title: Development of Power Assisting Suit for Assisting Nurse Labor

abstract: In order to realize a power assisting suit for assisting a nurse caring a patient in her arm, a hardness sensor of muscle using load cell and a pneumatic rotary actuator utilizing pressure cuffs have been developed. The power assisting suit consists of shoulders, arms, waist and legs made of aluminum, and is fitted on the nurse body. The power assisting suit is originated with the concept of a master and slave system in one body. The arms, waist and legs have the pneumatic rotary actuators. The pneumatic rotary actuators are constructed with pressure cuffs sandwiched between thin plates. The action of the arms, waist and legs of the nurse are sensed with the muscle hardness sensor utilizing load cell with diaphragm mounted on a sensing tip. The dent of the sensing tip corresponds to the hardness of the muscle so that exerting muscle force produces electric signal. This paper gives the design and characteristics of the power assisting suit using the cuff type pneumatic rotary actuators and the muscle hardness sensor verifying its practicability.


Simple Learning Algorithms for Training Support Vector Machines


title: Three-level buck converter for envelope tracking applications

abstract: This letter proposes a three-level buck converter for tracking applications such as envelope-tracking in radio frequency power amplifiers (RFPAs). It is shown that the three-level buck converter can offer advantages in terms of switching ripples, losses, bandwidth, or the size of magnetic components compared to a standard buck or a two-phase buck converter. Experimental results illustrate improved efficiency and ripple rejection in an RFPA envelope-tracking application representative for low-power battery-operated systems.


title: Learning Priors for Semantic 3D Reconstruction

abstract: We present a novel semantic 3D reconstruction framework which embeds variational regularization into a neural network. Our network performs a fixed number of unrolled multi-scale optimization iterations with shared interaction weights. In contrast to existing variational methods for semantic 3D reconstruction, our model is end-to-end trainable and captures more complex dependencies between the semantic labels and the 3D geometry. Compared to previous learning-based approaches to 3D reconstruction, we integrate powerful long-range dependencies using variational coarse-to-fine optimization. As a result, our network architecture requires only a moderate number of parameters while keeping a high level of expressiveness which enables learning from very little data. Experiments on real and synthetic datasets demonstrate that our network achieves higher accuracy compared to a purely variational approach while at the same time requiring two orders of magnitude less iterations to converge. Moreover, our approach handles ten times more semantic class labels using the same computational resources.


Top-k Hierarchical Classification.


title: The impact of the transient response of organic light emitting diodes on the design of active matrix OLED displays

abstract: Much of the organic light emitting diode (OLED) characterization published to date addresses the high current regime encountered in the operation of passively addressed displays. Higher efficiency and brightness can be obtained by driving with an active matrix, but the lower instantaneous pixel currents place the OLEDs in a completely different operating mode. Results at these low current levels are presented and their impact on active matrix display design is discussed.


title: Optimum Design of the Electric Vehicle Traction Motor Using the Hairpin Winding

abstract: Recently, according to Eco-technology accelerating, new types of traction motor with the high efficiency and power are demanded. This paper presents that the Hairpin winding is applied to a design of the electric vehicle traction motor for the high efficiency and power density. Generally, the winding method using the toroidal coil has the restriction which is the unnecessary spaces between coils. However the Hairpin winding method can reduce the gap between coils, therefore the space factor is higher than that of winding methods. However, because of using the full pitch winding and wave winding, this method has the disadvantages of increasing the THD of back electromotive force and torque ripple. Thus, to minimize the THD and torque ripple, the full factorial method, Response surface method and the step skew to the rotor are applied. The paper presents the motor design using the Hairpin winding for the high space factor, and the analysis result by using RSM, the step skew and full factorial method shows the reduced space harmonics.


title: Surgical precision JIT compilers

abstract: Just-in-time (JIT) compilation of running programs provides more optimization opportunities than offline compilation. Modern JIT compilers, such as those in virtual machines like Oracle's HotSpot for Java or Google's V8 for JavaScript, rely on dynamic profiling as their key mechanism to guide optimizations. While these JIT compilers offer good average performance, their behavior is a black box and the achieved performance is highly unpredictable. In this paper, we propose to turn JIT compilation into a precision tool by adding two essential and generic metaprogramming facilities: First, allow programs to invoke JIT compilation explicitly. This enables controlled specialization of arbitrary code at run-time, in the style of partial evaluation. It also enables the JIT compiler to report warnings and errors to the program when it is unable to compile a code path in the demanded way. Second, allow the JIT compiler to call back into the program to perform compile-time computation. This lets the program itself define the translation strategy for certain constructs on the fly and gives rise to a powerful JIT macro facility that enables "smart" libraries to supply domain-specific compiler optimizations or safety checks. We present Lancet, a JIT compiler framework for Java bytecode that enables such a tight, two-way integration with the running program. Lancet itself was derived from a high-level Java bytecode interpreter: staging the interpreter using LMS (Lightweight Modular Staging) produced a simple bytecode compiler. Adding abstract interpretation turned the simple compiler into an optimizing compiler. This fact provides compelling evidence for the scalability of the staged-interpreter approach to compiler construction. In the case of Lancet, JIT macros also provide a natural interface to existing LMS-based toolchains such as the Delite parallelism and DSL framework, which can now serve as accelerator macros for arbitrary JVM bytecode.


title: Region of Interest (ROI) Coding for Aerial Surveillance Video using AVC&HEVC

abstract: Aerial surveillance from Unmanned Aerial Vehicles (UAVs), i.e. with moving cameras, is of growing interest for police as well as disaster area monitoring. For more detailed ground images the camera resolutions are steadily increasing. Simultaneously the amount of video data to transmit is increasing significantly, too. To reduce the amount of data, Region of Interest (ROI) coding systems were introduced which mainly encode some regions in higher quality at the cost of the remaining image regions. We employ an existing ROI coding system relying on global motion compensation to retain full image resolution over the entire image. Different ROI detectors are used to automatically classify a video image on board of the UAV in ROI and non-ROI. We propose to replace the modified Advanced Video Coding (AVC) video encoder by a modified High Efficiency Video Coding (HEVC) encoder. Without any change of the detection system itself, but by replacing the video coding back-end we are able to improve the coding efficiency by 32% on average although regular HEVC provides coding gains of 12-30% only for the same test sequences and similar PSNR compared to regular AVC coding. Since the employed ROI coding mainly relies on intra mode coding of new emerging image areas, gains of HEVC-ROI coding over AVC-ROI coding compared to regular coding of the entire frames including predictive modes (inter) depend on sequence characteristics. We present a detailed analysis of bit distribution within the frames to explain the gains. In total we can provide coding data rates of 0.7-1.0 Mbit/s for full HDTV video sequences at 30 fps at reasonable quality of more than 37 dB.


title: A Computer Network Attack Taxonomy and Ontology

abstract: Copyright: 2012 IGI Global. This is an pre/post print version of the work. The definitive version is published in International Journal of Cyber Warfare and Terrorism, vol. 2(3), pp 12-25


title: Attachment-security priming attenuates amygdala activation to social and linguistic threat

abstract: A predominant expectation that social relationships with others are safe (a secure attachment style), has been linked with reduced threat-related amygdala activation. Experimental priming of mental representations of attachment security can modulate neural responding, but the effects of attachment-security priming on threat-related amygdala activation remains untested. Using functional magnetic resonance imaging, the present study examined the effects of trait and primed attachment security on amygdala reactivity to threatening stimuli in an emotional faces and a linguistic dot-probe task in 42 healthy participants. Trait attachment anxiety and attachment avoidance were positively correlated with amygdala activation to threatening faces in the control group, but not in the attachment primed group. Furthermore, participants who received attachment-security priming showed attenuated amygdala activation in both the emotional faces and dot-probe tasks. The current findings demonstrate that variation in state and trait attachment security modulates amygdala reactivity to threat. These findings support the potential use of attachment security-boosting methods as interventions and suggest a neural mechanism for the protective effect of social bonds in anxiety disorders.


title: The implementation of a full EMV smartcard for a point-of-sale transaction

abstract: This paper examines the changes in the payment card environment as they relate to EMV (named after Europay, MasterCard and Visa). This research shows that if the combined dynamic data authentication (CDA) card variant of the EMV card is deployed in a full EMV environment, given the relevant known vulnerabilities and attacks against the EMV technology, the consequences of unauthorized disclosure of the cardholder data is of significantly reduced value to a criminal.


title: Energy-Efficient Indoor Localization of Smart Hand-Held Devices Using Bluetooth

abstract: Indoor localization of smart hand-held devices is essential for location-based services of pervasive applications. The previous research mainly focuses on exploring wireless signal fingerprints for this purpose, and several shortcomings need to be addressed first before real-world usage, e.g., demanding a large number of access points or labor-intensive site survey. In this paper, through a systematic empirical study, we first gain in-depth understandings of Bluetooth characteristics, i.e., the impact of various factors, such as distance, orientation, and obstacles on the Bluetooth received signal strength indicator (RSSI). Then, by mining from historical data, a novel localization model is built to describe the relationship between the RSSI and the device location. On this basis, we present an energy-efficient indoor localization scheme that leverages user motions to iteratively shrink the search space to locate the target device. An Motion-assisted Device Tracking Algorithm has been prototyped and evaluated in several real-world scenarios. Extensive experiments show that our algorithm is efficient in terms of localization accuracy, searching time and energy consumption.


title: Proposal of communication standardization of industrial networks in Industry 4.0

abstract: This paper provides an overview of the current state of the use of Industry 4.0 standards in real practice and deals with application problems of Smart Factory technology in various industries. Essential conditions of a successful standards implementation, especially according to industrial networks communication are presented. In the second part of this paper consumption method for maintaining compatibility and safety communications in the control of industrial process is established. Part of the article is a communication model proposal suitable for the implementation.


title: Audio-to-text alignment for speech recognition with very limited resources.

abstract: In this paper we present our efforts in building a speech recognizer constrained by the availability of very limited resources. We consider that neither proper training databases nor initial acoustic models are available for the target language. Moreover, for the experiments shown here, we use grapheme-based speech recognizers. Most prior work in the area use initial acoustic models, trained on the target or a similar language, to force-align new data and then retrain the models with it. In the proposed approach a speech recognizer is trained from scratch by using audio recordings aligned with (sometimes approximate) text transcripts. All training data has been harvested online (e.g. audiobooks, parliamentary speeches). First, the audio is decoded into a phoneme sequence by an off-theshelf phonetic recognizer in Hungarian. Phoneme sequences are then aligned to the normalized text transcripts through dynamic programming. Correspondence between phonemes and graphemes is done through a matrix of approximate sound-tographeme matching. Finally, the aligned data is split into short audio/text segments and the speech recognizer is trained using Kaldi toolkit. Alignment experiments performed for Catalan and Spanish show the feasibility to obtain accurate alignments that can be used to successfully train a speech recognizer.


title: Bringing white-box testing to Service Oriented Architectures through a Service Oriented Approach

abstract: The attractive feature of Service Oriented Architecture (SOA) is that pieces of software conceived and developed by independent organizations can be dynamically composed to provide richer functionality. The same reasons that enable flexible compositions, however, also prevent the application of some traditional testing approaches, making SOA validation challenging and costly. Web services usually expose just an interface, enough to invoke them and develop some general (black-box) tests, but insufficient for a tester to develop an adequate understanding of the integration quality between the application and the independent web services. To address this lack we propose an approach that makes web services more transparent to testers through the addition of an intermediary service that provides coverage information. The approach, named Service Oriented Coverage Testing (SOCT), provides testers with feedback about how much a service is exercised by their tests without revealing the service internals. In SOCT, testing feedback is offered itself as a service, thus preserving SOA founding principles of loose coupling and implementation neutrality. In this paper we motivate and define the SOCT approach, and implement an instance of it. We also perform a study to asses SOCT feasibility and provide a preliminary evaluation of its viability and value.


title: Friendships and Favoritism on the Schoolground- A Framed Field Experiment ∗

abstract: We present experimental evidence on favouritism practices. Children compete in teams in a tournament. After the first round of a real effort task, children indicate which group member they would prefer to do the task in the second round, for the benefit of the team. Friends are much more likely to be chosen than others after controlling for performance. We also find that children who are favoured by their friend subsequently increase performance. Consequently, favouritism does not hurt efficiency. These results show the importance of observing performance ex post in order to properly evaluate the efficiency implications of favouritism.


title: An improved NSGA-III algorithm for feature selection used in intrusion detection

abstract: A scheme for the many-objective problem is proposed for feature selection in IDS.An improved many-objective optimization algorithm (I-NSGA-III) is proposed.I-NSGA-III can alleviate the imbalance problem in feature selection. Feature selection can improve classification accuracy and decrease the computational complexity of classification. Data features in intrusion detection systems (IDS) always present the problem of imbalanced classification in which some classifications only have a few instances while others have many instances. This imbalance can obviously limit classification efficiency, but few efforts have been made to address it. In this paper, a scheme for the many-objective problem is proposed for feature selection in IDS, which uses two strategies, namely, a special domination method and a predefined multiple targeted search, for population evolution. It can differentiate traffic not only between normal and abnormal but also by abnormality type. Based on our scheme, NSGA-III is used to obtain an adequate feature subset with good performance. An improved many-objective optimization algorithm (I-NSGA-III) is further proposed using a novel niche preservation procedure. It consists of a bias-selection process that selects the individual with the fewest selected features and a fit-selection process that selects the individual with the maximum sum weight of its objectives. Experimental results show that I-NSGA-III can alleviate the imbalance problem with higher classification accuracy for classes having fewer instances. Moreover, it can achieve both higher classification accuracy and lower computational complexity. Display Omitted


title: Polyploidization increases the sensitivity to DNA-damaging agents in mammalian cells.

abstract: Polyploidization occurs during normal development as well as during tumorigenesis. In this study, we investigated if the responses to genotoxic stress in cancer cells are influenced by the ploidy. Prolonged treatment of Hep3B cells with the spindle inhibitor nocodazole resulted in mitotic slippage, followed by re-replication of the DNA to produce polyploids. Reintroduction of p53 restored the checkpoints and suppressed polyploidization. Remarkably, a stable tetraploidy cell line could be generated from Hep3B by a transient nocodazole treatment followed by a period of recovery. Using this novel tetraploid system, we found that tetraploidization increased the cell volume without significantly affecting the cell cycle. Although tetraploidization was accompanied by an increase in centrosome number, the majority of mitoses in the tetraploid cells remained bipolar. Polyploidization sensitized cells to genotoxic stress inflicted by ionizing radiation and topoisomerase inhibitors without affecting the sensitivity to spindle inhibitors. Accordingly, more gamma-H2AX foci were induced by radiation in tetraploids than in normal Hep3B cells. Likewise, primary tetraploid human fibroblasts displayed higher gamma-H2AX foci formation than diploid human fibroblasts. An implication for chemotherapy is that some cancer cells can be sensitized to genotoxic agents by a preceding step that induces polyploidization.


title: Optimizing Intersection-Over-Union in Deep Neural Networks for Image Segmentation

abstract: We consider the problem of learning deep neural networks (DNNs) for object category segmentation, where the goal is to label each pixel in an image as being part of a given object (foreground) or not (background). Deep neural networks are usually trained with simple loss functions (e.g., softmax loss). These loss functions are appropriate for standard classification problems where the performance is measured by the overall classification accuracy. For object category segmentation, the two classes (foreground and background) are very imbalanced. The intersection-over-union (IoU) is usually used to measure the performance of any object category segmentation method. In this paper, we propose an approach for directly optimizing this IoU measure in deep neural networks. Our experimental results on two object category segmentation datasets demonstrate that our approach outperforms DNNs trained with standard softmax loss.


Review: diterpenoids from Croton genus(Euphorbiaceae) and their biological activity


title: Rage against the virtual machine: hindering dynamic analysis of Android malware

abstract: Antivirus companies, mobile application marketplaces, and the security research community, employ techniques based on dynamic code analysis to detect and analyze mobile malware. In this paper, we present a broad range of anti-analysis techniques that malware can employ to evade dynamic analysis in emulated Android environments. Our detection heuristics span three different categories based on (i) static properties, (ii) dynamic sensor information, and (iii) VM-related intricacies of the Android Emulator. To assess the effectiveness of our techniques, we incorporated them in real malware samples and submitted them to publicly available Android dynamic analysis systems, with alarming results. We found all tools and services to be vulnerable to most of our evasion techniques. Even trivial techniques, such as checking the value of the IMEI, are enough to evade some of the existing dynamic analysis frameworks. We propose possible countermeasures to improve the resistance of current dynamic analysis tools against evasion attempts.


title: Activity-Based Management for Electronic Commerce: A Structured Implementation Procedure

abstract: This paper explores the application of Activity-Based Costing and Activity-Based Management in ecommerce. The proposed application may lead to better firm performance of many companies in offering their products and services over the Internet. A case study of a fictitious Business-to-Customer (B2C) company is used to illustrate the proposed structured implementation procedure and effects of an Activity-Based Costing analysis. The analysis is performed by using matrixes in order to trace overhead. The Activity-Based Costing analysis is then used to demonstrate operational and strategic Activity-Based Management in e-commerce.


Indexing in an Actor-Oriented Database


title: Design and Analysis of RBFN-Based Single MPPT Controller for Hybrid Solar and Wind Energy System

abstract: In this paper, a radial basis function network-based single maximum power point tracking (MPPT) control algorithm for a hybrid solar and wind energy system is designed and analyzed for standalone and grid connected applications. The extraction of maximum power from the intermittent and erratic nature renewable energy sources is the main target in the hybrid renewable energy system. In the literature, many researchers developed an individual MPPT control algorithm for solar and wind energy system, which in turn increases the number of control algorithms in a hybrid system. In this paper, a single MPPT controller is proposed to extract maximum power from both the sources simultaneously. The performance of the proposed MPPT control algorithm is analyzed in both standalone and grid connected modes, under different weather conditions. The hybrid renewable energy system is designed by considering 560-W photovoltaic system and 500-W wind system with the conventional boost converter, and it is simulated in MATLAB/Simulink environment to analyze the performance of the proposed MPPT controller.


title: Paranoid Android: versatile protection for smartphones

abstract: Smartphone usage has been continuously increasing in recent years. Moreover, smartphones are often used for privacy-sensitive tasks, becoming highly valuable targets for attackers. They are also quite different from PCs, so that PC-oriented solutions are not always applicable, or do not offer comprehensive security. We propose an alternative solution, where security checks are applied on remote security servers that host exact replicas of the phones in virtual environments. The servers are not subject to the same constraints, allowing us to apply multiple detection techniques simultaneously. We implemented a prototype of this security model for Android phones, and show that it is both practical and scalable: we generate no more than 2KiB/s and 64B/s of trace data for high-loads and idle operation respectively, and are able to support more than a hundred replicas running on a single server.


title: CUP: Comprehensive User-Space Protection for C/C++

abstract: Memory corruption vulnerabilities in C/C++ applications enable attackers to execute code, change data, and leak information. Current memory sanitizers do not provide comprehensive coverage of a program»s data. In particular, existing tools focus primarily on heap allocations with limited support for stack allocations and globals. Orthogonally, existing tools focus on the main executable with limited support for system libraries. Existing tools also suffer from both false positives and false negatives. We present Comprehensive User-Space Protection for C/C++, \sysname, an LLVM sanitizer that provides complete spatial and probabilistic temporal memory safety for C/C++ programs on 64-bit architectures (with a prototype implementation for x86\_64). \sysname uses a hybrid metadata scheme that supports all program data including globals, heap, or stack and maintains Application Binary Interface (ABI) compatibility. Existing approaches have false positives and 8%-25% false negatives on the NIST Juliet test suite. In contrast, \sysname has no false negatives or false positives. \sysname instruments all user-space code, including libc and other system libraries, removing these libraries from the trusted computing base. Supporting all of user space allows \sysname to treat a missed check as a failed check, leading to no false negatives for \sysname. The overhead introduced by \sysname is half that of the state-of-the-art full memory protection on benchmarks where both mechanisms run, and imposes 1.58x overhead when compared to baseline on all benchmarks. Consequently, \sysname is intended as a sanitizer for use by system developers, and to protect truly critical systems.


title: CLARE: a joint approach to label classification and tag recommendation

abstract: Data classification and tag recommendation are both important and challenging tasks in social media. These two tasks are often considered independently and most efforts have been made to tackle them separately. However, labels in data classification and tags in tag recommendation are inherently related. For example, a Youtube video annotated with NCAA, stadium, pac12 is likely to be labeled as football, while a video/image with the class label of coast is likely to be tagged with beach, sea, water and sand. The existence of relations between labels and tags motivates us to jointly perform classification and tag recommendation for social media data in this paper. In particular, we provide a principled way to capture the relations between labels and tags, and propose a novel framework CLARE, which fuses data CLAssification and tag REcommendation into a coherent model. With experiments on three social media datasets, we demonstrate that the proposed framework CLARE achieves superior performance on both tasks compared to the state-of-the-art methods.


title: The prediction of abdominal visceral fat level from body composition and anthropometry: ROC analysis

abstract: OBJECTIVE: To evaluate the merit of body mass index (BMI), % body fat, waist circumference and waist-to-hip ratio as predictors of abdominal visceral fat (AVF) level.DESIGN: Cross-sectional measurements obtained from 458 female and 331 male subjects of French Canadian descent with an age range from 18–72 y.MEASUREMENTS: AVF level was assessed by computed tomography. BMI was calculated as weight (in kg) divided by stature2 (in m), body density was derived from underwater weighing and % body fat was computed from the estimate of body density with the Siri equation. Waist-to-hip ratio was calculated as waist circumference divided by hip circumference. Receiver operating characteristic (ROC) curves were used to identify the optimal cut-off points.RESULTS: In younger women (<40 y, n=258), waist-to-hip ratio was the poorest predictor of AVF level with areas under the ROC curves (Az) ranging from 0.684–0.716, sensitivity (Sen) from 63.3–68.8% and specificity (Spe) from 64.0–67.5%, whereas the Az, Sen and Spe for other predictors ranged from 0.924–0.983, 87.0–96.8, and 83.4–92.7, respectively. The same trend was observed in older (≥40 y, n=200) women, although differences between waist-to-hip ratio and other predictors were less pronounced. In older men, waist circumference was the best overall predictor (Az from 0.88–0.92), whereas BMI showed the lowest Az values (0.831–0.875, P≤0.001 vs waist circumference). In younger men, BMI had the smallest Az (P<0.007 vs others) with the lowest AVF cut-off point (100 cm2). However, with higher AVF cut-offs the differences were not significant.CONCLUSION: Waist circumference is the best overall predictor of abdominal visceral obesity, whereas in women waist-to-hip ratio is a poor indicator of AVF and its use as a surrogate measure of visceral fat should be avoided.


title: Allostatic load and health status of African Americans and whites.

abstract: OBJECTIVES ::: To compare health risks in 84 healthy African American and 45 white men and women after calculating allostatic load (AL) from biologic, psychosocial, and behavioral measures. ::: ::: ::: METHODS ::: Participants (18-45 years) ranging in weight from normal to obese and without hypertension or diabetes. Fitness, body fat, CRP, mood, social support, blood pressure, sleep and exercise habits, coping, and insulin responses were dichotomized as low/high risk and summed for AL. ::: ::: ::: RESULTS ::: African Americans (3.4±1.9) had significantly higher AL than that of whites (2.4±1.9; P<0.05). Significantly more African Americans had AL≥3 (67.9%) than did whites (48.9%). ::: ::: ::: CONCLUSIONS ::: Identifying cumulative AL may help identify and address the underpinnings of health disparities in African Americans.


title: Hardware/Software Design Considerations for Automotive Embedded Systems

abstract: An increasing number of safety-critical functions is taken over by embedded systems in today's automobiles. While standard microcontrollers are the dominant hardware platform in these systems, the decreasing costs of new devices as field programmable gate arrays (FPGAs) make it interesting to consider them for automotive applications. In this paper, a comparison of microcontrollers and FPGAs with respect to safety and reliability properties is presented. For this comparison, hardware fault handling was considered as well as software fault handling. Own empirical evaluations in the area of software fault handling identified advantages of FPGAs with respect to the encapsulation of real-time functions. On the other hand, several dependent failures were detected in versions developed independently on microcontrollers and FPGAs.


title: Versatility of the facial artery myomucosal island flap in neopharyngeal reconstruction

abstract: BACKGROUND ::: The facial artery myomucosal (FAMM) island flap is a cheek flap, pedicled on the facial artery and suitable for small to medium-sized reconstructions of the oral cavity and neighboring areas. A novel transposition of the flap to the hypopharynx after laryngectomy is presented in this report. ::: ::: ::: METHODS ::: A 58-year-old man, previously irradiated, was laryngopharyngectomized because of a new supraglottic carcinoma. An unexpected need for a flap was solved by tunneling a FAMM island flap lateral to the mandible. The reach and size of the flap were sufficient to close the defect. ::: ::: ::: RESULTS ::: Vitality of the flap was ascertained by fiber endoscopy. No donor-site morbidity was seen. Postoperatively, the patient had a minor stomal fistula with spontaneous healing. ::: ::: ::: CONCLUSION ::: The pedicled FAMM island flap may be considered for smaller reconstructions of the upper hypopharynx. Accessibility, low donor-site morbidity, and recipient tissue similarity count among the advantages. © 2016 Wiley Periodicals, Inc. Head Neck 39: E29-E33, 2017.


title: The Kappa Statistic: A Second Look

abstract: In recent years, the kappa coefficient of agreement has become the de facto standard for evaluating intercoder agreement for tagging tasks. In this squib, we highlight issues that affect κ and that the community has largely neglected. First, we discuss the assumptions underlying different computations of the expected agreement component of κ. Second, we discuss how prevalence and bias affect the κ measure.


title: International trade drives biodiversity threats in developing nations

abstract: Human activities are causing Earth's sixth major extinction event-an accelerating decline of the world's stocks of biological diversity at rates 100 to 1,000 times pre-human levels. Historically, low-impact intrusion into species habitats arose from local demands for food, fuel and living space. However, in today's increasingly globalized economy, international trade chains accelerate habitat degradation far removed from the place of consumption. Although adverse effects of economic prosperity and economic inequality have been confirmed, the importance of international trade as a driver of threats to species is poorly understood. Here we show that a significant number of species are threatened as a result of international trade along complex routes, and that, in particular, consumers in developed countries cause threats to species through their demand of commodities that are ultimately produced in developing countries. We linked 25,000 Animalia species threat records from the International Union for Conservation of Nature Red List to more than 15,000 commodities produced in 187 countries and evaluated more than 5 billion supply chains in terms of their biodiversity impacts. Excluding invasive species, we found that 30% of global species threats are due to international trade. In many developed countries, the consumption of imported coffee, tea, sugar, textiles, fish and other manufactured items causes a biodiversity footprint that is larger abroad than at home. Our results emphasize the importance of examining biodiversity loss as a global systemic phenomenon, instead of looking at the degrading or polluting producers in isolation. We anticipate that our findings will facilitate better regulation, sustainable supply-chain certification and consumer product labelling.


title: Sparse dynamic programming I: linear cost functions

abstract: Dynamic programming solutions to a number of different recurrence equations for sequence comparison and for RNA secondary structure prediction are considered. These recurrences are defined over a number of points that is quadratic in the input size; however only a sparse set matters for the result. Efficient algorithms for these problems are given, when the weight functions used in the recurrences are taken to be linear. The time complexity of the algorithms depends almost linearly on the number of points that need to be considered; when the problems are sparse this results in a substantial speed-up over known algorithms.


title: Simple and accurate frequency to voltage converter

abstract: A new technique has been developed for frequency to voltage converter. This technique, based on a very simple operating principle, comprises a differentiator, two RMS-DC converters, and a divider. The proposed converter provides an accurate output and a linear transfer characteristic. Moreover, its output response has input-amplitude independent characteristic. The performance of this converter is evaluated by computer simulation. The simulation results are in good accordance with the theoretical analyses.


title: Featureless visual processing for SLAM in changing outdoor environments

abstract: Vision-based SLAM is mostly a solved problem providing clear, sharp images can be obtained. However, in outdoor environments a number of factors such as rough terrain, high speeds and hardware limitations can result in these conditions not being met. High speed transit on rough terrain can lead to image blur and under/over exposure, problems that cannot easily be dealt with using low cost hardware. Furthermore, recently there has been a growth in interest in lifelong autonomy for robots, which brings with it the challenge in outdoor environments of dealing with a moving sun and lack of constant artificial lighting. In this paper, we present a lightweight approach to visual localization and visual odometry that addresses the challenges posed by perceptual change and low cost cameras. The approach combines low resolution imagery with the SLAM algorithm, RatSLAM. We test the system using a cheap consumer camera mounted on a small vehicle in a mixed urban and vegetated environment, at times ranging from dawn to dusk and in conditions ranging from sunny weather to rain. We first show that the system is able to provide reliable mapping and recall over the course of the day and incrementally incorporate new visual scenes from different times into an existing map. We then restrict the system to only learning visual scenes at one time of day, and show that the system is still able to localize and map at other times of day. The results demonstrate the viability of the approach in situations where image quality is poor and environmental or hardware factors preclude the use of visual features.


title: Applications of MATLAB's Toolbox to Recognize Handwritten Characters Part 2: Experimental Results

abstract: Handwritten character recognition is a challenging task in the field of research on image processing, artificial intelligence as well as machine vision since the handwriting varies from person to person. Moreover, the handwriting styles, sizes and its orientation make it even more complex to interpret the text. The numerous applications of handwritten text in reading bank cheques, Zip Code recognition and in removing the problem of handling documents manually has made it necessary to acquire digitally formatted data. This paper presents the recognition of handwritten characters using either a scanned document, or direct acquisition of image using Matlab, followed by the implementation of various other Matlab toolboxes like Image Processing and Neural Network Toolbox to process the scanned or acquired image. Experimental Results are given to present the proposed model in order to recognize handwritten characters accurately.


title: Active learning for online bayesian matrix factorization

abstract: The problem of large-scale online matrix completion is addressed via a Bayesian approach. The proposed method learns a factor analysis (FA) model for large matrices, based on a small number of observed matrix elements, and leverages the statistical model to actively select which new matrix entries/observations would be most informative if they could be acquired, to improve the model; the model inference and active learning are performed in an online setting. In the context of online learning, a greedy, fast and provably near-optimal algorithm is employed to sequentially maximize the mutual information between past and future observations, taking advantage of submodularity properties. Additionally, a simpler procedure, which directly uses the posterior parameters learned by the Bayesian approach, is shown to achieve slightly lower estimation quality, with far less computational effort. Inference is performed using a computationally efficient online variational Bayes (VB) procedure. Competitive results are obtained in a very large collaborative filtering problem, namely the Yahoo! Music ratings dataset.


title: DeepPap: Deep Convolutional Networks for Cervical Cell Classification

abstract: Automation-assisted cervical screening via Pap smear or liquid-based cytology (LBC) is a highly effective cell imaging based cancer detection tool, where cells are partitioned into “abnormal” and “normal” categories. However, the success of most traditional classification methods relies on the presence of accurate cell segmentations. Despite sixty years of research in this field, accurate segmentation remains a challenge in the presence of cell clusters and pathologies. Moreover, previous classification methods are only built upon the extraction of hand-crafted features, such as morphology and texture. This paper addresses these limitations by proposing a method to directly classify cervical cells-without prior segmentation- based on deep features, using convolutional neural networks (ConvNets). First, the ConvNet is pretrained on a natural image dataset. It is subsequently fine-tuned on a cervical cell dataset consisting of adaptively resampled image patches coarsely centered on the nuclei. In the testing phase, aggregation is used to average the prediction scores of a similar set of image patches. The proposed method is evaluated on both Pap smear and LBC datasets. Results show that our method outperforms previous algorithms in classification accuracy (98.3%), area under the curve (0.99) values, and especially specificity (98.3%), when applied to the Herlev benchmark Pap smear dataset and evaluated using five-fold cross validation. Similar superior performances are also achieved on the HEMLBC (H&E stained manual LBC) dataset. Our method is promising for the development of automation-assisted reading systems in primary cervical screening.


title: On the Impact of Data Set Size in Transfer Learning Using Deep Neural Networks

abstract: In this paper we study the effect of target set size on transfer learning in deep learning convolutional neural networks. This is an important problem as labelling is a costly task, or for new or specific classes the number of labelled instances available may simply be too small. We present results for a series of experiments where we either train on a target of classes from scratch, retrain all layers, or subsequently lock more layers in the network, for the Tiny-ImageNet and MiniPlaces2 data sets. Our findings indicate that for smaller target data sets freezing the weights for the initial layers of the network gives better results on the target set classes. We present a simple and easy to implement training heuristic based on these findings.


title: Vision Based Hand Gesture Recognition for Human Computer Interaction

abstract: This project presents an approach to develop a real-time hand gesture recognition enabling human-computer interaction. It is "Vision Based" that uses only a webcam and Computer Vision (CV) technology, such as image processing that can recognize several hand gestures. The applications of real time hand gesture recognition are numerous, due to the fact that it can be used almost anywhere where we interact with computers ranging from basic usage which involves small applications to domain-specific specialized applications. Currently, at this level our project is useful for the society but it can further be expanded to be readily used at the industrial level as well. Gesture recognition is an area of active current research in computer vision. Existing systems use hand detection primarily with some type of marker. Our system, however, uses a real-time hand image recognition system. Our system, however, uses a real-time hand image recognition without any marker, simply using bare hands.


title: Estimating the Dynamics of Mutual Fund Alphas and Betas ∗

abstract: This article develops a Kalman filter model to track dynamic mutual fund factor loadings. It then uses the estimates to analyze whether managers with market-timing ability can be identified ex ante. The primary findings are as follows: (i) Ordinary least squares (OLS) timing models produce false positives (nonzero alphas) at too high a rate with either daily or monthly data. In contrast, the Kalman filter model produces them at approximately the correct rate with monthly data; (ii) In monthly data, though the OLS models fail to detect any timing among fund managers, the Kalman filter does; (iii) The alpha and beta forecasts from the Kalman model are more accurate than those from the OLS timing models; (iv) The Kalman filter model tracks most fund alphas and betas better than OLS models that employ macroeconomic variables in addition to fund returns.


title: Sesquiterpene lactones and diterpenes from Mikania periplocifolia

abstract: Abstract The aerial parts of two collections of Mikania periplocifolia gave, in addition to mikanolide, miscandenin and several other known sesquiterpene dilactones related to them, the new dilactones 3β-hydroxy-11βH,13-dihydroisabelin, 11βH,13-dihydrodeoxymikanolide and mikaperiplocolide and three new geranylnerol derivatives.


title: Find your way by observing the sun and other semantic cues

abstract: In this paper we present a robust, efficient and affordable approach to self-localization which does not require neither GPS nor knowledge about the appearance of the world. Towards this goal, we utilize freely available cartographic maps and derive a probabilistic model that exploits semantic cues in the form of sun direction, presence of an intersection, road type, speed limit as well as the ego-car trajectory in order to produce very reliable localization results. Our experimental evaluation shows that our approach can localize much faster (in terms of driving time) with less computation and more robustly than competing approaches, which ignore semantic information.


title: A C-SVM Based Anomaly Detection Method for Multi-Dimensional Sequence over Data Stream

abstract: Anomaly detection over multi-dimensional data stream has attracted considerable attention recently in various fields, such as network, finance and aerospace. In many cases, anomalies are composed of a sequence of multi-dimensional data, and it's necessary to detect this type of anomalies accurately and efficiently over data stream. Existing online methods of anomaly detection merely focus on the single-dimensional sequence. What's more, current studies about multi-dimensional sequence are mainly concentrated on static database. However, the anomaly detection for multi-dimensional sequence over data stream is much more difficult, due to the complexity of multidimensional sequence processing, the dynamic nature of data stream and the unbalance between normal and abnormal data. Facing these challenges, we propose an anomaly detection method for multi-dimensional sequence over data stream based on cost sensitive support vector machine (C-SVM) called ADMS. First, to improve the accuracy and efficiency, the ADMS transforms multi-dimensional sequences into feature vectors in a lossless way and prunes worthless features of these vectors. And then, the ADMS can detect abnormal sequences over dynamically imbalanced data stream by lively testing these vectors based on C-SVM. Experiments show that the false negative rate (FNR) of the ADMS is lower than 5%, the false positive rate (FPR) is lower than 7%, and the throughput is improved 42% by pruning worthless features. In addition, the AMDS performs well when there are concept drifts over the data stream.


High-Resolution Side Channels for Untrusted Operating Systems


On the Analysis and Detection of Mobile Botnet Applications.


Lowering the Cost of Improved Cross-Lingual Sentiment Analysis by


title: Sales Demand Forecast in E-commerce using a Long Short-Term Memory Neural Network Methodology

abstract: Generating accurate and reliable sales forecasts is crucial in the E-commerce business. The current state-of-the-art techniques are typically univariate methods, which produce forecasts considering only the historical sales data of a single product. However, in a situation where large quantities of related time series are available, conditioning the forecast of an individual time series on past behaviour of similar, related time series can be beneficial. Since the product assortment hierarchy in an E-commerce platform contains large numbers of related products, in which the sales demand patterns can be correlated, our attempt is to incorporate this cross-series information in a unified model. We achieve this by globally training a Long Short-Term Memory network (LSTM) that exploits the non-linear demand relationships available in an E-commerce product assortment hierarchy. Aside from the forecasting framework, we also propose a systematic pre-processing framework to overcome the challenges in the E-commerce business. We also introduce several product grouping strategies to supplement the LSTM learning schemes, in situations where sales patterns in a product portfolio are disparate. We empirically evaluate the proposed forecasting framework on a real-world online marketplace dataset from Walmart.com. Our method achieves competitive results on category level and super-departmental level datasets, outperforming state-of-the-art techniques.


title: Passage Re-ranking with BERT

abstract: Recently, neural models pretrained on a language modeling task, such as ELMo (Peters et al., 2017), OpenAI GPT (Radford et al., 2018), and BERT (Devlin et al., 2018), have achieved impressive results on various natural language processing tasks such as question-answering and natural language inference. In this paper, we describe a simple re-implementation of BERT for query-based passage re-ranking. Our system is the state of the art on the TREC-CAR dataset and the top entry in the leaderboard of the MS MARCO passage retrieval task, outperforming the previous state of the art by 27% (relative) in MRR@10. The code to reproduce our results is available at this https URL


title: Facial expression recognition for neonatal pain assessment

abstract: Facial expressions are considered a critical factor in neonatal pain assessment. This paper attempts to apply modern facial expression recognition techniques to the task of distinguishing pain expression from non-pain expression. Firstly, 2D Gabor filter is applied to extract the expression features from facial images. Then we apply Adaboost as a feature selection tool to remove the redundant Gabor features. Finally, the Gabor features selected by Adaboost are fed into the support vector machines (SVMs) for final classification. 510 facial images are investigated by using SVMs. The best recognition rates of pain versus non-pain (85.29%), pain versus calm (94.24%), pain versus cry (78.24%) were obtained from an SVM with a polynomial kernel of degree 3. The results of this study indicate that the application of SVM technique in pain assessment is a promising area of investigation.


title: Bi-directional regulation between adiponectin and plasminogen activator-inhibitor-1 in 3T3-L1 cells.

abstract: BACKGROUND ::: Adiponectin (APN) and plasminogen activator inhibitor-1 (Pai-1) are adipocytokines, and low levels of serum APN and high levels of PAI-1 are observed in obese patients. Moreover, both APN and Pai-1 are known to be involved in colorectal carcinogenesis. Recently, we demonstrated that serum Pai-1 levels are elevated in APN-deficient mice. We hypothesized that Pai-1 expression levels could be depressed by APN. Thus, we aimed to clarify the bi-directional regulatory mechanisms between APN and Pai-1. ::: ::: ::: MATERIALS AND METHODS ::: We investigated the expression levels of APN and Pai-1 during 3T3-L1 pre-adipocyte differentiation, and examined the role of AMP-activated protein kinase (AMPK) and peroxisome proliferator-activated receptor (PPAR)-γ on APN and Pai-1 expression at early and late differentiation stages. ::: ::: ::: RESULTS ::: In the early phase of differentiation, Pai-1 expression increased and APN slightly decreased. Reduction of Pai-1 or activation of PPARγ resulted in elevation of APN, and supplementation of APN with activation of AMPK resulted in reduction of Pai-1. In the late phase of differentiation, APN increased its expression and Pai-1 decreased. Supplementation of Pai-1 resulted in a slight reduction of APN. ::: ::: ::: CONCLUSION ::: It is suggested that APN and Pai-1 expressions are inversely-regulated. Understanding of the regulatory system between APN and Pai-1 may lead to finding novel methods for colorectal cancer prevention.


title: Recurrent Chinese Restaurant Process with a Duration-based Discount for Event Identification from Twitter

abstract: Due to the fast development of social media on the Web, Twitter has become one of the major platforms for people to express themselves. Because of the wide adoption of Twitter, events like breaking news and release of popular videos can easily catch people’s attention and spread rapidly on Twitter, and the number of relevant tweets approximately reflects the impact of an event. Event identification and analysis on Twitter has thus become an important task. Recently the Recurrent Chinese Restaurant Process (RCRP) has been successfully used for event identification from news streams and news-centric social media streams. However, these models cannot be directly applied to Twitter based on our preliminary experiments mainly for two reasons: (1) Events emerge and die out fast on Twitter, while existing models ignore this burstiness property. (2) Most Twitter posts are personal interest oriented while only a small fraction is event related. Motivated by these challenges, we propose a new nonparametric model which considers burstiness. We further combine this model with traditional topic models to identify both events and topics simultaneously. Our quantitative evaluation provides sufficient evidence that our model can accurately detect meaningful events. Our qualitative evaluation also shows interesting analysis for events on Twitter.


title: Towards Trainable Media: Using Waves for Neural Network-Style Training

abstract: In this paper we study the concept of using the interaction between waves and a trainable medium in order to construct a matrix-vector multiplier. In particular we study such a device in the context of the backpropagation algorithm, which is commonly used for training neural networks. Here, the weights of the connections between neurons are trained by multiplying a `forward' signal with a backwards propagating `error' signal. We show that this concept can be extended to trainable media, where the gradient for the local wave number is given by multiplying signal waves and error waves. We provide a numerical example of such a system with waves traveling freely in a trainable medium, and we discuss a potential way to build such a device in an integrated photonics chip.


title: The Faces of Engagement: Automatic Recognition of Student Engagementfrom Facial Expressions

abstract: Student engagement is a key concept in contemporary education, where it is valued as a goal in its own right. In this paper we explore approaches for automatic recognition of engagement from students’ facial expressions. We studied whether human observers can reliably judge engagement from the face; analyzed the signals observers use to make these judgments; and automated the process using machine learning. We found that human observers reliably agree when discriminating low versus high degrees of engagement (Cohen’s $\kappa = 0.96$ ). When fine discrimination is required (four distinct levels) the reliability decreases, but is still quite high ( $\kappa = 0.56$ ). Furthermore, we found that engagement labels of 10-second video clips can be reliably predicted from the average labels of their constituent frames (Pearson $r=0.85$ ), suggesting that static expressions contain the bulk of the information used by observers. We used machine learning to develop automatic engagement detectors and found that for binary classification (e.g., high engagement versus low engagement), automated engagement detectors perform with comparable accuracy to humans. Finally, we show that both human and automatic engagement judgments correlate with task performance. In our experiment, student post-test performance was predicted with comparable accuracy from engagement labels ( $r=0.47$ ) as from pre-test scores ( $r=0.44$ ).


title: Mining Causal Association Rules

abstract: Discovering causal relationships is the ultimate goal of many scientific explorations. Causal relationships can be identified with controlled experiments, but such experiments are often very expensive and sometimes impossible to conduct. On the other hand, the collection of observational data has increased dramatically in recent decades. Therefore it is desirable to find causal relationships from the data directly. Significant progress has been made in the field of discovering causal relationships using the Causal Bayesian Network (CBN) theory. The applications of CBNs, however, are greatly limited due to the high computational complexity. In another direction, association rule mining has been shown to be an efficient data mining means for relationship discovery. However, although causal relationships imply associations, the reverse does not always hold. In this paper we study how to use an efficient association mining approach to discover potential causal rules in observational data. We make use of the idea of retrospective cohort studies, a widely used approach in medical and social research, to detect causal association rules. In comparison with the constraint-based methods within the CBN paradigm, the proposed approach is faster and is capable of finding a cause consisting of combined variables.


title: Trapping and cooling of 174Yb+ ions in a microfabricated surface trap

abstract: Trapped atomic ions are the leading candidate to act as individual quantum bits in a large quantum information processing system. Ion traps can be conformed to a two-dimensional surface, which provides a way to increase the number of ions while maintaining the ability to move individual ions in a two-dimensional grid. However, surface traps tend to suffer from shallow trap depths and higher motional heating rates. In this work, we report the design, fabrication, and testing of a simple gold-on-fused-silica ion trap optimized for a deep trapping potential and stable motional modes. The desirable trap characteristics include long lifetime, fully-compensated micromotion, and high secular trap frequencies that were measured using a trapped and cooled 174Yb+ ion. This trap design can be integrated with an optical cavity to enhance the ion-photon coupling.


title: Investigating Software Standards: A Lens of Sustainability for Software Crowdsourcing

abstract: Inevitably, the time has come that necessitates software systems to have long lasting social impact and environmental friendliness. Although sustainability related to environmental and social aspects have been well versed in the literature, software sustainability, for the way of development and as final product for use, is still not mature enough. With the increasing role of software crowdsourcing, a new working architecture for software development, in global software development (GSD) settings, has emerged challenges regarding sustainable software development and software sustainability, which are still to be elucidated. Software standards and models exist to assist co-located and global software development; however, seismic changes in the working architecture of software development as software crowdsourcing are highlighting a risk of software failure. Recently, statistics showed that only 30% of projects are successful, whereas 50% encounter challenges and the remaining 20% are absolute failures that implicate a challenge toward software success and its sustainability. This posits questions of how many IEEE and international standard organization standards exist that cover the sustainability aspects in particular to software development and how in-depth the development practices are addressing sustainable aspects in software crowdsourcing? Thus, the aim of this paper is to examine the IEEE software standards with a lens of sustainability to software crowdsourcing. After reviewing the existing IEEE standards related to sustainability, it has been observed that few standards exist which have discussed the importance of sustainability on an abstract level (energy and examining environmental influence by the computers) in software development. Moreover, standards are focusing less attention toward the sustainability of software development in the presence of new envision toward software crowdsourcing. Thus, a lack of any guideline toward sustainable software crowdsourcing is highlighted as one of the limitations in the software standards. This paper contributes to the GSD industry in two folds, first by laying down the importance and theoretical association on and around sustainable crowdsourcing, and second by highlighting that the IEEE standards, inadequacy toward the suppressing phenomena of sustainability in software crowdsourcing, if addressed adequately, can minimize the ripple effect toward software failure.


title: A survey on hardware-aware and heterogeneous computing on multicore processors and accelerators

abstract: In the last few years, the landscape of parallel computing has been subject to profound and highly dynamic changes. The paradigm shift towards multicore and manycore technologies coupled with accelerators in a heterogeneous environment is offering a great potential of computing power for scientific and industrial applications. However, for one to take full advantage of these new technologies, holistic approaches coupling the expertise ranging from hardware architecture and software design to numerical algorithms are a pressing necessity. Parallel computing is no longer limited to supercomputers and is now much more diversified – with a multitude of technologies, architectures, and programming approaches leading to increased complexity for developers and engineers. ::: ::: In this work, we give – from the perspective of numerical simulation and applications – an overview of existing and emerging multicore and manycore technologies as well as accelerator concepts. We emphasize the challenges associated with high-performance heterogeneous computing and discuss the interfaces needed to fill the gap between the hardware architecture and the implementation of efficient numerical algorithms. By means of this short survey – which stresses the necessity of hardware-aware computing – we aim at giving assistance to users in scientific computing entering this fascinating field and help understanding associated issues and capabilities. Copyright © 2011 John Wiley & Sons, Ltd.


title: Selecting skyline services for QoS-based web service composition

abstract: Web service composition enables seamless and dynamic integration of business applications on the web. The performance of the composed application is determined by the performance of the involved web services. Therefore, non-functional, quality of service aspects are crucial for selecting the web services to take part in the composition. Identifying the best candidate web services from a set of functionally-equivalent services is a multi-criteria decision making problem. The selected services should optimize the overall QoS of the composed application, while satisfying all the constraints specified by the client on individual QoS parameters. In this paper, we propose an approach based on the notion of skyline to effectively and efficiently select services for composition, reducing the number of candidate services to be considered. We also discuss how a provider can improve its service to become more competitive and increase its potential of being included in composite applications. We evaluate our approach experimentally using both real and synthetically generated datasets.


title: LIBRA: Lightweight Data Skew Mitigation in MapReduce

abstract: MapReduce is an effective tool for parallel data processing. One significant issue in practical MapReduce applications is data skew: the imbalance in the amount of data assigned to each task. This causes some tasks to take much longer to finish than others and can significantly impact performance. This paper presents LIBRA, a lightweight strategy to address the data skew problem among the reducers of MapReduce applications. Unlike previous work, LIBRA does not require any pre-run sampling of the input data or prevent the overlap between the map and the reduce stages. It uses an innovative sampling method which can achieve a highly accurate approximation to the distribution of the intermediate data by sampling only a small fraction of the intermediate data during the normal map processing. It allows the reduce tasks to start copying as soon as the chosen sample map tasks (only a small fraction of map tasks which are issued first) complete. It supports the split of large keys when application semantics permit and the total order of the output data. It considers the heterogeneity of the computing resources when balancing the load among the reduce tasks appropriately. LIBRA is applicable to a wide range of applications and is transparent to the users. We implement LIBRA in Hadoop and our experiments show that LIBRA has negligible overhead and can speed up the execution of some popular applications by up to a factor of 4.


title: Probabilistic inference using stochastic spiking neural networks on a neurosynaptic processor

abstract: Spiking neural networks are rapidly gaining popularity for their ability to perform efficient computation akin to the way a brain processes information. It has the potential to achieve low cost and high energy efficiency due to the distributed nature of neural computation and the use of low energy spikes for information exchange. A stochastic spiking neural network naturally can be used to realize Bayesian inference. IBM's TrueNorth is a neurosynaptic processor that has more than 1 million digital spiking neurons and 268 million digital synapses with less than 200 mW peak power. In this paper we propose the first work that converts an inference network to a spiking neural network that runs on the TrueNorth processor. Using inference-based sentence construction as a case study, we discuss algorithms that transform an inference network to a spiking neural network, and a spiking neural network to TrueNorth corelet designs. In our experiments, the TrueNorth spiking neural network constructed sentences have a matching accuracy of 88% while consuming an average power of 0.205 mW.


title: Understanding User Profiles on Social Media for Fake News Detection

abstract: Consuming news from social media is becoming increasingly popular nowadays. Social media brings benefits to users due to the inherent nature of fast dissemination, cheap cost, and easy access. However, the quality of news is considered lower than traditional news outlets, resulting in large amounts of fake news. Detecting fake news becomes very important and is attracting increasing attention due to the detrimental effects on individuals and the society. The performance of detecting fake news only from content is generally not satisfactory, and it is suggested to incorporate user social engagements as auxiliary information to improve fake news detection. Thus it necessitates an in-depth understanding of the correlation between user profiles on social media and fake news. In this paper, we construct real-world datasets measuring users trust level on fake news and select representative groups of both “experienced” users who are able to recognize fake news items as false and “naive” users who are more likely to believe fake news. We perform a comparative analysis over explicit and implicit profile features between these user groups, which reveals their potential to differentiate fake news. The findings of this paper lay the foundation for future automatic fake news detection research.


Classifying the Histology Image of Uterine Cervical Cancer


title: On Full Jacobian Decomposition of the Augmented Lagrangian Method for Separable Convex Programming

abstract: The augmented Lagrangian method (ALM) is a benchmark for solving a convex minimization model with linear constraints. We consider the special case where the objective is the sum of $m$ functions without coupled variables. For solving this separable convex minimization model, it is usually required to decompose the ALM subproblem at each iteration into $m$ smaller subproblems, each of which only involves one function in the original objective. Easier subproblems capable of taking full advantage of the functions' properties individually could thus be generated. In this paper, we focus on the case where full Jacobian decomposition is applied to ALM subproblems, i.e., all the decomposed ALM subproblems are eligible for parallel computation at each iteration. For the first time, we show, by an example, that the ALM with full Jacobian decomposition could be divergent. To guarantee the convergence, we suggest combining a relaxation step and the output of the ALM with full Jacobian decomposition. A novel analysis...


title: Truth Serums for Massively Crowdsourced Evaluation Tasks

abstract: Incentivizing effort and eliciting truthful responses from agents in the absence of verifiability is a major challenge faced while crowdsourcing many types of evaluation tasks like labeling images, grading assignments in online courses, etc. In this paper, we propose new reward mechanisms for such settings that, unlike most previously studied mechanisms, impose minimal assumptions on the structure and knowledge of the underlying generating model, can account for heterogeneity in the agents' abilities, require no extraneous elicitation from them, and furthermore allow their beliefs to be (almost) arbitrary. Moreover, these mechanisms have the simple and intuitive structure of output agreement mechanisms, which, despite not incentivizing truthful behavior, have nevertheless been quite popular in practice. We achieve this by leveraging a typical characteristic of many of these settings, which is the existence of a large number of similar tasks.


Data Driven Approaches in Digital Education


title: Learning the kernel matrix in discriminant analysis via quadratically constrained quadratic programming

abstract: The kernel function plays a central role in kernel methods. In this paper, we consider the automated learning of the kernel matrix over a convex combination of pre-specified kernel matrices in Regularized Kernel Discriminant Analysis (RKDA), which performs lineardiscriminant analysis in the feature space via the kernel trick. Previous studies have shown that this kernel learning problem can be formulated as a semidefinite program (SDP), which is however computationally expensive, even with the recent advances in interior point methods. Based on the equivalence relationship between RKDA and least square problems in the binary-class case, we propose a Quadratically Constrained Quadratic Programming (QCQP) formulation for the kernel learning problem, which can be solved more efficiently than SDP. While most existing work on kernel learning deal with binary-class problems only, we show that our QCQP formulation can be extended naturally to the multi-class case. Experimental results on both binary-class and multi-class benchmarkdata sets show the efficacy of the proposed QCQP formulations.


title: Modeling Order in Neural Word Embeddings at Scale

abstract: Natural Language Processing (NLP) systems commonly leverage bag-of-words co-occurrence techniques to capture semantic and syntactic word relationships. The resulting word-level distributed representations often ignore morphological information, though character-level embeddings have proven valuable to NLP tasks. We propose a new neural language model incorporating both word order and character order in its embedding. The model produces several vector spaces with meaningful substructure, as evidenced by its performance of 85.8% on a recent word-analogy task, exceeding best published syntactic word-analogy scores by a 58% error margin (Pennington et al., 2014). Furthermore, the model includes several parallel training methods, most notably allowing a skip-gram network with 160 billion parameters to be trained overnight on 3 multi-core CPUs, 14x larger than the previous largest neural network (Coates et al., 2013).


title: MUlti-Store Tracker (MUSTer): A cognitive psychology inspired approach to object tracking

abstract: Variations in the appearance of a tracked object, such as changes in geometry/photometry, camera viewpoint, illumination, or partial occlusion, pose a major challenge to object tracking. Here, we adopt cognitive psychology principles to design a flexible representation that can adapt to changes in object appearance during tracking. Inspired by the well-known Atkinson-Shiffrin Memory Model, we propose MUlti-Store Tracker (MUSTer), a dual-component approach consisting of short- and long-term memory stores to process target appearance memories. A powerful and efficient Integrated Correlation Filter (ICF) is employed in the short-term store for short-term tracking. The integrated long-term component, which is based on keypoint matching-tracking and RANSAC estimation, can interact with the long-term memory and provide additional information for output control. MUSTer was extensively evaluated on the CVPR2013 Online Object Tracking Benchmark (OOTB) and ALOV++ datasets. The experimental results demonstrated the superior performance of MUSTer in comparison with other state-of-art trackers.


title: High-resolution electrohydrodynamic jet printing

abstract: Efforts to adapt and extend graphic arts printing techniques for demanding device applications in electronics, biotechnology and microelectromechanical systems have grown rapidly in recent years. Here, we describe the use of electrohydrodynamically induced fluid flows through fine microcapillary nozzles for jet printing of patterns and functional devices with submicrometre resolution. Key aspects of the physics of this approach, which has some features in common with related but comparatively low-resolution techniques for graphic arts, are revealed through direct high-speed imaging of the droplet formation processes. Printing of complex patterns of inks, ranging from insulating and conducting polymers, to solution suspensions of silicon nanoparticles and rods, to single-walled carbon nanotubes, using integrated computer-controlled printer systems illustrates some of the capabilities. High-resolution printed metal interconnects, electrodes and probing pads for representative circuit patterns and functional transistors with critical dimensions as small as 1 mu m demonstrate potential applications in printed electronics.


title: Multilayer Brain Networks

abstract: The field of neuroscience is facing an unprecedented expanse in the volume and diversity of available data. Traditionally, network models have provided key insights into the structure and function of the brain. With the advent of big data in neuroscience, both more sophisticated models capable of characterizing the increasing complexity of the data and novel methods of quantitative analysis are needed. Recently multilayer networks, a mathematical extension of traditional networks, have gained increasing popularity in neuroscience due to their ability to capture the full information of multi-model, multi-scale, spatiotemporal data sets. Here, we review multilayer networks and their applications in neuroscience, showing how incorporating the multilayer framework into network neuroscience analysis has uncovered previously hidden features of brain networks. We specifically highlight the use of multilayer networks to model disease, structure-function relationships, network evolution, and link multi-scale data. Finally, we close with a discussion of promising new directions of multilayer network neuroscience research and propose a modified definition of multilayer networks designed to unite and clarify the use of the multilayer formalism in describing real-world systems.


Vision-Enhanced Lidar Odometry and Mapping


title: When is Chemical Similarity Significant? The Statistical Distribution of Chemical Similarity Scores and Its Extreme Values

abstract: As repositories of chemical molecules continue to expand and become more open, it becomes increasingly important to develop tools to search them efficiently and assess the statistical significance of chemical similarity scores. Here, we develop a general framework for understanding, modeling, predicting, and approximating the distribution of chemical similarity scores and its extreme values in large databases. The framework can be applied to different chemical representations and similarity measures but is demonstrated here using the most common binary fingerprints with the Tanimoto similarity measure. After introducing several probabilistic models of fingerprints, including the Conditional Gaussian Uniform model, we show that the distribution of Tanimoto scores can be approximated by the distribution of the ratio of two correlated Normal random variables associated with the corresponding unions and intersections. This remains true also when the distribution of similarity scores is conditioned on the size of the query molecules to derive more fine-grained results and improve chemical retrieval. The corresponding extreme value distributions for the maximum scores are approximated by Weibull distributions. From these various distributions and their analytical forms, Z-scores, E-values, and p-values are derived to assess the significance of similarity scores. In addition, the framework also allows one to predict the value of standard chemical retrieval metrics, such as sensitivity and specificity at fixed thresholds, or receiver operating characteristic (ROC) curves at multiple thresholds, and to detect outliers in the form of atypical molecules. Numerous and diverse experiments that have been performed, in part with large sets of molecules from the ChemDB, show remarkable agreement between theory and empirical results.


title: Multi-View Incident Ticket Clustering for Optimal Ticket Dispatching

abstract: We present a novel technique that optimizes the dispatching of incident tickets to the agents in an IT Service Support Environment. Unlike the common skill-based dispatching, our approach also takes empirical evidence on the agent's speed from historical data into account. Our solution consists of two parts. First, a novel technique clusters historic tickets into incident categories that are discriminative in terms of agent's performance. Second, a dispatching policy selects, for an incoming ticket, the fastest available agent according to the target cluster. We show that, for ticket data collected from several Service Delivery Units, our new dispatching technique can reduce service time between $35\%$ and $44\%$.


title: Navigation Among Movable Obstacles with learned dynamic constraints

abstract: In this paper we present the first planner for the problem of Navigation Among Movable Obstacles (NAMO) on a real robot that can handle environments with under-specified object dynamics. This result makes use of recent progress from two threads of the Reinforcement Learning literature. The first is a hierarchical Markov-Decision Process formulation of the NAMO problem designed to handle dynamics uncertainty. The second is a physics-based Reinforcement Learning framework which offers a way to ground this uncertainty in a compact model space that can be efficiently updated from data received by the robot online. Our results demonstrate the ability of a robot to adapt to unexpected object behavior in a real office scenario.


title: How to Explain Individual Classification Decisions

abstract: After building a classifier with modern tools of machine learning we typically have a black box at hand that is able to predict well for unseen data. Thus, we get an answer to the question what is the most likely label of a given unseen data point. However, most methods will provide no answer why the model predicted the particular label for a single instance and what features were most influential for that particular instance. The only method that is currently able to provide such explanations are decision trees. This paper proposes a procedure which (based on a set of assumptions) allows to explain the decisions of any classification method.


title: Smart wireless sensor network management based on software-defined networking

abstract: In this position paper, we propose the use of software-defined networking (SDN) in wireless sensor networks (WSNs) for smart management. We argue that smart management using SDN promises a solution to some of inherent problems in WSN management. Furthermore, we propose a generic architecture for a base station in a software-defined wireless sensor network. We also propose a general framework for a software-defined wireless sensor network where the controller is implemented at the base station. We then raise some important questions that need to be investigated in future research in software-defined wireless sensor networks.


title: Designing CAPTCHA Algorithm: Splitting and Rotating the Images against OCRs

abstract: Internet is being used for several activities by a great range of users. These activities include communication, e-commerce, education, and entertainment. Users are required to register regarding Website in order to enroll Web activities. However, registration can be done by automated hacking software. That software make false enrollments which occupy the resources of the Website by reducing the performance and efficiency of servers, even stop the entire Web service. It is crucial for the Websites to have a system which has the capability of differing human users and computer programs in reading images of text. Completely automated public Turing test to tell computers and human apart (CAPTCHA) is such a defense system against optical character recognition (OCR) software. OCR can be defined as a software which work for defeating CAPTCHA images and make countless number of registration on the Websites. This study focuses on a new method which is splitting CAPTCHA images into several parts with random rotation values, and drawing random lines on a grid background. Lines are in the same color with the CAPTCHA text and they provide a distortion of image with grid background. In this paper, the algorithm of our method is introduced in detail.


title: Personalized click shaping through lagrangian duality for online recommendation

abstract: Online content recommendation aims to identify trendy articles in a continuously changing dynamic content pool. Most of existing works rely on online user feedback, notably clicks, as the objective and maximize it by showing articles with highest click-through rates. Recently, click shaping was introduced to incorporate multiple objectives in a constrained optimization framework. The work showed that significant tradeoff among the competing objectives can be observed and thus it is important to consider multiple objectives. However, the proposed click shaping approach is segment-based and can only work with a few non-overlapping user segments. It remains a challenge of how to enable deep personalization in click shaping. In this paper, we tackle the challenge by proposing personalized click shaping. The main idea is to work with the Lagrangian duality formulation and explore strong convexity to connect dual and primal solutions. We show that our formulation not only allows efficient conversion from dual to primal for online personalized serving, but also enables us to solve the optimization faster by approximation. We conduct extensive experiments on a large real data set and our experimental results show that the personalized click shaping can significantly outperform the segmented one, while achieving the same ability to balance competing objectives.


title: Tabulator: Exploring and analyzing linked data on the semantic web

abstract: A web of linked RDF data may be enabled by standards specifying how links should be made in RDF and under what conditions they should be followed as well as powerful generic RDF browsers that can traverse an open web of RDF resources. The Tabulator is an RDF browser, which is designed both for new users to provoke interest in the Semantic Web and give them a means to access and interact with the entire web of RDF data, and for developers of RDF content to provide incentive for them to post their data in RDF, to refine and promote RDF linking standards, and to let providers see how their data interacts with the rest of the Semantic Web. A challenge for Semantic Web browsers is to bring the power of domain-specific applications to a generic program when new unexpected domains can be encountered in real time. The Tabulator project is an attempt to demonstrate and utilize the power of linked RDF data with a user-friendly Semantic Web browser that is able to recognize and follow RDF links to other RDF resources based on the user’s exploration and analysis.


title: Predictive data mining in clinical medicine: a focus on selected methods and applications

abstract: Predictive data mining in clinical medicine deals with learning models to predict patients' health. The models can be devoted to support clinicians in diagnostic, therapeutic, or monitoring tasks. Data mining methods are usually applied in clinical contexts to analyze retrospective data, thus giving healthcare professionals the opportunity to exploit large amounts of data routinely collected during their day-by-day activity. Moreover, clinicians can nowadays take advantage of data mining techniques to deal with the huge amount of research results obtained by molecular medicine, such as genetic or genomic signatures, which may allow transition from population-based to personalized medicine. The current challenge is to exploit data mining to build models able to take into account the dynamic and temporal nature of clinical care and to exploit the variety of information available at the bedside. This review describes the main features of predictive clinical data mining and focus on two specific aspects of particular interest: the methods able to deal with temporal data and the efforts performed to translate molecular medicine results into clinically useful data mining models. © 2011 John Wiley & Sons, Inc. WIREs Data Mining Knowl Discov 2011 1 416-430 DOI: 10.1002/widm.23


title: Intelligent sleep stage mining service with smartphones

abstract: Sleep quality plays a significant role in personal health. A great deal of effort has been paid to design sleep quality monitoring systems, providing services ranging from bedtime monitoring to sleep activity detection. However, as sleep quality is closely related to the distribution of sleep duration over different sleep stages, neither the bedtime nor the intensity of sleep activities is able to reflect sleep quality precisely. To this end, we present Sleep Hunter, a mobile service that provides a fine-grained detection of sleep stage transition for sleep quality monitoring and intelligent wake-up call. The rationale is that each sleep stage is accompanied by specific yet distinguishable body movements and acoustic signals. Leveraging the built-in sensors on smartphones, Sleep Hunter integrates these physical activities with sleep environment, inherent temporal relation and personal factors by a statistical model for a fine-grained sleep stage detection. Based on the duration of each sleep stage, Sleep Hunter further provides sleep quality report and smart call service for users. Experimental results from over 30 sets of nocturnal sleep data show that our system is superior to existing actigraphy-based sleep quality monitoring systems, and achieves satisfying detection accuracy compared with dedicated polysomnography-based devices.


title: A Systematic Review and Meta-Analysis of Mindfulness-Based (Baduanjin) Exercise for Alleviating Musculoskeletal Pain and Improving Sleep Quality in People with Chronic Diseases

abstract: Objective: we performed the first systematic review with meta-analyses of the existing studies that examined mindfulness-based Baduanjin exercise for its therapeutic effects for individuals with musculoskeletal pain or insomnia. Methods: Both English- (PubMed, Web of Science, Elsevier, and Google Scholar) and Chinese-language (CNKI and Wangfang) electronic databases were used to search relevant articles. We used a modified PEDro scale to evaluate risk of bias across studies selected. All eligible RCTS were considered for meta-analysis. The standardized mean difference was calculated for the pooled effects to determine the magnitude of the Baduanjin intervention effect. For the moderator analysis, we performed subgroup meta-analysis for categorical variables and meta-regression for continuous variables. Results: The aggregated result has shown a significant benefit in favour of Baduanjin at alleviating musculoskeletal pain (SMD = −0.88, 95% CI −1.02 to −0.74, p < 0.001, I2 = 10.29%) and improving overall sleep quality (SMD = −0.48, 95% CI −0.95 to −0.01, p = 004, I2 = 84.42%). Conclusions: Mindfulness-based Baduanjin exercise may be effective for alleviating musculoskeletal pain and improving overall sleep quality in people with chronic illness. Large, well-designed RCTs are needed to confirm these findings.


title: Scientific Information Extraction with Semi-supervised Neural Tagging

abstract: This paper addresses the problem of extracting keyphrases from scientific articles and categorizing them as corresponding to a task, process, or material. We cast the problem as sequence tagging and introduce semi-supervised methods to a neural tagging model, which builds on recent advances in named entity recognition. Since annotated training data is scarce in this domain, we introduce a graph-based semi-supervised algorithm together with a data selection scheme to leverage unannotated articles. Both inductive and transductive semi-supervised learning strategies outperform state-of-the-art information extraction performance on the 2017 SemEval Task 10 ScienceIE task.


title: Knowledge-based Transfer Learning Explanation

abstract: Machine learning explanation can significantly boost machine learning's application in decision making, but the usability of current methods is limited in human-centric explanation, especially for transfer learning, an important machine learning branch that aims at utilizing knowledge from one learning domain (i.e., a pair of dataset and prediction task) to enhance prediction model training in another learning domain. In this paper, we propose an ontology-based approach for human-centric explanation of transfer learning. Three kinds of knowledge-based explanatory evidence, with different granularities, including general factors, particular narrators and core contexts are first proposed and then inferred with both local ontologies and external knowledge bases. The evaluation with US flight data and DBpedia has presented their confidence and availability in explaining the transferability of feature representation in flight departure delay forecasting.


title: Cryptographic Hash-Function Basics: Definitions, Implications, and Separations for Preimage Resistance, Second-Preimage Resistance, and Collision Resistance

abstract: We consider basic notions of security for cryptographic hash functions: collision resistance, preimage resistance, and second-preimage resistance. We give seven different definitions that correspond to these three underlying ideas, and then we work out all of the implications and separations among these seven definitions within the concrete-security, provable-security framework. Because our results are concrete, we can show two types of implications, conventional and provisional, where the strength of the latter depends on the amount of compression achieved by the hash function. We also distinguish two types of separations, conditional and unconditional. When constructing counterexamples for our separations, we are careful to preserve specified hash-function domains and ranges; this rules out some pathological counterexamples and makes the separations more meaningful in practice. Four of our definitions are standard while three appear to be new; some of our relations and separations have appeared, others have not. Here we give a modern treatment that acts to catalog, in one place and with carefully-considered nomenclature, the most basic security notions for cryptographic hash functions.


title: Deep Multi-modal Vehicle Detection in Aerial ISR Imagery

abstract: Since the introduction of deep convolutional neural networks (CNNs), object detection in imagery has witnessed substantial breakthroughs in state-of-the-art performance. The defense community utilizes overhead image sensors that acquire large field-of-view aerial imagery in various bands of the electromagnetic spectrum, which is then exploited for various applications, including the detection and localization of man-made objects. In this work, we utilize a recent state-of-the art object detection algorithm, faster R-CNN, to train a deep CNN for vehicle detection in multimodal imagery. We utilize the vehicle detection in aerial imagery (VEDAI) dataset, which contains overhead imagery that is representative of an ISR setting. Our contribution includes modification of key parameters in the faster R-CNN algorithm for this setting where the objects of interest are spatially small, occupying less than 1:5×10-3 of the total image pixels. Our experiments show that (1) an appropriately trained deep CNN leads to average precision rates above 93% on vehicle detection, and (2) transfer learning between imagery modalities is possible, yielding average precision rates above 90% in the absence of fine-tuning.


Securing the Software Defined Network Control Layer.


title: A Systematic Assessment of Smartphone Usage Gaps

abstract: Researchers who analyse smartphone usage logs often make the assumption that users who lock and unlock their phone for brief periods of time (e.g., less than a minute) are continuing the same "session" of interaction. However, this assumption is not empirically validated, and in fact different studies apply different arbitrary thresholds in their analysis. To validate this assumption, we conducted a field study where we collected user-labelled activity data through ESM and sensor logging. Our results indicate that for the majority of instances where users return to their smartphone, i.e., unlock their device, they in fact begin a new session as opposed to continuing a previous one. Our findings suggest that the commonly used approach of ignoring brief standby periods is not reliable, but optimisation is possible. We therefore propose various metrics related to usage sessions and evaluate various machine learning approaches to classify gaps in usage.


title: Textbook Question Answering Under Instructor Guidance with Memory Networks

abstract: Textbook Question Answering (TQA) is a task to choose the most proper answers by reading a multi-modal context of abundant essays and images. TQA serves as a favorable test bed for visual and textual reasoning. However, most of the current methods are incapable of reasoning over the long contexts and images. To address this issue, we propose a novel approach of Instructor Guidance with Memory Networks (IGMN) which conducts the TQA task by finding contradictions between the candidate answers and their corresponding context. We build the Contradiction Entity-Relationship Graph (CERG) to extend the passage-level multi-modal contradictions to an essay level. The machine thus performs as an instructor to extract the essay-level contradictions as the Guidance. Afterwards, we exploit the memory networks to capture the information in the Guidance, and use the attention mechanisms to jointly reason over the global features of the multi-modal input. Extensive experiments demonstrate that our method outperforms the state-of-the-arts on the TQA dataset. The source code is available at https://github.com/freerailway/igmn.


title: AVIST: A GPU-Centric Design for Visual Exploration of Large Multidimensional Datasets

abstract: This paper presents the Animated VISualization Tool (AVIST), an exploration-oriented data visualization tool that enables rapidly exploring and filtering large time series multidimensional datasets. AVIST highlights interactive data exploration by revealing fine data details. This is achieved through the use of animation and cross-filtering interactions. To support interactive exploration of big data, AVIST features a GPU (Graphics Processing Unit)-centric design. Two key aspects are emphasized on the GPU-centric design: (1) both data management and computation are implemented on the GPU to leverage its parallel computing capability and fast memory bandwidth; (2) a GPU-based directed acyclic graph is proposed to characterize data transformations triggered by users’ demands. Moreover, we implement AVIST based on the Model-View-Controller (MVC) architecture. In the implementation, we consider two aspects: (1) user interaction is highlighted to slice big data into small data; and (2) data transformation is based on parallel computing. Two case studies demonstrate how AVIST can help analysts identify abnormal behaviors and infer new hypotheses by exploring big datasets. Finally, we summarize lessons learned about GPU-based solutions in interactive information visualization with big data.


title: ISoTrustSeq: a social recommender system based on implicit interest, trust and sequential behaviors of users using matrix factorization

abstract: Recommender systems try to propose a list of main interests of an on line social network user based on his predicted rating values. In the recent years, several methods are proposed such as Interest Social Recommendation method (ISoRec), and Social Recommendation method based on trust Sequence Matrix Factorization which employs matrix factorization techniques to address the trust propagation and sequential behaviors issues. Main drawback of these works is that they ignore implicit interest of users. Therefore, the main goal of this paper is to solve user-item rating based on the trust, sequential interest and the implicit interest of users, simultaneously. In order to solve this problem, our proposed method combines these parameters as its inputs. This method based on matrix factorization named as ISoTrustSeq. Experimental results show higher accuracy of predicted values in compared to the above-mentioned methods. Our results are also much better than these methods in terms of variation in the number of user-items features.