title: Spice up Your Chat: The Intentions and Sentiment Effects of Using Emoji

abstract: Emojis, as a new way of conveying nonverbal cues, are widely adopted in computer-mediated communications. In this paper, first from a message sender perspective, we focus on people's motives in using four types of emojis -- positive, neutral, negative, and non-facial. We compare the willingness levels of using these emoji types for seven typical intentions that people usually apply nonverbal cues for in communication. The results of extensive statistical hypothesis tests not only report the popularities of the intentions, but also uncover the subtle differences between emoji types in terms of intended uses. Second, from a perspective of message recipients, we further study the sentiment effects of emojis, as well as their duplications, on verbal messages. Different from previous studies in emoji sentiment, we study the sentiments of emojis and their contexts as a whole. The experiment results indicate that the powers of conveying sentiment are different between four emoji types, and the sentiment effects of emojis vary in the contexts of different valences.


Document Decomposition into Geometric and Logical Layout


title: Apparel classification with style

abstract: We introduce a complete pipeline for recognizing and classifying people's clothing in natural scenes. This has several interesting applications, including e-commerce, event and activity recognition, online advertising, etc. The stages of the pipeline combine a number of state-of-the-art building blocks such as upper body detectors, various feature channels and visual attributes. The core of our method consists of a multi-class learner based on a Random Forest that uses strong discriminative learners as decision nodes. To make the pipeline as automatic as possible we also integrate automatically crawled training data from the web in the learning process. Typically, multi-class learning benefits from more labeled data. Because the crawled data may be noisy and contain images unrelated to our task, we extend Random Forests to be capable of transfer learning from different domains. For evaluation, we define 15 clothing classes and introduce a benchmark data set for the clothing classification task consisting of over 80,000 images, which we make publicly available. We report experimental results, where our classifier outperforms an SVM baseline with 41.38 % vs 35.07 % average accuracy on challenging benchmark data.


title: Developments in the use of the hamstring/quadriceps ratio for the assessment of muscle balance.

abstract: Isokinetic moment ratios of the hamstrings (H) and quadriceps (Q) muscle groups, and their implication in muscle imbalance, have been investigated for more than three decades. The conventional concentric H/Q ratio with its normative value of 0.6 has been at the forefront of the discussion. This does not account for the joint angle at which moment occurs and the type of muscle action involved. Advances towards more functional analyses have occurred such that previous protocols are being re-examined raising questions about their ability to demonstrate a relationship between thigh muscle imbalance and increased incidence or risk of knee injury. This article addresses the function of the hamstring-quadriceps ratio in the interpretation of this relationship using the ratios Hecc/Qcon (ratio of eccentric hamstring strength to concentric quadriceps strength, representative of isolated knee extension) and Hcon/Qecc (ratio of concentric hamstring strength to eccentric quadriceps strength, representative of isolated knee flexion).


title: RNN-based sequence prediction as an alternative or complement to traditional recommender systems

abstract: The recurrent neural networks have the ability to grasp the temporal patterns withinthe data. This is a property that can be used in order to help a recommender system bettertaking into account the user past history. Still the dimensionality problem that raiseswithin the recommender system field also raises here as the number of items the systemhave to be aware of is susceptibility high.Recent research have studied the use of such neural networks at a user’s session level.This thesis rather examines the use of this technique at a whole user’s past history levelassociated with techniques such as embeddings and softmax sampling in order to accommodatewith the high dimensionality.The proposed method results in a sequence prediction model that can be used as is forthe recommender task or as a feature within a more complex system.


title: Dominance and age in bilingualism

abstract: The present article examines the relationship between age and dominance in bilingual populations. Age in bilingualism is understood as the point in development at which second language (L2) acquisition begins and as the chronological age of users of two languages. Age of acquisition (AoA) is a factor in determining which of a bilingual's two languages is dominant and to what degree, and it, along with age of first language (L1) attrition, may be associated with shifts in dominance from the L1 to the L2. In turn, dominance and chronological age, independently and in interaction with lexical frequency, predict performance on naming tasks. The article also considers the relevance of critical-period accounts of the relationships of AoA and age of L1 attrition to L2 dominance, and of usage-based and cognitive-aging accounts of the roles of age and dominance in naming.


title: High commitment HR practices, the employment relationship and job performance: A test of a mediation model

abstract: Abstract Purpose This study outlines and tests a high commitment model of human resource (HR) practices and its association with outcomes through a path including employee perceptions and attitudes, thereby seeking a new way of opening the so-called ‘black box’ between human resource management (HRM) and performance. Methodology Data were collected through a questionnaire survey with responses from 835 Spanish workers from three sectors (services, education and food manufacture). In order to test hypotheses, we conducted a path analysis. Findings High commitment HR practices were related to employee performance through the mediating effect of perceived organizational support, a fulfilled psychological contract and job security, as key features of the employment relationship, and job satisfaction. Research implications/limitations This study highlights the roles of high commitment HR and a social exchange model that places a positive employment relationship at the centre of the link between HRM and performance. In so doing, it supports a causal chain from input (HR practices) to perceptions (the employment relationship), attitudes (job satisfaction) and performance (employee behaviour). However, it is based on self-report and cross-sectional data, and hence future research should obtain independent performance data and should ideally be longitudinal. Originality/value This study is novel in its analysis of how high commitment HRM affects performance through the employment relationship within a social exchange analytic framework. As such, it offers an alternative, albeit complementary view of the HRM – performance link to the more dominant AMO (ability, motivation and opportunity) model.


title: A predictive model for the temporal dynamics of information diffusion in online social networks

abstract: Today, online social networks have become powerful tools for the spread of information. They facilitate the rapid and large-scale propagation of content and the consequences of an information -- whether it is favorable or not to someone, false or true -- can then take considerable proportions. Therefore it is essential to provide means to analyze the phenomenon of information dissemination in such networks. Many recent studies have addressed the modeling of the process of information diffusion, from a topological point of view and in a theoretical perspective, but we still know little about the factors involved in it. With the assumption that the dynamics of the spreading process at the macroscopic level is explained by interactions at microscopic level between pairs of users and the topology of their interconnections, we propose a practical solution which aims to predict the temporal dynamics of diffusion in social networks. Our approach is based on machine learning techniques and the inference of time-dependent diffusion probabilities from a multidimensional analysis of individual behaviors. Experimental results on a real dataset extracted from Twitter show the interest and effectiveness of the proposed approach as well as interesting recommendations for future investigation.


Mutual Causation in Highway Construction and Economic Development


title: Action Observation and Motor Imagery: Innovative Cognitive Tools in the Rehabilitation of Parkinson's Disease

abstract: Parkinson's disease (PD) is characterized by a progressive impairment of motor skills with deterioration of autonomy in daily living activities. Physiotherapy is regarded as an adjuvant to pharmacological and neurosurgical treatment and may provide small and short-lasting clinical benefits in PD patients. However, the development of innovative rehabilitation approaches with greater long-term efficacy is a major unmet need. Motor imagery (MI) and action observation (AO) have been recently proposed as a promising rehabilitation tool. MI is the ability to imagine a movement without actual performance (or muscle activation). The same cortical-subcortical network active during motor execution is engaged in MI. The physiological basis of AO is represented by the activation of the "mirror neuron system." Both MI and AO are involved in motor learning and can induce improvements of motor performance, possibly mediated by the development of plastic changes in the motor cortex. The review of available evidences indicated that MI ability and AO feasibility are substantially preserved in PD subjects. A few preliminary studies suggested the possibility of using MI and AO as parts of rehabilitation protocols for PD patients.


title: Fuzzy Keyword Search over Encrypted Data in Cloud Computing

abstract: As Cloud Computing becomes prevalent, more and more sensitive information are being centralized into the cloud. For the protection of data privacy, sensitive data usually have to be encrypted before outsourcing, which makes effective data utilization a very challenging task. Although traditional searchable encryption schemes allow a user to securely search over encrypted data through keywords and selectively retrieve files of interest, these techniques support only exact keyword search. That is, there is no tolerance of minor typos and format inconsistencies which, on the other hand, are typical user searching behavior and happen very frequently. This significant drawback makes existing techniques unsuitable in Cloud Computing as it greatly affects system usability, rendering user searching experiences very frustrating and system efficacy very low. In this paper, for the first time we formalize and solve the problem of effective fuzzy keyword search over encrypted cloud data while maintaining keyword privacy. Fuzzy keyword search greatly enhances system usability by returning the matching files when users' searching inputs exactly match the predefined keywords or the closest possible matching files based on keyword similarity semantics, when exact match fails. In our solution, we exploit edit distance to quantify keywords similarity and develop an advanced technique on constructing fuzzy keyword sets, which greatly reduces the storage and representation overheads. Through rigorous security analysis, we show that our proposed solution is secure and privacy-preserving, while correctly realizing the goal of fuzzy keyword search.


title: Process of Building Reference Ontology for Higher Education

abstract: Most ontologies are application ontologies, that are not reusable and are difficult to link together as they are too specific. Reference ontology is able to contribute significantly in reducing the issue of ontology applications specificity. Particularly considering higher education domain, we think that a reference ontology dedicated to this knowledge area, can be regarded as a valuable tool for several stakeholders interested in analyzing the system of higher education as a whole, especially in a context of academic systems diversity all over the world. Motivated by this potential application and even more, we decided to build a reference ontology called HERO ontology, which stands for "Higher Education Reference Ontology". In this paper we explain HERO ontology building process from requirements specification until ontology evaluation using NeOn methodology.


title: Adaptive Thresholding and Reweighting to Improve Domain Transfer Learning for Unbalanced Data with Applications to EEG Imbalance

abstract: Domain adaptation methods can be highly sensitive to class balance, particularly the usually unknown balance of the unlabeled test set. In this work, we analyze the effect of imbalance on a well-known algorithm, ARTL (Adaptation Regularization Transfer Learning) and propose four approaches for mitigating the adverse effects of imbalance. These include (1) balancing the training set for pseudo-label calculation, (2) applying adaptive thresholding to pseudo-label calculation, (3) using class reweighting in the optimization objective, and (4) applying adaptive thresholding to the output objective. We tested these methods with the UCI newsgroup dataset and on three types of imbalanced EEG (electroencephalogram) classification problems. We observed significant improvements, particularly for cases of extreme imbalance, which are not well addressed by standard classification techniques.


title: Estimation of human body shape and cloth field in front of a kinect

abstract: Abstract This paper describes an easy-to-use system to estimate the shape of a human body and his/her clothes. The system uses a Kinect to capture the human׳s RGB and depth information from different views. Using the depth data, a non-rigid deformation method is devised to compensate motions between different views, thus to align and complete the dressed shape. Given the reconstructed dressed shape, the skin regions are recognized by a skin classifier from the RGB images, and these skin regions are taken as a tight constraints for the body estimation. Subsequently, the body shape is estimated from the skin regions of the dressed shape by leveraging a statistical model of human body. After the body estimation, the body shape is non-rigidly deformed to fit the dressed shape, so as to extract the cloth field of the dressed shape. We demonstrate our system and the therein algorithms by several experiments. The results show the effectiveness of the proposed method.


title: How shall I trust the faceless and the intangible? A literature review on the antecedents of online trust

abstract: Trust is generally assumed to be an important precondition for people's adoption of electronic services. This paper provides an overview of the available research into the antecedents of trust in both commercial and non-commercial online transactions and services. A literature review was conducted covering empirical studies on people's trust in and adoption of computer-mediated services. Results are described using a framework of three clusters of antecedents: customer/client-based, website-based, and company/organization-based antecedents. Results show that there are many possible antecedents of trust in electronic services. The majority of the research has been conducted in the context of e-commerce; only few studies are available in the domains of e-government and e-health. For many antecedents, some empirical support can be found, but the results are far from univocal. The research calls for more, and particularly more systematic, research attention for the antecedents of trust in electronic services. The review presented in this paper offers practitioners an overview of possibly relevant variables that may affect people's trust in electronic services. It also gives a state-of-the-art overview of the empirical support for the relevance of these variables.


title: Evaluation of bidirectional LSTM for short-and long-term stock market prediction

abstract: Recently, there has been a rapidly growing interest in deep learning research and their applications to real-world problems. In this paper, we aim at evaluating and comparing LSTM deep learning architectures for short-and long-term prediction of financial time series. This problem is often considered as one of the most challenging real-world applications for time-series prediction. Unlike traditional recurrent neural networks, LSTM supports time steps of arbitrary sizes and without the vanishing gradient problem. We consider both bidirectional and stacked LSTM predictive models in our experiments and also benchmark them with shallow neural networks and simple forms of LSTM networks. The evaluations are conducted using a publicly available dataset for stock market closing prices.


title: A metaobject protocol for C++

abstract: This paper presents a metaobject protocol (MOP) for C++. This MOP was designed to bring the power of meta-programming to C++ programmers. It avoids penalties on runtime performance by adopting a new meta-architecture in which the metaobjects control the compilation of programs instead of being active during program execution. This allows the MOP to be used to implement libraries of efficient, transparent language extensions.


title: Knowle: A semantic link network based system for organizing large scale online news events

abstract: Abstract An explosive growth in the volume, velocity, and variety of the data available on the Internet has been witnessed recently. The data originated from multiple types of sources including mobile devices, sensors, individual archives, social networks, Internet of Things, enterprises, cameras, software logs, health data has led to one of the most challenging research issues of the big data era. In this paper, Knowle—an online news management system upon semantic link network model is introduced. Knowle is a news event centrality data management system. The core elements of Knowle are news events on the Web, which are linked by their semantic relations. Knowle is a hierarchical data system, which has three different layers including the bottom layer (concepts), the middle layer (resources), and the top layer (events). The basic blocks of the Knowle system—news collection, resources representation, semantic relations mining, semantic linking news events are given. Knowle does not require data providers to follow semantic standards such as RDF or OWL, which is a semantics-rich self-organized network. It reflects various semantic relations of concepts, news, and events. Moreover, in the case study, Knowle is used for organizing and mining health news, which shows the potential on forming the basis of designing and developing big data analytics based innovation framework in the health domain.


title: A VISION BASED SYSTEM FOR OBJECT DETECTION IN UNDERWATER IMAGES

abstract: In this paper, a vision-based system for underwater object detection is presented. The system is able to detect automatically a pipeline placed on the sea bottom, and some objects, e.g. trestles and anodes, placed in its neighborhoods. A color compensation procedure has been introduced in order to reduce problems connected with the light attenuation in the water. Artificial neural networks are then applied in order to classify in real-time the pixels of the input image into different classes, corresponding e.g. to different objects present in the observed scene. Geometric reasoning is applied to reduce the detection of false objects and to improve the accuracy of true detected objects. The results on real underwater images representing a pipeline structure in different scenarios are shown. The presence of seaweed and sand, different illumination conditions and water depth, different pipeline diameter and small variations of the camera tilt angle are considered to evaluate the algorithm performances.


TeleKinect : A Telepresence Platform for Shared Experiences in a Composited Video Environment


title: Pathophysiology and treatment of type 2 diabetes: perspectives on the past, present, and future

abstract: Glucose metabolism is normally regulated by a feedback loop including islet β cells and insulin-sensitive tissues, in which tissue sensitivity to insulin aff ects magnitude of β-cell response. If insulin resistance is present, β cells maintain normal glucose tolerance by increasing insulin output. Only when β cells cannot release suffi cient insulin in the presence of insulin resistance do glucose concentrations rise. Although β-cell dysfunction has a clear genetic component, environmental changes play an essential part. Modern research approaches have helped to establish the important role that hexoses, aminoacids, and fatty acids have in insulin resistance and β-cell dysfunction, and the potential role of changes in the microbiome. Several new approaches for treatment have been developed, but more eff ective therapies to slow progressive loss of β-cell function are needed. Recent fi ndings from clinical trials provide important information about methods to prevent and treat type 2 diabetes and some of the adverse eff ects of these interventions. However, additional long-term studies of drugs and bariatric surgery are needed to identify new ways to prevent and treat type 2 diabetes and thereby reduce the harmful eff ects of this disease.


title: Image set for deep learning: field images of maize annotated with disease symptoms

abstract: ObjectivesAutomated detection and quantification of plant diseases would enable more rapid gains in plant breeding and faster scouting of farmers’ fields. However, it is difficult for a simple algorithm to distinguish between the target disease and other sources of dead plant tissue in a typical field, especially given the many variations in lighting and orientation. Training a machine learning algorithm to accurately detect a given disease from images taken in the field requires a massive amount of human-generated training data.Data descriptionThis data set contains images of maize (Zea mays L.) leaves taken in three ways: by a hand-held camera, with a camera mounted on a boom, and with a camera mounted on a small unmanned aircraft system (sUAS, commonly known as a drone). Lesions of northern leaf blight (NLB), a common foliar disease of maize, were annotated in each image by one of two human experts. The three data sets together contain 18,222 images annotated with 105,705 NLB lesions, making this the largest publicly available image set annotated for a single plant disease.


title: The Causal Frame Problem: An Algorithmic Perspective

abstract: The Frame Problem (FP) is a puzzle in philosophy of mind and epistemology, articulated by the Stanford Encyclopedia of Philosophy as follows: "How do we account for our apparent ability to make decisions on the basis only of what is relevant to an ongoing situation without having explicitly to consider all that is not relevant?" In this work, we focus on the causal variant of the FP, the Causal Frame Problem (CFP). Assuming that a reasoner's mental causal model can be (implicitly) represented by a causal Bayes net, we first introduce a notion called Potential Level (PL). PL, in essence, encodes the relative position of a node with respect to its neighbors in a causal Bayes net. Drawing on the psychological literature on causal judgment, we substantiate the claim that PL may bear on how time is encoded in the mind. Using PL, we propose an inference framework, called the PL-based Inference Framework (PLIF), which permits a boundedly-rational approach to the CFP to be formally articulated at Marr's algorithmic level of analysis. We show that our proposed framework, PLIF, is consistent with a wide range of findings in causal judgment literature, and that PL and PLIF make a number of predictions, some of which are already supported by existing findings.


title: Face recognition from near-infrared images with convolutional neural network

abstract: Owing to the vigorous development of face recognition, near-infrared (NIR) face recognition technology with light insensitivity has attracted increasing attention. However, the traditional methods for NIR face recognition feature the hand-crafted feature design. In this paper, we present a convolutional neural network (CNN) for NIR face recognition. CNN is a multiplayer feed-forward neural network which can automatically learn the features from the raw images and provide partial invariance to illumination, scale and deformation. Experimental results on PolyU-NIRFD database show that our proposed CNN architecture has higher recognition rate compared with the traditional recognition methods, such as Gabor-directional binary code (GDBC), Zernike moments and Hermite kernels (ZMHK).


title: Data Mining with Big Data

abstract: In an Information technology world, the ability to effectively process massive datasets has become integral to a broad range of scientific and other academic disciplines. We are living in an era of data deluge and as a result, the term “Big Data” is appearing in many contexts. It ranges from meteorology, genomics, complex physics simulations, biological and environmental research, finance and business to healthcare. Big Data refers to data streams of higher velocity and higher variety. The infrastructure required to support the acquisition of Big Data must deliver low, predictable latency in both capturing data and in executing short, simple queries. To be able to handle very high transaction volumes, often in a distributed environment; and support flexible, dynamic data structures. Data processing is considerably more challenging than simply locating, identifying, understanding, and citing data. For effective large-scale analysis all of this has to happen in a completely automated manner. This requires differences in data structure and semantics to be expressed in forms that are computer understandable, and then “robotically” resolvable. There is a strong body of work in data integration, mapping and transformations. However, considerable additional work is required to achieve automated error-free difference resolution. This paper proposes a framework on recent research for the Data Mining using Big Data.


title: Functions that Emerge through End-to-end Reinforcement Lear

abstract: Recently, triggered by the impressive results in TV-games or game of Go by Google DeepMind, end-to-end reinforcement learning (RL) is collecting attentions. Although little is known, the author's group has propounded this framework for around 20 years and already has shown a variety of functions that emerge in a neural network (NN) through RL. In this paper, they are introduced again at this timing. ::: "Function Modularization" approach is deeply penetrated subconsciously. The inputs and outputs for a learning system can be raw sensor signals and motor commands. "State space" or "action space" generally used in RL show the existence of functional modules. That has limited reinforcement learning to learning only for the action-planning module. In order to extend reinforcement learning to learning of the entire function on a huge degree of freedom of a massively parallel learning system and to explain or develop human-like intelligence, the author has believed that end-to-end RL from sensors to motors using a recurrent NN (RNN) becomes an essential key. Especially in the higher functions, this approach is very effective by being free from the need to decide their inputs or outputs. ::: The functions that emerge, we have confirmed, through RL using a NN cover a broad range from real robot learning with raw camera pixel inputs to acquisition of dynamic functions in a RNN. Those are (1)image recognition, (2)color constancy (optical illusion), (3)sensor motion (active recognition), (4)hand-eye coordination and hand reaching movement, (5)explanation of brain activities, (6)communication, (7)knowledge transfer, (8)memory, (9)selective attention, (10)prediction, (11)exploration. The end-to-end RL enables the emergence of very flexible comprehensive functions that consider many things in parallel although it is difficult to give the boundary of each function clearly.


title: Automating preparation of exam questions: Exam Question Classification System (EQCS)

abstract: Currently, there is no any available computerized mechanism to classify the exam questions prepared by the instructors. Everything is done manually by the instructors according to their own style. Written exam question has always been a method for educators to assess the level of understanding of students. A good exam question should consist of different levels of difficulties in order to increase students' thinking skills. The template for exam questions is given in standard Microsoft Word template and the instructors will then fill the template with their own questions. Therefore, the questions prepared by the instructors are not standardized and are prone to be misguided from Bloom's Taxonomy. The Bloom's Taxonomy was introduced to assist educators to set a standard for their exam questions.


title: Unsupervised Domain Adaptation: from Simulation Engine to the RealWorld

abstract: Large-scale labeled training datasets have enabled deep neural networks to excel on a wide range of benchmark vision tasks. However, in many applications it is prohibitively expensive or time-consuming to obtain large quantities of labeled data. To cope with limited labeled training data, many have attempted to directly apply models trained on a large-scale labeled source domain to another sparsely labeled target domain. Unfortunately, direct transfer across domains often performs poorly due to domain shift and dataset bias. Domain adaptation is the machine learning paradigm that aims to learn a model from a source domain that can perform well on a different (but related) target domain. In this paper, we summarize and compare the latest unsupervised domain adaptation methods in computer vision applications. We classify the non-deep approaches into sample re-weighting and intermediate subspace transformation categories, while the deep strategy includes discrepancy-based methods, adversarial generative models, adversarial discriminative models and reconstruction-based methods. We also discuss some potential directions.


title: Adaptive boosting for transfer learning using dynamic updates

abstract: Instance-based transfer learning methods utilize labeled examples from one domain to improve learning performance in another domain via knowledge transfer. Boosting-based transfer learning algorithms are a subset of such methods and have been applied successfully within the transfer learning community. In this paper, we address some of the weaknesses of such algorithms and extend the most popular transfer boosting algorithm, TrAdaBoost. We incorporate a dynamic factor into TrAdaBoost to make it meet its intended design of incorporating the advantages of both AdaBoost and the "Weighted Majority Algorithm". We theoretically and empirically analyze the effect of this important factor on the boosting performance of TrAdaBoost and we apply it as a "correction factor" that significantly improves the classification performance. Our experimental results on several real-world datasets demonstrate the effectiveness of our framework in obtaining better classification results.


title: Paradigm shift in consciousness research: the child′s self-awareness and abnormalities in autism, ADHD and schizophrenia

abstract: Self-awareness is a pivotal component of any conscious experience and conscious self-regulation of behaviour. A paralimbic network is active, specific and causal in self-awareness. Its regions interact by gamma synchrony. Gamma synchrony develops throughout infancy, childhood and adolescence into adulthood and is regulated by dopamine and other neurotransmitters via GABA interneurons. Major derailments of this network and self-awareness occur in developmental disorders of conscious self-regulation like autism, attention deficit hyperactivity disorder (ADHD) and schizophrenia. ::: ::: ::: ::: Conclusion: Recent research on conscious experience is no longer limited to the study of neural ‘correlations’ but is increasingly lending itself to the study of causality. This paradigm shift opens new perspectives for understanding the neural mechanisms of the developing self and the causal effects of their disturbance in developmental disorders.


title: Parabolic Obstacle Problems. Quasi-convexity and Regularity

abstract: In a wide class of the so called Obstacle Problems of parabolic type it is shown how to improve the optimal regularity of the solution and as a consequence how to obtain space-time regularity of the corresponding free boundary.


title: The surviving rate of digraphs

abstract: Let G@? be a connected digraph with n>=2 vertices. Suppose that a fire breaks out at a vertex v of G@?. A firefighter starts to protect vertices. At each time interval, the firefighter protects k vertices not yet on fire. Afterward, the fire spreads to all unprotected neighbors that are heads of some arcs starting from the vertices on fire. Let sn"k(v) denote the maximum number of vertices in G@? that the firefighter can save when a fire breaks out at vertex v. The k-surviving rate @r"k(G@?) of G@? is defined as @?"v"@?"V"("G"@?")sn"k(v)/n^2. In this paper, we consider the k-surviving rate of digraphs. Main results are as follows: (1) if G@? is a k-degenerate digraph, then @r"k(G@?)>=1k+1; (2) if G@? is a planar digraph, then @r"2(G@?)>140; (3) if G@? is a planar digraph without 4-cycles, then @r"1(G@?)>151.


title: Image Based Mango Fruit Detection, Localisation and Yield Estimation Using Multiple View Geometry

abstract: This paper presents a novel multi-sensor framework to efficiently identify, track, localise and map every piece of fruit in a commercial mango orchard. A multiple viewpoint approach is used to solve the problem of occlusion, thus avoiding the need for labour-intensive field calibration to estimate actual yield. Fruit are detected in images using a state-of-the-art faster R-CNN detector, and pair-wise correspondences are established between images using trajectory data provided by a navigation system. A novel LiDAR component automatically generates image masks for each canopy, allowing each fruit to be associated with the corresponding tree. The tracked fruit are triangulated to locate them in 3D, enabling a number of spatial statistics per tree, row or orchard block. A total of 522 trees and 71,609 mangoes were scanned on a Calypso mango orchard near Bundaberg, Queensland, Australia, with 16 trees counted by hand for validation, both on the tree and after harvest. The results show that single, dual and multi-view methods can all provide precise yield estimates, but only the proposed multi-view approach can do so without calibration, with an error rate of only 1.36% for individual trees.


title: Evaluation of agile methods for quality assurance and quality control in ERP implementation

abstract: Most organizations are now adopting Enterprise Resource Planning (ERP) solutions to standardize, formalized and automate their processes. The organization usually hire IT Consulting Firms (ITCF) for ERP implementation due to its complexity, large scale, and domain knowledge. ERP brings radical changes in the environment, daily processes, and interactions of the organization. To make these changes gradual and incremental, ITCF are moving towards agile methodology. Quality assurance and quality control play a pivotal role in the success of an ERP implementation, with the use of agile methods the implementation becomes more complex. In this paper, grounded theory is used to study the rapid parallel processes of testing, validation, and verification for different phases of ERP implementation through using agile methods. In our study, 23 industry professionals participated from six different organizations. Each participant had experience of various ERP implementations in diverse geographical locations in various domains with different roles. This paper discuss and analyze different agile methods, which can reduce challenges in quality control and assurance during ERP implementation. The results show that agile methods (daily scrum meeting, pair programming and frequent reviews) reduce complexity and increase the quality of ERP implementation.


title: Millimeter-wave microstrip-to-waveguide transition operating over broad frequency bandwidth

abstract: Novel 3,4-disubstituted-N-acylanilines of the formula: wherein R1, R2 and ring A are defined in claim 1; wherein R3 is hydrogen or alkyl of up to 4 carbon atoms, or is joined to R5 as stated below; wherein R4 is alkyl of 2 to 4 carbon atoms, or has the formula -CX1X2X3 wherein X1, X2 and X3, which may be the same or different, each is hydrogen, fluorine or chlorine; wherein R5 is hydrogen, hydroxy or alkoxy or acyloxy each of up to 15 carbon atoms, or is joined to R3 to form an oxycarbonyl group such that together with the part of the molecule it forms an oxazolidinedione group; and wherein R6 is hydrogen or halogen. These compounds possess antiandrogenic activity and are useful for the treatment of androgen dependent or prostatic diseases. Representative of the compounds is 3,4-dichloro-N-(2-hydroxy-2-p-nitrophenylpropionyl)aniline. Also disclosed are processes for the manufacture of the compounds and pharmaceutical and veterinary compositions containing them.


title: Scene flow propagation for semantic mapping and object discovery in dynamic street scenes

abstract: Scene understanding is an important prerequisite for vehicles and robots that operate autonomously in dynamic urban street scenes. For navigation and high-level behavior planning, the robots not only require a persistent 3D model of the static surroundings—equally important, they need to perceive and keep track of dynamic objects. In this paper, we propose a method that incrementally fuses stereo frame observations into temporally consistent semantic 3D maps. In contrast to previous work, our approach uses scene flow to propagate dynamic objects within the map. Our method provides a persistent 3D occupancy as well as semantic belief on static as well as moving objects. This allows for advanced reasoning on objects despite noisy single-frame observations and occlusions. We develop a novel approach to discover object instances based on the temporally consistent shape, appearance, motion, and semantic cues in our maps. We evaluate our approaches to dynamic semantic mapping and object discovery on the popular KITTI benchmark and demonstrate improved results compared to single-frame methods.


title: Understanding the privacy behavior of adolescents on Facebook: The role of peers, popularity and trust

abstract: We study whether peer influence processes, popularity and trust predict privacy settings on Facebook. We use large-scale survey data from 3434 Dutch adolescents combined with observed privacy behavior on Facebook. The findings show that peer influence processes play a role and that adolescents imitate the privacy settings of their peers in the classroom. Such imitation processes are particularly pronounced for highly connected classrooms. The results show that more popular adolescents in the classroom are more likely to publicly display their Facebook profiles. Furthermore, we find that low-trust groups (ethnic minorities, lower educated and younger adolescents, and girls) more frequently opt for private Facebook profiles. We study the observed privacy settings of adolescents on Facebook.A combination of survey data and unobtrusive data obtained from Facebook is analyzed.We hypothesize about effects of peer influence, popularity and generalized trust.Popularity and classmates privacy settings are related to Facebook privacy.Low trust groups more often opt for private Facebook profiles.


title: Graphic medicine: use of comics in medical education and patient care

abstract: Graphic stories, or adult themed comics, are a popular new cultural trend. Michael J Green and Kimberly R Myers argue that they are also a valuable tool for medicine


title: Music and social bonding: “self-other” merging and neurohormonal mechanisms

abstract: It has been suggested that a key function of music during its development and spread amongst human populations was its capacity to create and strengthen social bonds amongst interacting group members. However, the mechanisms by which this occurs have not been fully discussed. In this paper we review evidence supporting two thus far independently investigated mechanisms for this social bonding effect: self-other merging as a consequence of inter-personal synchrony, and the release of endorphins during exertive rhythmic activities including musical interaction. In general, self-other merging has been experimentally investigated using dyads, which provide limited insight into large-scale musical activities. Given that music can provide an external rhythmic framework that facilitates synchrony, explanations of social bonding during group musical activities should include reference to endorphins, which are released during synchronised exertive movements. Endorphins (and the Endogenous Opioid System (EOS) in general) are involved in social bonding across primate species, and are associated with a number of human social behaviours (e.g. laughter, synchronised sports), as well as musical activities (e.g. singing and dancing). Furthermore, passively listening to music engages the EOS, so here we suggest that both self-other merging and the EOS are important in the social bonding effects of music. In order to investigate possible interactions between these two mechanisms, future experiments should recreate ecologically valid examples of musical activities.


title: Redirecting Walking and Driving for Natural Navigation in Immersive Virtual Environments

abstract: Walking is the most natural form of locomotion for humans, and real walking interfaces have demonstrated their benefits for several navigation tasks. With recently proposed redirection techniques it becomes possible to overcome space limitations as imposed by tracking sensors or laboratory setups, and, theoretically, it is now possible to walk through arbitrarily large virtual environments. However, walking as sole locomotion technique has drawbacks, in particular, for long distances, such that even in the real world we tend to support walking with passive or active transportation for longer-distance travel. In this article we show that concepts from the field of redirected walking can be applied to movements with transportation devices. We conducted psychophysical experiments to determine perceptual detection thresholds for redirected driving, and set these in relation to results from redirected walking. We show that redirected walking-and-driving approaches can easily be realized in immersive virtual reality laboratories, e. g., with electric wheelchairs, and show that such systems can combine advantages of real walking in confined spaces with benefits of using vehiclebased self-motion for longer-distance travel.


title: Epileptic Seizure Detection in Electroencephalograms using Time-Frequency Analysis

abstract: The detection of recorded epileptic seizure activity in electroencephalographic (EEG) segments is crucial for the localization and classification of epileptic seizures. However, since seizure evolution is typically a dynamic and nonstationary process and the signals are composed of multiple frequencies, visual and conventional frequency-based methods have limited application. In this paper, we demonstrate the suitability of the time-frequency (t-f) analysis to classify EEG segments for epileptic seizures and we compare several methods for t-f analysis of EEGs. Short-time Fourier transform (STFT) and several t-f distributions (TFDs) are used to calculate the power spectrum density (PSD) of each segment. The analysis is performed in three stages: (i) t-f analysis and calculation of the PSD of each EEG segment, (ii) feature extraction, measuring the signal segment fractional energy on specific t-f windows and (iii) classification of the EEG segment (existence of epileptic seizure or not), using artificial neural networks (ANN). The methods are evaluated using three classification problems obtained from a benchmark EEG dataset, and qualitative and quantitative results are presented.


title: Cascaded H-bridge multilevel converter for large-scale PV grid-integration with isolated DC-DC stage

abstract: The integration of large-scale PV systems to the grid is a growing trend in modern power systems. The cascaded H-bridge (CHB) converter is a suitable candidate for the grid interconnection due to its modular characteristics, high-quality output waveforms and capability of connecting to mediumvoltage grids. However, the CHB converter requires isolated DC sources. In order to avoid the leakage currents caused by the high potential differences across the parasitic capacitance of the PV panels to ground, an isolated DC-DC conversion stage is required when the CHB topology is used. The objective of this paper is to compare two PV system configurations based on the CHB multilevel converter using two isolated DC-DC converter topologies, namely the boost-half-bridge (BHB) and the flyback, for their performance on providing isolation and achieving individual MPPT at the DC-DC power conversion stage of large-scale PV power systems. Simulation results from a 263 kW PV system based on a seven-level CHB converter with the two aforementioned isolated DC-DC converters are provided for comparison and evaluation with different input PV voltages.


title: Energy-Efficient Adaptive Classifier Design for Mobile Systems

abstract: With the continuous increase in the amount of data that needs to be processed by digital mobile systems, energy-efficient computation has become a critical design constraint for mobile systems. In this paper, we propose an adaptive classifier that leverages the wide variability in data complexity to enable energy-efficient data classification operations for mobile systems. Our approach takes advantage of varying classification "hardness" across data to dynamically allocate resources and improve energy efficiency. On average, our adaptive classifier is ≈ 100× more energy efficient but has ≈ 1% higher error rate than a complex radial basis function classifier and is ≈ 10× less energy efficient but has ≈ 40% lower error rate than a simple linear classifier across a wide range of classification data sets.


title: Metaphor comprehension: A computational theory

abstract: Metaphor comprehension involves an interaction between the meaning of the topic and the vehicle terms of the metaphor. Meaning is represented by vectors in a high-dimensional semantic space. Predication modifies the topic vector by merging it with selected features of the vehicle vector. The resulting metaphor vector can be evaluated by comparing it with known landmarks in the semantic space. Thus, metaphorical predication is treated in the present model in exactly the same way as literal predication. Some experimental results concerning metaphor comprehension are simulated within this framework, such as the nonreversibility of metaphors, priming of metaphors with literal statements, and priming of literal statements with metaphors.


title: Clustering-Based Predictive Process Monitoring

abstract: Business process enactment is generally supported by information systems that record data about process executions, which can be extracted as event logs. Predictive process monitoring is concerned with exploiting such event logs to predict how running (uncompleted) cases will unfold up to their completion. In this paper, we propose a predictive process monitoring framework for estimating the probability that a given predicate will be fulfilled upon completion of a running case. The predicate can be, for example, a temporal logic constraint or a time constraint, or any predicate that can be evaluated over a completed trace. The framework takes into account both the sequence of events observed in the current trace, as well as data attributes associated to these events. The prediction problem is approached in two phases. First, prefixes of previous traces are clustered according to control flow information. Secondly, a classifier is built for each cluster using event data to discriminate between fulfillments and violations. At runtime, a prediction is made on a running case by mapping it to a cluster and applying the corresponding classifier. The framework has been implemented in the ProM toolset and validated on a log pertaining to the treatment of cancer patients in a large hospital.


title: Surrogate-Based Methods

abstract: Objective functions that appear in engineering practice may come from measurements of physical systems and, more often, from computer simulations. In many cases, optimization of such objectives in a straightforward way, i.e., by applying optimization routines directly to these functions, is impractical. One reason is that simulation-based objective functions are often analytically intractable (discontinuous, non-differentiable, and inherently noisy). Also, sensitivity information is usually unavailable, or too expensive to compute. Another, and in many cases even more important, reason is the high computational cost of measurement/simulations. Simulation times of several hours, days or even weeks per objective function evaluation are not uncommon in contemporary engineering, despite the increase of available computing power. Feasible handling of these unmanageable functions can be accomplished using surrogate models: the optimization of the original objective is replaced by iterative re-optimization and updating of the analytically tractable and computationally cheap surrogate. This chapter briefly describes the basics of surrogate-based optimization, various ways of creating surrogate models, as well as several examples of surrogate-based optimization techniques.


title: Video Super-Resolution With Convolutional Neural Networks

abstract: Convolutional neural networks (CNN) are a special type of deep neural networks (DNN). They have so far been successfully applied to image super-resolution (SR) as well as other image restoration tasks. In this paper, we consider the problem of video super-resolution. We propose a CNN that is trained on both the spatial and the temporal dimensions of videos to enhance their spatial resolution. Consecutive frames are motion compensated and used as input to a CNN that provides super-resolved video frames as output. We investigate different options of combining the video frames within one CNN architecture. While large image databases are available to train deep neural networks, it is more challenging to create a large video database of sufficient quality to train neural nets for video restoration. We show that by using images to pretrain our model, a relatively small video database is sufficient for the training of our model to achieve and even improve upon the current state-of-the-art. We compare our proposed approach to current video as well as image SR algorithms.


title: Materials, Mechanics, and Patterning Techniques for Elastomer-Based Stretchable Conductors

abstract: Stretchable electronics represent a new generation of electronics that utilize soft, deformable elastomers as the substrate or matrix instead of the traditional rigid printed circuit boards. As the most essential component of stretchable electronics, the conductors should meet the requirements for both high conductivity and the capability to maintain conductive under large deformations such as bending, twisting, stretching, and compressing. This review summarizes recent progresses in various aspects of this fascinating and challenging area, including materials for supporting elastomers and electrical conductors, unique designs and stretching mechanics, and the subtractive and additive patterning techniques. The applications are discussed along with functional devices based on these conductors. Finally, the review is concluded with the current limitations, challenges, and future directions of stretchable conductors.


title: Supplier selection using fuzzy AHP and fuzzy multi-objective linear programming for developing low carbon supply chain

abstract: Environmental sustainability of a supply chain depends on the purchasing strategy of the supply chain members. Most of the earlier models have focused on cost, quality, lead time, etc. issues but not given enough importance to carbon emission for supplier evaluation. Recently, there is a growing pressure on supply chain members for reducing the carbon emission of their supply chain. This study presents an integrated approach for selecting the appropriate supplier in the supply chain, addressing the carbon emission issue, using fuzzy-AHP and fuzzy multi-objective linear programming. Fuzzy AHP (FAHP) is applied first for analyzing the weights of the multiple factors. The considered factors are cost, quality rejection percentage, late delivery percentage, green house gas emission and demand. These weights of the multiple factors are used in fuzzy multi-objective linear programming for supplier selection and quota allocation. An illustration with a data set from a realistic situation is presented to demonstrate the effectiveness of the proposed model. The proposed approach can handle realistic situation when there is information vagueness related to inputs.


title: Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words

abstract: We present a novel unsupervised learning method for human action categories. A video sequence is represented as a collection of spatial-temporal words by extracting space-time interest points. The algorithm automatically learns the probability distributions of the spatial-temporal words and the intermediate topics corresponding to human action categories. This is achieved by using latent topic models such as the probabilistic Latent Semantic Analysis (pLSA) model and Latent Dirichlet Allocation (LDA). Our approach can handle noisy feature points arisen from dynamic background and moving cameras due to the application of the probabilistic models. Given a novel video sequence, the algorithm can categorize and localize the human action(s) contained in the video. We test our algorithm on three challenging datasets: the KTH human motion dataset, the Weizmann human action dataset, and a recent dataset of figure skating actions. Our results reflect the promise of such a simple approach. In addition, our algorithm can recognize and localize multiple actions in long and complex video sequences containing multiple motions.


title: USING INTELLECTUAL CAPITAL MANAGEMENT FOR FACILITATING ORGANIZATIONAL CHANGE

abstract: Purpose – This paper aims to examine the role of intellectual capital (IC) management in an organizational change process. The purpose is to obtain new empirical findings and an enhanced understanding of the role of IC in an organizational change process is obtained.Design/methodology/approach – First, the existing literature on change management and IC management is reviewed. Then, empirical research is carried out in terms of an action research project on IC development processes in three case companies. These processes and their outcomes are evaluated using interviews.Findings – An IC model can be a useful tool for change management as it helps to ensure the alignment of the change content with the strategic goals of the organization.Research limitations/implications – The study is based on applying the Danish IC management model in Finnish companies. Different results might have been obtained if another IC management model had been used or the companies representing other countries and cultures had be...


Satisficing Awakens : Models to Mitigate Uncertainty


title: Behavior change techniques in top-ranked mobile apps for physical activity.

abstract: BACKGROUND ::: Mobile applications (apps) have potential for helping people increase their physical activity, but little is known about the behavior change techniques marketed in these apps. ::: ::: ::: PURPOSE ::: The aim of this study was to characterize the behavior change techniques represented in online descriptions of top-ranked apps for physical activity. ::: ::: ::: METHODS ::: Top-ranked apps (n=167) were identified on August 28, 2013, and coded using the Coventry, Aberdeen and London-Revised (CALO-RE) taxonomy of behavior change techniques during the following month. Analyses were conducted during 2013. ::: ::: ::: RESULTS ::: Most descriptions of apps incorporated fewer than four behavior change techniques. The most common techniques involved providing instruction on how to perform exercises, modeling how to perform exercises, providing feedback on performance, goal-setting for physical activity, and planning social support/change. A latent class analysis revealed the existence of two types of apps, educational and motivational, based on their configurations of behavior change techniques. ::: ::: ::: CONCLUSIONS ::: Behavior change techniques are not widely marketed in contemporary physical activity apps. Based on the available descriptions and functions of the observed techniques in contemporary health behavior theories, people may need multiple apps to initiate and maintain behavior change. This audit provides a starting point for scientists, developers, clinicians, and consumers to evaluate and enhance apps in this market.


title: A semidefinite programming framework for controlling multi-robot systems in dynamic environments

abstract: In this paper, a discrete-time, semidefinite programming (SDP) framework is synthesized for controlling mobile robot teams operating in dynamic environments. Given an initially feasible configuration, the proffered framework embeds formation shape control and guarantees inter-agent and agent-obstacle collision avoidance and network interconnectivity across the formation given a sufficiently small Δt - provided that a feasible solution exists. Additionally, it affords goal-directed behaviors, which are explored, most notably, in terms of its application to directional coverage control, where the objective is to ensure that a set of mobile targets are being observed by at least a single member of the team at any given time. Central to our formulation is melding the recent application of shape theoretic constructs to globally optimal shape planning with state-dependent graphs whose enforced connectivity (gauged via their Fiedler value) implies satisfaction of the aforementioned constraints. Simulation results are presented to highlight the utility of our approach.


Cross-Lingual Trends Detection for Named Entities in News Texts with Dynamic Neural Embedding Models


title: Randoop : Feedback-Directed Random Testing for Java

abstract: R<scp>ANDOOP</scp> for Java generates unit tests for Java code using feedback-directed random test generation. Below we describe R<scp>ANDOOP</scp>'s input, output, and test generation algorithm. We also give an overview of RANDOOP's annotation-based interface for specifying configuration parameters that affect R<scp>ANDOOP</scp>'s behavior and output.


title: Kinematic analysis and design considerations for optimal base frame arrangement of humanoid shoulders

abstract: It is well known that kinematics can significantly affect the manipulation capabilities of robotic arms, traditionally illustrated by performance indices such as workspace volume, kinematic and force manipulability, and isotropy within the arm workspace. In the case of dual-arm systems and bimanual manipulation tasks, the kinematics effects to the above indices becomes even more apparent. However, in spite of the large number of dual-arm systems developed in the past, there is a little literature on the kinematic design analysis for the development of such systems. Particularly, the effects of configuration/ orientation of the shoulders' placement with respect to the torso structure have not sufficiently studied or considered, while many dual-arm systems with upward and/or forward tilt angle in shoulder base frame have been introduced. This paper addresses this problem and quantifies the effect of shoulders base frame orientation in a dual-arm manipulation system by looking at its effect on several important manipulation indices, such as the overall and common workspace, redundancy, global isotropy, dual-arm manipulability, and inertia ellipsoid index within the common workspace of the two arms. Consequently, a range of upward and forward tilt angles for the shoulder frames is identified for the design of a dual-arm torso system to render the most desired manipulation performance.


Exact Sampling with Coupled Markov Chains and Applications to Statistical Mechanics


title: Recent Developments in Human Motion Analysis

abstract: Visual analysis of human motion is currently one of the most active research topics in computer vision. This strong interest is driven by a wide spectrum of promising applications in many areas such as virtual reality, smart surveillance, perceptual interface, etc. Human motion analysis concerns the detection, tracking and recognition of people, and more generally, the understanding of human behaviors, from image sequences involving humans. This paper provides a comprehensive survey of research on computer-vision-based human motion analysis. The emphasis is on three major issues involved in a general human motion analysis system, namely human detection, tracking and activity understanding. Various methods for each issue are discussed in order to examine the state of the art. Finally, some research challenges and future directions are discussed.


title: Control flow based obfuscation

abstract: A software obfuscator is a program O to transform a source program P for protection against malicious reverse engineering. O should be correct (O(P) has same functionality with P), resilient (O(P) is resilient against attacks), and effective (O(P) is not too much slower than P). In this paper we describe the design of an obfuscator which consists of two parts. The first part extracts the control flow information from the program and saves it in another process named Monitor-process. The second part protects Monitor-process converting it into an Aucsmith like self-modifying version. We prove the correctness of the obfuscation scheme. We assess its resilience and efficiency to show that both are This supports the claim that our approach is practical.


title: Determining the Training Window for Small Sample Size Classification with Concept Drift

abstract: We consider classification of sequential data in the presence of frequent and abrupt concept changes. The current practice is to use the data after the change to train a new classifier. However, if the window with the new data is too small, the classifier will be undertrained and hence less accurate that the "old'' classifier. Here we propose a method (called WR*) for resizing the training window after detecting a concept change. Experiments with synthetic and real data demonstrate the advantages of WR* over other window resizing methods.


title: CLEX: Yet Another Supercomputer Architecture?

abstract: We propose the CLEX supercomputer topology and routing scheme. We prove that CLEX can utilize a constant fraction of the total bandwidth for point-to-point communication, at delays proportional to the sum of the number of intermediate hops and the maximum physical distance between any two nodes. Moreover, % applying an asymmetric bandwidth assignment to the links, all-to-all communication can be realized $(1+o(1))$-optimally both with regard to bandwidth and delays. This is achieved at node degrees of $n^{\varepsilon}$, for an arbitrary small constant $\varepsilon\in (0,1]$. In contrast, these results are impossible in any network featuring constant or polylogarithmic node degrees. Through simulation, we assess the benefits of an implementation of the proposed communication strategy. Our results indicate that, for a million processors, CLEX can increase bandwidth utilization and reduce average routing path length by at least factors $10$ respectively $5$ in comparison to a torus network. Furthermore, the CLEX communication scheme features several other properties, such as deadlock-freedom, inherent fault-tolerance, and canonical partition into smaller subsystems.


title: Semisupervised One-Class Support Vector Machines for Classification of Remote Sensing Data

abstract: This paper presents two semisupervised one-class support vector machine (OC-SVM) classifiers for remote sensing applications. In one-class image classification, one tries to detect pixels belonging to one of the classes in the image and reject the others. When few labeled pixels of only one class are available, obtaining a reliable classifier is a difficult task. In the particular case of SVM-based classifiers, this task is even harder because the free parameters of the model need to be finely adjusted, but no clear criterion can be adopted. In order to improve the OC-SVM classifier accuracy and alleviate the problem of free-parameter selection, the information provided by unlabeled samples present in the scene can be used. In this paper, we present two state-of-the-art algorithms for semisupervised one-class classification for remote sensing classification problems. The first proposed algorithm is based on modifying the OC-SVM kernel by modeling the data marginal distribution with the graph Laplacian built with both labeled and unlabeled samples. The second one is based on a simple modification of the standard SVM cost function which penalizes more the errors made when classifying samples of the target class. The good performance of the proposed methods is illustrated in four challenging remote sensing image classification scenarios where the goal is to detect one of the classes present on the scene. In particular, we present results for multisource urban monitoring, hyperspectral crop detection, multispectral cloud screening, and change-detection problems. Experimental results show the suitability of the proposed techniques, particularly in cases with few or poorly representative labeled samples.


title: Nonlinear elastic properties of graphene sheet using MM3 potential under finite deformation

abstract: Abstract MM3 interatomic potential is combined with Cauchy–Born rule for the first time to predict elastic properties of the graphene sheets at infinitesimal and finite strains. Further, using plane stress condition, basal plane stiffness and Poisson's ratio calculated using MM3 potential are found to be 310.59 nN/nm and 0.28, respectively. The elastic properties calculated using MM3 potential are somewhat better as compared to those of predicted through Tersoff–Brenner and second generation REBO potentials when compared with the experimental results. Elastic properties are also obtained with MM3 potential considering alternative empirical constants for bond stretch energy term. The present results are also compared with those obtained through modified Morse and first/second generation REBO potentials. The present multiscale model in the framework of MM3 potential will also facilitate the further investigations on the large atomistic structures with greater accuracy.


title: Big data analytics as a service for affective humanoid service robots

abstract: This paper identifies and analyses the advanced capability requirements for humanoid service robots to serve in highly complicated and intelligence demanding applications, such as children education and home care, in future smart home environments. In particular, a Distributed Collaboration and Continuous Learning (DCCL) mechanism is identified as the key capability of a humanoid service robot to succeed in these applications. Based on the latest Big Data Analytics tools with distributed machine learning technologies integrated as services, a novel DCCL middleware platform is developed to facilitate the realisation of the DCCL mechanism. A user preference based children toy recommendation application is introduced as a use case study of the DCCL mechanism and platform.


title: Improved splash-plate feed parabolic reflector antenna for Ka-Band VSAT applications

abstract: In this study, we propose a conical dielectric loaded circular waveguide opening to splash plate subreflector for parabolic antenna feed. Corrugations on the dielectric lens and splash-plate structure are optimized for antenna performance. The feed structure is designed as axi-symmetric to support zero-order azimuth currents for low cross-polarization. Resulting structure offers good gain, low sidelobe, good cross-polarization, and low VSWR for Ka-band VSAT applications where receive and transmit frequency bands are 20.2–21.2 GHz and 30–31 GHz, respectively.


title: Low-VDD Operation of SRAM Synaptic Array for Implementing Ternary Neural Network

abstract: For Internet of Things (IoT) edge devices, it is very attractive to have the local sensemaking capability instead of sending all the data back to the cloud for information processing. For image pattern recognition, neuro-inspired machine learning algorithms have demonstrated enormous powerfulness. To effectively implement learning algorithms on-chip for IoT edge devices, on-chip synaptic memory architectures have been proposed to implement the key operations such as weighted-sum or matrix-vector multiplication. In this paper, we proposed a low-power design of static random access memory (SRAM) synaptic array for implementing a low-precision ternary neural network. We experimentally demonstrated that the supply voltage (VDD) of the SRAM array could be aggressively reduced to a level, where the SRAM cell is susceptible to bit failures. The testing results from 65-nm SRAM chips indicate that VDD could be reduced from the nominal 1–0.55 V (or 0.5 V) with a bit error rate ~0.23% (or ~1.56%), which only introduced ~0.08% (or ~1.68%) degradation in the classification accuracy. As a result, the power consumption could be reduced by more than $8\times $ (or $10\times $ ).


title: Brain Region Segmentation using Convolutional Neural Network

abstract: Brain region segmentation or skull stripping is an essential step in neuroimaging application such as surgical, surface reconstruction, image registration etc. The accuracy of all existing methods depends on the registration and image geometry. When this fails, the probability of success is very less. In order to avoid this, Convolutional Neural Network (CNN) is used. For brain extraction which is free from geometry and registration. CNN learned the connectedness and shape of the brain. OASIS database is used which is publicly available benchmark dataset. In this method, training phase uses 30 images and 10 images are used for testing phase. The performance of CNN results is closer to the ground truth results given by experts.


title: DDoS defense system for web services in a cloud environment

abstract: Abstract Recently, a new kind of vulnerability has surfaced: application layer Denial-of-Service (DoS) attacks targeting web services. These attacks aim at consuming resources by sending Simple Object Access Protocol (SOAP) requests that contain malicious XML content. These requests cannot be detected on the network or transportation (TCP/IP) layer, as they appear as legitimate packets. Until now, there is no web service security specification that addresses this problem. Moreover, the current WS-Security standard induces crucial additional vulnerabilities threatening the availability of certain web service implementations. First, this paper introduces an attack-generating tool to test and confirm previously reported vulnerabilities. The results indicate that the attacks have a devastating impact on the web service availability, even whilst utilizing an absolute minimum of attack resources. Since these highly effective attacks can be mounted with relative ease, it is clear that defending against them is essential, looking at the growth of cloud and web services. Second, this paper proposes an intelligent, fast and adaptive system for detecting against XML and HTTP application layer attacks. The intelligent system works by extracting several features and using them to construct a model for typical requests. Finally, outlier detection can be used to detect malicious requests. Furthermore, the intelligent defense system is capable of detecting spoofing and regular flooding attacks. The system is designed to be inserted in a cloud environment where it can transparently protect the cloud broker and even cloud providers. For testing its effectiveness, the defense system was deployed to protect web services running on WSO2 with Axis2: the defacto standard for open source web service deployment. The proposed defense system demonstrates its capability to effectively filter out the malicious requests, whilst generating a minimal amount of overhead for the total response time.


title: A Pattern matching based Network Intrusion Detection System

abstract: Intrusion Detection System (IDS) has recently become a heated research topic due to its capability of preventing attacks from malicious network users. A pattern matching intrusion detection system has been proposed in this paper. The pattern matching based NIDS consists of four modules: Collection Module, Analyze Module, Response Module and Attack Rule Library. We base this system on CIDF architecture. Realizing that string matching is the bottleneck, our system has improved the performance of detection engines due to an improved algorithm based on the current BM algorithm. Testing results demonstrate superior performance in terms of the detection speed of IDS.


Severe Pulmonary Interstitial Emphysema in a Premature Baby


title: Science overlay maps: a new tool for research policy and library management

abstract: We present a novel approach to visually locate bodies of research within the sciences, both at each moment of time and dynamically. This article describes how this approach fits with other efforts to locally and globally map scientific outputs. We then show how these science overlay maps help benchmark, explore collaborations, and track temporal changes, using examples of universities, corporations, funding agencies, and research topics. We address conditions of application, with their advantages, downsides and limitations. Overlay maps especially help investigate the increasing number of scientific developments and organisations that do not fit within traditional disciplinary categories. We make these tools accessible to help researchers explore the ongoing socio-cognitive transformation of science and technology systems.


title: Personalized Recommendation Meets Your Next Favorite

abstract: A comprehensive understanding of user's item selection behavior is not only essential to many scientific disciplines, but also has a profound business impact on online recommendation. Recent researches have discovered that user's favorites can be divided into 2 categories: long-term and short-term. User's item selection behavior is a mixed decision of her long and short-term favorites. In this paper, we propose a unified model, namely States Transition pAir-wise Ranking Model (STAR), to address users' favorites mining for sequential-set recommendation. Our method utilizes a transition graph for collaborative filtering that accounts for mining user's short-term favorites, jointed with a generative topic model for expressing user's long-term favorites. Furthermore, a user's specific prior is introduced into our unified model for better modeling personalization. Technically, we develop a pair-wise ranking loss function for parameters learning. Empirically, we measure the effectiveness of our method using two real-world datasets and the results show that our method outperforms state-of-the-art methods.


search of how people change: Applications to addictive behaviors, American Psychologist 47


title: Just-In-Time Data Virtualization: Lightweight Data Management with ViDa

abstract: As the size of data and its heterogeneity increase, traditional database system architecture becomes an obstacle to data analysis. Integrating and ingesting (loading) data into databases is quickly becoming a bottleneck in face of massive data as well as increasingly heterogeneous data formats. Still, state-of-the-art approaches typically rely on copying and transforming data into one (or few) repositories. Queries, on the other hand, are often ad-hoc and supported by pre-cooked operators which are not adaptive enough to optimize access to data. As data formats and queries increasingly vary, there is a need to depart from the current status quo of static query processing primitives and build dynamic, fully adaptive architectures. We build ViDa, a system which reads data in its raw format and processes queries using adaptive, just-in-time operators. Our key insight is use of virtualization, i.e., abstracting data and manipulating it regardless of its original format, and dynamic generation of operators. ViDa’s query engine is generated just-in-time; its caches and its query operators adapt to the current query and the workload, while also treating raw datasets as its native storage structures. Finally, ViDa features a language expressive enough to support heterogeneous data models, and to which existing languages can be translated. Users therefore have the power to choose the language best suited for an analysis.


title: A Survey and Empirical Comparison of Modern Pseudo-Random Number Generators for Distributed Stochastic Simulations

abstract: Distributed stochastic simulations has become a popular tool for evaluating and testing complex stochastic dynamic systems. However there is some concern about the credibility of the final results of such simulation studies [11]. One of the important issues which need to be properly addressed for ensuring validity of the final results from any simulation study is application of an appropriate source of randomness. In the case of distributed stochastic simulation, the quality of the final results is highly dependent on the underlying parallel Pseudo-Random Number Generator (PRNG). Parallel PRNGs (PPRNGs) with the required empirical, analytical and deterministic properties are not trivial to find [9, 23, 10]. However, much research has resulted in several generators which we consider to be of high quality [6, 23, 24, 28, 32]. The effectiveness of simulations however depends not only on their accuracy but also on their efficiency and so simulations are also reliant on the speed and flexibility of these PPRNGs. In this paper, without a loss of generality, we examine the required features of modern PPRNGs from the point of view of their possible applications in Multiple Replications in Parallel (MRIP) paradigm of stochastic simulation. Having surveyed the most recommended generators of this class, we test their implementations in C and C++. The generators considered include: the combined multiple recursive generator MRG32k3a [6, 31], dynamic creation of Mersene Twisters [28] and the SPRNG Multiplicative Lagged-Fibonacci Generator (MLFG) [24]. For the purpose of comparison we


title: Are automated debugging techniques actually helping programmers?

abstract: Debugging is notoriously difficult and extremely time consuming. Researchers have therefore invested a considerable amount of effort in developing automated techniques and tools for supporting various debugging tasks. Although potentially useful, most of these techniques have yet to demonstrate their practical effectiveness. One common limitation of existing approaches, for instance, is their reliance on a set of strong assumptions on how developers behave when debugging (e.g., the fact that examining a faulty statement in isolation is enough for a developer to understand and fix the corresponding bug). In more general terms, most existing techniques just focus on selecting subsets of potentially faulty statements and ranking them according to some criterion. By doing so, they ignore the fact that understanding the root cause of a failure typically involves complex activities, such as navigating program dependencies and rerunning the program with different inputs. The overall goal of this research is to investigate how developers use and benefit from automated debugging tools through a set of human studies. As a first step in this direction, we perform a preliminary study on a set of developers by providing them with an automated debugging tool and two tasks to be performed with and without the tool. Our results provide initial evidence that several assumptions made by automated debugging techniques do not hold in practice. Through an analysis of the results, we also provide insights on potential directions for future work in the area of automated debugging.


title: Training Convolutional Neural Networks with Limited Training Data for Ear Recognition in the Wild

abstract: Identity recognition from ear images is an active field of research within the biometric community. The ability to capture ear images from a distance and in a covert manner makes ear recognition technology an appealing choice for surveillance and security applications as well as related application domains. In contrast to other biometric modalities, where large datasets captured in uncontrolled settings are readily available, datasets of ear images are still limited in size and mostly of laboratory-like quality. As a consequence, ear recognition technology has not benefited yet from advances in deep learning and convolutionalneural networks (CNNs) and is still lacking behind other modalities that experienced significant performance gains owing to deep recognition technology. In this paper we address this problem and aim at building a CNNbased ear recognition model. We explore different strategies towards model training with limited amounts of training data and show that by selecting an appropriate model architecture, using aggressive data augmentation and selective learning on existing (pre-trained) models, we are able to learn an effective CNN·based model using a little more than 1300training images. The result of our work is the first CNN·based approach to ear recognition that is also made publicly available to the research community. With our model we are able to improve on the rank one recognition rate of the previous state-of-the-art by more than 25% on a challenging dataset of ear images captured from the web (a.k.a, in the wild).


title: From phonemes to images: levels of representation in a recurrent neural model of visually-grounded language learning

abstract: We present a model of visually-grounded language learning based on stacked gated recurrent neural networks which learns to predict visual features given an image description in the form of a sequence of phonemes. The learning task resembles that faced by human language learners who need to discover both structure and meaning from noisy and ambiguous data across modalities. We show that our model indeed learns to predict features of the visual context given phonetically transcribed image descriptions, and show that it represents linguistic information in a hierarchy of levels: lower layers in the stack are comparatively more sensitive to form, whereas higher layers are more sensitive to meaning.


Generating E cient Mutation Operators for Search-Based Model-Driven Engineering


title: On the Theory and Practice of Privacy-Preserving Bayesian Data Analysis

abstract: Bayesian inference has great promise for the privacy-preserving analysis of sensitive data, as posterior sampling automatically preserves differential privacy, an algorithmic notion of data privacy, under certain conditions (Dimitrakakis et al., 2014; Wang et al., 2015). While this one posterior sample (OPS) approach elegantly provides privacy"for free,"it is data inefficient in the sense of asymptotic relative efficiency (ARE). We show that a simple alternative based on the Laplace mechanism, the workhorse of differential privacy, is as asymptotically efficient as non-private posterior inference, under general assumptions. This technique also has practical advantages including efficient use of the privacy budget for MCMC. We demonstrate the practicality of our approach on a time-series analysis of sensitive military records from the Afghanistan and Iraq wars disclosed by the Wikileaks organization.


Hardware Implementation of MAC using MATLAB Simulink and FPGA


title: 5.8 GHz bandpass filter design using planar couple microstrip lines

abstract: Using microstrip lines to design a planar coupled bandpass filter encounters some approximation errors due to the tolerance in PCB processing and coupling effects. Sometimes it causes unacceptable errors and it can take significant effort to reduce this error. We use admittance inverters to design a 5.8 GHz bandpass filter for a Chebyshev attenuation response of 0.5 dB. Modification of section lengths, widths and gaps by applying an iterative method used previously for synthesizing a coupler to compensate coupling effects and the tolerance in PCB processing. The ADS simulation tool and implemented filter measurements are performed to verify the adapted modified method and show very good results to our filter specifications. The method can be used to design any other bandpass filter.


title: Loss Max-Pooling for Semantic Image Segmentation

abstract: We introduce a novel loss max-pooling concept for handling imbalanced training data distributions, applicable as alternative loss layer in the context of deep neural networks for semantic image segmentation. Most real-world semantic segmentation datasets exhibit long tail distributions with few object categories comprising the majority of data and consequently biasing the classifiers towards them. Our method adaptively re-weights the contributions of each pixel based on their observed losses, targeting under-performing classification results as often encountered for under-represented object classes. Our approach goes beyond conventional cost-sensitive learning attempts through adaptive considerations that allow us to indirectly address both, inter- and intra-class imbalances. We provide a theoretical justification of our approach, complementary to experimental analyses on benchmark datasets. In our experiments on the Cityscapes and Pascal VOC 2012 segmentation datasets we find consistently improved results, demonstrating the efficacy of our approach.


title: Machine Learning and Deep Learning Algorithms for Bearing Fault Diagnostics -- A Comprehensive Review

abstract: In this survey paper, we systematically summarize existing literature on bearing fault diagnostics with deep learning (DL) algorithms. While conventional machine learning (ML) methods, including artificial neural network, principal component analysis, support vector machines, etc., have been successfully applied to the detection and categorization of bearing faults for decades, recent developments in DL algorithms in the last five years have sparked renewed interest in both industry and academia for intelligent machine health monitoring. In this paper, we first provide a brief review of conventional ML methods, before taking a deep dive into the state-of-the-art DL algorithms for bearing fault applications. Specifically, the superiority of DL based methods are analyzed in terms of fault feature extraction and classification performances; many new functionalities enabled by DL techniques are also summarized. In addition, to obtain a more intuitive insight, a comparative study is conducted on the classification accuracy of different algorithms utilizing the open source Case Western Reserve University (CWRU) bearing dataset. Finally, to facilitate the transition on applying various DL algorithms to bearing fault diagnostics, detailed recommendations and suggestions are provided for specific application conditions. Future research directions to further enhance the performance of DL algorithms on health monitoring are also discussed.


title: Emotion Recognition From EEG Using Higher Order Crossings

abstract: Electroencephalogram (EEG)-based emotion recognition is a relatively new field in the affective computing area with challenging issues regarding the induction of the emotional states and the extraction of the features in order to achieve optimum classification performance. In this paper, a novel emotion evocation and EEG-based feature extraction technique is presented. In particular, the mirror neuron system concept was adapted to efficiently foster emotion induction by the process of imitation. In addition, higher order crossings (HOC) analysis was employed for the feature extraction scheme and a robust classification method, namely HOC-emotion classifier (HOC-EC), was implemented testing four different classifiers [quadratic discriminant analysis (QDA), k-nearest neighbor, Mahalanobis distance, and support vector machines (SVMs)], in order to accomplish efficient emotion recognition. Through a series of facial expression image projection, EEG data have been collected by 16 healthy subjects using only 3 EEG channels, namely Fp1, Fp2, and a bipolar channel of F3 and F4 positions according to 10-20 system. Two scenarios were examined using EEG data from a single-channel and from combined-channels, respectively. Compared with other feature extraction methods, HOC-EC appears to outperform them, achieving a 62.3% (using QDA) and 83.33% (using SVM) classification accuracy for the single-channel and combined-channel cases, respectively, differentiating among the six basic emotions, i.e., happiness , surprise, anger, fear, disgust, and sadness. As the emotion class-set reduces its dimension, the HOC-EC converges toward maximum classification rate (100% for five or less emotions), justifying the efficiency of the proposed approach. This could facilitate the integration of HOC-EC in human machine interfaces, such as pervasive healthcare systems, enhancing their affective character and providing information about the user's emotional status (e.g., identifying user's emotion experiences, recurring affective states, time-dependent emotional trends).


title: Development of game testing method for measuring game quality

abstract: The quality of game is one of the factors that may stop the release of a game. Game testing can be used to measure the game quality. Unfortunately, most game testing use user-centric approach to test the quality of the game. In this paper, we propose a game testing method, which is developed from the existing game testing approach supplemented with software testing approach to measure the game quality. The game quality was derived from software engineering quality model standards and game quality model. The proposed game testing method is applied to several case studies. The case studies have demonstrated how to assess the games in order to obtain the meaningful results. The research found that the game quality results can be used by game developers to improve the game.


title: A new optimal adaptive under frequency load shedding Using Artificial Neural Networks

abstract: Different short circuits, load growth, generation shortage, and other faults which disturb the voltage and frequency stability are serious threats to the system security. The frequency and voltage instability causes dispersal of a power system into sub-systems, and leads to blackout as well as heavy damages of the system equipment. This paper presents a fast and optimal adaptive load shedding method, for isolated power system using Artificial Neural Networks (ANN). The proposed method is able to determine the necessary load shedding in all steps simultaneously and is much faster than conventional methods. This method has been tested on the New-England power system. The simulation results show that the proposed algorithm is fast, robust and optimal values of load shedding in different loading scenarios are obtained in comparison with conventional method.


An End-to-End Deep Learning Architecture for Graph Classification


title: A General Power Allocation Scheme to Guarantee Quality of Service in Downlink and Uplink NOMA Systems

abstract: In this paper, a novel dynamic power allocation scheme is proposed to downlink and uplink non-orthogonal multiple access (NOMA) scenarios with two users for more flexibly meeting various quality of service requirements. The exact expressions for the outage probability and the average rate achieved by the proposed scheme, as well as their high signal-to-noise ratio approximations, are established. Compared with the existing works, such as NOMA with fixed power allocation and cognitive radio inspired NOMA, the proposed scheme can: 1) strictly guarantee a performance gain over conventional orthogonal multiple access; and 2) offer more flexibility to realize different tradeoffs between the user fairness and system throughput. Monte Carlo simulation results are provided to demonstrate the accuracy of the developed analytical results and the performance gain of the proposed power allocation scheme.


title: Cascaded Segmentation-Detection Networks for Word-Level Text Spotting

abstract: We introduce an algorithm for word-level text spotting that is able to accurately and reliably determine the bounding regions of individual words of text "in the wild". Our system is formed by the cascade of two convolutional neural networks. The first network is fully convolutional and is in charge of detecting areas containing text. This results in a very reliable but possibly inaccurate segmentation of the input image. The second network (inspired by the popular YOLO architecture) analyzes each segment produced in the first stage, and predicts oriented rectangular regions containing individual words. No post-processing (e.g. text line grouping) is necessary. With execution time of 450 ms for a 1000-by-560 image on a Titan X GPU, our system achieves the highest score to date among published algorithms on the ICDAR 2015 Incidental Scene Text dataset benchmark.


title: Service Quality Influence on Patient Loyalty: Customer Relationship Management as Mediation Variable (Study on Private Hospital Industry in Denpasar)

abstract: Private Hospital Industry has currently been advanced and prolific rapidly along with the improvement of technology and medical science. Its circumstance prompts a strong effort to achieve and satisfy consumers in order that they become loyal ones. The study aimed to identify and examine (1) service quality influence on patient loyalty; and (2) service quality influence on patient loyalty mediated by CRM. The population was patients of Private Hospitals accredited by minimum five standards, and had BOR (Bed Occupancy Rate) score of minimum 50% in Denpasar. Collecting sample of study used two levels of judgment sampling. The first level was purposive sampling to determine the means of choosing sample. The amount of sample was 100 respondents analyzed by Generalized Structured Component Analysis (GSCA). The results showed service quality positively and significantly influenced customer loyalty; Implementation of Customer Relationship Management (CRM) mediated service quality influence on customer loyalty. When customers of Private Hospital in Denpasar felt the service quality met their expectation or more, they would be satisfied and loyal. Theoretically, the study is able to enhance and complement the realm of management science particularly concepts of CRM service, Customer Loyalty, and to be beneficial for academics and practitioners.


title: A Systematic Review of Factors That Influence Youths Career Choices—the Role of Culture

abstract: Good career planning leads to life fulfillment; however cultural heritage can conflict with youths' personal interests. This systematic review examined existing literature on factors that influence youths' career choices in both collectivist and individualistic cultural settings from around the globe with the aim of identifying knowledge gaps and providing direction for future research. ::: ::: A systematic review strategy using the Joana Briggs Institute's format was conducted. The ERIC, PsychInfo, Scopus and Informit Platform databases were searched for articles published between January 1997 and May 2018. ::: ::: A total of 30 articles were included in the review, findings revealed that youth from collectivist cultures were mainly influenced by family expectations, whereby higher career congruence with parents increased career confidence and self-efficacy. Personal interest was highlighted as the major factor that influenced career choice in individualistic settings, and the youth were more independent in their career decision making. Bicultural youth who were more acculturated to their host countries were more intrinsically motivated in their career decision making. ::: ::: Further research is imperative to guide the understanding of parental influence and diversity, particularly for bicultural youths' career prospects and their ability to use the resources available in their new environments to attain meaningful future career goals.


title: Physics-based ball tracking and 3D trajectory reconstruction with applications to shooting location estimation in basketball video

abstract: The demand for computer-assisted game study in sports is growing dramatically. This paper presents a practical video analysis system to facilitate semantic content understanding. A physics-based algorithm is designed for ball tracking and 3D trajectory reconstruction in basketball videos and shooting location statistics can be obtained. The 2D-to-3D inference is intrinsically a challenging problem due to the loss of 3D information in projection to 2D frames. One significant contribution of the proposed system lies in the integrated scheme incorporating domain knowledge and physical characteristics of ball motion into object tracking to overcome the problem of 2D-to-3D inference. With the 2D trajectory extracted and the camera parameters calibrated, physical characteristics of ball motion are involved to reconstruct the 3D trajectories and estimate the shooting locations. Our experiments on broadcast basketball videos show promising results. We believe the proposed system will greatly assist intelligence collection and statistics analysis in basketball games.