of Motion Estimation and Segmentation for Cardiac MR Image Sequences

abstract: Cardiac motion estimation and segmentation play important roles in quantitatively assessing cardiac function and diagnosing cardiovascular diseases. In this paper, we propose a novel deep learning method for joint estimation of motion and segmentation from cardiac MR image sequences. The proposed network consists of two branches: a cardiac motion estimation branch which is built on a novel unsupervised Siamese style recurrent spatial transformer network, and a cardiac segmentation branch that is based on a fully convolutional network. In particular, a joint multi-scale feature encoder is learned by optimizing the segmentation branch and the motion estimation branch simultaneously. This enables the weakly-supervised segmentation by taking advantage of features that are unsupervisedly learned in the motion estimation branch from a large amount of unannotated data. Experimental results using cardiac MlRI images from 220 subjects show that the joint learning of both tasks is complementary and the proposed models outperform the competing methods significantly in terms of accuracy and speed.


title: Video Super-Resolution Algorithm Using Bi-Directional Overlapped Block Motion Compensation and On-the-Fly Dictionary Training

abstract: This paper presents a video super-resolution algorithm to interpolate an arbitrary frame in a low resolution video sequence from sparsely existing high resolution key-frames. First, a hierarchical block-based motion estimation is performed between an input and low resolution key-frames. If the motion-compensated error is small, then an input low resolution patch is temporally super-resolved via bi-directional overlapped block motion compensation. Otherwise, the input patch is spatially super-resolved using the dictionary that has been already learned from the low resolution and its corresponding high resolution key-frame pair. Finally, possible blocking artifacts between temporally super-resolved patches and spatially super-resolved patches are concealed using a specific de-blocking filter. The experimental results show that the proposed algorithm provides significantly better subjective visual quality as well as higher peak-to-peak signal-to-noise ratio than those by previous interpolation algorithms.


title: Towards Managing IT Complexity: An IT Governance Framework to Measure Business-IT Responsibility Sharing and Structural IT Organization

abstract: For large enterprises, IT governance is a major lever to influence the way how IT enables business. The authors hypothesize that a part of IT governance, namely business-IT responsibility sharing and structural IT organization, has a strong influence on IT architecture complexity of large enterprises. IT complexity is challenging CIOs of large enterprises all over the world day-by-day. Yet, IT complexity so far has been analyzed by scientific research only to a small degree. To prove or falsify the hypothesis that IT governance influences IT architecture complexity, the authors developed concepts to measure both aspects with a framework. In this research, the concept for business-IT responsibility sharing and its scientific background are presented. The IT complexity measurement concept is shown, focusing on IT architecture complexity. An outlook for this research, an empirical study that is currently in progress is given.


title: Publishing of personal information on Facebook with regard to gender: Comparison of pupils and university students

abstract: The goal of this research was to explore and compare personal information Facebook publishing behaviour of male and female pupils and university students. One important finding is that very small percentage of all examinees publish items in most sensitive data types regarding the privacy: religious and political views, mobile phone, email, address. Additionally, it was established that a similar percentage of male and female pupils publish information in almost all data types. The exception is a Books data type where female pupils publish about two times more than male pupils. Also, on average, female pupils publish about 7.5 times more often information in Books data type than male pupils. Male pupils, on average, publish information in Sports data type about 4.5 times more often than female pupils. Considerably higher percentage of male students publish information in data types: High School, Apps and Games, Music, Friends, TV Shows, Movies. On average, male students publish about 8 times more often information in Sports data type than female students. Female students, on average, publish information considerably more frequently in Check-Ins data type: about 3.5 times more often than male students.


title: The role of working memory in processing L2 input: Insights from eye-tracking

abstract: Our study investigated how attention paid to a target syntactic construction causative had is related the storage capacity and attention regulation function of working memory (WM) and how these WM abilities moderate the change of knowledge of the target construction in different input conditions. 80 Sri Lankan learners of English were exposed to examples of the target construction in explicit and implicit learning conditions and their eye movements were tracked as they read the input. Correlational and multiple regression analyses indicated a very strong relationship between WM abilities and gains in the knowledge of the target construction. WM scores were closely associated with gains in receptive knowledge in all input conditions, but they had a weaker link to the improvement of productive knowledge in the implicit learning conditions. The amount of attention paid to input was also strongly related to WM abilities.


title: More on the Fragility of Performance: Choking Under Pressure in Mathematical Problem Solving.

abstract: In 3 experiments, the authors examined mathematical problem solving performance under pressure. In Experiment 1, pressure harmed performance on only unpracticed problems with heavy working memory demands. In Experiment 2, such high-demand problems were practiced until their answers were directly retrieved from memory. This eliminated choking under pressure. Experiment 3 dissociated practice on particular problems from practice on the solution algorithm by imposing a high-pressure test on problems practiced 1, 2, or 50 times each. Infrequently practiced high-demand problems were still performed poorly under pressure, whereas problems practiced 50 times each were not. These findings support distraction theories of choking in math, which contrasts with considerable evidence for explicit monitoring theories of choking in sensorimotor skills. This contrast suggests a skill taxonomy based on real-time control structures.


Variational Autoencoder based Anomaly Detection using Reconstruction Probability


title: Dictionary Learning Algorithms for Sparse Representation

abstract: Algorithms for data-driven learning of domain-specific overcomplete dictionaries are developed to obtain maximum likelihood and maximum a posteriori dictionary estimates based on the use of Bayesian models with concave/Schur-concave (CSC) negative log priors. Such priors are appropriate for obtaining sparse representations of environmental signals within an appropriately chosen (environmentally matched) dictionary. The elements of the dictionary can be interpreted as concepts, features, or words capable of succinct expression of events encountered in the environment (the source of the measured signals). This is a generalization of vector quantization in that one is interested in a description involving a few dictionary entries (the proverbial "25 words or less"), but not necessarily as succinct as one entry. To learn an environmentally adapted dictionary capable of concise expression of signals generated by the environment, we develop algorithms that iterate between a representative set of sparse representations found by variants of FOCUSS and an update of the dictionary using these sparse representations.Experiments were performed using synthetic data and natural images. For complete dictionaries, we demonstrate that our algorithms have improved performance over other independent component analysis (ICA) methods, measured in terms of signal-to-noise ratios of separated sources. In the overcomplete case, we show that the true underlying dictionary and sparse sources can be accurately recovered. In tests with natural images, learned overcomplete dictionaries are shown to have higher coding efficiency than complete dictionaries; that is, images encoded with an overcomplete dictionary have both higher compression (fewer bits per pixel) and higher accuracy (lower mean square error).


title: Real-time Empirical Mode Decomposition for EEG signal enhancement

abstract: Electroencephalography (EEG) recordings are used for brain research. However, in most cases, the recordings not only contain brain waves, but also artifacts of physiological or technical origins. A recent approach used for signal enhancement is Empirical Mode Decomposition (EMD), an adaptive data-driven technique which decomposes non-stationary data into so-called Intrinsic Mode Functions (IMFs). Once the IMFs are obtained, they can be used for denoising and detrending purposes. This paper presents a real-time implementation of an EMD-based signal enhancement scheme. The proposed implementation is used for removing noise, for suppressing muscle artifacts, and for detrending EEG signals in an automatic manner and in real-time. The proposed algorithm is demonstrated by application to a simulated and a real EEG data set from an epilepsy patient. Moreover, by visual inspection and in a quantitative manner, it is shown that after the EMD in real-time, the EEG signals are enhanced.


title: Technological pedagogical content knowledge - a review of the literature

abstract: Technological Pedagogical Content Knowledge (TPACK) has been introduced as a conceptual framework for the knowledge base teachers need to effectively teach with technology. The framework stems from the notion that technology integration in a specific educational context benefits from a careful alignment of content, pedagogy and the potential of technology, and that teachers who want to integrate technology in their teaching practice therefore need to be competent in all three domains. This study is a systematic literature review about TPACK of 55 peer-reviewed journal articles (and one book chapter), published between 2005 and 2011. The purpose of the review was to investigate the theoretical basis and the practical use of TPACK. Findings showed different understandings of TPACK and of technological knowledge. Implications of these different views impacted the way TPACK was measured. Notions about TPACK in subject domains were hardly found in the studies selected for this review. Teacher knowledge (TPACK) and beliefs about pedagogy and technology are intertwined. Both determine whether a teacher decides to teach with technology. Active involvement in (re)design and enactment of technology-enhanced lessons was found as a promising strategy for the development of TPACK in (student-)teachers. Future directions for research are discussed


title: Preoperative neutrophil-to-lymphocyte ratio and tumor-related factors to predict lymph node metastasis in patients with pancreatic ductal adenocarcinoma (PDAC)

abstract: // Lianyuan Tao 1 , Lingfu Zhang 1 , Ying Peng 1 , Ming Tao 1 , Gang Li 1 , Dianrong Xiu 1 , Chunhui Yuan 1 , Chaolai Ma 1 and Bin Jiang 1 1 Department of General Surgery, Peking University Third Hospital, Beijing, China Correspondence to: Dianrong Xiu, email: // Keywords : pancreatic ductal adenocarcinoma; lymph node metastasis; prognosis; neutrophil-to-lymphocyte ratio Received : May 09, 2016 Accepted : July 19, 2016 Published : August 02, 2016 Abstract As a poor prognosis indicator in patients with pancreatic ductal adenocarcinoma (PDCA), lymph node (LN) metastasis is of great importance in treatment. Present study was performed to evaluate the predictive value of preoperative neutrophil-to-lymphocyte ratio (NLR), Platelet-to-lymphocyte ratio (PLR) and possible clinical parameters on the LN metastasis in PDCA patients. A total of 159 operable patients with PDCA were enrolled in our study. The clinical utility of NLR and other clinical parameters was evaluated by receiver operating characteristic (ROC) curves. Overall survival analysis indicated that LN metastasis is an independent prognostic factor. The logistic analysis was used to determine the independent parameters associated with LN metastasis. Ideal cutoff values for predicting LN metastasis are 2.12 for NLR and 130.96 for PLR according to the ROC curve. Multivariate analyses indicate that NLR (HR 2.588; 95% CI 1.246-5.376; P = 0.011), CA125 (HR 6.348; 95% CI 2.056-19.594; P = 0.001) and CA19-9 (HR 2.738; 95% CI 1.151-6.515; P = 0.023) are associated significantly with LN metastasis independently. Preoperative NLR, CA125 and CA19-9 are useful biomarkers for the prediction of LN metastasis in PDCA patients.


title: A Controlled Set-Up Experiment to Establish Personalized Baselines for Real-Life Emotion Recognition

abstract: We design, conduct and present the results of a highly personalized baseline emotion recognition experiment, which aims to set reliable ground-truth estimates for the subject's emotional state for real-life prediction under similar conditions using a small number of physiological sensors. We also propose an adaptive stimuli-selection mechanism that would use the user's feedback as guide for future stimuli selection in the controlled-setup experiment and generate optimal ground-truth personalized sessions systematically. Initial results are very promising (85% accuracy) and variable importance analysis shows that only a few features, which are easy-to-implement in portable devices, would suffice to predict the subject's emotional state.


title: Mining Students' Data for Prediction Performance

abstract: A country's growth is strongly measured by quality of its education system. Education sector, across the globe has witnessed sea change in its functioning. Today it is recognized as an industry and like any other industry it is facing challenges, the major challenges of higher education being decrease in students' success rate and their leaving a course without completion. An early prediction of students' failure may help the management provide timely counseling as well coaching to increase success rate and student retention. We use different classification techniques to build performance prediction model based on students' social integration, academic integration, and various emotional skills which have not been considered so far. Two algorithms J48 (Implementation of C4.5) and Random Tree have been applied to the records of MCA students of colleges affiliated to Guru Gobind Singh Indraprastha University to predict third semester performance. Random Tree is found to be more accurate in predicting performance than J48 algorithm.


title: IoT-Based Smart Rehabilitation System

abstract: Internet of Things (IoT) makes all objects become interconnected and smart, which has been recognized as the next technological revolution. As its typical case, IoT-based smart rehabilitation systems are becoming a better way to mitigate problems associated with aging populations and shortage of health professionals. Although it has come into reality, critical problems still exist in automating design and reconfiguration of such a system enabling it to respond to the patient's requirements rapidly. This paper presents an ontology-based automating design methodology (ADM) for smart rehabilitation systems in IoT. Ontology aids computers in further understanding the symptoms and medical resources, which helps to create a rehabilitation strategy and reconfigure medical resources according to patients' specific requirements quickly and automatically. Meanwhile, IoT provides an effective platform to interconnect all the resources and provides immediate information interaction. Preliminary experiments and clinical trials demonstrate valuable information on the feasibility, rapidity, and effectiveness of the proposed methodology.


title: Exploring Health Topics in Chinese Social Media: An Analysis of Sina Weibo

abstract: This paper seeks to identify and characterize health-related topics discussed on the Chinese microblogging website, Sina Weibo. We identified nearly 1 million messages containing health-related keywords, filtered from a dataset of 93 million messages spanning five years. We applied probabilistic topic models to this dataset and identified the prominent health topics. We show that a variety of health topics are discussed in Sina Weibo, and that four flu-related topics are correlated with monthly influenza case rates in China.


title: Image Processing with Spiking Neuron Networks

abstract: Artificial neural networks have been well developed so far. First two generations of neural networks have had a lot of successful applications. Spiking Neuron Networks (SNNs) are often referred to as the third generation of neural networks which have potential to solve problems related to biological stimuli. They derive their strength and interest from an accurate modeling of synaptic interactions between neurons, taking into account the time of spike emission.


title: A CNN Regression Approach for Real-Time 2D/3D Registration

abstract: In this paper, we present a Convolutional Neural Network (CNN) regression approach to address the two major limitations of existing intensity-based 2-D/3-D registration technology: 1) slow computation and 2) small capture range. Different from optimization-based methods, which iteratively optimize the transformation parameters over a scalar-valued metric function representing the quality of the registration, the proposed method exploits the information embedded in the appearances of the digitally reconstructed radiograph and X-ray images, and employs CNN regressors to directly estimate the transformation parameters. An automatic feature extraction step is introduced to calculate 3-D pose-indexed features that are sensitive to the variables to be regressed while robust to other factors. The CNN regressors are then trained for local zones and applied in a hierarchical manner to break down the complex regression task into multiple simpler sub-tasks that can be learned separately. Weight sharing is furthermore employed in the CNN regression model to reduce the memory footprint. The proposed approach has been quantitatively evaluated on 3 potential clinical applications, demonstrating its significant advantage in providing highly accurate real-time 2-D/3-D registration with a significantly enlarged capture range when compared to intensity-based methods.


title: A Survey of Traffic Data Visualization

abstract: Data-driven intelligent transportation systems utilize data resources generated within intelligent systems to improve the performance of transportation systems and provide convenient and reliable services. Traffic data refer to datasets generated and collected on moving vehicles and objects. Data visualization is an efficient means to represent distributions and structures of datasets and reveal hidden patterns in the data. This paper introduces the basic concept and pipeline of traffic data visualization, provides an overview of related data processing techniques, and summarizes existing methods for depicting the temporal, spatial, numerical, and categorical properties of traffic data.


title: Local Expectation Gradients for Black Box Variational Inference

abstract: We introduce local expectation gradients which is a general purpose stochastic variational inference algorithm for constructing stochastic gradients by sampling from the variational distribution. This algorithm divides the problem of estimating the stochastic gradients over multiple variational parameters into smaller sub-tasks so that each sub-task explores intelligently the most relevant part of the variational distribution. This is achieved by performing an exact expectation over the single random variable that most correlates with the variational parameter of interest resulting in a Rao-Blackwellized estimate that has low variance. Our method works efficiently for both continuous and discrete random variables. Furthermore, the proposed algorithm has interesting similarities with Gibbs sampling but at the same time, unlike Gibbs sampling, can be trivially parallelized.


title: A frequency-domain analysis of head-motion prediction

abstract: The use of prediction to eliminate or reduce the effects of system delays in Head-Mounted Display systems has been the subject of several recent papers. A variety of methods have been proposed but almost all the analysis has been empirical, making comparisons of results difficult and providing little direction to the designer of new systems. In this paper, we characterize the performance of two classes of head-motion predictors by analyzing them in the frequency domain. The first predictor is a polynomial extrapolation and the other is based on the Kalman filter. Our analysis shows that even with perfect, noise-free inputs, the error in predicted position grows rapidly with increasing prediction intervals and input signal frequencies. Given the spectra of the original head motion, this analysis estimates the spectra of the predicted motion, quantifying a predictor's performance on different systems and applications. Acceleration sensors are shown to be more useful to a predictor than velocity sensors. The methods described will enable designers to determine maximum acceptable system delay based on maximum tolerable error and the characteristics of user motions in the application. CR


title: A combined SMOTE and PSO based RBF classifier for two-class imbalanced problems

abstract: This contribution proposes a powerful technique for two-class imbalanced classification problems by combining the synthetic minority over-sampling technique (SMOTE) and the particle swarm optimisation (PSO) aided radial basis function (RBF) classifier. In order to enhance the significance of the small and specific region belonging to the positive class in the decision region, the SMOTE is applied to generate synthetic instances for the positive class to balance the training data set. Based on the over-sampled training data, the RBF classifier is constructed by applying the orthogonal forward selection procedure, in which the classifier's structure and the parameters of RBF kernels are determined using a PSO algorithm based on the criterion of minimising the leave-one-out misclassification rate. The experimental results obtained on a simulated imbalanced data set and three real imbalanced data sets are presented to demonstrate the effectiveness of our proposed algorithm.


title: Ultrathin Suspended Nanopores with Surface Plasmon Resonance Fabricated by Combined Colloidal Lithography and Film Transfer

abstract: Suspended plasmonic nanopores in ultrathin film layers were fabricated through a simple and widely applicable method combining colloidal lithography and thin film transfer, which allows mass production of short-range ordered nanopore arrays on a large scale. By this combined method, mechanically stable and flexible free-standing nanopore membranes with a thickness down to 15–30 nm were produced. The plasmon resonances of the ultrathin plasmonic nanopores fabricated in AlN/Au/AlN trilayer and single layer Au membranes were tuned to lie in the vis–NIR wavelength range by properly designing their dimensions. The optical responses to the refractive index changes were tested and applied to adlayer sensing. The trilayer nanopore membrane showed a unique property to support water only on one side of the membrane, which was confirmed by the resonance shift and comparison with numerical simulation. Pore size reduction down to 10 nm can be achieved through additional material deposition. The filtering function of s...


title: Musical neurofeedback for treating depression in elderly people

abstract: We introduce a new neurofeedback approach, which allows users to manipulate expressive parameters in music performances using their emotional state, and we present the results of a pilot clinical experiment applying the approach to alleviate depression in elderly people. Ten adults (9 female and 1 male, mean = 84, SD = 5.8) with normal hearing participated in the neurofeedback study consisting of 10 sessions (2 sessions per week) of 15 min each. EEG data was acquired using the Emotiv EPOC EEG device. In all sessions, subjects were asked to sit in a comfortable chair facing two loudspeakers, to close their eyes, and to avoid moving during the experiment. Participants listened to music pieces preselected according to their music preferences, and were encouraged to increase the loudness and tempo of the pieces, based on their arousal and valence levels. The neurofeedback system was tuned so that increased arousal, computed as beta to alpha activity ratio in the frontal cortex corresponded to increased loudness, and increased valence, computed as relative frontal alpha activity in the right lobe compared to the left lobe, corresponded to increased tempo. Pre and post evaluation of six participants was performed using the BDI depression test, showing an average improvement of 17.2% (1.3) in their BDI scores at the end of the study. In addition, an analysis of the collected EEG data of the participants showed a significant decrease of relative alpha activity in their left frontal lobe (p = 0.00008), which may be interpreted as an improvement of their depression condition.


A SURVEY ON ARTIFICIAL INTELLIGENCE BASED BRAIN PATHOLOGY IDENTIFICATION TECHNIQUES IN MAGNETIC RESONANCE IMAGES


title: Longitudinal Face Modeling via Temporal Deep Restricted Boltzmann Machines

abstract: Modeling the face aging process is a challenging task due to large and non-linear variations present in different stages of face development. This paper presents a deep model approach for face age progression that can efficiently capture the non-linear aging process and automatically synthesize a series of age-progressed faces in various age ranges. In this approach, we first decompose the longterm age progress into a sequence of short-term changes and model it as a face sequence. The Temporal Deep Restricted Boltzmann Machines based age progression model together with the prototype faces are then constructed to learn the aging transformation between faces in the sequence. In addition, to enhance the wrinkles of faces in the later age ranges, the wrinkle models are further constructed using Restricted Boltzmann Machines to capture their variations in different facial regions. The geometry constraints are also taken into account in the last step for more consistent age-progressed results. The proposed approach is evaluated using various face aging databases, i.e. FGNET, Cross-Age Celebrity Dataset (CACD) and MORPH, and our collected large-scale aging database named AginG Faces in the Wild (AGFW). In addition, when ground-truth age is not available for input image, our proposed system is able to automatically estimate the age of the input face before aging process is employed.


title: Neural Tree Indexers for Text Understanding

abstract: Neural networks with recurrent or recursive architecture have shown promising results on various natural language processing (NLP) tasks. The recurrent and recursive architectures have their own strength and limitations. The recurrent networks process input text sequentially and model the conditional transition between word tokens. In contrast, the recursive networks explicitly model the compositionality and the recursive structure of natural language. Current recursive architecture is based on syntactic tree, thus limiting its practical applicability in different NLP applications. In this paper, we introduce a class of tree structured model, Neural Tree Indexers (NTI) that provides a middle ground between the sequential RNNs and the syntactic tree-based recursive models. NTI constructs a full n-ary tree by processing the input text with its node function in a bottom-up fashion. Attention mechanism can then be applied to both structure and different forms of node function. We demonstrated the effectiveness and the flexibility of a binary-tree model of NTI, showing the model achieved the state-of-the-art performance on three different NLP tasks: natural language inference, answer sentence selection, and sentence classification.


title: Build Watson: an overview of DeepQA for the Jeopardy! challenge

abstract: Computer systems that can directly and accurately answer peoples' questions over a broad domain of human knowledge have been envisioned by scientists and writers since the advent of computers themselves. Open domain question answering holds tremendous promise for facilitating informed decision making over vast volumes of natural language content. Applications in business intelligence, healthcare, customer support, enterprise knowledge management, social computing, science and government would all benefit from deep language processing. The DeepQA project (www.ibm.com/deepqa) is aimed at illustrating how the advancement and integration of Natural Language Processing (NLP), Information Retrieval (IR), Machine Learning (ML), massively parallel computation and Knowledge Representation and Reasoning (KR&R) can greatly advance open-domain automatic Question Answering. An exciting proof-point in this challenge is to develop a computer system that can successfully compete against top human players at the Jeopardy! quiz show (www.jeopardy.com). Attaining champion-level performance Jeopardy! requires a computer to rapidly answer rich open-domain questions, and to predict its own performance on any given category/question. The system must deliver high degrees of precision and confidence over a very broad range of knowledge and natural language content and with a 3-second response time. To do this DeepQA generates, evidences and evaluates many competing hypotheses. A key to success is automatically learning and combining accurate confidences across an array of complex algorithms and over different dimensions of evidence. Accurate confidences are needed to know when to "buzz in" against your competitors and how much to bet. Critical for winning at Jeopardy!, High precision and accurate confidence computations are just as critical for providing real value in business settings where helping users focus on the right content sooner and with greater confidence can make all the difference. The need for speed and high precision demands a massively parallel compute platform capable of generating, evaluating and combing 1000's of hypotheses and their associated evidence. In this talk I will introduce the audience to the Jeopardy! Challenge and describe our technical approach and our progress on this grand-challenge problem.


title: Languages, Automata, and Logic

abstract: The subject of this chapter is the study of formal languages (mostly languages recognizable by finite automata) in the framework of mathematical logic.


title: Template-Based Information Extraction without the Templates

abstract: Standard algorithms for template-based information extraction (IE) require predefined template schemas, and often labeled data, to learn to extract their slot fillers (e.g., an embassy is the Target of a Bombing template). This paper describes an approach to template-based IE that removes this requirement and performs extraction without knowing the template structure in advance. Our algorithm instead learns the template structure automatically from raw text, inducing template schemas as sets of linked events (e.g., bombings include detonate, set off, and destroy events) associated with semantic roles. We also solve the standard IE task, using the induced syntactic patterns to extract role fillers from specific documents. We evaluate on the MUC-4 terrorism dataset and show that we induce template structure very similar to hand-created gold structure, and we extract role fillers with an F1 score of .40, approaching the performance of algorithms that require full knowledge of the templates.


Geothermobarometry of cordierite-bearing metapelites near the Morin anorthosite complex, Grenville Province, Quebec


title: Improving Privacy and Security in Decentralizing Multi-Authority Attribute-Based Encryption in Cloud Computing

abstract: Decentralizing multi-authority attribute-based encryption (ABE) has been adopted for solving problems arising from sharing confidential corporate data in cloud computing. For decentralizing multi-authority ABE systems that do not rely on a central authority, collusion resistance can be achieved using a global identifier. Therefore, identity needs to be managed globally, which results in the crucial problems of privacy and security. A scheme is developed that does not use a central authority to manage users and keys, and only simple trust relations need to be formed by sharing the public key between each attribute authority (AA). User identities are unique by combining a user’s identity with the identity of the AA where the user is located. Once a key request needs to be made to an authority outside the domain, the request needs to be performed by the authority in the current domain rather than by the users, so, user identities remain private to the AA outside the domain, which will enhance privacy and security. In addition, the key issuing protocol between AA is simple as the result of the trust relationship of AA. Moreover, extensibility for authorities is also supported by the scheme presented in this paper. The scheme is based on composite order bilinear groups. A proof of security is presented that uses the dual system encryption methodology.


title: Unsupervised Salient Object Detection via Inferring From Imperfect Saliency Models

abstract: Visual saliency detection has become an active research direction in recent years. A large number of saliency models, which can automatically locate objects of interest in images, have been developed. As these models take advantage of different kinds of prior assumptions, image features, and computational methodologies, they have their own strengths and weaknesses and may cope with only one or a few types of images well. Inspired by these facts, this paper proposes a novel salient object detection approach with the idea of inferring a superior model from a variety of previous imperfect saliency models via optimally leveraging the complementary information among them. The proposed approach mainly consists of three steps. First, a number of existing unsupervised saliency models are adopted to provide weak/imperfect saliency predictions for each region in the image. Then, a fusion strategy is used to fuse each image region's weak saliency predictions into a strong one by simultaneously considering the performance differences among various weak predictions and various characteristics of different image regions. Finally, a local spatial consistency constraint that ensures high similarity of the saliency labels for neighboring image regions with similar features is proposed to refine the results. Comprehensive experiments on five public benchmark datasets and comparisons with a number of state-of-the-art approaches can demonstrate the effectiveness of the proposed work.


title: Process to enhance the quality of software requirement specification document

abstract: The failure and success of any software mainly depends on a technical document known as Software Requirement Specification (SRS) document, as it contains all requirements and features of the product. In the past, many developments had been done to improve the quality of the SRS, with respect to different attributes of the product, but the product success rate is not satisfactory and the room for improvement is still there. We have developed a different approach to resolve those issues. Our methodology consist of four processes i.e. Parsing Requirement (PR), Requirement Mapping using Matrix (RMM), Addition of Requirements in SRS template and Third Party Inspection. Requirement Engineering Process will provide the required inputs to PR after the implementation of its ontology rules completion of requirements will be achieved. RMM will be generated to minimize ambiguities and incorrectness with concerns of all stakeholders. Outputs of the previous processes will be added to IEEE standard format. A third party inspection will be conducted to check the requirements of the client and SRS. After inspecting SRS using inspection models and assigning Total Quality Score (TQS) third party will submit a detailed report to team of Requirement Engineers (RE). This practice will not only identify the problem but will solve the issue on its way.


title: Research on improved Apriori algorithm based on MapReduce and HBase

abstract: In order to improve the efficiency of Apriori algorithm for mining frequent item sets, MH-Apriori algorithm was designed for big data to address the poor efficiency problem. MH-Apriori takes advantages of MapReduce and HBase together to optimize Apriori algorithm. Compared with the improved Apriori algorithm simply based on MapReduce framework, timestamp of HBase is utilized in this algorithm to avoid generating a large number of key/value pairs. It saves the pattern matching time and scans the database only once. Also, to obtain transaction marks automatically, transaction mark column is added to set list for computing support numbers. MH-Apriori was executed on Hadoop platform. The experimental results show that MH-Apriori has higher efficiency and scalability.


title: Classifying eye and head movement artifacts in EEG signals

abstract: Brain Computer Interfaces has some exciting prospects such as controlling devices at the speed of thought. However BCI technology is far from attaining this goal. A significant challenge the EEG-based system has is the interference of artifacts in the EEG generated by eye and head movement. This paper presents the use of machine learning techniques to classify artifacts in the EEG. Successful artifact classification was then be applied to improve existing artifact removal techniques. The experiment used a state-of-the-art EEG system to gather the classifier input. An eye tracker and motion sensor were also used to measure and provide the ground truth for the classification experiments. The data from these devices were captured using custom built software developed for this research. The classifiers tested showed potential to classify artifacts in the EEG when trained on a per-person basis. This research paves the way for further work to be carried out to explore subject-independent artifact classification.


title: Verification of Recurrent Neural Networks Through Rule Extraction

abstract: The verification problem for neural networks is verifying whether a neural network will suffer from adversarial samples, or approximating the maximal allowed scale of adversarial perturbation that can be endured. While most prior work contributes to verifying feed-forward networks, little has been explored for verifying recurrent networks. This is due to the existence of a more rigorous constraint on the perturbation space for sequential data, and the lack of a proper metric for measuring the perturbation. In this work, we address these challenges by proposing a metric which measures the distance between strings, and use deterministic finite automata (DFA) to represent a rigorous oracle which examines if the generated adversarial samples violate certain constraints on a perturbation. More specifically, we empirically show that certain recurrent networks allow relatively stable DFA extraction. As such, DFAs extracted from these recurrent networks can serve as a surrogate oracle for when the ground truth DFA is unknown. We apply our verification mechanism to several widely used recurrent networks on a set of the Tomita grammars. The results demonstrate that only a few models remain robust against adversarial samples. In addition, we show that for grammars with different levels of complexity, there is also a difference in the difficulty of robust learning of these grammars.


title: An Efficient Method of Edge Detection using Fuzzy Logic

abstract: Edge detection algorithms have been proposed in the literature for extracting the edges from the image. But after emerging the fuzzy logic concept, a lot of Researcher of image processing has been shifted towards the fuzzy logic and its applicability in the field of image processing. This paper presents a fuzzy rule base algorithm, in MATLAB environment, which is capable of detecting edges of an input image by scanning it throughout using a 2*2 pixel window efficiently from the gray scale images. Fuzzy inference system designed has four inputs, which corresponds to four pixels of instantaneous scanning matrix, one output that tells whether the pixel under consideration is "low", "medium" or "high" pixel. Rule base comprises of seven rules, which classify the target pixel. Also, a Graphical User Interface (GUI) in MATLAB has been designed to aid the loading of the image, and to display the resultant image at different intermediate levels of processing. Threshold level for the image can be set from the slider control of GUI. Main feature of the algorithm is that it has been designed by the smallest possible mask with less number of rules i.e. 2*2 with seven rules unlike 3*3 or bigger masks found in the literature.


title: Linear and nonlinear associations between general intelligence and personality in Project TALENT.

abstract: Research on the relations of personality traits to intelligence has primarily been concerned with linear associations. Yet, there are no a priori reasons why linear relations should be expected over nonlinear ones, which represent a much larger set of all possible associations. Using 2 techniques, quadratic and generalized additive models, we tested for linear and nonlinear associations of general intelligence (g) with 10 personality scales from Project TALENT (PT), a nationally representative sample of approximately 400,000 American high school students from 1960, divided into 4 grade samples (Flanagan et al., 1962). We departed from previous studies, including one with PT (Reeve, Meyer, & Bonaccio, 2006), by modeling latent quadratic effects directly, controlling the influence of the common factor in the personality scales, and assuming a direction of effect from g to personality. On the basis of the literature, we made 17 directional hypotheses for the linear and quadratic associations. Of these, 53% were supported in all 4 male grades and 58% in all 4 female grades. Quadratic associations explained substantive variance above and beyond linear effects (mean R² between 1.8% and 3.6%) for Sociability, Maturity, Vigor, and Leadership in males and Sociability, Maturity, and Tidiness in females; linear associations were predominant for other traits. We discuss how suited current theories of the personality�intelligence interface are to explain these associations, and how research on intellectually gifted samples may provide a unique way of understanding them. We conclude that nonlinear models can provide incremental detail regarding personality and intelligence associations. (PsycINFO Database Record (c) 2014 APA, all rights reserved)


title: What is Your Organization 'Like'?: A Study of Liking Activity in the Enterprise

abstract: The 'like' button, introduced by Facebook several years ago, has become one of the most prominent icons of social media. Similarly to other popular social media features on the web, enterprises have also recently adopted it. In this paper, we present a first comprehensive study of liking activity in the enterprise. We studied the logs of an enterprise social media platform within a large global organization along a period of seven months, in which 393,720 'likes' were performed. In addition, we conducted a survey of 571 users of the platform's 'like' button. Our evaluation combines quantitative and qualitative analysis to inspect what employees like, why they use the 'like' button, and to whom they give their 'likes'.


title: A 3.2 Gbps/pin 8 Gbit 1.0 V LPDDR4 SDRAM With Integrated ECC Engine for Sub-1 V DRAM Core Operation

abstract: A 1.0 V 8 Gbit LPDDR4 SDRAM with 3.2 Gbps/pin speed and integrated ECC engine for sub-1 V DRAM core is presented. DRAM internal read-modify-write operation for data masked write makes the integrated ECC engine possible in a commodity DRAM. Time interleaved latency and IO control circuits enable 1.0 V operation at target speed. To reach 3.2 Gbps with improved power efficiency over conventional mobile DRAMs, the following IO features are introduced: Low voltage swing terminated logic drivers with VOH level calibration and periodic ZQ calibration, unmatched DQ/DQS scheme and DQS oscillator for DQS tree delay tracking. This chip is fabricated in 25 nm DRAM process on 88.1 mm 2 die area.


title: Lung organoids: current uses and future promise

abstract: Lungs are composed of a system of highly branched tubes that bring air into the alveoli, where gas exchange takes place. The proximal and distal regions of the lung contain epithelial cells specialized for different functions: basal, secretory and ciliated cells in the conducting airways and type II and type I cells lining the alveoli. Basal, secretory and type II cells can be grown in three-dimensional culture, with or without supporting stromal cells, and under these conditions they give rise to self-organizing structures known as organoids. This Review summarizes the different methods for generating organoids from cells isolated from human and mouse lungs, and compares their final structure and cellular composition with that of the airways or alveoli of the adult lung. We also discuss the potential and limitations of organoids for addressing outstanding questions in lung biology and for developing new drugs for disorders such as cystic fibrosis and asthma.


title: Airfare prices prediction using machine learning techniques

abstract: This paper deals with the problem of airfare prices prediction. For this purpose a set of features characterizing a typical flight is decided, supposing that these features affect the price of an air ticket. The features are applied to eight state of the art machine learning (ML) models, used to predict the air tickets prices, and the performance of the models is compared to each other. Along with the prediction accuracy of each model, this paper studies the dependency of the accuracy on the feature set used to represent an airfare. For the experiments a novel dataset consisting of 1814 data flights of the Aegean Airlines for a specific international destination (from Thessaloniki to Stuttgart) is constructed and used to train each ML model. The derived experimental results reveal that the ML models are able to handle this regression problem with almost 88% accuracy, for a certain type of flight features.


title: Effects of Plant Litter on Foraging and Nesting Behavior of Prairie Rodents

abstract: Interspecific differences in seed use and location of nest sites of Peromyscus leucopus, P. maniculatus, Reithrodontomys megalotis , and Microtus ochrogaster were found among experimental patches of habitat that differed only in amount of plant litter. P. leucopus and P. maniculatus foraged selectively in patches with sparse litter and used nest boxes in these patches more than expected. R. megalotis and M. ochrogaster foraged selectively and nested in patches with moderate litter; both nested more in litter than in nest boxes. All species consumed more seeds in patches with moderate litter on bright than dark nights. P. leucopus consumed fewer seeds and P. maniculatus tended to reduce consumption from dark to bright nights; however, R. megalotis and M. ochrogaster consumed similar amounts of seeds under both light conditions. P. leucopus and P. maniculatus forage in open habitats and may perceive changes in light intensity more readily than either R. megalotis or M. ochrogaster that forage in structurally complex habitats. Interspecific differences in activity between dark and bright nights may be related to the ability of each species to detect changes in risk of predation related to variation in light intensity.


title: Forensic Acquisition and Analysis of VMware Virtual Hard Disks

abstract: With the advancement in virtualization technology, virtual machines (VMs) are becoming a common and integral part of datacenters. As the popularity and the use of VMs increases, incidents involving them are also on the rise. There is substantial research on using VMs and virtual appliances to aid forensic investigation, but research on the appropriate forensics procedures for collecting and analyzing evidence within a VM following is


title: A methodology for community detection in Twitter

abstract: The microblogging service Twitter is one of the world's most popular online social networks and assembles a huge amount of data produced by interactions between users. A careful analysis of this data allows identifying groups of users who share similar traits, opinions, and preferences. We call community detection the process of user group identification, which grants valuable insights not available upfront. In order to extract useful knowledge from Twitter data many methodologies have been proposed, which define the attributes to be used in community detection problems by manual and empirical criteria - oftentimes guided by the aimed type of community and what the researcher attaches importance to. However, such approach cannot be generalized because it is well known that the task of finding out an appropriate set of attributes leans on context, domain, and data set. Aiming to the advance of community detection domain, reduce computational cost and improve the quality of related researches, this paper proposes a standard methodology for community detection in Twitter using feature selection methods. Results of the present research directly affect the way community detection methodologies have been applied to Twitter and quality of outcomes produced.


title: Sensor faults: Detection methods and prevalence in real-world datasets

abstract: Various sensor network measurement studies have reported instances of transient faults in sensor readings. In this work, we seek to answer a simple question: How often are such faults observed in real deploymentsq We focus on three types of transient faults, caused by faulty sensor readings that appear abnormal. To understand the prevalence of such faults, we first explore and characterize four qualitatively different classes of fault detection methods. Rule-based methods leverage domain knowledge to develop heuristic rules for detecting and identifying faults. Estimation methods predict “normal” sensor behavior by leveraging sensor correlations, flagging anomalous sensor readings as faults. Time-series-analysis-based methods start with an a priori model for sensor readings. A sensor measurement is compared against its predicted value computed using time series forecasting to determine if it is faulty. Learning-based methods infer a model for the “normal” sensor readings using training data, and then statistically detect and identify classes of faults. We find that these four classes of methods sit at different points on the accuracy/robustness spectrum. Rule-based methods can be highly accurate, but their accuracy depends critically on the choice of parameters. Learning methods can be cumbersome to train, but can accurately detect and classify faults. Estimation methods are accurate, but cannot classify faults. Time-series-analysis-based methods are more effective for detecting short duration faults than long duration ones, and incur more false positives than the other methods. We apply these techniques to four real-world sensor datasets and find that the prevalence of faults as well as their type varies with datasets. All four methods are qualitatively consistent in identifying sensor faults, lending credence to our observations. Our work is a first step towards automated online fault detection and classification.


title: Evolutionary algorithm for seed selection in social influence process

abstract: Nowadays, in the world of limited attention, the techniques that maximize the spread of social influence are more than welcomed. Companies try to maximize their profits on sales by providing customers with free samples believing in the power of word-of-mouth marketing, governments and non-governmental organizations often want to introduce positive changes in the society by appropriately selecting individuals or election candidates want to spend least budget yet still win the election. In this work we propose the use of evolutionary algorithm as a mean for selecting seeds in social networks. By framing the problem as genetic algorithm challenge we show that it is possible to outperform well-known greedy algorithm in the problem of influence maximization for the linear threshold model in both: quality (up to 16% better) and efficiency (up to 35 times faster). We implemented these two algorithms by using GPGPU approach showing that also the evolutionary algorithm can benefit from GPU acceleration making it efficient and scaling better than the greedy algorithm. As the experiments conducted by using three real world datasets reveal, the evolutionary approach proposed in this paper outperforms the greedy algorithm in terms of the outcome and it also scales much better than the greedy algorithm when the network size is increasing. The only drawback in the GPGPU approach so far is the maximum size of the network that can be processed - it is limited by the memory of the GPU card. We believe that by showing the superiority of the evolutionary approach over the greedy algorithm, we will motivate the scientific community to look for an idea to overcome this limitation of the GPU approach - we also suggest one of the possible paths to explore. Since the proposed approach is based only on topological features of the network, not on the attributes of nodes, the applications of it are broader than the ones that are dataset-specific.


title: Scalable and Crash-Tolerant Load Balancing Based on Switch Migration for Multiple Open Flow Controllers

abstract: As the size of networks continues to increase, the scalability of the centralized controller becomes increasingly issues in Software Defined Networking. Distributed controllers have been proposed to solve the problem that the centralized controllers such as NOX and Floodlight suffer from. That logically centralized, but physically distributed architecture divide the network into zones with separate multiple controllers to achieve a better scalability control plane. However, Such distributed architecture introduces a new challenge to the load rebalancing of controllers when uneven load distribution among the controllers due to the statically configured mapping between a switch and a controller. Therefore, under variable traffic conditions in real networks, keeping load balance dynamically among the controller clusters is essential for realizing a high performance and scalability control plane. To address these issues, we propose a dynamic load rebalancing method based on switch migration mechanism for clustered controllers. The multiple controllers use Groups to coordinate actions for switch migration. The whole network is divided into several groups and each group is set up one controller cluster. Our proposed method can dynamically shift the load across the multiple controllers through switch migration. The mechanism support controller failover without switch disconnection avoiding the single point of failure problem. We also implemented a prototype system based on Open Day light Hydrogen controller to evaluated the performance of our design. Our preliminary result shows that the method enables controllers to relieve the overload via switch migration and can improve throughput and reduce the response time of the control plane.


title: The Impact of the Sarbanes-Oxley Act on IT Project Management: A Case Study

abstract: In 2002, the Sarbanes-Oxley Act was passed into law requiring all U.S. based, publicly traded companies to report on the status of their internal controls governing the reporting of financial information. Because of the close relationship between financial reporting and IT, the requirements of the Sarbanes-Oxley (SOX) Act has also greatly impacted IT Governance and the way IT projects are managed. This study is investigating the impact of SOX on IT Project Management within a large corporation. The study is evaluating three areas of impact: 1) The introduction and formalization of internal controls as defined by the COBIT framework, 2) The positive and negative effects on IT project implementation, and 3) The additional costs to an IT project to maintain compliance to the SOX requirement. In addition, the study also considers if the introduction of internal controls has impacted the organization’s development maturity when evaluated against standard maturity models.


title: Resting-state fMRI in the Human Connectome Project

abstract: Resting-state functional magnetic resonance imaging (rfMRI) allows one to study functional connectivity in the brain by acquiring fMRI data while subjects lie inactive in the MRI scanner, and taking advantage of the fact that functionally related brain regions spontaneously co-activate. rfMRI is one of the two primary data modalities being acquired for the Human Connectome Project (the other being diffusion MRI). A key objective is to generate a detailed in vivo mapping of functional connectivity in a large cohort of healthy adults (over 1000 subjects), and to make these datasets freely available for use by the neuroimaging community. In each subject we acquire a total of 1. h of whole-brain rfMRI data at 3. T, with a spatial resolution of 2. ×. 2. ×. 2. mm and a temporal resolution of 0.7. s, capitalizing on recent developments in slice-accelerated echo-planar imaging. We will also scan a subset of the cohort at higher field strength and resolution. In this paper we outline the work behind, and rationale for, decisions taken regarding the rfMRI data acquisition protocol and pre-processing pipelines, and present some initial results showing data quality and example functional connectivity analyses. © 2013 Elsevier Inc.


title: Automated Evaluation of Scientific Writing: AESW Shared Task Proposal

abstract: The goal of the Automated Evaluation of Scientific Writing (AESW) Shared Task is to analyze the linguistic characteristics of scientific writing to promote the development of automated writing evaluation tools that can assist authors in writing scientific papers. The proposed task is to predict whether a given sentence requires editing to ensure its “fit” with the scientific writing genre. We describe the proposed task, training, development, and test data sets, and evaluation metrics. Quality means doing it right when no one is looking. – Henry Ford


title: A Hybrid Genetic Algorithm for the Multidimensional Knapsack Problem

abstract: During the last two decades solving combinatorial optimization problems, using genetic algorithms (GA), has attracted the attention of many researchers. In this paper a strong initial population is created by Dantzig algorithm for solving single knapsack problems. This algorithm is hybridized with the genetic algorithm. A number of novel penalty functions are designed which drive the population toward feasibility rapidly. The algorithm is then applied to some standard knapsack test problems, Computational results shows that our hybridized algorithm outperforms a state of the are algorithm.


title: Inspiring and Modeling Multi-Robot Search with Particle Swarm Optimization

abstract: Within the field of multi-robot systems, multi-robot search is one area which is currently receiving a lot of research attention. One major challenge within this area is to design effective algorithms that allow a team of robots to work together to find their targets. Techniques have been adopted for multi-robot search from the particle swarm optimization algorithm, which uses a virtual multi-agent search to find optima in a multi-dimensional function space. We present here a multi-search algorithm inspired by particle swarm optimization. Additionally, we exploit this inspiration by modifying the particle swarm optimization algorithm to mimic the multi-robot search process, thereby allowing us to model at an abstracted level the effects of changing aspects and parameters of the system such as number of robots and communication range


Destigmatizing Migraine


title: BeamSpy: enabling robust 60 GHz links under blockage

abstract: Due to high directionality and small wavelengths, 60 GHz links are highly vulnerable to human blockage. To overcome blockage, 60 GHz radios can use a phased-array antenna to search for and switch to unblocked beam directions. However, these techniques are reactive, and only trigger after the blockage has occurred, and hence, they take time to recover the link. In this paper, we propose BeamSpy, that can instantaneously predict the quality of 60 GHz beams, even under blockage, without the costly beam searching. BeamSpy captures unique spatial and blockage-invariant correlation among beams through a novel prediction model, exploiting which we can immediately select the best alternative beam direction whenever the current beam's quality degrades. We apply BeamSpy to a run-time fast beam adaptation protocol, and a blockage-risk assessment scheme that can guide blockage-resilient link deployment. Our experiments on a reconfigurable 60 GHz platform demonstrate the effectiveness of BeamSpy's prediction framework, and its usefulness in enabling robust 60 GHz links.


title: AMPF: Application-aware Multipath Packet Forwarding using Machine Learning and SDN

abstract: This paper proposes an application-aware multipath packet forwarding framework that integrates Machine Learning Techniques (MLT) and Software Defined Networks (SDN). As the Internet provides a variety of services and their performance requirement has become heterogeneous, it is common to come across the scenario of multiple flows competing for a constrained resource such as bandwidth, less jitter or low latency path. Such factors are application specific requirement that is beyond the knowledge of a simple combination of protocol type and port number. Better overall performance could be achieved if the network is able to prioritize the flows and assign resources based on their application specific requirement. Our system prioritizes each of the flows using MLT and routes it through a path according to the flow priority and network state using SDN. The proof of concept implementation has been done on OpenvSwitch and evaluation results involving a large number of flows exhibited a significant improvement over the traditional network setup. We also report that the port number and protocol are not contributing to determine the application in the decision-making process of Machine Learning (ML).


title: "Attention"for Detecting Unreliable News in the Information Age

abstract: An Unreliable news is any piece of information which is false or misleading, deliberately spread to promote political, ideological and financial agendas. Recently the problem of unreliable news has got a lot of attention as the number instances of using news and social media outlets for propaganda have increased rapidly. This poses a serious threat to society, which calls for technology to automatically and reliably identify unreliable news sources. This paper is an effort made in this direction to build systems for detecting unreliable news articles. In this paper, various NLP algorithms were built and evaluated on Unreliable News Data 2017 dataset. Variants of hierarchical attention networks (HAN) are presented for encoding and classifying news articles which achieve the best results of 0.944 ROC-AUC. Finally, Attention layer weights are visualized to understand and give insight into the decisions made by HANs. The results obtained are very promising and encouraging to deploy and use these systems in the real world to mitigate the problem of unreliable news.


title: Improving TCP throughput over two-way asymmetric links: analysis and solutions

abstract: The sharing of a common buffer by TCP data segments and acknowledgments in a network or internet has been known to produce the effect of ack compression, often causing dramatic reductions in throughput. We study several schemes for improving the performance of two-way TCP traffic over asymmetric links where the bandwidths in the two directions may differ substantially, possibly by many orders of magnitude. These approaches reduce the effect of ack compression by carefully controlling the flow of data packets and acknowledgments. We first examine a scheme where acknowledgments are transmitted at a higher priority than data. By analysis and simulation, we show that prioritizing acks can lead to starvation of the low-bandwidth connection. Next, we introduce and analyze a connection-level backpressure mechanism designed to limit the maximum amount of data buffered in the outgoing IP queue of the source of the low-bandwidth connection. We show that this approach, while minimizing the queueing delay for acks, results in unfair bandwidth allocation on the slow link. Finally, our preferred solution separates the acks from data packets in the outgoing queue, and makes use of a connection-level bandwidth allocation mechanism to control their bandwidth shares. We show that this scheme overcomes the limitations of the previous approaches, provides isolation, and enables precise control of the connection throughputs. We present analytical models of the dynamic behavior of each of these approaches, derive closed-form expressions for the expected connection efficiencies in each case, and validate them with simulation results.


title: Semi-supervised Semantic Pattern Discovery with Guidance from Unsupervised Pattern Clusters

abstract: We present a simple algorithm for clustering semantic patterns based on distributional similarity and use cluster memberships to guide semi-supervised pattern discovery. We apply this approach to the task of relation extraction. The evaluation results demonstrate that our novel bootstrapping procedure significantly outperforms a standard bootstrapping. Most importantly, our algorithm can effectively prevent semantic drift and provide semi-supervised learning with a natural stopping criterion.


title: Cardiovascular collapse after myocardial infarction due to centipede bite

abstract: Centipede bites have been reported to cause localized and/or systemic symptoms including local pain, erythema and edema, nausea and vomiting, palpitations, headache, lymphadenopathy, and rhabdomyolysis. However, acute myocardial infarction due to centipede envenomation is reported in only three cases in English medical literature. We present a case of 31-year-old male bitten by a golden colored centipede leading to myocardial infarction and cardiopulmonary arrest which is seen very rarely. The patient was admitted to emergency department with a swollen and painful right foot. However, typical chest pain became the major complaint and cardiopulmonary arrest developed while electrocardiography was being obtained. The patient was resuscitated successfully for 5 min and acute infero-posterolateral myocardial infarction was detected on electrocardiography.


title: Efficient Maximum Power Point Tracking for a Distributed PV System under Rapidly Changing Environmental Conditions

abstract: When conventional maximum power point tracking (MPPT) techniques are required to operate fast under rapidly changing environmental conditions, a large power loss can be caused by slow tracking speed, output power fluctuation, or additionally required ad hoc parameters. This paper proposes a fast and efficient MPPT technique that minimizes the power loss with the adaptively binary-weighted step (ABWS) followed by the monotonically decreased step (MDS) without causing output power fluctuation or requiring additional ad hoc parameter. The proposed MPPT system for a photovoltaic (PV) module is implemented by a boost converter with a microcontroller unit. The theoretical analysis and the simulation results show that the proposed MPPT provides fast and accurate tracking under rapidly changing environmental conditions. The experimental results based on a distributed PV system demonstrate that the proposed MPPT technique is superior to the conventional perturb and observe (P&O) technique, which reduces the tracking time and the overall power loss by up to 82.95%, 91.51% and 82.46%, 97.71% for two PV modules, respectively.


title: IT CONTROL IN THE AUSTRALIAN PUBLIC SECTOR : AN INTERNATIONAL COMPARISON

abstract: Despite widespread adoption of IT control frameworks, little academic empirical research has been undertaken to investigate their use. This paper reports upon research to benchmark the maturity levels of 15 key IT control processes from the Control Objectives for Information and Related Technology (COBIT) in public sector organisations across Australia. It also makes a comparison against a similar benchmark for a mixed sector group from a range of nations, a mixed sector group from Asian-Oceanic nations, and for public sector organisations for all geographic areas. The Australian data were collected in a mail survey of the 387 non-financial public sector organisations identified as having more than 50 employees, which returned a 27% response rate. Patterns seen in the original international survey undertaken by the IS Audit and Control Association in 2002 were also seen in the Australian data. However, the Australian public sector performed better than sectors in all the international benchmarks for the 15 most important IT processes.


title: The Theoretical Maximum Throughput Calculation for the IEEE802.11g Standard

abstract: ††† Summary Estimating the throughput of a WiFi connection can be quite complex, even when considering simple scenarios. Indeed, the varying number of parameters specified in the standards makes it hard to understand their impact in terms of delay and throughput. The main contribution of this work is to present a scheme to compute the exact maximum throughput for an IEEE802.11g network. The proposed scheme incorporates all the timings and settings, which allow one to calculate the throughput for different channel spacing and modulation techniques specified in the standard. Experimental and simulated results show the accuracy of the proposed scheme.


title: Development of Millimeter-Wave Planar Diplexers Based on Complementary Characters of Dual-Mode Substrate Integrated Waveguide Filters With Circular and Elliptic Cavities

abstract: A novel high-performance millimeter-wave planar diplexer is developed based on the complementary characters of substrate integrated waveguide (SIW) dual-mode filters with circular and elliptic cavities by making the tradeoff between the isolation, insertion loss, and selectivity. The responses of the dual-mode SIW circular and elliptic cavities are first investigated. It can be found that the upper side response of the circular cavity and the lower side response of the elliptic cavity are very steep. The diplexers with high isolation performance are then designed based on the complementary response characters of circular and elliptic cavities. A diplexer with two dual-mode SIW circular and elliptic cavities is designed and fabricated with a normal printed circuit board process. The measured insertion losses are 1.95 and 2.09 dB in the upper and lower passbands centered at 26 and 25 GHz with the fractional bandwidths of 5.2% and 5.4%. The isolation is lower than - 50dB


title: Air filter particulate loading detection using smartphone audio and optimized ensemble classification

abstract: Abstract Automotive engine intake filters ensure clean air delivery to the engine, though over time these filters load with contaminants hindering free airflow. Today’s open-loop approach to air filter maintenance has drivers replace elements at predetermined service intervals, causing costly and potentially harmful over- and under-replacement. The result is that many vehicles consistently operate with reduced power, increased fuel consumption, or excessive particulate-related wear which may harm the catalyst or damage machined engine surfaces. We present a method of detecting filter contaminant loading from audio data collected by a smartphone and a stand microphone. Our machine learning approach to filter supervision uses Mel-Cepstrum, Fourier and Wavelet features as input into a classification model and applies feature ranking to select the best-differentiating features. We demonstrate the robustness of our technique by showing its efficacy for two vehicle types and different microphones, finding a best result of 79.7% accuracy when classifying a filter into three loading states. Refinements to this technique will help drivers supervise their filters and aid in optimally timing their replacement. This will result in an improvement in vehicle performance, efficiency, and reliability, while reducing the cost of maintenance to vehicle owners.


title: Self-balancing robot implementing the inverted pendulum concept

abstract: The inverted pendulum is a classic automation problem that has numerous theoretical approaches as well as a multitude of practical applications. The current paper explores the challenges and identified solutions in implementing a small scale robot for didactical and show-room applications, considering specific mechanical and complexity constrains limitations. The proposed model is validated by a hardware implementation, while different practical PID tuning methods are discusses and reached results presented.


title: Blockchain-Based Ubiquitous Code Ownership Management System without Hierarchical Structure

abstract: The ubiquitous ID (uID) architecture provides dynamic and flexible context-awareness frameworks necessary in ubiquitous computing. Its basic idea is to assign 128 bit unique identifiers, each of which is called a ucode (ubiquitous code), to real-world entities. To guarantee the uniqueness of ucodes, current ucode ownreships are maintained by a hierarchical structure like DNS. In this traditional system, processing ucode transactions between organizations is cumbersome and inefficient because the reliability of owner information is dependent on individual organizations. To solve this problem, we designed a ucode ownership management system on a blockchain which is a decentralized platform. In this system, we proposed a blockchain-based ucode allocation method, which requires a simple procedure and only one transaction. This proposed method is user-friendly and efficient compared with ucode allocation methods to which we simply apply current blockchain-based name registration methods requiring a complex procedure and at least two transactions. Finally, we conducted a case study about these ucode allocation methods built on the Ethereum blockchain. From this result, we compared the usability and security of each ucode allocation method in Ethereum.


title: Backdooring Convolutional Neural Networks via Targeted Weight Perturbations

abstract: We present a new type of backdoor attack that exploits a vulnerability of convolutional neural networks (CNNs) that has been previously unstudied. In particular, we examine the application of facial recognition. Deep learning techniques are at the top of the game for facial recognition, which means they have now been implemented in many production-level systems. Alarmingly, unlike other commercial technologies such as operating systems and network devices, deep learning-based facial recognition algorithms are not presently designed with security requirements or audited for security vulnerabilities before deployment. Given how young the technology is and how abstract many of the internal workings of these algorithms are, neural network-based facial recognition systems are prime targets for security breaches. As more and more of our personal information begins to be guarded by facial recognition (e.g., the iPhone X), exploring the security vulnerabilities of these systems from a penetration testing standpoint is crucial. Along these lines, we describe a general methodology for backdooring CNNs via targeted weight perturbations. Using a five-layer CNN and ResNet-50 as case studies, we show that an attacker is able to significantly increase the chance that inputs they supply will be falsely accepted by a CNN while simultaneously preserving the error rates for legitimate enrolled classes.


title: Shared representations between self and other: a social cognitive neuroscience view

abstract: The abilities to identify with others and to distinguish between self and other play a pivotal role in intersubjective transactions. Here, we marshall evidence from developmental science, social psychology and neuroscience (including clinical neuropsychology) that support the view of a common representation network (both at the computational and neural levels) between self and other. However, sharedness does not mean identicality, otherwise representations of self and others would completely overlap, and lead to confusion. We argue that self-awareness and agency are integral components for navigating within these shared representations. We suggest that within this shared neural network the inferior parietal cortex and the prefrontal cortex in the right hemisphere play a special role in interpersonal awareness.


Intestinal Behçet's Disease: A True Inflammatory Bowel Disease or Merely an Intestinal Complication of Systemic Vasculitis?


Vascular structures in dermoscopy*


OPTICAL CHARACTER RECOGNITION TECHNIQUE ALGORITHMS


title: Machine Learning approach for Predictive Maintenance in Industry 4.0

abstract: Condition monitoring together with predictive maintenance of electric motors and other equipment used by the industry avoids severe economic losses resulting from unexpected motor failures and greatly improves the system reliability. This paper describes a Machine Learning architecture for Predictive Maintenance, based on Random Forest approach. The system was tested on a real industry example, by developing the data collection and data system analysis, applying the Machine Learning approach and comparing it to the simulation tool analysis. Data has been collected by various sensors, machine PLCs and communication protocols and made available to Data Analysis Tool on the Azure Cloud architecture. Preliminary results show a proper behavior of the approach on predicting different machine states with high accuracy.


Controlling AI Engines in Dynamic Environments


title: Language access and theory of mind reasoning: evidence from deaf children in bilingual and oralist environments.

abstract: This investigation examined whether access to sign language as a medium for instruction influences theory of mind (ToM) reasoning in deaf children with similar home language environments. Experiment 1 involved 97 deaf Italian children ages 4-12 years: 56 were from deaf families and had LIS (Italian Sign Language) as their native language, and 41 had acquired LIS as late signers following contact with signers outside their hearing families. Children receiving bimodal/bilingual instruction in LIS together with Sign-Supported and spoken Italian significantly outperformed children in oralist schools in which communication was in Italian and often relied on lipreading. Experiment 2 involved 61 deaf children in Estonia and Sweden ages 6-16 years. On a wide variety of ToM tasks, bilingually instructed native signers in Estonian Sign Language and spoken Estonian succeeded at a level similar to age-matched hearing children. They outperformed bilingually instructed late signers and native signers attending oralist schools. Particularly for native signers, access to sign language in a bilingual environment may facilitate conversational exchanges that promote the expression of ToM by enabling children to monitor others' mental states effectively.


Semi-supervised Learning for Multi-label Classification ∗