title: A Graphene-Based Resistive Pressure Sensor with Record-High Sensitivity in a Wide Pressure Range

abstract: Pressure sensors are a key component in electronic skin (e-skin) sensing systems. Most reported resistive pressure sensors have a high sensitivity at low pressures ( 5 kPa), which is inadequate for practical applications. For example, actions like a gentle touch and object manipulation have pressures below 10 kPa, and 10–100 kPa, respectively. Maintaining a high sensitivity in a wide pressure range is in great demand. Here, a flexible, wide range and ultra-sensitive resistive pressure sensor with a foam-like structure based on laser-scribed graphene (LSG) is demonstrated. Benefitting from the large spacing between graphene layers and the unique v-shaped microstructure of the LSG, the sensitivity of the pressure sensor is as high as 0.96 kPa−1 in a wide pressure range (0 ~ 50 kPa). Considering both sensitivity and pressure sensing range, the pressure sensor developed in this work is the best among all reported pressure sensors to date. A model of the LSG pressure sensor is also established, which agrees well with the experimental results. This work indicates that laser scribed flexible graphene pressure sensors could be widely used for artificial e-skin, medical-sensing, bio-sensing and many other areas.


title: A Novel Feature Hashing With Efficient Collision Resolution for Bag-of-Words Representation of Text Data

abstract: Text Mining is widely used in many areas transforming unstructured text data from all sources such as patients' record, social media network, insurance data, and news, among others into an invaluable source of information. The Bag Of Words (BoW) representation is a means of extracting features from text data for use in modeling. In text classification, a word in a document is assigned a weight according to its frequency and frequency between different documents; therefore, words together with their weights form the BoW. One way to solve the issue of voluminous data is to use the feature hashing method or hashing trick. However, collision is inevitable and might change the result of the whole process of feature generation and selection. Using the vector data structure, the lookup performance is improved while resolving collision and the memory usage is also efficient.


title: Electromagnetic compatibility tests for multirotor UAS

abstract: The diffusion of multirotors in different fields of application comes together with a variety of critical issues. For instance, they often have to operate close to high electric and magnetic fields, like in power lines areas. In order to ensure sufficient reliability in this kind of missions, a small quadcopter has been tested by performing electromagnetic compatibility tests. The experiment was carried out inside a laboratory where a voltage up to 200 kV could be generated. Tests consist of two phases: in the first one the quadcopter was fastened to a table to prevent its premature loss or damages. In the second phase the aircraft could soar, while tethered by a cable to the ground. Results were satisfactory also for high tensions and proved that the multirotor can operate close to power line plants.


title: The effects of monetary and social rewards on task performance in children and adolescents: Liking is not enough

abstract: The current study compared the effects of reward anticipation on task performance in children and adolescents (8–16 years old) using monetary and various social rewards. Eighty-five typically developing children undertook the Monetary Incentive Delay (MID) task. Of these 44 also undertook the Social Incentive Delay (SID-basic) task where social reward was operationalized as a smiling face and spoken compliments. Forty-one children participated in the SID-plus where points were added to a pictogram with written compliments. In a preparatory validation study participants were asked howmuch they liked the SID-basic rewards.Results showed that there was an effect of reward size on accuracy and RT in both the MID task and SID-plus, but not SID-basic. Subjective value of the SID-basic rewards was rated higher with hypothesized increasing reward intensity. In conclusion, although the social rewards in SID-basic were liked by children andadolescents in the validation study, they had no effect on the behaviour. Only when points were added (SID-plus), anticipated social reward affected task performance. Thus our results highlight (i) the difference between likeability andreinforcing quality and (ii) the need for a quantifiable element to rewards for themto be reinforcing for children. Copyright © 2012 John Wiley & Sons, Ltd.


title: An investigation of current models of second language speech perception: The case of Japanese adults’ perception of English consonants

abstract: This study reports the results of two experiments with native speakers of Japanese. In experiment 1, near-monolingual Japanese listeners participated in a cross-language mapping experiment in which they identified English and Japanese consonants in terms of a Japanese category, then rated the identifications for goodness-of-fit to that Japanese category. Experiment 2 used the same set of stimuli in a categorial discrimination test. Three groups of Japanese speakers varying in English-language experience, and one group of native English speakers participated. Contrast pairs composed of two English consonants, two Japanese consonants, and one English and one Japanese consonant were tested. The results indicated that the perceived phonetic distance of second language (L2) consonants from the closest first language (L1) consonant predicted the discrimination of L2 sounds. In addition, this study investigated the role of experience in learning sounds in a second language. Some of the consonant contrasts tested showed evidence of learning (i.e., significantly higher scores for the experienced than the relatively inexperienced Japanese groups). The perceived phonetic distance of L1 and L2 sounds was found to predict learning effects in discrimination of L1 and L2 sounds in some cases. The results are discussed in terms of models of cross-language speech perception and L2 phonetic learning.


A Virtual World Installation in an Art Exhibition: Providing a Shared Interaction Space for Local and Remote Visitors


title: CayleyNets: Graph Convolutional Neural Networks With Complex Rational Spectral Filters

abstract: The rise of graph-structured data such as social networks, regulatory networks, citation graphs, and functional brain networks, in combination with resounding success of deep learning in various applications, has brought the interest in generalizing deep learning models to non-Euclidean domains. In this paper, we introduce a new spectral domain convolutional architecture for deep learning on graphs. The core ingredient of our model is a new class of parametric rational complex functions (Cayley polynomials) allowing to efficiently compute spectral filters on graphs that specialize on frequency bands of interest. Our model generates rich spectral filters that are localized in space, scales linearly with the size of the input data for sparsely-connected graphs, and can handle different constructions of Laplacian operators. Extensive experimental results show the superior performance of our approach, in comparison to other spectral domain convolutional architectures, on spectral image classification, community detection, vertex classification and matrix completion tasks.


title: Database Security - Attacks and Control Methods

abstract: In today's world, data is generated at a very rapid speed and final destination of such data is database. Data is stored in database for easy and efficient way to manage these data. All the operations of data manipulation and maintenance are done using Database Management System. Considering the importance of data in organization, it is absolutely essential to secure the data present in the database. A secure database is the one which is reciprocated from different possible database attacks. Security models are required to develop for databases. These models are different in many aspects as they are dealing with different issues of the database security. They may different also because of they are taking different assumptions about what constitutes a secure database. So, it becomes very difficult for database security seekers to select appropriate model for securing their database. In this paper, we have discussed some of the attacks that can be possible with its counter measures and its control methods that can be possible. Securing database is important approach for the planning of explicit and directive based database security requirements. Ensuring security for database is very critical issues for the companies. As complexity of database increases, we may tend to have more complex security issues of database.


title: Social and Parasocial Relationships on Social Network Sites and Their Differential Relationships with Users' Psychological Well-Being

abstract: Abstract With the advent of social network sites (SNSs), people can efficiently maintain preexisting social relationships and make online friendships without offline encounters. While such technological features of SNSs hold a variety of potential for individual and collective benefits, some scholars warn that use of SNSs might lead to socially negative consequences, such as social isolation, erosion of social cohesion, or SNS addiction. This study distinguishes types of SNS relationships, and investigates their relationships with social isolation, interpersonal trust, and SNS addiction. We classify SNS relationships into two types: (a) social relationships based on reciprocity between a user and his/her friends, and (b) parasocial relationships in which an ordinary user is aware of activities of a celebrity (e.g., famous actors, athletes, and others) but not vice versa. Based on achievements in studies of media effect and social psychology, we constructed a set of hypotheses, and tested them using a subs...


title: A low-cost GPS/inertial attitude heading reference system (AHRS) for general aviation applications

abstract: An inexpensive Attitude Heading Reference System (AHRS) for general aviation applications is developed by fusing low cost ($20-$1000) automotive grade inertial sensors with GPS. The inertial sensor suit consists of three orthogonally mounted solid state rate gyros. GPS is used for attitude determination in a triple antenna ultra short baseline configuration. A complementary filter is used to combine the information from the inertial sensors with the attitude information derived from GPS. The inertial sensors provide attitude information at a sufficiently high bandwidth to drive an inexpensive glass-cockpit type display for pilot-in-the-loop control. The low bandwidth GPS attitude is used to calibrate the rate gyro biases on-line. Data collected during laboratory testing is used to construct error models for the inertial sensors. Analysis based on these models shows that the system can coast through momentary GPS outages lasting 2 minutes with attitude errors less than 6 degrees. Actual performance observed during ground and flight tests with GPS off was found to be substantially better than that predicted by manufacturer supplied specification sheets. Based on this, it is concluded that off-line calibration combined with GPS based in-flight calibration can dramatically improve the performance of inexpensive automotive grade inertial sensors. Data collected from flight tests indicate that some of the automotive grade inertial sensors (180 deg/hr) can perform near the low end of tactical grade (10 deg/hr) sensors for short periods of time after being calibrated on-line by GPS.


Who ’ s Watching ? De-anonymization of Netflix Reviews using Amazon Reviews


title: Learning Hierarchical Item Categories from Implicit Feedback Data for Efficient Recommendations and Browsing

abstract: Searching, browsing, and recommendations are common ways in which the"choice overload"faced by users in the online marketplace can be mitigated. In this paper we propose the use of hierarchical item categories, obtained from implicit feedback data, to enable efficient browsing and recommendations. We present a method of creating hierarchical item categories from implicit feedback data only i.e., without any other information on the items like name, genre etc. Categories created in this fashion are based on users' co-consumption of items. Thus, they can be more useful for users in finding interesting and relevant items while they are browsing through the hierarchy. We also show that this item hierarchy can be useful in making category based recommendations, which makes the recommendations more explainable and increases the diversity of the recommendations without compromising much on the accuracy. Item hierarchy can also be useful in the creation of an automatic item taxonomy skeleton by bypassing manual labeling and annotation. This can especially be useful for small vendors. Our data-driven hierarchical categories are based on hierarchical latent tree analysis (HLTA) which has been previously used for text analysis. We present a scaled up learning algorithm \emph{HLTA-Forest} so that HLTA can be applied to implicit feedback data.


title: Injection of botulinum A toxin into the gastrocnemius muscle of patients with cerebral palsy: a 3-dimensional motion analysis study

abstract: Abstract Botulinum A toxin (BOTOX ® ) was injected into the gastrocnemius muscle of 26 cerebral palsy subjects with equinus gait. All subjects were equinus walkers without fixed contracture of the triceps-surae muscle. Injections were performed at 3 month intervals, if needed, as determined by the treating clinician. There were 14 subjects with spastic hemiplegia, 11 subjects with spastic diplegia and 1 subject with spastic quadriplegia. In the case of those subjects with bilateral equinus gait the dose was divided and given into both the right and left gastrocnemius muscle. Gait analysis data was collected prior to the first injection and subsequently at 3 month intervals for 1 year. Kinematic and electromyographic data was obtained. This data was analyzed to provide objective information about the outcome of treatment. Four subjects moved away and were lost to follow-up. Seven subjects left the study to have surgery. The data collected revealed statistically significant improvements in dynamic ankle dorsiflexion in both stance and swing phases, stride length, and electromyography of the tibialis anterior. There were no complications. While the results of this study are promising, additional prospective studies are needed to determine the feasibility of preventing muscle contractures over a longer time period. Furthermore, there is a need for inclusion of other muscles in future research. Future research should also compare BOTOX ® treatment with alternative methods of dealing with muscle spasticity such as: casting, orthotic devices, physical therapy, selective dorsal rhizotomy, and surgical lengthening.


title: Aspect based Sentiment Analysis in Hindi: Resource Creation and Evaluation.

abstract: Due to the phenomenal growth of online product reviews, sentiment analysis (SA) has gained huge attention, for example, by online service providers. A number of benchmark datasets for a wide range of domains have been made available for sentiment analysis, especially in resource-rich languages. In this paper we assess the challenges of SA in Hindi by providing a benchmark setup, where we create an annotated dataset of high quality, build machine learning models for sentiment analysis in order to show the effective usage of the dataset, and finally make the resource available to the community for further advancement of research. The dataset comprises of Hindi product reviews crawled from various online sources. Each sentence of the review is annotated with aspect term and its associated sentiment. As classification algorithms we use Conditional Random Filed (CRF) and Support Vector Machine (SVM) for aspect term extraction and sentiment analysis, respectively. Evaluation results show the average F-measure of 41.07% for aspect term extraction and accuracy of 54.05% for sentiment classification.


ANALYTICS ON BIG FAST DATA USING REAL TIME STREAM DATA PROCESSING ARCHITECTURE


title: A Probabilistic Theory of Deep Learning

abstract: A grand challenge in machine learning is the development of computational algorithms that match or outperform humans in perceptual inference tasks that are complicated by nuisance variation. For instance, visual object recognition involves the unknown object position, orientation, and scale in object recognition while speech recognition involves the unknown voice pronunciation, pitch, and speed. Recently, a new breed of deep learning algorithms have emerged for high-nuisance inference tasks that routinely yield pattern recognition systems with near- or super-human capabilities. But a fundamental question remains: Why do they work? Intuitions abound, but a coherent framework for understanding, analyzing, and synthesizing deep learning architectures has remained elusive. We answer this question by developing a new probabilistic framework for deep learning based on the Deep Rendering Model: a generative probabilistic model that explicitly captures latent nuisance variation. By relaxing the generative model to a discriminative one, we can recover two of the current leading deep learning systems, deep convolutional neural networks and random decision forests, providing insights into their successes and shortcomings, as well as a principled route to their improvement.


title: Organizational perspective of CRM implementation factors in hospital

abstract: Customer Relationship Management (CRM) projects implementation have been popular in the organizations and during three consecutive years, CRM related issues were in the top nine lists most concern for the chief executive officers in the USA because failure rate of IT project implementation is quite high. In reality, there have been many failed CRM projects. According to previous literatures, failure rate is between 30% and 70%. Although many investigations had been done on Critical Success Factors (CSF) and were produced long list of CSFs and Critical failure factors (CFF), but guidance in this era is very little and need more study. Specifically, more powerful theoretical models of CRM system implementation are needed for managers to get better understanding of the success factors. To this end, current article focused on organizational CRM implementation success factors in hospital and proposed a model to clarify the potential risk by reviewing and introducing effective success factors for implementing CRM. This paper zoomed on the organizational factors and software and patient factors are considered for further work. Practical investigations are needed to validate proposed model.


title: Heart rate and oxygen demand of powered exoskeleton-assisted walking in persons with paraplegia.

abstract: UNLABELLED ::: Historically, persons with paralysis have limited options for overground ambulation. Recently, powered exoskeletons have become available, which are systems that translate the user's body movements to activate motors to move the lower limbs through a predetermined gait pattern. As part of an ongoing clinical study (NCT01454570), eight nonambulatory persons with paraplegia were trained to ambulate with a powered exoskeleton. Measurements of oxygen uptake (VO2) and heart rate (HR) were recorded for 6 min each during each maneuver while sitting, standing, and walking. The average value of VO2 during walking (11.2 +/- 1.7 mL/kg/min) was significantly higher than those for sitting and standing (3.5 +/- 0.4 and 4.3 +/- 0.9 mL/kg/min, respectively; p < 0.001). The HR response during walking was significantly greater than that of either sitting or standing (118 +/- 21vs 70 +/- 10 and 81 +/- 12 beats per minute, respectively: p < 0.001). Persons with paraplegia were able to ambulate efficiently using the powered exoskeleton for overground ambulation, providing potential for functional gain and improved fitness. ::: ::: ::: CLINICAL TRIAL REGISTRATION ::: ClinicalTrials.gov; NCT01454570; "The ReWalk Exoskeletal Walking System for Persons with Paraplegia (VA_ReWalk)"; https://clinicaltrials.gov/ct2/show/NCT01454570.


title: Waveform Equations, Output Power, and Power Conversion Efficiency for Class-E Inverter Outside Nominal Operation

abstract: This paper presents analytical expressions for steady-state waveforms, output power, and power conversion efficiency for the class-E inverter outside the class-E zero-voltage switching and zero-derivative switching conditions. The analytical expressions in this paper include the MOSFET-body-diode effect. By carrying out PSpice simulations and circuit experiments, it is shown that the analytical predictions agree with the simulated and experimental results quantitatively, which indicates the validity of the analytical expressions. Additionally, the switching-pattern distribution maps are also given by using the analytical waveform equations.


title: Pressure characteristics at the stump/socket interface in transtibial amputees using an adaptive prosthetic foot.

abstract: BACKGROUND ::: The technological advances that have been made in developing highly functional prostheses are promising for very active patients but we do not yet know whether they cause an increase in biomechanical load along with possibly negative consequences for pressure conditions in the socket. Therefore, this study monitored the socket pressure at specific locations of the stump when using a microprocessor-controlled adaptive prosthetic ankle under different walking conditions. ::: ::: ::: METHODS ::: Twelve unilateral transtibial amputees between 43 and 59 years of age were provided with the Proprio-Foot (Ossur) and underwent an instrumented 3D gait analysis in level, stair, and incline walking, including synchronous data capturing of socket pressure. Peak pressures and pressure time integrals (PTI) at three different locations were compared for five walking conditions with and without using the device's ankle adaptation mode. ::: ::: ::: FINDINGS ::: Highest peak pressures of 2.4 k Pa/kg were found for incline ascent at the calf muscle as compared to 2.1 k Pa/kg in level walking with large inter-individual variance. In stair ascent a strong correlation was found between maximum knee moment and socket pressure. The most significant pressure changes relative to level walking were seen in ramp descent anteriorly towards the stump end, with PTI values being almost twice as high as those in level walking. Adapting the angle of the prosthesis on stairs and ramps modified the pressure data such that they were closer to those in level walking. ::: ::: ::: INTERPRETATION ::: Pressure at the stump depends on the knee moments involved in each walking condition. Adapting the prosthetic ankle angle is a valuable means of modifying joint kinetics and thereby the pressure distribution at the stump. However, large inter-individual differences in local pressures underline the importance of individual socket fitting.


title: PERSISTENT JOINT LAXITY AND CONGENITAL DISLOCATION OF THE HIP

abstract: 1. General joint laxity affecting more than three joints was found in 7 per cent of normal schoolchildren. Similar laxity was found in fourteen of a random series of forty-eight girls, and in nineteen of twenty-six boys, with non-familial congenital dislocation of the hip. Such laxity was also found in four of seven girls and five of seven boys with familial (first degree relative affected) congenital dislocation of the hip. 2. It is concluded that persistent generalised joint laxity, which is often familial, is an important predisposing factor to congenital dislocation of the hip in boys. It is less important in girls, except perhaps in familial cases, as in girls there is an alternative temporary hormonal cause of joint laxity.


title: Modeling TCAM power for next generation network devices

abstract: Applications in computer networks often require high throughput access to large data structures for lookup and classification. Many advanced algorithms exist to speed these search primitives on network processors, general purpose machines, and even custom ASICs. However, supporting these applications with standard memories requires very careful analysis of access patterns, and achieving worst case performance can be quite difficult and complex. A simple solution is often possible if a Ternary CAM (content addressable memory) is used to perform a fully parallel search across the entire data set. Unfortunately, this parallelism means that large portions of the chip are switching during each cycle, causing large amounts of power to be consumed. While researchers have begun to explore new ways of managing the power consumption, quantifying design alternatives is difficult due to a lack of available models. In this paper, we examine the structure inside a modern TCAM and present a simple, yet accurate, power model. We present techniques to estimate the dynamic power consumption of a large TCAM. We validate the model using industrial TCAM datasheets and prior published works. We present an extensive analysis of the model by varying various architectural parameters. We also describe how new network algorithms have the potential to address the growing problem of power management in next-generation network devices.


title: Rethinking the Value of Network Pruning

abstract: Network pruning is widely used for reducing the heavy inference cost of deep models in low-resource settings. A typical pruning algorithm is a three-stage pipeline, i.e., training (a large model), pruning and fine-tuning. During pruning, according to a certain criterion, redundant weights are pruned and important weights are kept to best preserve the accuracy. In this work, we make several surprising observations which contradict common beliefs. For all state-of-the-art structured pruning algorithms we examined, fine-tuning a pruned model only gives comparable or worse performance than training that model with randomly initialized weights. For pruning algorithms which assume a predefined target network architecture, one can get rid of the full pipeline and directly train the target network from scratch. Our observations are consistent for multiple network architectures, datasets, and tasks, which imply that: 1) training a large, over-parameterized model is often not necessary to obtain an efficient final model, 2) learned "important" weights of the large model are typically not useful for the small pruned model, 3) the pruned architecture itself, rather than a set of inherited "important" weights, is more crucial to the efficiency in the final model, which suggests that in some cases pruning can be useful as an architecture search paradigm. Our results suggest the need for more careful baseline evaluations in future research on structured pruning methods. We also compare with the "Lottery Ticket Hypothesis" (Frankle & Carbin 2019), and find that with optimal learning rate, the "winning ticket" initialization as used in Frankle & Carbin (2019) does not bring improvement over random initialization.


Healthy Menus Recommendation: Optimizing the Use of the Pantry


title: First Impressions From Faces

abstract: Although cultural wisdom warns us not to judge a book by its cover, we seem unable to inhibit this tendency even though it can lead to inaccurate impressions of people’s psychological traits and has significant social consequences. One explanation for this paradox is that first impressions from faces reflect overgeneralizations of adaptive impressions of categories of people with structurally similar faces (including babies, familiar or unfamiliar people, evolutionarily unfit people, and people expressing a variety of emotions). Research testing these overgeneralization hypotheses has elucidated why we form first impressions from faces, what impressions we form, and what cues influence these impressions. This article focuses on commonalities in impressions across diverse perceivers, with additional brief attention given to individual differences in impressions and impression accuracy.


title: Few-Shot Image Recognition by Predicting Parameters from Activations

abstract: In this paper, we are interested in the few-shot learning problem. In particular, we focus on a challenging scenario where the number of categories is large and the number of examples per novel category is very limited, e.g. 1, 2, or 3. Motivated by the close relationship between the parameters and the activations in a neural network associated with the same category, we propose a novel method that can adapt a pre-trained neural network to novel categories by directly predicting the parameters from the activations. Zero training is required in adaptation to novel categories, and fast inference is realized by a single forward pass. We evaluate our method by doing few-shot image recognition on the ImageNet dataset, which achieves the state-of-the-art classification accuracy on novel categories by a significant margin while keeping comparable performance on the large-scale categories. We also test our method on the MiniImageNet dataset and it strongly outperforms the previous state-of-the-art methods.


title: Cooperative formation control of quadrotors with obstacle avoidance and self collisions based on a hierarchical MPC approach

abstract: This paper presents a hierarchical model predictive control (MPC) approach for cooperative formation flight control and stabilization of a team of quadrotors under constraints. The proposed control structure is a hierarchical scheme with two layers. At the top layer, a linear MPC controller is developed to generate the optimized reference trajectory to be used in the lower-layer with an MPC attitude and altitude controllers. The MPC controller units in the lower-layer are considered separately for each quadrotor. At the top-layer, data transfer between the quadrotors is implemented cooperatively. Three cost functions are considered for keeping the quadrotors in a desired formation while avoiding obstacles and inner-vehicle collisions. The simulation results show that the overall hierarchical MPC approach effectively tracks the quadrotor reference trajectory while avoiding self and obstacle collisions.


title: Using Text Mining Techniques for Classical Music Scores Analysis

abstract: Music Classification is a particular area of Computational Musicology that provides valuable insights about the evolving of composition patterns and assists in catalogue generation. The proposed work detaches from former works by classifying music based on music score information. Text Mining techniques support music score processing while Classification techniques are used in the construction of decision models. Although research is still at its earliest beginnings, the work already provides valuable contributes to symbolic music representation processing and subsequent analysis. Score processing involved the counting of ascending and descending chromatic intervals, note duration and metainformation tagging. Analysis involved feature selection and the evaluation of several data mining algorithms, ensuring extensibility towards larger repositories or more complex problems. Experiments report the analysis of composition epochs on a subset of the Mutopia project open archive of classical LilyPond-annotated music scores.


title: The Discriminative Validity of “Nociceptive,” “Peripheral Neuropathic,” and “Central Sensitization” as Mechanisms-based Classifications of Musculoskeletal Pain

abstract: ObjectivesEmpirical evidence of discriminative validity is required to justify the use of mechanisms-based classifications of musculoskeletal pain in clinical practice. The purpose of this study was to evaluate the discriminative validity of mechanisms-based classifications of pain by identifying di


title: From grid cells to place cells : a mathematical model.

abstract: Anatomical connectivity and recent neurophysiological results imply that grid cells in the medial entorhinal cortex are the principal cortical inputs to place cells in the hippocampus. The authors propose a model in which place fields of hippocampal pyramidal cells are formed by linear summation of appropriately weighted inputs from entorhinal grid cells. Single confined place fields could be formed by summing input from a modest number (10–50) of grid cells with relatively similar grid phases, diverse grid orientations, and a biologically plausible range of grid spacings. When the spatial phase variation in the grid-cell input was higher, multiple, and irregularly spaced firing fields were formed. These observations point to a number of possible constraints in the organization of functional connections between grid cells and place cells. © 2006 Wiley-Liss, Inc.


title: An intra-amygdala circuit specifically regulates social fear learning

abstract: Effective social behavior requires comprehension of social cues and use of those cues to guide behavior. This study uncovers an amygdala circuit that is necessary for socially driven valuation of environmental cues. The strength of this circuit correlates with social learning, and augmentation of this circuit enhances abnormal social learning.


title: Understanding social media marketing: a case study on topics, categories and sentiment on a Facebook brand page

abstract: Social networks have changed the way information is delivered to the customers, shifting from traditional one-to-many to one-to-one communication. Opinion mining and sentiment analysis offer the possibility to understand the user-generated comments and explain how a certain product or a brand is perceived. Classification of different types of content is the first step towards understanding the conversation on the social media platforms. Our study analyses the content shared on Facebook in terms of topics, categories and shared sentiment for the domain of a sponsored Facebook brand page. Our results indicate that Product, Sales and Brand are the three most discussed topics, while Requests and Suggestions, Expressing Affect and Sharing are the most common intentions for participation. We discuss the implications of our findings for social media marketing and opinion mining.


title: Pure exploration in multi-armed bandits problems

abstract: We consider the framework of stochastic multi-armed bandit problems and study the possibilities and limitations of strategies that perform an online exploration of the arms. The strategies are assessed in terms of their simple regret, a regret notion that captures the fact that exploration is only constrained by the number of available rounds (not necessarily known in advance), in contrast to the case when the cumulative regret is considered and when exploitation needs to be performed at the same time.We believe that this performance criterion is suited to situations when the cost of pulling an arm is expressed in terms of resources rather than rewards. We discuss the links between the simple and the cumulative regret. The main result is that the required exploration-exploitation trade-offs are qualitatively different, in view of a general lower bound on the simple regret in terms of the cumulative regret.


title: CasCADe: A Novel 4D Visualization System for Virtual Construction Planning

abstract: Building Information Modeling (BIM) provides an integrated 3D environment to manage large-scale engineering projects. The Architecture, Engineering and Construction (AEC) industry explores 4D visualizations over these datasets for virtual construction planning. However, existing solutions lack adequate visual mechanisms to inspect the underlying schedule and make inconsistencies readily apparent. The goal of this paper is to apply best practices of information visualization to improve 4D analysis of construction plans. We first present a review of previous work that identifies common use cases and limitations. We then consulted with AEC professionals to specify the main design requirements for such applications. These guided the development of CasCADe, a novel 4D visualization system where task sequencing and spatio-temporal simultaneity are immediately apparent. This unique framework enables the combination of diverse analytical features to create an information-rich analysis environment. We also describe how engineering collaborators used CasCADe to review the real-world construction plans of an Oil & Gas process plant. The system made evident schedule uncertainties, identified work-space conflicts and helped analyze other constructability issues. The results and contributions of this paper suggest new avenues for future research in information visualization for the AEC industry.


title: Development of a Multi-fingered Robot Hand with Softness-changeable Skin Mechanism

abstract: This paper develops a multi-fingered robot hand with skin mechanism enabling softness change. We show how the softness of the skin affects grasping and manipulation. Elastic skin provides stable grasping while precise manipulation is lost. Hard skin provides precise manipulation while stability of grasp is lost. We will try to change the softness of the skin according to situation, object, and so on. In this paper, we develop a novel human like robot hand with softness-changeable skin mechanism.


title: Language and the Newborn Brain: Does Prenatal Language Experience Shape the Neonate Neural Response to Speech?

abstract: Previous research has shown that by the time of birth, the neonate brain responds specially to the native language when compared to acoustically similar non-language stimuli. In the current study, we use Near Infrared Spectroscopy to ask how prenatal language experience might shape the brain response to language in newborn infants. To do so, we examine the neural response of neonates when listening to familiar versus unfamiliar language, as well as to non-linguistic backwards language. Twenty monolingual English-exposed neonates aged 0-3 days were tested. Each infant heard low-pass filtered sentences of forward English (familiar language), forward Tagalog (unfamiliar language), and backwards English and Tagalog. During exposure, neural activation was measured across twelve channels on each hemisphere. Our results indicate a bilateral effect of language familiarity on neonates’ brain response to language. Differential brain activation was seen when neonates listened to forward Tagalog (unfamiliar language) as compared to other types of language stimuli. We interpret these results as evidence that the pre-natal experience with the native language gained in utero influences how the newborn brain responds to language across brain regions sensitive to speech processing.


title: The role of nervus intermedius in side specific nasal responses.

abstract: BACKGROUND ::: Nervus intermedius (NI) dysfunction is common in patients who have had vestibular schwannoma (VS) surgery. Such patients have a unilateral parasympathetic-denervated nasal cavity. A number of side-specific nasal reflexes have been demonstrated in normal individuals, including hand cold-water immersion. It is not understood whether these reflexes have parasympathetic or sympathic efferent pathways. We aimed to evaluate the side specific nasal reflex to cold-water immersion in post-operative VS patients with NI dysfunction, in order to determine the nature of the efferent pathway of these reflexes. ::: ::: ::: METHOD ::: Side specific responses to cold-water immersion were tested by acoustic rhinometry in 10 normal individuals and 18 patients with NI dysfunction (proven by Schirmer s test) after VS surgery. ::: ::: ::: RESULTS ::: A consistent pattern of ipsilateral congestion and contralateral decongestion after the cold-water immersion was seen in normal individuals (p smaller than 0.001). We found no consistent response in VS patients both ipsilateral and contralateral to the side of NI dysfunction. ::: ::: ::: CONCLUSIONS ::: We confirm the consistent side-specific nasal reflexes to cold-water hand immersion in normal individuals. This is disturbed in patients with NI dysfunction. We have also shown unexpectantly that the contralateral side-specific reflex is disturbed in these patients. These data suggest that the reflex is parasympathetic and crosses the midline.


Enhancement of dissolution rate of Meloxicam by co-grinding technique using Hydroxypropyl methylcellulose


title: IVE-GAN: Invariant Encoding Generative Adversarial Networks

abstract: Generative adversarial networks (GANs) are a powerful framework for generative tasks. However, they are difficult to train and tend to miss modes of the true data generation process. Although GANs can learn a rich representation of the covered modes of the data in their latent space, the framework misses an inverse mapping from data to this latent space. We propose Invariant Encoding Generative Adversarial Networks (IVE-GANs), a novel GAN framework that introduces such a mapping for individual samples from the data by utilizing features in the data which are invariant to certain transformations. Since the model maps individual samples to the latent space, it naturally encourages the generator to cover all modes. We demonstrate the effectiveness of our approach in terms of generative performance and learning rich representations on several datasets including common benchmark image generation tasks.


title: A Formal Calculus for International Relations Computation and Evaluation

abstract: This publication presents a relation computation or calculus for international relations using a mathematical modeling. It examined trust for international relations and its calculus, which related to Bayesian inference, Dempster-Shafer theory and subjective logic. Based on an observation in the literature, we found no literature discussing the calculus method for the international relations. To bridge this research gap, we propose a relation algebra method for international relations computation. The proposed method will allow a relation computation which is previously subjective and incomputable. We also present three international relations as case studies to demonstrate the proposed method is a real-world scenario. The method will deliver the relation computation for the international relations that to support decision makers in a government such as foreign ministry, defense ministry, presidential or prime minister office. The Department of Defense (DoD) may use our method to determine a nation that can be identified as a friendly, neutral or hostile nation.


title: Field lines and players detection and recognition in soccer video

abstract: Objects like field lines and players are important for semantic analysis in soccer video. In this paper, we propose effective methods for field lines and players detection and recognition. Regions of field lines and players are first segmented from shot of wide angle view. Gray value top-hat transform is then performed on the segmented region to detect field lines. Mid-lines, end-lines and penalty-lines are then recognized based on their positions. Template matching method is adopted to detect and recognize players. Four types of templates for players are matched on the segmented region. Accurate position of player can be obtained after template matching, and the type of player is also identified. Experiment results on various data-sets with different production styles show effectiveness of our method.


title: Mining (Social) Network Graphs to Detect Random Link Attacks

abstract: Moderncommunication networks arevulnerable toattackers whosendunsolicited messages toinnocent users, wasting networkresources andusertime.Someexamples of suchattacks arespamemails, annoying tele-marketing phone calls, viral marketing insocial networks, etc. Existing techniques toidentify these attacks aretailored tocertain specific domains (like emailspamfiltering), butarenotapplicable toamajority ofothernetworks. We provide a generic abstraction ofsuch attacks, called theRandomLinkAttack (RLA), thatcanbeused todescribe alarge class ofattacks incommunication networks. InanRLA,themalicious usercreates asetoffalse identities and usesthemtocommunicate withalarge, randomsetofinnocent users. Weminethesocial networking graph extracted fromuser interactions inthecommunication network tofindRLAs.Tothe bestofourknowledge, this isthefirst attempt toconceptualize theattack definition, applicable toavariety ofcommunication networks. Inthispaper, we formally define RLA andshowthatthe problemoffinding anRLA isNP-complete. We alsoprovide twoefficient heuristics tominesubgraphs satisfying theRLA property; thefirst (GREEDY) isbasedongreedy set-expansion, andthesecond (TRWALK) onrandomized graphtraversal. Our experiments withareal-life datasetdemonstrate theeffectiveness ofthese algorithms.


title: Age Estimation Based on Complexity-Aware Features

abstract: The research related to age estimation using face images has become increasingly important. We propose an age estimator using two kinds of local features, the gradient features which well describe the local characteristic, and the Gabor wavelets which reflect the multi-scale directional information. The RealAdaBoost algorithm with a complexity penalty term in the feature selection module is applied to choose meaningful regions from human face for feature extraction, while balancing the discriminative capability and the computation cost at the same time. Furthermore, the hierarchical classifier, which is composed of an age group classification (e.g., 15–39 years old, 40–59 years old etc.) and a detailed age estimation (e.g. 19, 53 years old, etc.) are utilized to get the final age. Experimental results show that the proposed approach outperforms the methods using single feature on PAL and FG-NET database. It also achieves competitive accuracy with the state-of-the-art algorithms.


title: Energy-Efficient Routing Protocols in Wireless Sensor Networks: A Survey

abstract: This paper represents energy efficient routing protocols in WSN. It is a collection of sensor nodes with a set of limited Processor and limited memory unit embedded in it. Reliable routing of packets from the sensor node to its base station is the most important task for the networks. The routing protocols applied for the other networks cannot be used here due to its battery powered nodes This paper gives an overview of the different routing strategies used in wireless sensor networks and gives a brief working model of energy efficient routing protocols in WSN. It also shows the comparison of these different routing protocols based on metrics such as mobility support, stability, issues and latency.


title: Learning Invariance From Transformation Sequences

abstract: The visual system can reliably identify objects even when the retinal image is transformed considerably by commonly occurring changes in the environment. A local learning rule is proposed, which allows a network to learn to generalize across such transformations. During the learning phase, the network is exposed to temporal sequences of patterns undergoing the transformation. An application of the algorithm is presented in which the network learns invariance to shift in retinal position. Such a principle may be involved in the development of the characteristic shift invariance property of complex cells in the primary visual cortex, and also in the development of more complicated invariance properties of neurons in higher visual areas.


title: Mobiles, music, and materiality

abstract: Building on recent HCI contributions that assert the materiality of digital information, we examine the material nature of digital media and information technology in the context of mobile music production, reproduction, and reception in rural and semi-urban India. We use ethnographic methods to study the recent adoption and use of mobile technology and discuss our findings in relation to the evolving materiality of music. We also investigate the sociotechnical configurations that emerge as a consequence of this materiality. Thus we contribute to HCI research by showing how the material representations of digital media affect the interactions of humans with technology.


title: Face recognition using PCA and different distance classifiers

abstract: A facial recognition system is a computer application for automatically identifying or verifying a person from a digital image or a video frame from a video source. One of the way is to do this is by comparing selected facial features from the image and a facial database. It is typically used in security systems and can be compared to other biometrics such as fingerprint or eye iris recognition systems. In this paper we focus on 3-D facial recognition system and biometric facial recognition system. We do critics on facial recognition system giving effectiveness and weaknesses. This paper also introduces scope of recognition system in India. Face recognition has received substantial attention from researchers in biometrics, pattern recognition field and computer vision communities. Face recognition can be applied in Security measure at Air ports, Passport verification, Criminals list verification in police department, Visa processing, Verification of Electoral identification and Card Security measure at ATM's. Principal Component Analysis (PCA) is a technique among the most common feature extraction techniques used in Face Recognition. In this paper, a face recognition system for personal identification and verification using Principal Component Analysis with different distance classifiers is proposed. The test results in the ORL face database produces interesting results from the point of view of recognition success, rate, and robustness of the face recognition algorithm. Different classifiers were used to match the image of a person to a class (a subject) obtained from the training data. These classifiers are: the City-Block Distance Classifier, the Euclidian distance classifier, the Squared Euclidian Distance Classifier, and


title: Non-invasive measurement of blood glucose of diabetic based on IR spectroscopy

abstract: This paper describes a Non-invasive measurement of blood glucose of diabetic based on infrared spectroscopy. We measured the spectrum of human finger by using the Fourier transform infrared spectroscopy (FT-IR) of attenuated total reflection (ATR). In this paper, We would like to report the accuracy of the calibration models when we measured the blood glucose of diabetic.


title: Distributed Training Strategies for the Structured Perceptron

abstract: Perceptron training is widely applied in the natural language processing community for learning complex structured models. Like all structured prediction learning frameworks, the structured perceptron can be costly to train as training complexity is proportional to inference, which is frequently non-linear in example sequence length. In this paper we investigate distributed training strategies for the structured perceptron as a means to reduce training times when computing clusters are available. We look at two strategies and provide convergence bounds for a particular mode of distributed structured perceptron training based on iterative parameter mixing (or averaging). We present experiments on two structured prediction problems -- named-entity recognition and dependency parsing -- to highlight the efficiency of this method.


title: Active-source-pump (ASP) technique for ESD design window expansion and ultra-thin gate oxide protection in sub-90nm technologies

abstract: This paper presents a novel active-source-pump (ASP) circuit technique to significantly lower the ESD sensitivity of ultrathin gate inputs in advanced sub-90nm CMOS technologies. As demonstrated by detailed experimental analysis, an ESD design window expansion of more than 100% can be achieved. This revives conventional ESD solutions for ultrasensitive input protection also enabling low-capacitance RF protection schemes with a high ESD design flexibility at IC-level. ASP IC application examples, and the impact of ASP on normal RF operation performance, are discussed.


title: Intellectual, behavioral, and emotional functioning in children with syndromic craniosynostosis.

abstract: OBJECTIVES ::: To examine intellectual, behavioral, and emotional functioning of children who have syndromic craniosynostosis and to explore differences between diagnostic subgroups. ::: ::: ::: METHODS ::: A national sample of children who have syndromic craniosynostosis participated in this study. Intellectual, behavioral, and emotional outcomes were assessed by using standardized measures: Wechsler Intelligence Scale for Children, Third Edition, Child Behavior Checklist (CBCL)/6-18, Disruptive Behavior Disorder rating scale (DBD), and the National Institute of Mental Health Diagnostic Interview Schedule for Children. ::: ::: ::: RESULTS ::: We included 82 children (39 boys) aged 6 to 13 years who have syndromic craniosynostosis. Mean Full-Scale IQ (FSIQ) was in the normal range (M = 96.6; SD = 21.6). However, children who have syndromic craniosynostosis had a 1.9 times higher risk for developing intellectual disability (FSIQ < 85) compared with the normative population (P < .001) and had more behavioral and emotional problems compared with the normative population, including higher scores on the CBCL/6-18, DBD Total Problems (P < .001), Internalizing (P < .01), social problems (P < .001), attention problems (P < .001), and the DBD Inattention (P < .001). Children who have Apert syndrome had lower FSIQs (M = 76.7; SD = 13.3) and children who have Muenke syndrome had more social problems (P < .01), attention problems (P < .05), and inattention problems (P < .01) than normative population and with other diagnostic subgroups. ::: ::: ::: CONCLUSIONS ::: Although children who have syndromic craniosynostosis have FSIQs similar to the normative population, they are at increased risk for developing intellectual disability, internalizing, social, and attention problems. Higher levels of behavioral and emotional problems were related to lower levels of intellectual functioning.


title: The NarrativeQA Reading Comprehension Challenge

abstract: Reading comprehension (RC)---in contrast to information retrieval---requires integrating information and reasoning about events, entities, and their relations across a full document. Question answering is conventionally used to assess RC ability, in both artificial agents and children learning to read. However, existing RC datasets and tasks are dominated by questions that can be solved by selecting answers using superficial information (e.g., local context similarity or global term frequency); they thus fail to test for the essential integrative aspect of RC. To encourage progress on deeper comprehension of language, we present a new dataset and set of tasks in which the reader must answer questions about stories by reading entire books or movie scripts. These tasks are designed so that successfully answering their questions requires understanding the underlying narrative rather than relying on shallow pattern matching or salience. We show that although humans solve the tasks easily, standard RC models struggle on the tasks presented here. We provide an analysis of the dataset and the challenges it presents.


title: Constrained bearings-only target motion analysis via Markov chain Monte Carlo methods

abstract: The aim of this paper is to develop methods for estimating the range of a moving target from bearings-only observations and for weakly observable scenarios, by including general constraints about the target trajectories. Throughout this manuscript, it is assumed that the target motion is conditionally deterministic, which leads us to focus on batch algorithms. Another common assumption is poor observability, which results from another constraint: (very) limited amplitude of the observer maneuvers. Classical batch algorithms are based on iterative methods (such as gradient techniques), but they perform very poorly in this context and including constraints is not easy and not reliable in this way. Instead, we consider simulation-based methods, i.e., Monte Carlo Markov chain (MCMC) sampling, developed in a Bayesian context. In this way, it is possible to take into account any type of constraint, and to drastically improve estimation of weakly observable parameters. As a by-product, an estimate of confidence intervals is obtained. This is the aim of the highest probability density (HPD) intervals. This study is illustrated by simulation results, while the benefits of constraint inclusion are analyzed via geometric methods


title: A cognitive developmental approach to morality: investigating the psychopath

abstract: Various social animal species have been noted to inhibit aggressive attacks when a conspecific displays submission cues. Blair (1993) has suggested that humans possess a functionally similar mechanism which mediates the suppression of aggression in the context of distress cues. He has suggested that this mechanism is a prerequisite for the development of the moral/conventional distinction; the consistently observed distinction in subject's judgments between moral and conventional transgressions. Psychopaths may lack this violence inhibitor. A causal model is developed showing how the lack of this mechanism would explain the core behavioural symptoms associated with the psychopathic disorder. A prediction of such a causal model would be that psychopaths should fail to make the moral/conventional distinction. This prediction was confirmed. The implication of this finding for other theories of morality is discussed.


title: Coupled-slotline-hybrid sampling mixer integrated with step-recovery-diode pulse generator for UWB applications

abstract: A new compact low-cost sampling mixer with internal local strobe-pulse generator for (carrierless) ultra-wideband (UWB) applications, especially for sub-sampling of UWB video pulse signals, is presented along with detailed design information. The sampler employs a double-sided planar structure to facilitate interface with the strobe-pulse generator and the baseband circuit without using wire connections or air bridges, enabling hybrid microwave integrated-circuit integration with significant reduction in assembly effort. A novel coupling structure for both coupling and termination of the RF and local-oscillator signals to the Schottky-diode sampling bridge was designed to suppress ringing in the strobe pulse signal, resulting in harmonic-distortion reduction in the baseband output signal. Development of strobe-pulse generators using step recovery diodes for good output power efficiency is also presented along with design information for relatively low clock frequencies. The developed sampler achieves an unprecedented conversion loss of 4.5-7.5 dB (without a baseband amplifier) and conversion gain from 6.5 to 9.5 dB (with an amplifier) over a 5.5-GHz RF bandwidth, a dynamic range of over 50 dB, and low harmonic distortion in the baseband output. A new intrinsic conversion-loss equation that allows the sampler's conversion loss and operating RF bandwidth to be predicted accurately was also derived.


title: Learning to Learn

abstract: Preface. Part I: Overview Articles. 1. Learning to Learn: Introduction and Overview S. Thrun, L. Pratt. 2. A Survey of Connectionist Network Reuse Through Transfer L. Pratt, B. Jennings. 3. Transfer in Cognition A. Robins. Part II: Prediction. 4. Theoretical Models of Learning to Learn J. Baxter. 5. Multitask Learning R. Caruana. 6. Making a Low-Dimensional Representation Suitable for Diverse Tasks N. Intrator, S. Edelman. 7. The Canonical Distortion Measure for Vector Quantization and Function Approximation J. Baxter. 8. Lifelong Learning Algorithms S. Thrun. Part III: Relatedness. 9. The Parallel Transfer of Task Knowledge Using Dynamic Learning Rates Based on a Measure of Relatedness D.L. Silver, R.E. Mercer. 10. Clustering Learning Tasks and the Selective Cross-Task Transfer of Knowledge S. Thrun, J. O'Sullivan. Part IV: Control. 11. CHILD: A First Step Towards Continual Learning M.B. Ring. 12. Reinforcement Learning with Self-Modifying Policies J. Schmidhuber, et al. 13. Creating Advice-Taking Reinforcement Learners R. Maclin, J.W. Shavlik. Contributing Authors. Index.


title: Balance equilibrium manifold and control of rider-bikebot systems

abstract: Postural balance control of machines is one of the most important human motor skills. We present a balance equilibrium manifold (BEM) concept to study how a human rider balances a bicycle-like robot (i.e., bikebot) while maintaining tracking a desired trajectory. The BEM is built on the external-internal convertible (EIC) structure of the rider-bikebot dynamics to capture the balance properties of the rider-bikebot systems. We analyze the contribution of the upper-body movements and the steering inputs to the task of balance control of the systems. The results reveal that steering actuation is more effective than upper-body movement for balance task. A performance metric is also introduced to quantify the balance motor skills using the BEM. Extensive experiments are conducted to validate and demonstrate the analyses and the proposed balance skill metrics.


Fetus in Fetu: Case Report and Brief Review of Literature on Embryologic Origin, Clinical Presentation, Imaging and Differential Diagnosis


title: Identity-Aware Convolutional Neural Network for Facial Expression Recognition

abstract: Facial expression recognition suffers under realworldconditions, especially on unseen subjects due to highinter-subject variations. To alleviate variations introduced bypersonal attributes and achieve better facial expression recognitionperformance, a novel identity-aware convolutional neuralnetwork (IACNN) is proposed. In particular, a CNN with a newarchitecture is employed as individual streams of a bi-streamidentity-aware network. An expression-sensitive contrastive lossis developed to measure the expression similarity to ensure thefeatures learned by the network are invariant to expressionvariations. More importantly, an identity-sensitive contrastiveloss is proposed to learn identity-related information from identitylabels to achieve identity-invariant expression recognition.Extensive experiments on three public databases including aspontaneous facial expression database have shown that theproposed IACNN achieves promising results in real world.


title: KBFS: K-Best-First Search

abstract: We introduce a new algorithm, K-best-first search (KBFS), which is a generalization of the well known best-first search (BFS). In KBFS, each iteration simultaneously expands the K best nodes from the open-list (rather than just the best as in BFS). We claim that KBFS outperforms BFS in domains where the heuristic function has large errors in estimation of the real distance to the goal state or does not predict dead-ends in the search tree. We present empirical results that confirm this claim and show that KBFS outperforms BFS by a factor of 15 on random trees with dead-ends, and by a factor of 2 and 7 on the Fifteen and Twenty-Four tile puzzles, respectively. KBFS also finds better solutions than BFS and hill-climbing for the number partitioning problem. KBFS is only appropriate for finding approximate solutions with inadmissible heuristic functions.


title: Exploiting user comments for audio-visual content indexing and retrieval

abstract: State-of-the-art content sharing platforms often require users to assign tags to pieces of media in order to make them easily retrievable. Since this task is sometimes perceived as tedious or boring, annotations can be sparse. Commenting on the other hand is a frequently used means of expressing user opinion towards shared media items. This work makes use of time series analyses in order to infer potential tags and indexing terms for audio-visual content from user comments. In this way, we mitigate the vocabulary gap between queries and document descriptors. Additionally, we show how large-scale encyclopaedias such as Wikipedia can aid the task of tag prediction by serving as surrogates for high-coverage natural language vocabulary lists. Our evaluation is conducted on a corpus of several million real-world user comments from the popular video sharing platform YouTube, and demonstrates significant improvements in retrieval performance.


title: Automatic Tooth Segmentation of Dental Mesh Based on Harmonic Fields

abstract: An important preprocess in computer-aided orthodontics is to segment teeth from the dental models accurately, which should involve manual interactions as few as possible. But fully automatic partition of all teeth is not a trivial task, since teeth occur in different shapes and their arrangements vary substantially from one individual to another. The difficulty is exacerbated when severe teeth malocclusion and crowding problems occur, which is a common occurrence in clinical cases. Most published methods in this area either are inaccurate or require lots of manual interactions. Motivated by the state-of-the-art general mesh segmentation methods that adopted the theory of harmonic field to detect partition boundaries, this paper proposes a novel, dental-targeted segmentation framework for dental meshes. With a specially designed weighting scheme and a strategy of a priori knowledge to guide the assignment of harmonic constraints, this method can identify teeth partition boundaries effectively. Extensive experiments and quantitative analysis demonstrate that the proposed method is able to partition high-quality teeth automatically with robustness and efficiency.


title: Circular-buffered architecture for Cellular Neural Networks-based analog Viterbi decoder

abstract: The Cellular Neural Network (CNN) based analog Viterbi decoder with a circular-buffered architecture is proposed for decoding partial response maximum likelihood (PRML) signals. The Viterbi decoder is an error correcting method utilizing the dynamic programming which is an efficient algorithm for finding the optimal path with the identical local computation performed at each node. In the previous CNN-based analog Viterbi decoder, a circularly connected cylindrical structure was presented. In this paper, a multiplexer-based cellular 2D structure is presented in which positions of its decoding and output stages are fixed and a multiplexer which distributes input data sequence to appropriate CNN trellis stages is employed. The proposed CNN-based Viterbi decoder is simpler, requires less silicon area, higher speed and has better performance than the previous one. The principle of the new architecture is uncovered and its decoding performance is compared with that of the previous architecture in this paper.


title: Precisiated natural language (PNL)

abstract: This article is a sequel to an article title "A New Direction in AI--Toward a Computational Theory of Perceptions," which appeared in the Spring 2001 issue of AI Magazine (volume 22, No. 1, 73-84). The concept of precisiated natural language (PNL) was briefly introduced in that article, and PNL was employed as a basis for computation with perceptions. In what follows, the conceptual structure of PNL is described in greater detail, and PNL's role in knowledge representation, deduction, and concept definition is outlined and illustrated by examples. What should be understood is that PNL is in its initial stages of development and that the exposition that follows is an outline of the basic ideas that underlie PNL rather than a definitive theory.


title: Kneser–Ney Smoothing With a Correcting Transformation for Small Data Sets

abstract: We present a technique which improves the Kneser-Ney smoothing algorithm on small data sets for bigrams, and we develop a numerical algorithm which computes the parameters for the heuristic formula with a correction. We give motivation for the formula with correction on a simple example. Using the same example, we show the possible difficulties one may run into with the numerical algorithm. Applying the algorithm to test data we show how the new formula improves the results on cross-entropy.


title: Image segmentation via adaptive K-mean clustering and knowledge-based morphological operations with biomedical applications

abstract: Image segmentation remains one of the major challenges in image analysis. In medical applications, skilled operators are usually employed to extract the desired regions that may be anatomically separate but statistically indistinguishable. Such manual processing is subject to operator errors and biases, is extremely time consuming, and has poor reproducibility. We propose a robust algorithm for the segmentation of three-dimensional (3-D) image data based on a novel combination of adaptive K-mean clustering and knowledge-based morphological operations. The proposed adaptive K-mean clustering algorithm is capable of segmenting the regions of smoothly varying intensity distributions. Spatial constraints are incorporated in the clustering algorithm through the modeling of the regions by Gibbs random fields. Knowledge-based morphological operations are then applied to the segmented regions to identify the desired regions according to the a priori anatomical knowledge of the region-of-interest. This proposed technique has been successfully applied to a sequence of cardiac CT volumetric images to generate the volumes of left ventricle chambers at 16 consecutive temporal frames. Our final segmentation results compare favorably with the results obtained using manual outlining. Extensions of this approach to other applications can be readily made when a priori knowledge of a given object is available.


title: Learning from partially annotated sequences

abstract: We study sequential prediction models in cases where only fragments of the sequences are annotated with the ground-truth. The task does not match the standard semi-supervised setting and is highly relevant in areas such as natural language processing, where completely labeled instances are expensive and require editorial data. We propose to generalize the semi-supervised setting and devise a simple transductive loss-augmented perceptron to learn from inexpensive partially annotated sequences that could for instance be provided by laymen, the wisdom of the crowd, or even automatically. Experiments on mono- and crosslingual named entity recognition tasks with automatically generated partially annotated sentences from Wikipedia demonstrate the effectiveness of the proposed approach. Our results show that learning from partially labeled data is never worse than standard supervised and semi-supervised approaches trained on data with the same ratio of labeled and unlabeled tokens.


title: Towards Automated Knowledge Discovery of Hepatocellular Carcinoma: Extract Patient Information from Chinese Clinical Reports

abstract: Objectives: To accurately determine significant prognostic risk factors, patient information must be quantified accurately according to their extent of disease. An essential step for prediction of prognostic risk factors requires the determination of patient features which are typically hidden in electronic medical record(EMR). The goal of this study is to extract clinical entities of Chinese clinical reports, enabling automated hepatocellular carcinoma knowledge extraction. Materials and Methods: In this paper, we annotated hepatocellular carcinoma corpora with patient records from EMR database. We present an information extraction solution based on assembled method. Our evaluation dataset contains 3996 training sentences and 1570 test sentences. The evaluation metrics are precision, recall, F1 of extract matching. Results and Conclusions: NER of admission reports, radiology reports and discharge summaries with F1 of 0.8449, 0.5935 and 0.7320 respectively. RE of overall F1 is 0.9129. This study prepares a foundation for larger population studies to identify clinical features of hepatocellular carcinoma.


title: Image Stitching for Dual Fisheye Cameras

abstract: Panoramic photography creates stunning immersive visual experiences for viewers. In this paper, we investigate how to seamlessly stitch a pair of images captured by two uncalibrated, back-to-back, 195-degree fisheye cameras to generate a surround view of a 3D scene. It is a challenging task because the two camera centers are displaced and because the common region is the most distorted area. To enhance the robustness of feature matching and hence the quality of stitching, we propose a novel technique that projects the image rectilinearly onto an equirectangular plane. Unlike most previous global warping methods that are sensitive to the disparity between back-to-back fisheye images, our method employs local warping for image alignment while preserving the geometric structure of the scene. Experimental results show that our method effectively produces high-quality seamless panoramic images without stitching artifacts.


title: A New Fuzzy Support Vector Machine for Credit Scoring

abstract: With China's rapid economic development, credit scoring has become very important. This paper presents a new fuzzy support vector machine algorithm used to solve the problems of credit scoring. The empirical results show that the proposed fuzzy membership model is valid ,the algorithm has good prediction accuracy and anti-noise ability.


title: ADHD comorbidity findings from the MTA study: comparing comorbid subgroups

abstract: ABSTRACT Objectives Previous research has been inconclusive whether attention-deficit/hyperactivity disorder (ADHD), when comorbid with disruptive disorders (oppositional defiant disorder [ODD] or conduct disorder [CD]), with the internalizing disorders (anxiety and/or depression), or with both, should constitute separate clinical entities. Determination of the clinical significance of potential ADHD + internalizing disorder or ADHD + ODD/CD syndromes could yield better diagnostic decision-making, treatment planning, and treatment outcomes. Method Drawing upon cross-sectional and longitudinal information from 579 children (aged 7–9.9 years) with ADHD participating in the NIMH Collaborative Multisite Multimodal Treatment Study of Children With Attention-Deficit/Hyperactivity Disorder (MTA), investigators applied validational criteria to compare ADHD subjects with and without comorbid internalizing disorders and ODD/CD. Results Substantial evidence of main effects of internalizing and externalizing comorbid disorders was found. Moderate evidence of interactions of parent-reported anxiety and ODD/CD status were noted on response to treatment, indicating that children with ADHD and anxiety disorders (but no ODD/CD) were likely to respond equally well to the MTA behavioral and medication treatments. Children with ADHD-only or ADHD with ODD/CD (but without anxiety disorders) responded best to MTA medication treatments (with or without behavioral treatments), while children with multiple comorbid disorders (anxiety and ODD/CD) responded optimally to combined (medication and behavioral) treatments. Conclusions Findings indicate that three clinical profiles, ADHD co-occurring with internalizing disorders (principally parent-reported anxiety disorders) absent any concurrent disruptive disorder (ADHD + ANX), ADHD co-occurring with ODD/CD but no anxiety (ADHD + ODD/CD), and ADHD with both anxiety and ODD/CD (ADHD + ANX + ODD/CD) may be sufficiently distinct to warrant classification as ADHD subtypes different from "pure" ADHD with neither comorbidity. Future clinical, etiological, and genetics research should explore the merits of these three ADHD classification options.


title: Measuring Online Learning Systems Success: Applying the Updated DeLone and McLean Model

abstract: Based on a survey of 232 undergraduate students, this study used the updated DeLone and McLean information systems success model to examine the determinants for successful use of online learning systems (OLS). The results provided an expanded understanding of the factors that measure OLS success. The results also showed that system quality, information quality, and service quality had a significant effect on actual OLS use through user satisfaction and behavioral intention to use OLS.


title: Big Graph Processing Systems: State-of-the-Art and Open Challenges

abstract: Graph is a fundamental data structure that captures relationships between different data entities. In practice, graphs are widely used for modeling complicated data in different application domains such as social networks, protein networks, transportation networks, bibliographical networks, knowledge bases and many more. Currently, graphs with millions and billions of nodes and edges have become very common. In principle, graph analytics is an important big data discovery technique. Therefore, with the increasing abundance of large graphs, designing scalable systems for processing and analyzing large scale graphs has become one of the most timely problems facing the big data research community. In general, distributed processing of big graphs is a challenging task due to their size and the inherent irregular structure of graph computations. Thus, in recent years, we have witnessed an unprecedented interest in building big graph processing systems that attempted to tackle these challenges. To better understand the challenges of developing scalable graph processing systems, in this paper, we provide a comprehensive overview of the state-of-the art. In addition, we identify a set of the current open research challenges and discuss some promising directions for future research.


Sticking mechanism during hot rolling of two stainless steels


title: Improving resolution by image registration

abstract: Abstract Image resolution can be improved when the relative displacements in image sequences are known accurately, and some knowledge of the imaging process is available. The proposed approach is similar to back-projection used in tomography. Examples of improved image resolution are given for gray-level and color images, when the unknown image displacements are computed from the image sequence.


title: Making sense of sensing systems: five questions for designers and researchers

abstract: This paper borrows ideas from social science to inform the design of novel "sensing" user-interfaces for computing technology. Specifically, we present five design challenges inspired by analysis of human-human communication that are mundanely addressed by traditional graphical user interface designs (GUIs). Although classic GUI conventions allow us to finesse these questions, recent research into innovative interaction techniques such as 'Ubiquitous Computing' and 'Tangible Interfaces' has begun to expose the interaction challenges and problems they pose. By making them explicit we open a discourse on how an approach similar to that used by social scientists in studying human-human interaction might inform the design of novel interaction mechanisms that can be used to handle human-computer communication accomplishments


title: Palm vein recognition with Local Binary Patterns and Local Derivative Patterns

abstract: Palm vein feature extraction from near infrared images is a challenging problem in hand pattern recognition. In this paper, a promising new approach based on local texture patterns is proposed. First, operators and histograms of multi-scale Local Binary Patterns (LBPs) are investigated in order to identify new efficient descriptors for palm vein patterns. Novel higher-order local pattern descriptors based on Local Derivative Pattern (LDP) histograms are then investigated for palm vein description. Both feature extraction methods are compared and evaluated in the framework of verification and identification tasks. Extensive experiments on CASIA Multi-Spectral Palmprint Image Database V1.0 (CASIA database) identify the LBP and LDP descriptors which are better adapted to palm vein texture. Tests on the CASIA datasets also show that the best adapted LDP descriptors consistently outperform their LBP counterparts in both palm vein verification and identification.


title: On-Board Dual-Stereo-Vision for the Navigation of an Autonomous MAV

abstract: We present a quadrotor Micro Aerial Vehicle (MAV) equipped with four cameras, which are arranged in two stereo configurations. The MAV is able to perform stereo matching for each camera pair on-board and in real-time, using an efficient sparse stereo method. In case of the camera pair that is facing forward, the stereo matching results are used for a reduced stereo SLAM system. The other camera pair, which is facing downwards, is used for ground plane detection and tracking. Hence, we are able to obtain a full 6DoF pose estimate from each camera pair, which we fuse with inertial measurements in an extended Kalman filter. Special care is taken to compensate various drift errors. In an evaluation we show that using two instead of one camera pair significantly increases the pose estimation accuracy and robustness.


title: Deep recurrent neural networks for predicting intraoperative and postoperative outcomes and trends

abstract: Intraoperative vital signals convey a wealth of complex temporal information that can provide significant insights into a patient's physiological status during the surgery, as well as outcomes after the surgery. Our study involves the use of a deep recurrent neural network architecture to predict patient's outcomes after the surgery, as well as to predict the immediate changes in the intraoperative signals during the surgery. More specifically, we will use a Long Short-Term Memory (LSTM) model which is a gated deep recurrent neural network architecture. We have performed two experiments on a large intraoperative dataset of 12,036 surgeries containing information on 7 intraoperative signals including body temperature, respiratory rate, heart rate, diastolic blood pressure, systolic blood pressure, fraction of inspired O 2 and end-tidal CO 2 . We first evaluated the capability of LSTM in predicting the immediate changes in intraoperative signals, and then we evaluated its performance on predicting each patient's length of stay outcome. Our experiments show the effectiveness of LSTM with promising results on both tasks compared to the traditional models.


title: Echo Chamber: A Persuasive Game on Climate Change Rhetoric

abstract: Echo Chamber is a game that persuades players to re-examine their argumentation style and adopt new rhetorical techniques procedurally delivered through gameplay. Several games have been made addressing the environmental impacts of climate change; none have examined the gap between scientific and public discourse over climate change, and our goal was to teach players more effective communication techniques for conveying climate change in public venues. Our game provides other developers insight into persuasion through game mechanics with good design practices for similar persuasive games.


title: Social network use and personality

abstract: Studies have shown a connection between the individual personality of the user and the way he or she behaves on line. Today many millions of people around the world are connected by being members of various Internet social networks. Ross et al. (2009) studied the connection between the personality of the individual users and their behavior on a social network. They based their study on the self-reports of users of Facebook, one of the most popular social networks, and measured five personality factors using the NEO-PI-R (Costa & McCrae, 1992) questionnaire. They found that while there was a connection between the personalities of surfers and their behavior on Facebook, it was not strong. This study is based on that of Ross et al. (2009), but in our study the self-reports of subjects, were replaced by more objective criteria, measurements of the user-information upload on Facebook. A strong connection was found between personality and Facebook behavior. Implications of the results are discussed.


title: Mango (Mangifera indica L.) cv. Kent fruit mesocarp de novo transcriptome assembly identifies gene families important for ripening

abstract: Fruit ripening is a physiological and biochemical process genetically programmed to regulate fruit quality parameters like firmness, flavor, odor and color, as well as production of ethylene in climacteric fruit. In this study, a transcriptomic analysis of mango (Mangifera indica L.) mesocarp cv. “Kent” was done to identify key genes associated with fruit ripening. Using the Illumina sequencing platform, 67,682,269 clean reads were obtained and a transcriptome of 4.8 Gb. A total of 33,142 coding sequences were predicted and after functional annotation, 25,154 protein sequences were assigned with a product according to Swiss-Prot database and 32,560 according to non-redundant database. Differential expression analysis identified 2,306 genes with significant differences in expression between mature-green and ripe mango (1,178 up-regulated and 1,128 down-regulated (FDR≤0.05)). The expression of ten genes evaluated by both qRT-PCR and RNA-seq data was highly correlated (R=0.97), validating the differential expression data from RNA-seq alone. Gene Ontology enrichment analysis, showed significantly represented terms associated to fruit ripening like “cell wall”, “carbohydrate catabolic process” and “starch and sucrose metabolic process” among others. Mango genes were assigned to 327 metabolic pathways according to Kyoto Encyclopedia of Genes and Genomes database, among them those involved in fruit ripening such as plant hormone signal transduction, starch and sucrose metabolism, galactose metabolism, terpenoid backbone and carotenoid biosynthesis. This study provides a mango transcriptome that will be very helpful to identify genes for expression studies in early and late flowering mangos during fruit ripening.


title: Fast ICP Algorithms for Shape Registration

abstract: Shape registration plays an important role in applications such as 3D object modeling or object recognition. The iterative closest point (ICP) algorithm is widely used for the registration of geometric data. One of its main drawback is its time complexity O(N2), quadratic with the shape size N, which implies long processing time, especially when using high resolution data. Several methods were proposed to accelerate the process. One of the most effective one uses a tree search (k-D tree) to establish closest point relationships and reduces the complexity to O(N logN). This paper reviews several of the existing methods and proposes and analyses a new, even less complex ICP algorithm, that uses a heuristic approach to find the closest points. Based on a local search it permits to reduce the complexity to O(N) and to greatly accelerate the process. A comprehensive analysis and a comparison of the considered algorithm with a tree search method are presented.


title: Effects of basic fibroblast growth factor on survival and choline acetyltransferase development of spinal cord neurons.

abstract: To investigate the biological role of basic fibroblast growth factor (bFGF) for the development of the spinal cord we studied the in vitro and in vivo effects of this protein on survival and choline acetyltransferase (ChAT)-activity of embryonic chick and rat spinal cord neurons. In vitro, bFGF (ED50 1-2.8 ng/ml) supported the survival of embryonic neurons from the ventral part of the rat spinal cord (ventral spinal cord, vsc), including motoneurons. Addition of bFGF (100 ng/ml) increased the ChAT-activity in embryonic chick vsc cultures to 150% as compared to untreated cultures (100%). The effect of bFGF was dose-dependent. In vivo-application of bFGF resulted in a similar increase of ChAT-activity in chick spinal cord. Since bFGF stimulates the ChAT-activity of spinal cord neurons in vivo and in vitro we therefore conclude that this protein may have a physiological function for the transmitter development of cholinergic spinal cord neurons.


title: Real-Time Superpixel Segmentation by DBSCAN Clustering Algorithm

abstract: In this paper, we propose a real-time image superpixel segmentation method with 50 frames/s by using the density-based spatial clustering of applications with noise (DBSCAN) algorithm. In order to decrease the computational costs of superpixel algorithms, we adopt a fast two-step framework. In the first clustering stage, the DBSCAN algorithm with color-similarity and geometric restrictions is used to rapidly cluster the pixels, and then, small clusters are merged into superpixels by their neighborhood through a distance measurement defined by color and spatial features in the second merging stage. A robust and simple distance function is defined for obtaining better superpixels in these two steps. The experimental results demonstrate that our real-time superpixel algorithm (50 frames/s) by the DBSCAN clustering outperforms the state-of-the-art superpixel segmentation methods in terms of both accuracy and efficiency.


title: Explaining online purchase intentions: A multi-channel store image perspective

abstract: This study is one of the few empirical works addressing the impact of offline and online store impressions on consumer online purchase intentions. Building upon the literature on store image and consumer online purchasing, we propose positive effects of online store image and suggest mixed influences of offline store image perceptions. Drawing on a sample of 630 customers of one of the largest music retail stores in the Netherlands, hypotheses are tested. The empirical results clearly support the assumed positive effect of online store image, and confirm that the influence of offline store image on online purchase intentions can be positive as well negative. We discuss the implications of our research, and conclude with directions for further research.


title: Iterative Channel Estimation in LTE Systems

abstract: In this paper, we discuss iterative channel estimation for the Long Term Evolution (LTE) downlink. The LTE standard utilizes coherent detection and therefore to achieve a high data rate over mobile radio channels, it is essential to obtain high accurate channel state information at the receiver side. For channel estimation purposes, the LTE standard provides training data known as pilot symbols. The open question is whether the accuracy of channel estimation based on pilot symbols is sufficient to achieve such high data rate transmission. The quality of the channel estimates can be further enhanced if soft or hard information bits, which are available after the decoding stage, are utilized by the channel estimator to re-estimate the channel. In order to quantify the gain provided by the iterative channel estimation, several scenarios have been considered in this work.


title: A Security Risk Assessment Framework for Smart Car

abstract: As the automobile industry has recently adopted information technologies, the latter are being used to replace mechanical systems with electronically-controlled systems. Moreover, automobiles are evolving into smart cars or connected cars as they are connected to various IT devices and networks such as VANET (Vehicular Ad hoc NETwork). Although there were no concerns about the hacking of automobiles in the past, various security threats are now emerging as electronic systems are gradually filling up the interiors of many automobiles, which are in turn being connected to external networks. As such, researchers have begun studying smart car security, leading to the disclosure of security threats through the testing or development of various automobile security technologies. However, the security threats facing smart cars do not occur frequently and, practically speaking, it is unrealistic to attempt to cope with every possible security threat when considering such factors as performance, compatibility, and so forth. Moreover, the excessive application of security technology will increase the overall vehicle cost and lower the effectiveness of investment. Therefore, smart car security risks should be assessed and prioritized to establish efficient security measures. To that end, this study constructed a security risk assessment framework in a bid to establish efficient measures for smart car security. The proposed security risk assessment framework configured the assessment procedure based on the conventional security risk analysis model GMITS (ISO13335) and utilized 'attack tree analysis' to assess the threats and vulnerabilities.


title: ViziQuer: a tool to explore and query SPARQL endpoints

abstract: The presented tool uses a novel approach to explore and query a SPARQL endpoint. The tool is simple to use as a user needs only to enter an address of a SPARQL endpoint of one's interest. The tool will extract and visualize graphically the data schema of the endpoint. The user will be able to overview the data schema and use it to construct a SPARQL query according to the data schema. The tool can be downloaded from http://viziquer.lumii.lv. There is also additional information and help on how to use it in practice.


title: Hybrid map-based navigation for intelligent wheelchair

abstract: A navigation system based on hybrid map for intelligent wheelchair is presented. The system is consisted of hybrid map building, localization, path planning and trajectory following. The hybrid map includes a series of small probabilistic grid maps (PGM) and a global topological map (GTM). They are built simultaneously and easily using the human-guided method. Then on the hybrid map, the localization and the real-time path planning algorithms are realized smartly and effectively. The experiments and applications results show that the human-guided method integrates both the computer's modeling ability and the human's sensory perception to the environment. The hybrid map is easy to solve the loop-closure and doorway problems that enhances the robustness against uncertainty of sensors. It also can improve the efficiency in large-scale SLAM. Further more, at an elderly home we did the activities of daily living (ADL) testing and at Shanghai Expo 2010 we demonstrated the wheelchair system by offering trial rides to visitors.


title: The structure of musical preferences: A five-factor model.

abstract: Music is a cross-cultural universal, a ubiquitous activity found in every known human culture. Individuals demonstrate manifestly different preferences in music, and yet relatively little is known about the underlying structure of those preferences. Here, we introduce a model of musical preferences based on listeners’ affective reactions to excerpts of music from a wide variety of musical genres. The findings from three independent studies converged to suggest that there exists a latent five-factor structure underlying music preferences that is genre-free, and reflects primarily emotional/affective responses to music. We have interpreted and labeled these factors as: 1) a Mellow factor comprising smooth and relaxing styles; 2) an Urban factor defined largely by rhythmic and percussive music, such as is found in rap, funk, and acid jazz; 3) a Sophisticated factor that includes classical, operatic, world, and jazz; 4) an Intense factor defined by loud, forceful, and energetic music; and 5) a Campestral factor comprising a variety of different styles of direct, and rootsy music such as is often found in country and singer-songwriter genres. The findings from a fourth study suggest that preferences for the MUSIC factors are affected by both the social and auditory characteristics of the music.


title: Weighted hybrid recommendation for heterogeneous networks

abstract: Social media sites accumulate a wide variety of information about users: likes and ratings, friend and follower links, annotations, posts, media uploads, just to name a few. Key challenges for recommender systems research are (a) to synthesize of all of this data into an integrated recommendation model and (b) to support a wide variety of recommendation types simultaneously (items, friends, tags, etc.) One approach that has been explored in recent research is to view this multi-faceted data as a heterogeneous network and use network-based methods of generating recommendations. However, most such approaches involve computationally-intensive model generation resulting in a single-purpose recommender system. Our approach is to create a component-based hybrid model whose components can be reused for multiple recommendation tasks. In this paper, we show how this model can be applied to heterogeneous networks.


title: Integrating Refined Kano Model and QFD for Service Quality Improvement in Healthy Fast-Food Chain Restaurants

abstract: People are paying greater attention to health. To maintain a good health status and obtain food fast, customers may go to healthy fast-food chain restaurants such as Subway more often than before in China and Taiwan. Healthy fast-food chain restaurants come with a healthy spin, seeking to differentiate themselves from other fast-food restaurants. This paper combined the refined Kano model and the quality function deployment (QFD) method. The refined Kano model was used to understand how customers perceive service attributes developed based on DINESERV measurements. QFD was employed to describe the relationships among the critical service attributes and corresponding improvements as well as to identify the priority for these improvements. The analysis results revealed that providing limited offers (due to periods, seasons, and regions) should be at the top of their improvement list, followed by staff suggestions for ingredients, and a temperature display to enhance the image of fresh ingredients. Other improvement actions include providing regular launches of new flavors/products, designing new and attractive slogans, and providing restaurant apps.