title: Photosynthesis under drought and salt stress: regulation mechanisms from whole plant to cell.

abstract: BACKGROUND ::: Plants are often subjected to periods of soil and atmospheric water deficits during their life cycle as well as, in many areas of the globe, to high soil salinity. Understanding how plants respond to drought, salt and co-occurring stresses can play a major role in stabilizing crop performance under drought and saline conditions and in the protection of natural vegetation. Photosynthesis, together with cell growth, is among the primary processes to be affected by water or salt stress. ::: ::: ::: SCOPE ::: The effects of drought and salt stresses on photosynthesis are either direct (as the diffusion limitations through the stomata and the mesophyll and the alterations in photosynthetic metabolism) or secondary, such as the oxidative stress arising from the superimposition of multiple stresses. The carbon balance of a plant during a period of salt/water stress and recovery may depend as much on the velocity and degree of photosynthetic recovery, as it depends on the degree and velocity of photosynthesis decline during water depletion. Current knowledge about physiological limitations to photosynthetic recovery after different intensities of water and salt stress is still scarce. From the large amount of data available on transcript-profiling studies in plants subjected to drought and salt it is becoming apparent that plants perceive and respond to these stresses by quickly altering gene expression in parallel with physiological and biochemical alterations; this occurs even under mild to moderate stress conditions. From a recent comprehensive study that compared salt and drought stress it is apparent that both stresses led to down-regulation of some photosynthetic genes, with most of the changes being small (ratio threshold lower than 1) possibly reflecting the mild stress imposed. When compared with drought, salt stress affected more genes and more intensely, possibly reflecting the combined effects of dehydration and osmotic stress in salt-stressed plants.


title: PANTO -- a portable natural language interface to ontologies

abstract: Providing a natural language interface to ontologies will not only offer ordinary users the convenience of acquiring needed information from ontologies, but also expand the influence of ontologies and the semantic web consequently. This paper presents PANTO, a Portable nAtural laNguage inTerface to Ontologies, which accepts generic natural language queries and outputs SPARQL queries. Based on a special consideration on nominal phrases, it adopts a triple-based data model to interpret the parse trees output by an off-the-shelf parser. Complex modifications in natural language queries such as negations, superlative and comparative are investigated. The experiments have shown that PANTO provides state-of-the-art results.


title: Classification Under Streaming Emerging New Classes: A Solution Using Completely-Random Trees

abstract: This paper investigates an important problem in stream mining, i.e., classification under streaming emerging new classes or SENC . The SENC problem can be decomposed into three subproblems: detecting emerging new classes, classifying known classes, and updating models to integrate each new class as part of known classes. The common approach is to treat it as a classification problem and solve it using either a supervised learner or a semi-supervised learner. We propose an alternative approach by using unsupervised learning as the basis to solve this problem. The proposed method employs completely-random trees which have been shown to work well in unsupervised learning and supervised learning independently in the literature. The completely-random trees are used as a single common core to solve all three subproblems: unsupervised learning, supervised learning, and model update on data streams. We show that the proposed unsupervised-learning-focused method often achieves significantly better outcomes than existing classification-focused methods.


title: Effect of web-based assertion training for stress management of Japanese nurses

abstract: Aim and Background The purpose of this study was to examine the effects of web-based assertion training programmes for Japanese hospital nurses based on their assertion knowledge, attitude and behaviour, job stress and depression. Job stress has been reported to be high among Japanese hospital nurses, and it is thought that assertion, one type of communication skill, could help nurses to better manage their job stress. ::: ::: Method Twenty-five nurses from an urban tertiary hospital in western Japan completed the 70-minute assertion programme during 3 weeks. The changes between pretraining and post-training and between pretraining and 1 month after the training were tested. ::: ::: Result Results showed that assertion knowledge and voluntary behaviour in assertive behaviour had increased at post-training and remained higher a month later. With regard to job stress, mental workload decreased. ::: ::: Conclusion The results show the effects of web-based assertion training on assertion skills and stress management for Japanese hospital nurses.


title: Renewing and Revising SemLink

abstract: This research describes SemLink, a comprehensive resource for Natural Language Processing that maps and unifies several highquality lexical resources: PropBank, VerbNet, FrameNet, and the recently added OntoNotes sense groupings. Each of these resources was created for slightly different purposes, and therefore each carries unique strengths and limitations. SemLink allows users to leverage the strengths of each resource and provides the groundwork for incorporating these lexical resources effectively into linked data resources. SemLink and the resources included therein are discussed with a focus on the value of using lexical resources in a complementary fashion. Recent improvements to SemLink, including the addition of a new resource, the OntoNotes sense groupings, are described. Work to address future goals, including further expansion of SemLink, is also discussed.


Tree-based Convolution for Sentence Modeling.


title: Weakly Supervised Scene Parsing with Point-based Distance Metric Learning

abstract: Semantic scene parsing is suffering from the fact that pixellevel annotations are hard to be collected. To tackle this issue, we propose a Point-based Distance Metric Learning (PDML) in this paper. PDML does not require dense annotated masks and only leverages several labeled points that are much easier to obtain to guide the training process. Concretely, we leverage semantic relationship among the annotated points by encouraging the feature representations of the intra- and intercategory points to keep consistent, i.e. points within the same category should have more similar feature representations compared to those from different categories. We formulate such a characteristic into a simple distance metric loss, which collaborates with the point-wise cross-entropy loss to optimize the deep neural networks. Furthermore, to fully exploit the limited annotations, distance metric learning is conducted across different training images instead of simply adopting an image-dependent manner. We conduct extensive experiments on two challenging scene parsing benchmarks of PASCALContext and ADE 20K to validate the effectiveness of our PDML, and competitive mIoU scores are achieved.


title: A General Theory of Equivariant CNNs on Homogeneous Spaces

abstract: We present a general theory of Group equivariant Convolutional Neural Networks (G-CNNs) on homogeneous spaces such as Euclidean space and the sphere. Feature maps in these networks represent fields on a homogeneous base space, and layers are equivariant maps between spaces of fields. The theory enables a systematic classification of all existing G-CNNs in terms of their symmetry group, base space, and field type. We also answer a fundamental question: what is the most general kind of equivariant linear map between feature spaces (fields) of given types? We show that such maps correspond one-to-one with generalized convolutions with an equivariant kernel, and characterize the space of such kernels.


title: Dental caries: intervened--interrupted--interpreted. Concluding remarks and cariography.

abstract: This paper comprizes a summarizing discussion for a set of 13 papers on the theme caries decline. The complexity of the issue is highlighted, and it is concluded that there is not one single factor explaining the changes observed. Actually, in one and the same population, different explanations may be relevant for different individuals, for different age groups, for different teeth and for different periods of time. A new model for understanding the interaction of various caries etiological factors is proposed. The model can illustrate how in one situation caries activity can increase (or decrease) due to one such factor, while in another situation different factors are more important. As it, in a graphic way, maps the interactions of relevant factors, the author has chosen to call it a cariogram, and the process of preparing such graphs, cariography.


title: ∑oφoς: Forward Secure Searchable Encryption

abstract: Searchable Symmetric Encryption aims at making possible searching over an encrypted database stored on an untrusted server while keeping privacy of both the queries and the data, by allowing some small controlled leakage to the server. Recent work shows that dynamic schemes -- in which the data is efficiently updatable -- leaking some information on updated keywords are subject to devastating adaptative attacks breaking the privacy of the queries. The only way to thwart this attack is to design forward private schemes whose update procedure does not leak if a newly inserted element matches previous search queries. This work proposes Sophos as a forward private SSE scheme with performance similar to existing less secure schemes, and that is conceptually simpler (and also more efficient) than previous forward private constructions. In particular, it only relies on trapdoor permutations and does not use an ORAM-like construction. We also explain why Sophos is an optimal point of the security/performance tradeoff for SSE. Finally, an implementation and evaluation results demonstrate its practical efficiency.


Cross-modal Image-Graphics Retrieval by Neural Transfer Learning


title: Centrality based Document Ranking

abstract: Abstract : In this paper, we address the problem of ranking clinical documents using centrality based approach. We model the documents to be ranked as nodes in a graph and place edges between documents based on their similarity. Given a query, we compute similarity of the query with respect to every document in the graph. Based on these similarity values, documents are ranked for a given query. Initially, Lucene1 is used to retrieve top fifty documents that are relevant to the query and then our proposed approach is applied on these retrieved documents to re-rank them. Experimental results show that our approach did not perform well as the documents retrieved by Lucene are not among the top 50 documents in the Gold Standard.


title: VMPBL: Identifying Vulnerable Functions Based on Machine Learning Combining Patched Information and Binary Comparison Technique by LCS

abstract: Nowadays, most vendors apply the same open source code to their products, which is dangerous. In addition, when manufacturers release patches, they generally hide the exact location of the vulnerabilities. So, identifying vulnerabilities in binaries is crucial. However, just searching source program has a lower identifying accuracy of vulnerability, which requires operators further to differentiate searched results. Under this context, we propose VMPBL to enhance identifying the accuracy of vulnerability with the help of patch files. VMPBL, compared with other proposed schemes, uses patched functions according to its vulnerable functions in patch file to further distinguish results. We establish a prototype of VMPBL, which can effectively identify vulnerable function types and get rid of safe functions from results. Firstly, we get the potential vulnerable-patched functions by binary comparison technique based on K-Trace algorithm. Then we combine the functions with vulnerability and patch knowledge database to classify these function pairs and identify the possible vulnerable functions and the vulnerability types. Finally, we test some programs containing real-world CWE vulnerabilities, and one of the experimental results about CWE415 shows that the results returned from only searching source program are about twice as much as the results from VMPBL. We can see that using VMPBL can significantly reduce the false positive rate of discovering vulnerabilities compared with analyzing source files alone.


title: Data Mining Based Strategy for Detecting Malicious PDF Files

abstract: Portable Document Format (PDF) is one of the widely-accepted document format. However, it becomes one of the most attractive targets for exploitation by malware developers and vulnerability researchers. Malicious PDF files can be used in Advanced Persistent Threats (APTs) targeting individuals, governments, and financial sectors. The existing tools such as intrusion detection systems (IDSs) and antivirus packages are inefficient to mitigate this kind of attacks. This is because these techniques need regular updates with the new malicious PDF files which are increasing every day. In this paper, a new algorithm is presented for detecting malicious PDF files based on data mining techniques. The proposed algorithm consists of feature selection stage and classification stage. The feature selection stage is used to the select the optimum number of features extracted from the PDF file to achieve high detection rate and low false positive rate with small computational overhead. Experimental results show that the proposed algorithm can achieve 99.77% detection rate, 99.84% accuracy, and 0.05% false positive rate.


Smart cities : definitions , dimensions , and performance


title: Key distribution mechanism in secure ADS-B networks

abstract: Automatic Dependent Surveillance Broadcast (ADS-B) has been gaining attention as a replacement and strengthening of primary and secondary RADAR systems. Nevertheless, there have been several security concerns about this protocol because messages are sent in clear text, which makes it vulnerable to several attacks. In recent work, we addressed these concerns using a hash-based message authentication code (HMAC) that provides ADS-B with integrity and authentication capabilities. In this paper, we further develop this concept by proposing a key distribution mechanism that provides the proper infrastructure for the added safety and security features. The proposed mechanism addresses several common usage scenarios, including those that arise from air space being shared by manned and unmanned aerial vehicles.


title: Goal-oriented requirements engineering: an extended systematic mapping study

abstract: Over the last two decades, much attention has been paid to the area of goal-oriented requirements engineering (GORE), where goals are used as a useful conceptualization to elicit, model, and analyze requirements, capturing alternatives and conflicts. Goal modeling has been adapted and applied to many sub-topics within requirements engineering (RE) and beyond, such as agent orientation, aspect orientation, business intelligence, model-driven development, and security. Despite extensive efforts in this field, the RE community lacks a recent, general systematic literature review of the area. In this work, we present a systematic mapping study, covering the 246 top-cited GORE-related conference and journal papers, according to Scopus. Our literature map addresses several research questions: we classify the types of papers (e.g., proposals, formalizations, meta-studies), look at the presence of evaluation, the topics covered (e.g., security, agents, scenarios), frameworks used, venues, citations, author networks, and overall publication numbers. For most questions, we evaluate trends over time. Our findings show a proliferation of papers with new ideas and few citations, with a small number of authors and papers dominating citations; however, there is a slight rise in papers which build upon past work (implementations, integrations, and extensions). We see a rise in papers concerning adaptation/variability/evolution and a slight rise in case studies. Overall, interest in GORE has increased. We use our analysis results to make recommendations concerning future GORE research and make our data publicly available.


title: Multiscale Visual Attention Networks for Object Detection in VHR Remote Sensing Images

abstract: Object detection plays an active role in remote sensing applications. Recently, deep convolutional neural network models have been applied to automatically extract features, generate region proposals, and predict corresponding object class. However, these models face new challenges in VHR remote sensing images due to the orientation and scale variations and the cluttered background. In this letter, we propose an end-to-end multiscale visual attention networks (MS-VANs) method. We use skip-connected encoder–decoder model to extract multiscale features from a full-size image. For feature maps in each scale, we learn a visual attention network, which is followed by a classification branch and a regression branch, so as to highlight the features from object region and suppress the cluttered background. We train the MS-VANs model by a hybrid loss function which is a weighted sum of attention loss, classification loss, and regression loss. Experiments on a combined data set consisting of Dataset for Object Detection in Aerial Images and NWPU VHR-10 show that the proposed method outperforms several state-of-the-art approaches.


title: Scene Classification With Recurrent Attention of VHR Remote Sensing Images

abstract: Scene classification of remote sensing images has drawn great attention because of its wide applications. In this paper, with the guidance of the human visual system (HVS), we explore the attention mechanism and propose a novel end-to-end attention recurrent convolutional network (ARCNet) for scene classification. It can learn to focus selectively on some key regions or locations and just process them at high-level features, thereby discarding the noncritical information and promoting the classification performance. The contributions of this paper are threefold. First, we design a novel recurrent attention structure to squeeze high-level semantic and spatial features into several simplex vectors for the reduction of learning parameters. Second, an end-to-end network named ARCNet is proposed to adaptively select a series of attention regions and then to generate powerful predictions by learning to process them sequentially. Third, we construct a new data set named OPTIMAL-31, which contains more categories than popular data sets and gives researchers an extra platform to validate their algorithms. The experimental results demonstrate that our model makes great promotion in comparison with the state-of-the-art approaches.


title: Classification of TrashNet Dataset Based on Deep Learning Models

abstract: Waste recycling is very important in terms of economy and climate balance of the world. For this reason, classifying recyclable garbage is an important goal for humanity and Deep Learning models can be used for this purpose. In this study, we tested well-known Deep Learning models to provide the most efficient approach. In this study, Densenet121, DenseNet169, InceptionResnetV2, MobileNet, Xception architectures were used for Trashnet dataset and Adam and Adadelta were used as the optimizer in neural network models. Based on the findings obtained in this study, Adam provided better test accuracies compared to Adadelta. Besides, the data augmentation process was applied to increase classification accuracy because of limited samples of the Trashnet dataset. As a result of the conducted experiments, the best results were found in the DenseNet121 using fine-tuning with a test accuracy rate of 95%. A similar success rate was also found in the InceptionResNetV2 model using fine-tuning with a test accuracy of 94%.


title: DiskAccel: Accelerating Disk-Based Experiments by Representative Sampling

abstract: Disk traces are typically used to analyze real-life workloads and for replay-based evaluations. This approach benefits from capturing important details such as varying behavior patterns, bursty activity, and diurnal patterns of system activity, which are often missing from the behavior of workload synthesis tools. However, accurate capture of such details requires recording traces containing long durations of system activity, which are difficult to use for replay-based evaluation. One way of solving the problem of long storage trace duration is the use of disk simulators. While publicly available disk simulators can greatly accelerate experiments, they have not kept up with technological innovations in the field. The variety, complexity, and opaque nature of storage hardware make it very difficult to implement accurate simulators. The alternative, replaying the whole traces on real hardware, suffers from either long run-time or required manual reduction of experimental time, potentially at the cost of reduced accuracy. On the other hand, burstiness, auto-correlation, and complex spatio-temporal properties of storage workloads make the known methods of sampling workload traces less effective. In this paper, we present a methodology called DiskAccel to efficiently select key intervals of a trace as representatives and to replay them to estimate the response time of the whole workload. Our methodology extracts a variety of spatial and temporal features from each interval and uses efficient data mining techniques to select the representative intervals. To verify the proposed methodology, we have implemented a tool capable of running whole traces or selective intervals on real hardware, warming up hardware state in an accelerated manner, and emulating request causality while minimizing request inter-arrival time error. Based on our experiments, DiskAccel manages to speed up disk replay by more than two orders of magnitude, while keeping average estimation error at 7.6%.


title: Quality management in systems development: an organizational system perspective

abstract: We identify top management leadership, a sophisticated management infrastructure, process management efficacy, and stakeholder participation as important elements of a quality-oriented organizational system for software development. A model interrelating these constructs and quality performance is proposed. Data collected through a national survey of IS executives in Fortune 1000 companies and government agencies was used to test the model using a Partial Least Squares analysis methodology. Our results suggest that software quality goals are best attained when top management creates a management infrastructure that promotes improvements in process design and encourages stakeholders to evolve the design of the development processes. Our results also suggest that all elements of the organizational system need to be developed in order to attain quality goals and that piecemeal adoption of select quality management practices are unlikely to be effective. Implications of this research for IS theory and practice are discussed.


title: Creating an Online Dictionary of Abbreviations from MEDLINE

abstract: Objective: The growth of the biomedical literature presents special challenges for both human readers and automatic algorithms. One such challenge derives from the common and uncontrolled use of abbreviations in the literature. Each additional abbreviation increases the effective size of the vocabulary for a field. Therefore, to create an automatically generated and maintained lexicon of abbreviations, we have developed an algorithm to match abbreviations in text with their expansions. ::: ::: Design: Our method uses a statistical learning algorithm, logistic regression, to score abbreviation expansions based on their resemblance to a training set of human-annotated abbreviations. We applied it to Medstract , a corpus of MEDLINE abstracts in which abbreviations and their expansions have been manually annotated. We then ran the algorithm on all abstracts in MEDLINE, creating a dictionary of biomedical abbreviations. To test the coverage of the database, we used an independently created list of abbreviations from the China Medical Tribune . ::: ::: Measurements: We measured the recall and precision of the algorithm in identifying abbreviations from the Medstract corpus. We also measured the recall when searching for abbreviations from the China Medical Tribune against the database. ::: ::: Results: On the Medstract corpus, our algorithm achieves up to 83% recall at 80% precision. Applying the algorithm to all of MEDLINE yielded a database of 781,632 high-scoring abbreviations. Of all the abbreviations in the list from the China Medical Tribune , 88% were in the database. ::: ::: Conclusion: We have developed an algorithm to identify abbreviations from text. We are making this available as a public abbreviation server at \url{ }.


title: MySpace and Facebook: Applying the Uses and Gratifications Theory to Exploring Friend-Networking Sites

abstract: ABSTRACT The increased use of the Internet as a new tool in communication has changed the way people interact. This fact is even more evident in the recent development and use of friend-networking sites. However, no research has evaluated these sites and their impact on college students. Therefore, the present study was conducted to evaluate: (a) why people use these friend-networking sites, (b) what the characteristics are of the typical college user, and (c) what uses and gratifications are met by using these sites. Results indicated that the vast majority of college students are using these friend-networking sites for a significant portion of their day for reasons such as making new friends and locating old friends. Additionally, both men and women of traditional college age are equally engaging in this form of online communication with this result holding true for nearly all ethnic groups. Finally, results showed that many uses and gratifications are met by users (e.g., “keeping in touch with friends”...


Pixel Perfect: Fingerprinting Canvas in HTML5


title: Question Answering Over Knowledge Graphs: Question Understanding Via Template Decomposition

abstract: The gap between unstructured natural language and structured data makes it challenging to build a system that supports using natural language to query large knowledge graphs. Many existing methods construct a structured query for the input question based on a syntactic parser. Once the input question is parsed incorrectly, a false structured query will be generated, which may result in false or incomplete answers. The problem gets worse especially for complex questions. In this paper, we propose a novel systematic method to understand natural language questions by using a large number of binary templates rather than semantic parsers. As sufficient templates are critical in the procedure, we present a low-cost approach that can build a huge number of templates automatically. To reduce the search space, we carefully devise an index to facilitate the online template decomposition. Moreover, we design effective strategies to perform the two-level disambiguations (i.e., entity-level ambiguity and structure-level ambiguity) by considering the query semantics. Extensive experiments over several benchmarks demonstrate that our proposed approach is effective as it significantly outperforms state-of-the-art methods in terms of both precision and recall.


title: Simulation-based optimization of StarCraft tactical AI through evolutionary computation

abstract: The development of competent AI for real-time strategy games such as StarCraft is made difficult by the myriad of strategic and tactical reasonings which must be performed concurrently. A significant portion of StarCraft gameplay is in managing tactical conflict with opposing forces. We present a modular framework for simulating AI vs. AI conflicts through an XML specification, whereby the behavioural and tactical components for each force can be varied. Evolutionary computation can be employed on aspects of the scenario to yield superior solutions. Through evolution, a StarCraft AI tournament bot achieved a success rate of 68% against its original version. We also demonstrate the use of evolutionary computation to yield a tactical attack path to maximise enemy casualties. We believe that our framework can be used to perform automatic refinement on AI bots in StarCraft.


Smart Jump : Automated Navigation Suggestion for Videos in MOOCs


title: Language Independent Extractive Summarization

abstract: We demonstrate TextRank -- a system for unsupervised extractive summarization that relies on the application of iterative graph-based ranking algorithms to graphs encoding the cohesive structure of a text. An important characteristic of the system is that it does not rely on any language-specific knowledge resources or any manually constructed training data, and thus it is highly portable to new languages or domains.


title: Particle swarm optimization based fixed order controller design and system simulation

abstract: Design of fixed order controllers is an open problem which takes controller complexity into account. Different solutions exist in literature for the fixed order controller design problem. However, major difficulty in these results is that they are not simple to develop an algorithm that guarantees the decisive solutions coupled with computations. To overcome this difficulty H ∞ loop shaping control under an order specified controller has been proposed. The performance specification is formulated as cost function, which is to be minimized. Particle swarm optimization has been adopted for optimization of cost function and parameters of the specified controller. To check the success of proposed techniques, resulting controller parameters are evaluated via system simulations. Simulation results demonstrate the efficiency of proposed technique.


title: Accelerating aggregation using intra-cycle parallelism

abstract: Modern CPUs have word width of 64 bits but real data values are usually represented using bits fewer than a CPU word. This underutilization of CPU at register level has motivated the recent development of bit-parallel algorithms that carry out data processing operations (e.g., filter scan) on CPU words packed with data values (e.g., 8 data values are packed into one 64-bit word). Bit-parallel algorithms fully unleash the intra-cycle parallelism of modern CPUs and they are especially attractive to main-memory column stores whose goal is to process data at the speed of the “bare metal”. Main-memory column stores generally focus on analytical queries, where aggregation is a common operation. Current bit-parallel algorithms, however, have not covered aggregation yet. In this paper, we present a suite of bit-parallel algorithms to accelerate all standard aggregation operations: SUM, MIN, MAX, AVG, MEDIAN, COUNT. The algorithms are designed to fully leverage the intra-cycle parallelism in CPU cores when aggregating words of packed values. Experimental evaluation shows that our bit-parallel aggregation algorithms exhibit significant performance benefits compared with non-bit-parallel methods.


title: When Events Change Their Nature: The Neurocognitive Mechanisms Underlying Aspectual Coercion

abstract: The verb "pounce" describes a single, near-instantaneous event. Yet, we easily understand that, "For several minutes the cat pounced'" describes a situation in which multiple pounces occurred, although this interpretation is not overtly specified by the sentence's syntactic structure or by any of its individual words—a phenomenon known as "aspectual coercion." Previous psycholinguistic studies have reported processing costs in association with aspectual coercion, but the neurocognitive mechanisms giving rise to these costs remain contentious. Additionally, there is some controversy about whether readers commit to a full interpretation of the event when the aspectual information becomes available, or whether they leave it temporarily underspecified until later in the sentence. Using ERPs, we addressed these questions in a design that fully crossed context type (punctive, durative, frequentative) with verb type (punctive, durative). We found a late, sustained negativity to punctive verbs in durative contexts, but not in frequentative (e.g., explicitly iterative) contexts. This effect was distinct from the N400 in both its time course and scalp distribution, suggesting that it reflected a different underlying neurocognitive mechanism. We also found that ERPs to durative verbs were unaffected by context type. Together, our results provide strong evidence that neural activity associated with aspectual coercion is driven by the engagement of a morphosyntactically unrealized semantic operator rather than by violations of real-world knowledge, more general shifts in event representation, or event iterativity itself. More generally, our results add to a growing body of evidence that a set of late-onset sustained negativities reflect elaborative semantic processing that goes beyond simply combining the meaning of individual words with syntactic structure to arrive at a final representation of meaning.


title: Convolutional Long Short-Term Memory Networks for Recognizing First Person Interactions

abstract: In this paper, we present a novel deep learning based approach for addressing the problem of interaction recognition from a first person perspective. The proposed approach uses a pair of convolutional neural networks, whose parameters are shared, for extracting frame level features from successive frames of the video. The frame level features are then aggregated using a convolutional long short-term memory. The hidden state of the convolutional long short-term memory, after all the input video frames are processed, is used for classification in to the respective categories. The two branches of the convolutional neural network perform feature encoding on a short time interval whereas the convolutional long short term memory encodes the changes on a longer temporal duration. In our network the spatio-temporal structure of the input is preserved till the very final processing stage. Experimental results show that our method outperforms the state of the art on most recent first person interactions datasets that involve complex ego-motion. In particular, on UTKinect-FirstPerson it competes with methods that use depth image and skeletal joints information along with RGB images, while it surpasses all previous methods that use only RGB images by more than 20% in recognition accuracy.


title: MPTLsim: a cycle-accurate, full-system simulator for x86-64 multicore architectures with coherent caches

abstract: The introduction of multicore microprocessors in the recent years has made it imperative to use cycleaccurate and full-system simulators in the architecture research community. We introduce MPTLsim - a multicore simulator for the X86 ISA that meets this need. MPTLsim is a uop-accurate, cycle-accurate, full-system simulator for multicore designs based on the X86-64 ISA. MPTLsim extends PTLsim, a publicly available single core simulator, with a host of additional features to support hyperthreading within a core and multiple cores, with detailed models for caches, on-chip interconnections and the memory data flow. MPTLsim incorporates detailed simulation models for cache controllers, interconnections and has built-in implementations of a number of cache coherency protocols.


Image Classification Using Deep Learning and Prior Knowledge.


Tailored Sequence to Sequence Models to Different Conversation Scenarios


title: Expedition: A Time-Aware Exploratory Search System Designed for Scholars

abstract: Archives are an important source of study for various scholars. Digitization and the web have made archives more accessible and led to the development of several time-aware exploratory search systems. However these systems have been designed for more general users rather than scholars. Scholars have more complex information needs in comparison to general users. They also require support for corpus creation during their exploration process. In this paper we present Expedition - a time-aware exploratory search system that addresses the requirements and information needs of scholars. Expedition possesses a suite of ad-hoc and diversity based retrieval models to address complex information needs; a newspaper-style user interface to allow for larger textual previews and comparisons; entity filters to more naturally refine a result list and an interactive annotated timeline which can be used to better identify periods of importance.


title: A Neural Attention Model for Sentence Summarization

abstract: Summarization based on text extraction is inherently limited, but generation-style abstractive methods have proven challenging to build. In this work, we propose a fully data-driven approach to abstractive sentence summarization. Our method utilizes a local attention-based model that generates each word of the summary conditioned on the input sentence. While the model is structurally simple, it can easily be trained end-to-end and scales to a large amount of training data. The model shows significant performance gains on the DUC-2004 shared task compared with several strong baselines.


title: Human and Fire Detection from High Altitude UAV Images

abstract: Illegal migration as well as wildfires constitute commonplace situations in southern European countries, where the mountainous terrain and thick forests make the surveillance and location of these incidents a tall task. This territory could benefit from Unmanned Aerial Vehicles (UAVs) equipped with optical and thermal sensors in conjunction with sophisticated image processing and computer vision algorithms, in order to detect suspicious activity or prevent the spreading of a fire. Taking into account that the flight height is about to two kilometers, human and fire detection algorithms are mainly based on blob detection. For both processes thermal imaging is used in order to improve the accuracy of the algorithms, while in the case of human recognition information like movement patterns as well as shadow size and shape are also considered. For fire detection a blob detector is utilized in conjunction with a color based descriptor, applied to thermal and optical images, respectively. Unlike fire, human detection is a more demanding process resulting in a more sophisticated and complex algorithm. The main difficulty of human detection originates from the high flight altitude. In images taken from high altitude where the ground sample distance is not small enough, people appear as small blobs occupying few pixels, leading corresponding research works to be based on blob detectors to detect humans. Their shadows as well as motion detection and object tracking can then be used to determine whether these regions of interest do depict humans. This work follows this motif as well, nevertheless, its main novelty lies in the fact that the human detection process is adapted for high altitude and vertical shooting images in contrast with the majority of other similar works where lower altitudes and different shooting angles are considered. Additionally, in the interest of making our algorithms as fast as possible in order for them to be used in real time during the UAV flights, parallel image processing with the help of a specialized hardware device based on Field Programmable Gate Array (FPGA) is being worked on.


title: Multi-objective optimal design of an axial flux permanent magnet wheel motor for electric scooters

abstract: This paper proposes a systematic process of a multi-objective optimal design of an axial-flux permanent-magnet motor for electric scooters. In the preliminary design, the zero-dimensional magnetic circuit model is used to determine the numbers of slots and poles and the initial size of the motor according to the driving requirements of scooter. In the optimal design process, the one-dimensional magnetic circuit model with an effective air-gap distribution function is used while searching a set of motor parameters that minimize torque ripple and maximize torque and torque density. The final design is verified and refined by the 3-dimensioanl finite element method. Experimental results on a motor prototype show that the proposed design process results in an axial-flux permanentmagnet motor with a high torque density for electric scooters.


Blackwell handbook of social psychology: Intraindividual processes


title: Anisotropic noise

abstract: Programmable graphics hardware makes it possible to generate procedural noise textures on the fly for interactive rendering. However, filtering and antialiasing procedural noise involves a tradeoff between aliasing artifacts and loss of detail. In this paper we present a technique, targeted at interactive applications, that provides high-quality anisotropic filtering for noise textures. We generate noise tiles directly in the frequency domain by partitioning the frequency domain into oriented subbands. We then compute weighted sums of the subband textures to accurately approximate noise with a desired spectrum. This allows us to achieve high-quality anisotropic filtering. Our approach is based solely on 2D textures, avoiding the memory overhead of techniques based on 3D noise tiles. We devise a technique to compensate for texture distortions to generate uniform noise on arbitrary meshes. We develop a GPU-based implementation of our technique that achieves similar rendering performance as state-of-the-art algorithms for procedural noise. In addition, it provides anisotropic filtering and achieves superior image quality.


title: Low power memristor-based ReRAM design with Error Correcting Code

abstract: The emerging memristor-based Resistive RAM (ReRAM) has shown great potential as one of the most promising memory technologies, with the unique properties such as high density, low-power, good-scalability, and non-volatility. However, as the process technology scales, the process variation will cause the deviation of the actual electrical behavior of memristor. Recently, researchers have observed that the probability of a single ReRAM cell switching successfully follows a function of the logarithm of the total programming time. As a result, the uncertainty of the electrical behavior results in different degrees of error rates in ReRAM-based memory. Traditional ECC (Error Correcting Code) design for conventional DRAM memory is used to detect and correct the errors in the memory system. In this paper, based on the mathematical analysis of the error patterns in memristor-based ReRAM and the study of ECC designs, we proposed to use ECC code to relax the BER (Bit Error Rate) requirement of a single memory to improve the write energy consumption and latency for both the MOS based and cross-point based memristor ReRAM designs. In addition, the performance/power/area overhead of the proposed design options is also evaluated in detail.


title: A non-randomized comparison of mindfulness-based stress reduction and healing arts programs for facilitating post-traumatic growth and spirituality in cancer outpatients

abstract: GOALS OF WORK ::: The aim of this study was to compare a mindfulness-based stress reduction (MBSR) program and a healing through the creative arts (HA) program on measures of post-traumatic growth (PTGI-R), spirituality (FACIT-Sp), stress (SOSI), and mood disturbance (POMS) in cancer patients. ::: ::: ::: MATERIALS AND METHODS ::: A sample of cancer outpatients (MBSR, n = 60; HA, n = 44) with a variety of diagnoses chose to attend either an 8-week MBSR program or a 6-week HA program and were assessed pre- and post-intervention. The majority of participants were female, married, and had breast cancer. ::: ::: ::: MAIN RESULTS ::: Repeated measures analysis of variance indicated that participants in both groups improved significantly over time on overall post-traumatic growth (p = 0.015). Participants in the MBSR group improved on measures of spirituality more than those in the HA group (p = 0.029). Participants in the MBSR group also showed more improvement than those in HA on measures of anxiety (POMS, p = 0.038), anger (POMS, p = 0.004), overall stress symptoms (SOSI, p = 0.041), and mood disturbance (POMS, p = 0.023). Several main effects of time were also observed in both groups. These results were found despite attrition in both groups. ::: ::: ::: CONCLUSIONS ::: Both programs may improve facilitation of positive growth after traumatic life experiences for those who choose to participate. MBSR may be more helpful than HA in enhancing spirituality and reducing stress, depression, and anger.


title: Distributed Representation of Subgraphs

abstract: Network embeddings have become very popular in learning effective feature representations of networks. Motivated by the recent successes of embeddings in natural language processing, researchers have tried to find network embeddings in order to exploit machine learning algorithms for mining tasks like node classification and edge prediction. However, most of the work focuses on finding distributed representations of nodes, which are inherently ill-suited to tasks such as community detection which are intuitively dependent on subgraphs. Here, we propose sub2vec, an unsupervised scalable algorithm to learn feature representations of arbitrary subgraphs. We provide means to characterize similarties between subgraphs and provide theoretical analysis of sub2vec and demonstrate that it preserves the so-called local proximity. We also highlight the usability of sub2vec by leveraging it for network mining tasks, like community detection. We show that sub2vec gets significant gains over state-of-the-art methods and node-embedding methods. In particular, sub2vec offers an approach to generate a richer vocabulary of features of subgraphs to support representation and reasoning.


title: Improving naive Bayes classifiers using neuro-fuzzy learning

abstract: Naive Bayes classifiers are a well-known and powerful type of classifier that can easily be induced from a dataset of sample cases. However, the strong conditional independence and distribution assumptions underlying them can sometimes lead to poor classification performance. Another prominent type of classifier are neuro-fuzzy classification systems which derive (fuzzy) classifiers from data using neural network inspired learning methods. Since there are certain structural similarities between a neuro-fuzzy classifier and a naive Bayes classifier, the idea suggests itself to mapping the latter to the former in order to improve its capabilities.


title: Experience Report: System Log Analysis for Anomaly Detection

abstract: Anomaly detection plays an important role in managementof modern large-scale distributed systems. Logs, whichrecord system runtime information, are widely used for anomalydetection. Traditionally, developers (or operators) often inspectthe logs manually with keyword search and rule matching. Theincreasing scale and complexity of modern systems, however, make the volume of logs explode, which renders the infeasibilityof manual inspection. To reduce manual effort, many anomalydetection methods based on automated log analysis are proposed. However, developers may still have no idea which anomalydetection methods they should adopt, because there is a lackof a review and comparison among these anomaly detectionmethods. Moreover, even if developers decide to employ ananomaly detection method, re-implementation requires a nontrivialeffort. To address these problems, we provide a detailedreview and evaluation of six state-of-the-art log-based anomalydetection methods, including three supervised methods and threeunsupervised methods, and also release an open-source toolkitallowing ease of reuse. These methods have been evaluated ontwo publicly-available production log datasets, with a total of15,923,592 log messages and 365,298 anomaly instances. Webelieve that our work, with the evaluation results as well asthe corresponding findings, can provide guidelines for adoptionof these methods and provide references for future development.


title: Example Selection For Dictionary Learning

abstract: In unsupervised learning, an unbiased uniform sampling strategy is typically used, in order that the learned features faithfully encode the statistical structure of the training data. In this work, we explore whether active example selection strategies - algorithms that select which examples to use, based on the current estimate of the features - can accelerate learning. Specifically, we investigate effects of heuristic and saliency-inspired selection algorithms on the dictionary learning task with sparse activations. We show that some selection algorithms do improve the speed of learning, and we speculate on why they might work.


title: On Linear Variational Surface Deformation Methods

abstract: This survey reviews the recent advances in linear variational mesh deformation techniques. These methods were developed for editing detailed high-resolution meshes like those produced by scanning real-world objects. The challenge of manipulating such complex surfaces is threefold: The deformation technique has to be sufficiently fast, robust, intuitive, and easy to control to be useful for interactive applications. An intuitive and, thus, predictable deformation tool should provide physically plausible and aesthetically pleasing surface deformations, which, in particular, requires its geometric details to be preserved. The methods that we survey generally formulate surface deformation as a global variational optimization problem that addresses the differential properties of the edited surface. Efficiency and robustness are achieved by linearizing the underlying objective functional such that the global optimization amounts to solving a sparse linear system of equations. We review the different deformation energies and detail preservation techniques that were proposed in recent years, together with the various techniques to rectify the linearization artifacts. Our goal is to provide the reader with a systematic classification and comparative description of the different techniques, revealing the strengths and weaknesses of each approach in common editing scenarios.


title: Susceptibility of Helicobacter pylori to the antibacterial activity of manuka honey.

abstract: Honey is a traditional remedy for dyspepsia, and is still used for this by some medical practitioners although there is no rational basis for its use. The finding that Helicobacter pylori is probably the causative agent in many cases of dyspepsia has raised the possibility that the therapeutic action of honey may be due to its antibacterial properties. Consequently, the sensitivity of Helicobacter pylori to honey was tested, using isolates from biopsies of gastric ulcers. It was found that all five isolates tested were sensitive to a 20% (v/v) solution of manuka honey in an agar well diffusion assay, but none showed sensitivity to a 40% solution of a honey in which the antibacterial activity was due primarily to its content of hydrogen peroxide. Assessment of the minimum inhibitory concentration by inclusion of manuka honey in the agar showed that all seven isolates tested had visible growth over the incubation period of 72 h prevented completely by the presence of 5% (v/v) honey.


title: Exploration opportunities enabled by the space launch system

abstract: A variety of scientific and human beyond Earth orbit exploration missions are enabled by the Space Launch System (SLS). The SLS will play a critical part in enabling the next steps in human space exploration beyond Earth orbit. Benefits include very large fairing diameters (up to 10 meters), heavy lift capability (up to 130mt to LEO) and fast in-space transfers via direct trajectories to scientifically rich outer planet destinations.


title: Social computing: an intersection of recommender systems, trust/reputation systems, and social networks

abstract: Computational applications now go beyond personal computing, facilitating collaboration and social interactions. Social computing is an area of information technology concerned with the intersection of human and social studies connected by computer networks. The primary goal of this article is to provide a brief survey of three popular social computing services: recommender systems, trust/reputation systems, and social networks. We approach these services from a data representation perspective and discuss two of their main challenges: network sparsity and coldstart problems. We also present a novel graph model, which provides an abstract taxonomy and a common data representation model for the three services. We are mainly motivated by the power of graph theory in data representation and analysis for social computing services. Through this model, we believe that it becomes clearer that data from different contexts can be related such that new solutions can be explored; thus, it may provide illumination for the aforementioned problems and stimulate new research.


title: Vertex Sparsification in Trees

abstract: Given an unweighted tree $T=(V,E)$ with terminals $K \subset V$, we show how to obtain a $2$-quality vertex flow and cut sparsifier $H$ with $V_H = K$. We prove that our result is essentially tight by providing a $2-o(1)$ lower-bound on the quality of any cut sparsifier for stars. In addition we give improved results for quasi-bipartite graphs. First, we show how to obtain a $2$-quality flow sparsifier with $V_H = K$ for such graphs. We then consider the other extreme and construct exact sparsifiers of size $O(2^{k})$, when the input graph is unweighted.


title: Estimation of Detection Thresholds for Redirected Walking Techniques

abstract: In immersive virtual environments (IVEs), users can control their virtual viewpoint by moving their tracked head and walking through the real world. Usually, movements in the real world are mapped one-to-one to virtual camera motions. With redirection techniques, the virtual camera is manipulated by applying gains to user motion so that the virtual world moves differently than the real world. Thus, users can walk through large-scale IVEs while physically remaining in a reasonably small workspace. In psychophysical experiments with a two-alternative forced-choice task, we have quantified how much humans can unknowingly be redirected on physical paths that are different from the visually perceived paths. We tested 12 subjects in three different experiments: (E1) discrimination between virtual and physical rotations, (E2) discrimination between virtual and physical straightforward movements, and (E3) discrimination of path curvature. In experiment E1, subjects performed rotations with different gains, and then had to choose whether the visually perceived rotation was smaller or greater than the physical rotation. In experiment E2, subjects chose whether the physical walk was shorter or longer than the visually perceived scaled travel distance. In experiment E3, subjects estimate the path curvature when walking a curved path in the real world while the visual display shows a straight path in the virtual world. Our results show that users can be turned physically about 49 percent more or 20 percent less than the perceived virtual rotation, distances can be downscaled by 14 percent and upscaled by 26 percent, and users can be redirected on a circular arc with a radius greater than 22 m while they believe that they are walking straight.


title: Immunohistochemical and Biochemical Analysis of a Human Sertoli-Leydig Cell Tumor: Autonomous Steroid Production Characteristic of Ovarian Theca Cells

abstract: To ascertain the steroidogenic profile and location of steroidogenic enzymes in a steroid-secreting Sertoli-Leydig cell tumor of the ovary. Steroid levels from peripheral, left ovarian (tumor), and right ovarian venous blood were measured. Tumor tissue was examined for the steroidogenic enzymes 17α-hydroxylase (P450c17) and 3β-hydroxy steroid dehydrogenase (3βHSD) by immunohistochemistry. Tumor cells were isolated and incubated in serum-free media. Thereafter, media were analyzed for steroid production, steroidogenic response to effectors, and metabolism of radiolabeled pregnenolone and androstenedione. Levels of C19 steroids and 17-hydroxyprogesterone (17OHP) were elevated in peripheral blood. The majority (80%) of steroids in serum from the left ovarian vein (tumor) were C19 steroids (dehydroepiandrosterone [DHEA], 45%; androstenedione, 21%, testosterone, 7%, with 13% 17OHP, 7% progesterone, and less than 1% estradiol (E2). Immunoreactivity for both P450c17 and 3βHSD was identified in clusters of large cells surrounded by nonimmunore-active cells composing cord-like structures. Of the steroids that accumulated in the incubation medium of unstimulated, freshly isolated tumor cells, 84% were C19 steroids (DHEA, 44%; androstenedione, 36%; testosterone 2%, with 16% 17OHP and less than 1% progesterone and E2. Basal steroid production was not stimulated by LH or FSH. However, treatment with forskolin (10 μmol/L), dibutyryl cAMP (1 mmol/L), or steroid precursors (22-hydroxycholesterol, 1 μmol/L; pregnenolone, 1 μmol/L) increased the production of all steroids measured. Forskolin treatment increased androstenedione (fivefold), DHEA (tenfold), and 17OHP (40-fold) compared with basal levels. Incubation of freshly isolated cells with [3H]preg-nenolone demonstrated the ability of these cells to metabolize this C21 steroid precursor to androstenedione, DHEA, and Í70HP. However, [3H]androstenedione was not readily metabolized by these cells to either estrone or testosterone. The steroidogenic properties of a steroid hormone-producing tumor were described. Cells isolated from this tumor produced steroids similar to those secreted by ovarian theca cells. These properties suggest that certain ovarian steroidogenic tumor cells may be an appropriate model for ovarian theca cells and could be used to develop steroid-secreting cell lines.


title: Gut microbiota: next frontier in understanding human health and development of biotherapeutics

abstract: The gut microbiota is a remarkable asset for human health. As a key element in the development and prevention of specific diseases, its study has yielded a new field of promising biotherapeutics. This review provides comprehensive and updated knowledge of the human gut microbiota, its implications in health and disease, and the potentials and limitations of its modification by currently available biotherapeutics to treat, prevent and/or restore human health, and future directions. Homeostasis of the gut microbiota maintains various functions which are vital to the maintenance of human health. Disruption of the intestinal ecosystem equilibrium (gut dysbiosis) is associated with a plethora of human diseases, including autoimmune and allergic diseases, colorectal cancer, metabolic diseases, and bacterial infections. Relevant underlying mechanisms by which specific intestinal bacteria populations might trigger the development of disease in susceptible hosts are being explored across the globe. Beneficial modulation of the gut microbiota using biotherapeutics, such as prebiotics, probiotics, and antibiotics, may favor health-promoting populations of bacteria and can be exploited in development of biotherapeutics. Other technologies, such as development of human gut models, bacterial screening, and delivery formulations eg, microencapsulated probiotics, may contribute significantly in the near future. Therefore, the human gut microbiota is a legitimate therapeutic target to treat and/or prevent various diseases. Development of a clear understanding of the technologies needed to exploit the gut microbiota is urgently required.


title: A NOVEL ARCHITECTURE FOR HIGH SPEED 1-BIT FULL ADDER AT 45nm TECHNOLOGY

abstract: Adder’s area unit basic building block of Arithmetic VLSI circuits found in processors and microcontroller within Arithmetic and Logic units. Improvement of adder is so vital so as to extend the performance of those units wherever adders realize application. Full adders, till now, are designed wide selection of structures for improvement of varied parameters like power consumption, performance of speed and physical size. The paper here describes a comparative analysis of 10T and 28T full adder with the aim of accelerating power potency and reducing physical size at 45nm technology. The design carried out by using Tanner e EDA tool at 45nm technology.


title: A Generalized Framework for Automatic Scripting Language Parallelization

abstract: Computational scientists are typically not expert programmers, and thus work in easy to use dynamic languages. However, they have very high performance requirements, due to their large datasets and experimental setups. Thus, the performance required for computational science must be extracted from dynamic languages in a manner that is transparent to the programmer. Current approaches to optimize and parallelize dynamic languages, such as just-in-time compilation and highly optimized interpreters, require a huge amount of implementation effort and are typically only effective for a single language. However, scientists in different fields use different languages, depending upon their needs.This paper presents techniques to enable automatic extraction of parallelism within scripts that are universally applicable across multiple different dynamic scripting languages. The key insight is that combining a script with its interpreter, through program specialization techniques, will embed any parallelism within the script into the combined program that can then be extracted via automatic parallelization techniques. Additionally, this paper presents several enhancements to existing speculative automatic parallelization techniques to handle the dependence patterns created by the specialization process. A prototype of the proposed technique, called Partial Evaluation with Parallelization (PEP), is evaluated against two open-source script interpreters with 6 input linear algebra kernel scripts each. The resulting geomean speedup of 5.10× on a 24-core machine shows the potential of the generalized approach in automatic extraction of parallelism in dynamic scripting languages.


title: Preprocessing techniques for context recognition from accelerometer data

abstract: The ubiquity of communication devices such as smartphones has led to the emergence of context-aware services that are able to respond to specific user activities or contexts. These services allow communication providers to develop new, added-value services for a wide range of applications such as social networking, elderly care and near-emergency early warning systems. At the core of these services is the ability to detect specific physical settings or the context a user is in, using either internal or external sensors. For example, using built-in accelerometers, it is possible to determine whether a user is walking or running at a specific time of day. By correlating this knowledge with GPS data, it is possible to provide specific information services to users with similar daily routines. This article presents a survey of the techniques for extracting this activity information from raw accelerometer data. The techniques that can be implemented in mobile devices range from classical signal processing techniques such as FFT to contemporary string-based methods. We present experimental results to compare and evaluate the accuracy of the various techniques using real data sets collected from daily activities.


title: Human facial beauty

abstract: It is hypothesized that human faces judged to be attractive by people possess two features—averageness and symmetry—that promoted adaptive mate selection in human evolutionary history by way of production of offspring with parasite resistance. Facial composites made by combining individual faces are judged to be attractive, and more attractive than the majority of individual faces. The composites possess both symmetry and averageness of features. Facial averageness may reflect high individual protein heterozygosity and thus an array of proteins to which parasites must adapt. Heterozygosity may be an important defense of long-lived hosts against parasites when it occurs in portions of the genome that do not code for the essential features of complex adaptations. In this case heterozygosity can create a hostile microenvironment for parasites without disrupting adaptation. Facial bilateral symmetry is hypothesized to affect positive beauty judgments because symmetry is a certification of overall phenotypic quality and developmental health, which may be importantly influenced by parasites. Certain secondary sexual traits are influenced by testosterone, a hormone that reduces immunocompetence. Symmetry and size of the secondary sexual traits of the face (e.g., cheek bones) are expected to correlate positively and advertise immunocompetence honestly and therefore to affect positive beauty judgments. Facial attractiveness is predicted to correlate with attractive, nonfacial secondary sexual traits; other predictions from the view that parasite-driven selection led to the evolution of psychological adaptations of human beauty perception are discussed. The view that human physical attractiveness and judgments about human physical attractiveness evolved in the context of parasite-driven selection leads to the hypothesis that both adults and children have a species-typical adaptation to the problem of identifying and favoring healthy individuals and avoiding parasite-susceptible individuals. It is proposed that this adaptation guides human decisions about nepotism and reciprocity in relation to physical attractiveness.


Wind power prediction with different artificial intelligence models


title: A Comparison of Search Strategies for Geometric Branch and Bound Algorithms

abstract: Over the last decade, a number of methods for geometric matching based on a branch-and-bound approach have been proposed. Such algorithms work by recursively subdividing transformation space and bounding the quality of match over each subdivision. No direct comparison of the major implementation strategies has been made so far, so it has been unclear what the relative performance of the different approaches is. This paper examines experimentally the relative performance of different implementation choices in the implementation of branch-and-bound algorithms for geometric matching: alternatives for the computation of upper bounds across a collection of features, and alternatives the order in which search nodes are expanded. Two major approaches to computing the bounds have been proposed: the matchlist based approach, and approaches based on point location data structures. A second issue that is addressed in the paper is the question of search strategy; branch-and-bound algorithms traditionally use a "best-first" search strategy, but a "depth-first" strategy is a plausible alternative. These alternative implementations are compared on an easily reproducible and commonly used class of test problems, a statistical model of feature distributions and matching within the COIL-20 image database. The experimental results show that matchlist based approaches outperform point location based approaches on common tasks. The paper also shows that a depth-first approach to matching results in a 50-200 fold reduction in memory usage with only a small increase in running time. Since matchlist-based approaches are significantly easier to implement and can easily cope with a much wider variety of feature types and error bounds that point location based approaches, they should probably the primary implementation strategy for branch-and-bound based methods for geometric matching.


title: Generators and irreducible polynomials over finite fields

abstract: Weil's character sum estimate is used to study the problem of constructing generators for the multiplicative group of a finite field. An application to the distribution of irreducible polynomials is given, which confirms an asymptotic version of a conjecture of Hansen-Mullen.


She 'Likes' it, He Doesn't: Gender Differences in Facebook Communication Behaviors


title: Service-Oriented E-Learning Platforms: From Monolithic Systems to Flexible Services

abstract: The learning management system (LMS) has dominated Internet-based education for the past two decades. However, the traditional LMS is failing to keep pace with advances in Internet technologies and social interactions online. To support technological diversity, current frameworks such as the e-learning framework (ELF), the IMS abstract framework, and the open knowledge initiative (OKI) have defined the initial steps toward service-oriented e-learning platforms. Next-generation platforms will be based on these service-oriented visions. The authors discuss LMS evolution and present core challenges that must be addressed to achieve information interoperability in next-generation e-learning platforms


An ECC/DCT-Based Robust Video Steganography Algorithm for Secure Data Communication


title: Structure and action mechanism of ligninolytic enzymes.

abstract: Lignin is the most abundant renewable source of aromatic polymer in nature, and its decomposition is indispensable for carbon recycling. It is chemically recalcitrant to breakdown by most organisms because of the complex, heterogeneous structure. The white-rot fungi produce an array of extracellular oxidative enzymes that synergistically and efficiently degrade lignin. The major groups of ligninolytic enzymes include lignin peroxidases, manganese peroxidases, versatile peroxidases, and laccases. The peroxidases are heme-containing enzymes with catalytic cycles that involve the activation by H2O2 and substrate reduction of compound I and compound II intermediates. Lignin peroxidases have the unique ability to catalyze oxidative cleavage of C-C bonds and ether (C-O-C) bonds in non-phenolic aromatic substrates of high redox potential. Manganese peroxidases oxidize Mn(II) to Mn(III), which facilitates the degradation of phenolic compounds or, in turn, oxidizes a second mediator for the breakdown of non-phenolic compounds. Versatile peroxidases are hybrids of lignin peroxidase and manganese peroxidase with a bifunctional characteristic. Laccases are multi-copper-containing proteins that catalyze the oxidation of phenolic substrates with concomitant reduction of molecular oxygen to water. This review covers the chemical nature of lignin substrates and focuses on the biochemical properties, molecular structures, reaction mechanisms, and related structures/functions of these enzymes.


title: A Cardiac Early Warning System with Multi Channel SCG and ECG Monitoring for Mobile Health

abstract: Use of information and communication technology such as smart phone, smart watch, smart glass and portable health monitoring devices for healthcare services has made Mobile Health (mHealth) an emerging research area. Coronary Heart Disease (CHD) is considered as a leading cause of death world wide and an increasing number of people die prematurely due to CHD. Under such circumstances, there is a growing demand for a reliable cardiac monitoring system to catch the intermittent abnormalities and detect critical cardiac behaviors which lead to sudden death. Use of mobile devices to collect Electrocardiography (ECG), Seismocardiography (SCG) data and efficient analysis of those data can monitor a patient’s cardiac activities for early warning. This paper presents a novel cardiac data acquisition method and combined analysis of Electrocardiography (ECG) and multi channel Seismocardiography (SCG) data. An early warning system is implemented to monitor the cardiac activities of a person and accuracy assessment of the early warning system is conducted for the ECG data only. The assessment shows 88% accuracy and effectiveness of our proposed analysis, which implies the viability and applicability of the proposed early warning system.


Easy Questions First? A Case Study on Curriculum Learning for Question Answering


title: CPS approach to checking norm operation of a brake-by-wire system

abstract: For better controllability and energy-efficiency, more vehicle functions are being implemented via electronic control systems in place of traditional mechanical control systems. However, such transitions are creating new, unprecedented risks such as software bugs or hardware glitches, all of which can lead to serious safety risks. Recent real-world examples and research literature have been covering them under the name of vehicle misbehavior. In this paper, we present a new way of checking norm operations, called BAD (Brake Anomaly Detection), which detects any vehicle misbehavior in the Brake-by-Wire system. We focus on the braking system since it is a prototypical safety-critical and cyber-physical system. We first propose a new method for constructing norm models of braking and then show how anomalies are detected by BAD using the constructed models. Finally, we discuss how to verify the results, especially in the context of false positives. Our evaluation results show that BAD can effectively detect various types of anomaly in the braking system.


title: Cyberterrorism Definition Patterns and Mitigation Strategies: A Literature Review

abstract: This paper aim to contribute to the body of knowledge on cyber terrorism, improves awareness of cyber terrorism definition, boundaries, potential targets, crime patterns and effective mitigation strategies through analysis of relevant literature on the issue produced in recent years. It details the definitional origins of the concept and It looks at motivational factors and level of destruction necessary to classify a cyber-attack as cyberterrorism. The second sections is devoted to literature distinguishing cyberterrorism from hacking and cybercrime. Section three details the stages of a cyber attack, Section four lists the types of infrastructure targeted for cyber terrorists and Section five focuses on mitigation strategies.


title: Interactive Movie Recommendation Through Latent Semantic Analysis and Storytelling

abstract: Recommendation has become one of the most important components of online services for improving sale records, however visualization work for online recommendation is still very limited. This paper presents an interactive recommendation approach with the following two components. First, rating records are the most widely used data for online recommendation, but they are often processed in high-dimensional spaces that can not be easily understood or interacted with. We propose a Latent Semantic Model (LSM) that captures the statistical features of semantic concepts on 2D domains and abstracts user preferences for personal recommendation. Second, we propose an interactive recommendation approach through a storytelling mechanism for promoting the communication between the user and the recommendation system. Our approach emphasizes interactivity, explicit user input, and semantic information convey; thus it can be used by general users without any knowledge of recommendation or visualization algorithms. We validate our model with data statistics and demonstrate our approach with case studies from the MovieLens100K dataset. Our approaches of latent semantic analysis and interactive recommendation can also be extended to other network-based visualization applications, including various online recommendation systems.


title: Pedestrian Behavior Understanding and Prediction with Deep Neural Networks

abstract: In this paper, a deep neural network (Behavior-CNN) is proposed to model pedestrian behaviors in crowded scenes, which has many applications in surveillance. A pedestrian behavior encoding scheme is designed to provide a general representation of walking paths, which can be used as the input and output of CNN. The proposed Behavior-CNN is trained with real-scene crowd data and then thoroughly investigated from multiple aspects, including the location map and location awareness property, semantic meanings of learned filters, and the influence of receptive fields on behavior modeling. Multiple applications, including walking path prediction, destination prediction, and tracking, demonstrate the effectiveness of Behavior-CNN on pedestrian behavior modeling.


title: EMOTIONS AND ADVICE 1 Running Head: EMOTIONS AND ADVICE Blinded by Anger or Feeling the Love: How Emotions Influence Advice Taking Forthcoming: Journal of Applied Psychology

abstract: Across 2 experiments, the authors demonstrate that emotional states influence how receptive people are to advice. The focus of these experiments is on incidental emotions, emotions triggered by a prior experience that is irrelevant to the current situation. The authors demonstrate that people who feel incidental gratitude are more trusting and more receptive to advice than are people in a neutral emotional state, and people in a neutral state are more trusting and more receptive to advice than are people who feel incidental anger. In these experiments, greater receptivity to advice increased judgment accuracy. People who felt incidental gratitude were more accurate than were people in a neutral state, and people in a neutral state were more accurate than were people who felt incidental anger. The results offer insight into how people use advice, and the authors identify conditions under which leaders, policy makers, and advisors may be particularly influential.


title: Revisiting Semi-Supervised Learning with Graph Embeddings

abstract: We present a semi-supervised learning framework based on graph embeddings. Given a graph between instances, we train an embedding for each instance to jointly predict the class label and the neighborhood context in the graph. We develop both transductive and inductive variants of our method. In the transductive variant of our method, the class labels are determined by both the learned embeddings and input feature vectors, while in the inductive variant, the embeddings are defined as a parametric function of the feature vectors, so predictions can be made on instances not seen during training. On a large and diverse set of benchmark tasks, including text classification, distantly supervised entity extraction, and entity classification, we show improved performance over many of the existing models.


Principles of Neural Science


LDA v. LSA: A Comparison of Two Computational Text Analysis Tools for the Functional Categorization of Patents


Weight initialization methods for multilayer feedforward


title: Reduction of Long Line Effects in Single-Layer Slotted Waveguide Arrays With an Embedded Partially Corporate Feed

abstract: Single-layer slotted waveguide arrays have various advantages such as low cost and mass productivity for fabrication, high gain and efficiency especially in millimeter wave band. They have traveling wave operation and the bandwidth becomes inherently narrower due to the long line effect as the antenna gain increases. This paper proposes the partially corporate feed in the single-layer structure, which inherits the advantage of low cost in fabrication. The bandwidth enhancement as well as the degradation due to the non radiating area in the aperture is assessed. The feasibility/potential of the embedded partially corporate feed is demonstrated by the experiments using the E- to H-plane power divider with very small blockage.


title: Gravitating towards mobile phone (GoToMP) during lecture periods by students: Why are they using it? and how can it be measured?

abstract: With the growing amount of research on the use of technology, especially the mobile phones, the paucity of research in the area of mobile phone use that focus on the reasons why students use their mobile phones during lecture periods is somewhat surprising. Findings from the previous studies have documented that students continue to use their mobile phones during lecture periods even in classes where its use is banned. Thus, the motives that determine students' continued intention to use their mobile phones during lecture periods becomes an important issue that deserves urgent attention from researchers and education policymakers. In the present investigation, we tried to fill this gap by examining the determinants of students' continuance use of mobile phones during lecture periods. To achieve this, three stages of instrument development (item generation, scale development, and instrument testing) were undertaken through two studies. The results of the exploratory and confirmatory factor analysis confirmed the existence of six dimensions in the proposed measurement instrument (which we referred to as gravitating towards mobile phone (GoToMP)); the dimensions are boredom, class-related use, social connection, emergency, addiction, and perceived behavioral control. The combined findings from the two studies reported here provide initial evidence that GoToMP exhibits excellent psychometric properties and can be used in future empirical research on mobile phone use behaviors of students. New scale is developed to measure the motives behind students use of mobile phones.The scale has good psychometric properties.It can be used in future empirical research on mobile phone use behaviors of students.The scale is very simple to use and administer.


title: Preventive staff-support interventions for health workers

abstract: BACKGROUND ::: Healthcare workers need to be supported to maintain sufficient levels of motivation and productivity, and to prevent the debilitating effects of stress on mental and physical well-being. ::: ::: ::: OBJECTIVES ::: To assess the effects of preventive staff-support interventions to healthcare workers. ::: ::: ::: SEARCH STRATEGY ::: We searched The Cochrane Effective Practice and Organisation of Care Group (EPOC) Specialised Register (and the database of studies awaiting assessment), Biblioweb (searched 28 August 2008); The Cochrane Central Register of Controlled Trials (The Cochrane Library 2008, Issue 3) (searched 28 August 2008); MEDLINE, Ovid 1950 to August Week 2 2008 (searched 26 August 2008); CINAHL, Ovid 1982 to August Week 4 2008 (searched 26 August 2008); EMBASE, Ovid 1980 to 2008 Week 34 (searched 26 August 2008); PsycINFO, Ovid 1806 to July Week 5 2008 (searched 27 August 2008); Soc iological Abstracts, CBA 1952 to present (searched 28 August 2008). ::: ::: ::: SELECTION CRITERIA ::: Randomised controlled trials of interventions to support healthcare workers in coping with work-related stress, preventing burnout and improving job satisfaction, without changing contractual conditions of service or physical work environment. Three types of interventions were included in this review: (1) support groups for staff; (2) training in stress management techniques; and (3) management interventions for supporting staff. ::: ::: ::: DATA COLLECTION AND ANALYSIS ::: Two authors independently performed study selection, quality assessments and data abstraction. ::: ::: ::: MAIN RESULTS ::: Ten studies involving 716 participants met the criteria for inclusion. None assessed the effects of support groups for health workers. Eight studies assessed the effects of training interventions in various stress management techniques on measures of stress and/or job satisfaction, and two studies assessed the effects of management interventions on stress, job satisfaction and absenteeism.Three studies demonstrated a beneficial effect of stress management training intervention on job stress. Only one of these showed that this effect is sustainable over the medium-term. One study demonstrated the beneficial effect of a high intensity, stress management training intervention on burnout. Low and moderate intensity stress management training interventions failed to demonstrate benefit on burnout or staff satisfaction.Management interventions demonstrated increases in job satisfaction, but failed to show effect on absenteeism.Most studies had several methodological shortcomings leaving them vulnerable to potential biases. ::: ::: ::: AUTHORS' CONCLUSIONS ::: There is insufficient evidence for the effectiveness of stress management training interventions to reduce job stress and prevent burnout among healthcare workers beyond the intervention period. Low quality evidence suggests that longer-term interventions with refresher or booster sessions may have more sustained positive effect, but this needs to be rigorously evaluated in further trials.Low quality evidence exists to show that management interventions may improve some measures of job satisfaction. However, further trials are needed to assess whether this finding is replicable in other settings. There was insufficient evidence of the benefit of management interventions on staff absenteeism.Rigorous trials are needed to assess the effects of longer-term stress management training and management interventions in primary care and developing country settings.


title: Energy management of Renewable Energy Sources in a microgrid using Cuckoo Search Algorithm

abstract: In this paper, the Renewable Energy Resources (RES) are used in a specific small area which is known as microgrid. Microgrid consists of microsources, battery storage and loads. In this paper the research is focused on the islanded mode microgrid. In the microgrid, the energy management system having a problem of Economic Load Dispatch (ELD) and it is optimized by meta-heuristic techniques. The newly introduced Cuckoo Search Algorithm (CSA) is implemented for the solution of ELD problem in the MATLAB environment. The minimization of total cost is obtained for four different scenarios like all sources included, all sources without solar energy, all sources without wind energy and all sources without solar and wind energy. In both scenarios, the result shows the comparison of CSA with the Reduced Gradient Method (RGM) for the ELD problem solution. The results are calculated for different power demand of 24 hours. The results obtained with CSA gives better cost reduction in less iterations as compared to RGM which shows the effectiveness of the suggested algorithm.


title: On the Recognition of Printed Characters of Any Font and Size

abstract: We describe the current state of a system that recognizes printed text of various fonts and sizes for the Roman alphabet. The system combines several techniques in order to improve the overall recognition rate. Thinning and shape extraction are performed directly on a graph of the run-length encoding of a binary image. The resulting strokes and other shapes are mapped, using a shape-clustering approach, into binary features which are then fed into a statistical Bayesian classifier. Large-scale trials have shown better than 97 percent top choice correct performance on mixtures of six dissimilar fonts, and over 99 percent on most single fonts, over a range of point sizes. Certain remaining confusion classes are disambiguated through contour analysis, and characters suspected of being merged are broken and reclassified. Finally, layout and linguistic context are applied. The results are illustrated by sample pages.


title: An Improved RNS Variant of the BFV Homomorphic Encryption Scheme

abstract: We present an optimized variant of the Brakerski/Fan-Vercauteren (BFV) homomorphic encryption scheme and its efficient implementation in PALISADE. Our algorithmic improvements focus on optimizing decryption and homomorphic multiplication in the Residue Number System (RNS), using the Chinese Remainder Theorem (CRT) to represent and manipulate the large coefficients in the ciphertext polynomials. These improvements are based on our original general-purpose techniques for CRT basis extension and scaling that can be applied to many other lattice-based cryptographic primitives. Our variant is simpler and significantly more efficient than the RNS variant proposed by Bajard et al. both in terms of noise growth and the computational complexity of the underlying CRT basis extension and scaling procedures.


title: Automatic generation of fill-in clues and answers from raw texts for crosswords

abstract: This paper presents a method to generate fill-in clues and answers for building automatically a crossword. Answers are capitalised words present in an input sentence and clues are segments of the dependency syntactic structure of that sentence. The pairs (Clue, ANSWER) are extracted from a collection of raw sentences related to the history of Sarawak. This work is at its early stage, and thus the proposed method that generates automatically fill-in clues, was tested on a small set of sentences and the obtained results are promising. Near 53% of the generated fill-in clues are considered correct. The major contribution of this work is the innovative strategy used to read the result of a pre-order depth-first search applied on a dependency graph to generate the clues. The clues and answers generator is implemented in Python.


title: mtDNA and the Islands of the North Atlantic: Estimating the Proportions of Norse and Gaelic Ancestry

abstract: A total of 1,664 new mtDNA control-region sequences were analyzed in order to estimate Gaelic and Scandinavian matrilineal ancestry in the populations of Iceland, Orkney, the Western Isles, and the Isle of Skye and to investigate other aspects of their genetic history. A relative excess of private lineages in the Icelanders is indicative of isolation, whereas the scarcity of private lineages in Scottish island populations may be explained by recent gene flow and population decline. Differences in the frequencies of lineage clusters are observed between the Scandinavian and the Gaelic source mtDNA pools, and, on a continent-wide basis, such differences between populations seem to be associated with geography. A multidimensional scaling analysis of genetic distances, based on mtDNA lineage-cluster frequencies, groups the North Atlantic islanders with the Gaelic and the Scandinavian populations, whereas populations from the central, southern, and Baltic regions of Europe are arranged in clusters in broad agreement with their geographic locations. This pattern is highly significant, according to a Mantel correlation between genetic and geographic distances (r=.716). Admixture analyses indicate that the ancestral contributions of mtDNA lineages from Scandinavia to the populations of Iceland, Orkney, the Western Isles, and the Isle of Skye are 37.5%, 35.5%, 11.5%, and 12.5%, respectively.


title: Exosomes — beyond stem cells for restorative therapy in stroke and neurological injury

abstract: Stroke is a leading cause of disability worldwide, and brain injuries devastate patients and their families, but currently no drugs on the market promote neurological recovery. Limited spontaneous recovery of function as a result of brain remodelling after stroke or injury does occur, and cell-based therapies have been used to promote these endogenous processes. Increasing evidence is demonstrating that the positive effects of such cell-based therapy are mediated by exosomes released from the administered cells and that the microRNA cargo in these exosomes is largely responsible for the therapeutic effects. This evidence raises the possibility that isolated exosomes could be used alone as a neurorestorative therapy and that these exosomes could be tailored to maximize clinical benefit. The potential of exosomes as a therapy for brain disorders is therefore being actively investigated. In this Review, we discuss the current knowledge of exosomes and advances in our knowledge of their effects on endogenous neurovascular remodelling events. We also consider the opportunities for exosome-based approaches to therapeutic amplification of brain repair and improvement of recovery after stroke, traumatic brain injury and other diseases in which neurorestoration could be a viable treatment strategy.


title: Sequential Convolutional Neural Networks for Slot Filling in Spoken Language Understanding

abstract: We investigate the usage of convolutional neural networks (CNNs) for the slot filling task in spoken language understanding. We propose a novel CNN architecture for sequence labeling which takes into account the previous context words with preserved order information and pays special attention to the current word with its surrounding context. Moreover, it combines the information from the past and the future words for classification. Our proposed CNN architecture outperforms even the previously best ensembling recurrent neural network model and achieves state-of-the-art results with an F1-score of 95.61% on the ATIS benchmark dataset without using any additional linguistic knowledge and resources.


title: P3P and P2P problems with known camera and object vertical directions

abstract: In this paper the problem of estimating the relative orientation and position between a camera and an object is considered. It is assumed that both the camera and the object are provided with an IMU capable to give their inclinations with respect to the gravity vector. It is moreover assumed that the object contains a feature of 3 points whose position in the object coordinate frame is known. Using the image provided by the camera and the information on the gravity vector by the IMUs we propose an algorithm capable of estimating the relative pose of the object in the camera reference frame by solving a modified P2P or P3P problem. It will be shown that the P2P problem always gives 2 solutions, except in a few singular configurations, while the P3P problem usually gives a single solution. The effectiveness of the proposed approach will be shown by contrasting it with other algorithms presented in the literature.


title: Kannada named entity recognition and classification (nerc) based on multinomial na\"ive bayes (mnb) classifier

abstract: Named Entity Recognition and Classification (NERC) is a process of identification of proper nouns in the text and classification of those nouns into certain predefined categories like person name, location, organization, date, and time etc. NERC in Kannada is an essential and challenging task. The aim of this work is to develop a novel model for NERC, based on Multinomial Na\"ive Bayes (MNB) Classifier. The Methodology adopted in this paper is based on feature extraction of training corpus, by using term frequency, inverse document frequency and fitting them to a tf-idf-vectorizer. The paper discusses the various issues in developing the proposed model. The details of implementation and performance evaluation are discussed. The experiments are conducted on a training corpus of size 95,170 tokens and test corpus of 5,000 tokens. It is observed that the model works with Precision, Recall and F1-measure of 83%, 79% and 81% respectively.


title: Classification of histopathological images using convolutional neural network

abstract: In this work, classification of cellular structures in the high resolutional histopathological images and the discrimination of cellular and non-cellular structures have been investigated. The cell classification is a very exhaustive and time-consuming process for pathologists in medicine. The development of digital imaging in histopathology has enabled the generation of reasonable and effective solutions to this problem. Morever, the classification of digital data provides easier analysis of cell structures in histopathological data. Convolutional neural network (CNN), constituting the main theme of this study, has been proposed with different spatial window sizes in RGB color spaces. Hence, to improve the accuracies of classification results obtained by supervised learning methods, spatial information must also be considered. So, spatial dependencies of cell and non-cell pixels can be evaluated within different pixel neighborhoods in this study. In the experiments, the CNN performs superior than other pixel classification methods including SVM and k-Nearest Neighbour (k-NN). At the end of this paper, several possible directions for future research are also proposed.


title: A survey on optical character recognition for Bangla and Devanagari scripts

abstract: The past few decades have witnessed an intensive research on optical character recognition (OCR) for Roman, Chinese, and Japanese scripts. A lot of work has been also reported on OCR efforts for various Indian scripts, like Devanagari, Bangla, Oriya, Tamil, Telugu, Malayalam, Kannada, Gurmukhi, Gujarati, etc. In this paper, we present a review of OCR work on Indian scripts, mainly on Bangla and Devanagari—the two most popular scripts in India. We have summarized most of the published papers on this topic and have also analysed the various methodologies and their reported results. Future directions of research in OCR for Indian scripts have been also given.


Information Systems and Healthcare XXXII: Understanding the Multidimensionality of Information Systems Use: A Study of Nurses’ Use of a Mandated Electronic Medical Record System