title: Creating Value in Networks: A Value Network Mapping Method for Assessing the Current and Potential Value Networks in Cross-Sector Collaboration

abstract: ABSTRACTHow to manage value creation and how to maximize the potential of collaborations are challenging issues in the management of cross-sector collaborations. One strategic tool for addressing these areas of concern is value network mapping; this serves as the starting point for value network analysis, which is used to maximize innovations by forging stronger value creation links with strategic partners. The objective of this study was to design and implement a method for value network mapping that would increase our understanding of how new value can be created in cross-sector collaborations. The proposed mapping method also permits the assessment of current and potential value networks; e.g., what value is exchanged in the current collaborative state and what new value can be created through the proper management of a value network? Using the case study of a collaboration among the public, private, and non-profit sectors for elderly care, this paper describes, step by step, how the proposed value network mapping method can be applied in practice to help managers and experts understand how to develop complex value network maps, how new values can be created, and how to maximize the potential of a collaboration.Key Words: Value network, value network mapping, value creation, cross-sector collaboration, conceptual framework, elderly careIntroductionHow to manage value creation and how to maximize the potential of collaborations are challenging issues in the management of cross-sector collaborations. In today's dynamic economic environment, which is facing complex socioeconomic problems, cross-sector collaborations have continuously increased (Handy, 1996; Lipman-Blumen, 1996; Cleveland, 2002; Le Ber and Branzei, 2010; Oystein et al., 2012). However, despite sustained efforts to find or develop innovative and powerful methods for effective management and value creation within the context of cross-sector collaborations, this remains a challenge.Collaborations and partnerships enable people and organizations to support each other by combining and leveraging their complementary strengths and capabilities (Dyer and Singh, 1998; Adegbesan and Higgins, 2011). The power of a partnership is determined by the value network that results from the collaboration, and managing value creation in a knowledge economy requires a strong appreciation of the intangible aspects of a business model and an understanding of network dynamics (Allee, 2002).Traditionally, value creation has been seen as a linear process (i.e., value is created through a value chain) (Vargoa, Magliob ,and Akakaa, 2008). In a knowledge economy, however, the situation is much more complex. Organizations themselves are highly complex systems consisting of many variables that cannot easily be controlled, which complicates the management of value creation (Chatain and Zemsky, 2011). Managers will often use visual tools like strategic maps to more easily piece together a huge amount of information (de Benedetto and Klemes, 2009). Value network maps (Allee, 2011) are one such visual tool used to manage value creation. Value network mapping is the starting point in value network analysis, which is used to maximize innovations by forging stronger value creation links between strategic partners (Allee and Taug, 2006).Although the literature on value networks and value network analysis has reached an increasingly interest (Allee, 2002, 2006, 2008; Lock Lee, 2007; Meggitt and Allee, 2011; Optimice Pty. Ltd., 2008; Anger, 2008; Plambeck and Denend, 2008), there is not a specific method for describing in detail how to map a value network. Allee (2011) has described the basics of value network mapping but provides no details about how to identify the added value that every partner brings to a network or how to identify the participants' assets (e.g., the value flows within the network). Therefore, more research is needed in this area.The objective and benefits of the studyThe main objective of this study was to design a method for value network mapping that provides increased understanding of the value creation process. …


title: Self-regulation of goal setting : Turning free fantasies about the future into binding goals

abstract: Fantasy realization theory states that when people contrast their fantasies about a desired future with reflections on present reality, a necessity to act is induced that leads to the activation and use of relevant expectations. Strong goal commitment arises in light of favorable expectations, and weak goal commitment arises in light of unfavorable expectations. To the contrary, when people only fantasize about a desired future or only reflect on present reality, expectancy-independent moderate goal commitment emerges. Four experiments pertaining to various life domains supported these hypotheses. Strength of goal commitment was assessed in cognitive (e.g., making plans), affective (e.g., felt attachment), and behavioral terms (e.g., effort expenditure, quality of performance). Implications for theories on goal setting and goal striving are discussed.


title: Multiple Hypothesis Tracking for Cluttered Biological Image Sequences

abstract: In this paper, we present a method for simultaneously tracking thousands of targets in biological image sequences, which is of major importance in modern biology. The complexity and inherent randomness of the problem lead us to propose a unified probabilistic framework for tracking biological particles in microscope images. The framework includes realistic models of particle motion and existence and of fluorescence image features. For the track extraction process per se, the very cluttered conditions motivate the adoption of a multiframe approach that enforces tracking decision robustness to poor imaging conditions and to random target movements. We tackle the large-scale nature of the problem by adapting the multiple hypothesis tracking algorithm to the proposed framework, resulting in a method with a favorable tradeoff between the model complexity and the computational cost of the tracking procedure. When compared to the state-of-the-art tracking techniques for bioimaging, the proposed algorithm is shown to be the only method providing high-quality results despite the critically poor imaging conditions and the dense target presence. We thus demonstrate the benefits of advanced Bayesian tracking techniques for the accurate computational modeling of dynamical biological processes, which is promising for further developments in this domain.


title: ChemSpot: a hybrid system for chemical named entity recognition

abstract: Motivation: The accurate identification of chemicals in text is important for many applications, including computer-assisted reconstruction of metabolic networks or retrieval of information about substances in drug development. But due to the diversity of naming conventions and traditions for such molecules, this task is highly complex and should be supported by computational tools. Results: We present ChemSpot, a named entity recognition (NER) tool for identifying mentions of chemicals in natural language texts, including trivial names, drugs, abbreviations, molecular formulas and International Union of Pure and Applied Chemistry entities. Since the different classes of relevant entities have rather different naming characteristics, ChemSpot uses a hybrid approach combining a Conditional Random Field with a dictionary. It achieves an F1 measure of 68.1% on the SCAI corpus, outperforming the only other freely available chemical NER tool, OSCAR4, by 10.8 percentage points. Availability: ChemSpot is freely available at: http://www.informatik.hu-berlin.de/wbi/resources Contact: leser@informatik.hu-berlin.de


title: QRS Detection Based on Multiscale Mathematical Morphology for Wearable ECG Devices in Body Area Networks

abstract: A novel wearable electrocardiograph (ECG) QRS detection algorithm for wearable ECG devices in body area networks is presented in this paper, which utilizes the multistage multiscale mathematical morphology filtering to suppress the impulsive noise and uses the multiframe differential modulus accumulation to remove the baseline drift and enhance the signal. The proposed algorithm, verified with data from the MIT/BIH Arrhythmia Database and wearable ECG devices, achieves an average QRS detection rate of 99.61%, a sensitivity of 99.81%, and a positive prediction of 99.80%. It compares favorably to the published methods.


title: An efficient reconciliation algorithm for social networks

abstract: People today typically use multiple online social networks (Facebook, Twitter, Google+, LinkedIn, etc.). Each online network represents a subset of their "real" ego-networks. An interesting and challenging problem is to reconcile these online networks, that is, to identify all the accounts belonging to the same individual. Besides providing a richer understanding of social dynamics, the problem has a number of practical applications. At first sight, this problem appears algorithmically challenging. Fortunately, a small fraction of individuals explicitly link their accounts across multiple networks; our work leverages these connections to identify a very large fraction of the network. ::: ::: Our main contributions are to mathematically formalize the problem for the first time, and to design a simple, local, and efficient parallel algorithm to solve it. We are able to prove strong theoretical guarantees on the algorithm's performance on well-established network models (Random Graphs, Preferential Attachment). We also experimentally confirm the effectiveness of the algorithm on synthetic and real social network data sets.


title: Integrating the Smart Home into the Digital Calendar

abstract: With the growing adoption of smart home technologies, inhabitants are faced with the challenge of making sense of the data that their homes can collect to configure automated behaviors that benefit their routines. Current commercial smart home interfaces usually provide information on individual devices instead of a more comprehensive overview of a home's behavior. To reduce the complexity of smart home data and integrate it better into inhabitants' lives, we turned to the familiar metaphor of a calendar and developed our smart home interface Casalendar. In order to investigate the concept and evaluate our goals to facilitate the understanding of smart home data, we created a prototype that we installed in two commercial smart homes for a month. The results we present in this paper are based on our analysis of user data from questionnaires, semi-structured interviews, participant-driven audio and screenshot feedback as well as logged interactions with our system. Our findings exposed advantages and disadvantages of this metaphor, emerging usage patterns, privacy concerns and challenges for information visualization. We further report on implications for design and open challenges we revealed through this work.


title: Tractable Particle Filters for Robot Fault Diagnosis

abstract: This article presents a number of complementary algorithms for detecting faults on-board operating robots, where a fault is defined as a deviation from expected behavior. The algorithms focus on faults that cannot directly be detected from current sensor values but require inference from a sequence of time-varying sensor values. Each algorithm provides an independent improvement over the basic approach. These improvements are not mutually exclusive, and the algorithms may be combined to suit the application domain. All the approaches presented require dynamic models representing the behavior of each of the fault and operational states. These models can be built from analytical models of the robot dynamics, data from simulation, or from the real robot. All the approaches presented detect faults from a finite number of known fault conditions, although there may potentially be a very large number of these faults.


title: Assessment of Motor Functioning in the Preschool Period

abstract: The assessment of motor functioning in young children has become increasingly important in recent years with the acknowledgement that motor impairment is linked with cognitive, language, social and emotional difficulties. However, there is no one gold standard assessment tool to investigate motor ability in children. The aim of the current paper was to discuss the issues related to the assessment of motor ability in young pre-school children and to provide guidelines on the best approach for motor assessment. The paper discusses the maturational changes in brain development at the preschool level in relation to motor ability. Other issues include sex differences in motor ability at this young age, and evidence for this in relation to sociological versus biological influences. From the previous literature it is unclear what needs to be assessed in relation to motor functioning. Should the focus be underlying motor processes or movement skill assessment? Several key assessment tools are discussed that produce a general measure of motor performance followed by a description of tools that assess specific skills, such as fine and gross motor, ball and graphomotor skills. The paper concludes with recommendations on the best approach in assessing motor function in pre-school children.


title: WIQ: Work-Intensive Query Scheduling for In-Memory Database Systems

abstract: We propose a novel admission control policy for database queries. Our methodology uses system measurements of CPU utilization and query backlogs to determine interference between queries in execution on the same database server. Query interference may arise due to the concurrent access of hardware and software resources and can affect performance in positive and negative ways. Specifically our admission control considers the mix of jobs in service and prioritizes the query classes consuming CPU resources more efficiently. The policy ignores I/O subsystems and is therefore highly appropriate for in-memory databases. We validate our approach in trace-driven simulation and show performance increases of query slowdowns and throughputs compared to first-come first-served and shortest expected processing time first scheduling. Simulation experiments are parameterized from system traces of a SAP HANA in-memory database installation with TPC-H type workloads.


title: An assertion language for debugging SDN applications

abstract: Software Defined Networking (SDN) provides opportunities for network verification and debugging by offering centralized visibility of the data plane. This has enabled both offline and online data-plane verification. However, little work has gone into the verification of time-varying properties (e.g., dynamic access control), where verification conditions change dynamically in response to application logic, network events, and external stimulus (e.g., operator requests). This paper introduces an assertion language to support verifying and debugging SDN applications with dynamically changing verification conditions. The language allows programmers to annotate controller applications with C-style assertions about the data plane. Assertions consist of regular expressions on paths to describe path properties for classes of packets, and universal and existential quantifiers that range over programmer-defined sets of hosts, switches, or other network entities. As controller programs dynamically add and remove elements from these sets, they generate new verification conditions that the existing data plane must satisfy. This work proposes an incremental data structure together with an underlying verification engine, to avoid naively re-verifying the entire data plane as these verification conditions change. To validate our ideas, we have implemented a debugging library on top of a modified version of VeriFlow, which is easily integrated into existing controller systems with minimal changes. Using this library, we have verified correctness properties for applications on several controller platforms.


title: ScribbleSup: Scribble-Supervised Convolutional Networks for Semantic Segmentation

abstract: Large-scale data is of crucial importance for learning semantic segmentation models, but annotating per-pixel masks is a tedious and inefficient procedure. We note that for the topic of interactive image segmentation, scribbles are very widely used in academic research and commercial software, and are recognized as one of the most userfriendly ways of interacting. In this paper, we propose to use scribbles to annotate images, and develop an algorithm to train convolutional networks for semantic segmentation supervised by scribbles. Our algorithm is based on a graphical model that jointly propagates information from scribbles to unmarked pixels and learns network parameters. We present competitive object semantic segmentation results on the PASCAL VOC dataset by using scribbles as annotations. Scribbles are also favored for annotating stuff (e.g., water, sky, grass) that has no well-defined shape, and our method shows excellent results on the PASCALCONTEXT dataset thanks to extra inexpensive scribble annotations. Our scribble annotations on PASCAL VOC are available at http://research.microsoft.com/en-us/um/ people/jifdai/downloads/scribble_sup.


title: Tensor space model for document analysis

abstract: Vector Space Model (VSM) has been at the core of information retrieval for the past decades. VSM considers the documents as vectors in high dimensional space.In such a vector space, techniques like Latent Semantic Indexing (LSI), Support Vector Machines (SVM), Naive Bayes, etc., can be then applied for indexing and classification. However, in some cases, the dimensionality of the document space might be extremely large, which makes these techniques infeasible due to the curse of dimensionality. In this paper, we propose a novel Tensor Space Model for document analysis. We represent documents as the second order tensors, or matrices. Correspondingly, a novel indexing algorithm called Tensor Latent Semantic Indexing (TensorLSI) is developed in the tensor space. Our theoretical analysis shows that TensorLSI is much more computationally efficient than the conventional Latent Semantic Indexing, which makes it applicable for extremely large scale data set. Several experimental results on standard document data sets demonstrate the efficiency and effectiveness of our algorithm.


title: Unsupervised Polyglot Text-to-speech

abstract: We present a TTS neural network that is able to produce speech in multiple languages. The proposed network is able to transfer a voice, which was presented as a sample in a source language, into one of several target languages. Training is done without using matching or parallel data, i.e., without samples of the same speaker in multiple languages, making the method much more applicable. The conversion is based on learning a polyglot network that has multiple per-language sub-networks and adding loss terms that preserve the speaker’s identity in multiple languages. We evaluate the proposed polyglot neural network for three languages with a total of more than 400 speakers and demonstrate convincing conversion capabilities.


title: SIRIUS: Dual, Symmetric, Interactive Dimension Reductions

abstract: Much research has been done regarding how to visualize and interact with observations and attributes of high-dimensional data for exploratory data analysis. From the analyst's perceptual and cognitive perspective, current visualization approaches typically treat the observations of the high-dimensional dataset very differently from the attributes. Often, the attributes are treated as inputs (e.g., sliders), and observations as outputs (e.g., projection plots), thus emphasizing investigation of the observations. However, there are many cases in which analysts wish to investigate both the observations and the attributes of the dataset, suggesting a symmetry between how analysts think about attributes and observations. To address this, we define SIRIUS (Symmetric Interactive Representations In a Unified System), a symmetric, dual projection technique to support exploratory data analysis of high-dimensional data. We provide an example implementation of SIRIUS and demonstrate how this symmetry affords additional insights.


title: A Large-Scale Empirical Study on Android Runtime-Permission Rationale Messages

abstract: After Android 6.0 introduces the runtime-permission system, many apps provide runtime-permission-group rationales for the users to better understand the permissions requested by the apps. To understand the patterns of rationales and to what extent the rationales can improve the users' understanding of the purposes of requesting permission groups, we conduct a large-scale measurement study on five aspects of runtime rationales. We have five main findings: (1) less than 25% apps under study provide rationales; (2) for permission-group purposes that are difficult to understand, the proportions of apps that provide rationales are even lower; (3) the purposes stated in a significant proportion of rationales are incorrect; (4) a large proportion of customized rationales do not provide more information than the default permission-requesting message of Android; (5) apps that provide rationales are more likely to explain the same permission group's purposes in their descriptions than apps that do not provide rationales. We further discuss important implications from these findings


title: The Role of Abnormal Methylation of Wnt5a Gene Promoter Regions in Human Epithelial Ovarian Cancer: A Clinical and Experimental Study

abstract: Objective ::: In the current study, the role of abnormal methylation of Wnt5a gene promoter regions in human epithelial ovarian cancer was investigated. ::: ::: ::: Methods ::: Wnt5a expressions were examined by immunohistochemistry in epithelial ovarian tissues (30 normal and 79 human EOC tissues). SKOV3 cells were treated with different concentrations of 5-Aza-CdR (0.5, 5, and 50 μmol/L). The methylation status of the Wnt5a promoter was analyzed using a methylation-specific polymerase chain reaction (MSP), and the expression level of Wnt5a mRNA was detected using quantitative real-time polymerase chain reaction (qRT-PCR). Cell proliferation was measured by MTT assay, and apoptosis was analyzed using flow cytometry. ::: ::: ::: Results ::: (1) Compared with normal tissues, Wnt5a expressions were reduced or lost in EOC (P < 0.05). Wnt5a expression had a close relationship with histological grade, FIGO stage, and lymph node metastasis (P = 0.005, P = 0.022, and P = 0.037, resp.). (2) Wnt5a abnormal methylation status existed in ovarian cancer tissues and was higher than that of normal ovarian tissue (P < 0.01). (3) Before treatment with 5-Aza-CdR, the promoter of the Wnt5a gene was methylated in SKOV3 cells; accordingly, Wnt5a mRNA levels were low to absent in SKOV3 cells. (4) Following 5-Aza-CdR treatment, MSP analysis revealed complete demethylation of the Wnt5a promoter in the SKOV3 cell line, particularly at 5 μmol/L 5-Aza-CdR. Wnt5a expression increased in SKOV3 cells following treatment with a demethylating agent (P ≤ 0.001). (5) The growth rate of the cells was inhibited in a dose-dependent manner by treatment with 5-Aza-CdR. (6) The cell apoptosis rate increased gradually after treatment with 0.5, 5, and 50 μmol/L 5-Aza-CdR. The apoptosis rate exists in a dose-dependent relationship with 5-Aza-CdR concentration (F = 779.73, P < 0.01). ::: ::: ::: Conclusions ::: Wnt5a gene region promoter aberrant methylation existed in epithelial ovarian cancer, and abnormal methylation of Wnt5a gene promoter regions may be a new target for the treatment of epithelial ovarian cancer.


I NTELLIGENT SYNAPSES FOR MULTITASK AND TRANSFER LEARNING


title: Distributed Representations of Geographically Situated Language

abstract: We introduce a model for incorporating contextual information (such as geography) in learning vector-space representations of situated language. In contrast to approaches to multimodal representation learning that have used properties of the object being described (such as its color), our model includes information about the subject (i.e., the speaker), allowing us to learn the contours of a word’s meaning that are shaped by the context in which it is uttered. In a quantitative evaluation on the task of judging geographically informed semantic similarity between representations learned from 1.1 billion words of geo-located tweets, our joint model outperforms comparable independent models that learn meaning in isolation.


title: Analysis of the Potential for Use of Floating Photovoltaic Systems on Mine Pit Lakes: Case Study at the Ssangyong Open-Pit Limestone Mine in Korea

abstract: Recently, the mining industry has introduced renewable energy technologies to resolve power supply problems at mines operating in polar regions or other remote areas, and to foster substitute industries, able to benefit from abandoned sites of exhausted mines. However, little attention has been paid to the potential placement of floating photovoltaic (PV) systems operated on mine pit lakes because it was assumed that the topographic characteristics of open-pit mines are unsuitable for installing any type of PV systems. This study analyzed the potential of floating PV systems on a mine pit lake in Korea to break this misconception. Using a fish-eye lens camera and digital elevation models, a shading analysis was performed to identify the area suitable for installing a floating PV system. The layout of the floating PV system was designed in consideration of the optimal tilt angle and array spacing of the PV panels. The System Advisor Model (SAM) by National Renewable Energy Laboratory, USA, was used to conduct energy simulations based on weather data and the system design. The results indicated that the proposed PV system could generate 971.57 MWh/year. The economic analysis (accounting for discount rate and a 20-year operational lifetime) showed that the net present value would be $897,000 USD, and a payback period of about 12.3 years. Therefore, we could know that the economic effect of the floating PV system on the mine pit lake is relatively higher than that of PV systems in the other abandoned mines in Korea. The annual reduction of greenhouse gas emissions was analyzed and found to be 471.21 tCO2/year, which is twice the reduction effect achieved by forest restoration of an abandoned mine site. The economic feasibility of a floating PV system on a pit lake of an abandoned mine was thus established, and may be considered an efficient reuse option for abandoned mines.


title: Blending Face Details: Synthesizing a Face Using Multiscale Face Models

abstract: Creating realistic 3D face models is a challenging problem in computer graphics because humans are so sensitive to facial abnormalities. The authors propose a method to synthesize a 3D face model using weighted blending of multiscale details from different face models. Using multiscale continuous displacement maps (CDMs), they achieve full correspondences across multiple scales in the parameter space. Their results demonstrate detail transfer across faces with highly different proportions, such as between humans and nonhuman creatures. An artist evaluation also indicated the proposed approach is intuitive and easy to use.


title: Radon-Gabor barcodes for medical image retrieval

abstract: In recent years, with the explosion of digital images on the Web, content-based retrieval has emerged as a significant research area. Shapes, textures, edges and segments may play a key role in describing the content of an image. Radon and Gabor transforms are both powerful techniques that have been widely studied to extract shape-texture-based information. The combined Radon-Gabor features may be more robust against scale/rotation variations, presence of noise, and illumination changes. The objective of this paper is to harness the potentials of both Gabor and Radon transforms in order to introduce expressive binary features, called barcodes, for image annotation/tagging tasks. We propose two different techniques: Gabor-of-Radon-Image Barcodes (GRIBCs), and Guided-Radon-of-Gabor Barcodes (GRGBCs). For validation, we employ the IRMA x-ray dataset with 193 classes, containing 12,677 training images and 1,733 test images. A total error score as low as 322 and 330 were achieved for GRGBCs and GRIBCs, respectively. This corresponds to ≈ 81% retrieval accuracy for the first hit.


Comparative Models of the File Assignment Problem


title: A Storage Solution for Massive IoT Data Based on NoSQL

abstract: Storage is an important research direction of the data management of the Internet of Things. Massive and heterogeneous data of the Internet of Things brings the storage huge challenges. Based on the analysis of the IoT data characteristics, this paper proposed a storage management solution called IOTMDB based on NoSQL as current storage solutions are not well support storing massive and heterogeneous IoT data. Besides, the storage strategies about expressing and organizing IoT data in a uniform manner were proposed, some evaluations were carried out. Furthermore, we not only just concerned about descripting the data itself, but also cared for sharing of the data, so a data sharing mechanism based on ontology was proposed. Finally, index was studied and a set of query syntaxes to meet the needs of different kinds of IoT queries based NoSQL was given.


title: A pilot study evaluating the perceptions of Certified Registered Nurse Anesthetists toward human patient simulation.

abstract: An evaluation was conducted of perceptions of practicing Certified Registered Nurse Anesthetists (CRNAs) toward use of simulation for initial certification, continuing education, and recertification. The hypothesis was that a 1-day simulation experience would improve their perceptions toward simulation use. The first phase developed content-valid questions to examine CRNAs' perceptions toward simulation. The second phase used these questions to survey practicing CRNAs in the District of Columbia, Maryland, and Virginia. During the third phase, 9 CRNAs were selected to complete a 1-day simulation experience and complete the perception questionnaire before and after the experience. Through content validity index calculations, 25 of 27 questions were retained for use in this study. A total of 378 CRNAs responded to the questionnaire. There was consensus that human patient simulation (HPS) is an important part of anesthesia provider training, and 85.7% strongly agreed, agreed, or somewhat agreed that HPS should be a required component for initial certification. Additionally, 52.9% of respondents agreed (somewhat agreed or strongly agreed) that continuing education units from HPS should be required for recertification. After the simulation, a significant (P < .05) positive change in level of agreement was noted for 13 of 25 questions, including questions related to initial training and recertification.


Characterizing Online Discussion Using Coarse Discourse Sequences


title: A High Performance Gated Voltage Level Translator with Integrated Multiplexer

abstract: Multiple supply voltages are commonly used in designs to enable better power performance through dedicated control of the supply voltage of the various functional units. In multiple supply voltage designs, circuits are partitioned into voltage islands that operate at their optimum supply voltages which necessitates the use of voltage level translators between them. This paper presents a high performance voltage level translator design aimed at minimizing insertion penalty by minimizing logic contention and thereby improving latency. In addition, the proposed voltage level translator design has an integrated logic multiplexer function built in through an enable signal. Simulation results of the proposed voltage level translator in comparison with the conventional voltage level translator shows upto 42% delay reduction, combined with a power benefit upto 15%, for supply voltage ranging from near-threshold to above-threshold levels.


title: Fast-LSTM acoustic model for distant speech recognition

abstract: The distant-talking automatic speech recognition (ASR) currently becomes an important task in a speech recognition area. Traditionally, hybrid Gaussian Mixture Model-Hidden Markov Model (GMM-HMM) approach are used for ASR. This paper will discuss some deep neural network (DNN) techniques for acoustic modeling, as well as lattice rescoring techniques for ASR. The proposed Fast-long short-term memory neural network (Fast-LSTM) acoustic model combines the time delay neural network (TDNN) and LSTM network to reduce the training time of the standard LSTM acoustic model.


title: Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks

abstract: Data poisoning an attack on machine learning models wherein the attacker adds examples to the training set to manipulate the behavior of the model at test time. This paper explores poisoning attacks on neural nets. The proposed attacks use ::: clean-labels''; they don't require the attacker to have any control over the labeling of training data. They are also targeted; they control the behavior of the classifier on a specific test instance without degrading overall classifier performance. For example, an attacker could add a seemingly innocuous image (that is properly labeled) to a training set for a face recognition engine, and control the identity of a chosen person at test time. Because the attacker does not need to control the labeling function, poisons could be entered into the training set simply by putting them online and waiting for them to be scraped by a data collection bot. ::: We present an optimization-based method for crafting poisons, and show that just one single poison image can control classifier behavior when transfer learning is used. For full end-to-end training, we present a ::: watermarking'' strategy that makes poisoning reliable using multiple (approx. 50) poisoned training instances. We demonstrate our method by generating poisoned frog images from the CIFAR dataset and using them to manipulate image classifiers.


Nested Propositions in Open Information Extraction


title: MEDAL: A Cost-Effective High-Frequency Energy Data Acquisition System for Electrical Appliances

abstract: Traditional energy measurement fails to provide support to consumers to make intelligent decisions to save energy. Non-intrusive load monitoring is one solution that provides disaggregated power consumption profiles. Machine learning approaches rely on public datasets to train parameters for their algorithms, most of which only provide low-frequency appliance-level measurements, thus limiting the available feature space for recognition. In this paper, we propose a low-cost measurement system for high-frequency energy data. Our work utilizes an off-the-shelf power strip with a voltage-sensing circuit, current sensors, and a single-board PC as data aggregator. We develop a new architecture and evaluate the system in real-world environments. The self-contained unit for six monitored outlets can achieve up to 50 kHz for all signals simultaneously. A simple design and off-the-shelf components allow us to keep costs low. Equipping a building with our measurement systems is more feasible compared to expensive existing solutions. We used the outlined system architecture to manufacture 20 measurement systems to collect energy data over several months of more than 50 appliances at different locations, with an aggregated size of 15 TB.


title: High avalanche capability specific diode part structure of RC-IGBT based upon CSTBT™

abstract: A thin RC-IGBT is facing to a difficulty of certifying a stable BV characteristic among the three portions, i.e. IGBT (pnp), Diode (pin) and termination (pnp too). We propose a high avalanche capability specific Diode part structure of the RC-IGBT based upon CSTBT™. This stabilization of the BV characteristic was realized by adjusting the BV balance of three portions described above. One of the successful approach was lowering the Diode's BV by widening Diode's trench pitch (W DTP ).


title: Interpreting Embedding Models of Knowledge Bases: A Pedagogical Approach

abstract: Knowledge bases are employed in a variety of applications from natural language processing to semantic web search; alas, in practice their usefulness is hurt by their incompleteness. Embedding models attain state-of-the-art accuracy in knowledge base completion, but their predictions are notoriously hard to interpret. In this paper, we adapt "pedagogical approaches" (from the literature on neural networks) so as to interpret embedding models by extracting weighted Horn rules from them. We show how pedagogical approaches have to be adapted to take upon the large-scale relational aspects of knowledge bases and show experimentally their strengths and weaknesses.


Is Yoga an Effective Treatment for Post-Traumatic Stress Disorder (PTSD)?


title: Sleep Apnea Detection Based on Thoracic and Abdominal Movement Signals of Wearable Piezoelectric Bands

abstract: Physiologically, the thoracic (THO) and abdominal (ABD) movement signals, captured using wearable piezoelectric bands, provide information about various types of apnea, including central sleep apnea (CSA) and obstructive sleep apnea (OSA). However, the use of piezoelectric wearables in detecting sleep apnea events has been seldom explored in the literature. This study explored the possibility of identifying sleep apnea events, including OSA and CSA, by solely analyzing one or both the THO and ABD signals. An adaptive nonharmonic model was introduced to model the THO and ABD signals, which allows us to design features for sleep apnea events. To confirm the suitability of the extracted features, a support vector machine was applied to classify three categories - normal and hypopnea, OSA, and CSA. According to a database of 34 subjects, the overall classification accuracies were on average 75.9% ± 11.7% and 73.8% ± 4.4%, respectively, based on the cross validation. When the features determined from the THO and ABD signals were combined, the overall classification accuracy became 81.8% ± 9.4%. These features were applied for designing a state machine for online apnea event detection. Two event-by-event accuracy indexes, S and I, were proposed for evaluating the performance of the state machine. For the same database, the S index was 84.01% ± 9.06% and the I index was 77.21% ± 19.01%. The results indicate the considerable potential of applying the proposed algorithm to clinical examinations for both screening and homecare purposes.


title: Active learning for on-road vehicle detection: a comparative study

abstract: In recent years, active learning has emerged as a powerful tool in building robust systems for object detection using computer vision. Indeed, active learning approaches to on-road vehicle detection have achieved impressive results. While active learning approaches for object detection have been explored and presented in the literature, few studies have been performed to comparatively assess costs and merits. In this study, we provide a cost-sensitive analysis of three popular active learning methods for on-road vehicle detection. The generality of active learning findings is demonstrated via learning experiments performed with detectors based on histogram of oriented gradient features and SVM classification (HOG---SVM), and Haar-like features and Adaboost classification (Haar---Adaboost). Experimental evaluation has been performed on static images and real-world on-road vehicle datasets. Learning approaches are assessed in terms of the time spent annotating, data required, recall, and precision.


title: A compact flip chip single die WiFi FEM for smart phone application

abstract: A flip chip single die WiFi FEM is developed using Bi-FET (HBT+E/D-PHEMT) technology for smart phone application. High thermal conductive copper-pillar bumps were developed for the flip chip process. This FEM flip chip die consists of a high-pass filter (HPF), a 2GHz WiFi PA with on-chip regulator, PAON logic and detector circuit, and an SP3T. It showed good over-voltage and over-temperature performance when mounted on test LTCC module. Thermal modeling and design optimization kept junction temperatures comparable to wirebond versions of the design. A complete WiFi front-end LTCC module was developed using flip chip FEIC, integrated balun and SAW filter, with 3.2mm×3.2mm size for Smart Phone Application.


title: PiCam: an ultra-thin high performance monolithic camera array

abstract: We present PiCam (Pelican Imaging Camera-Array), an ultra-thin high performance monolithic camera array, that captures light fields and synthesizes high resolution images along with a range image (scene depth) through integrated parallax detection and superresolution. The camera is passive, supporting both stills and video, low light capable, and small enough to be included in the next generation of mobile devices including smartphones. Prior works [Rander et al. 1997; Yang et al. 2002; Zhang and Chen 2004; Tanida et al. 2001; Tanida et al. 2003; Duparre et al. 2004] in camera arrays have explored multiple facets of light field capture - from viewpoint synthesis, synthetic refocus, computing range images, high speed video, and micro-optical aspects of system miniaturization. However, none of these have addressed the modifications needed to achieve the strict form factor and image quality required to make array cameras practical for mobile devices. In our approach, we customize many aspects of the camera array including lenses, pixels, sensors, and software algorithms to achieve imaging performance and form factor comparable to existing mobile phone cameras. Our contributions to the post-processing of images from camera arrays include a cost function for parallax detection that integrates across multiple color channels, and a regularized image restoration (superresolution) process that takes into account all the system degradations and adapts to a range of practical imaging conditions. The registration uncertainty from the parallax detection process is integrated into a Maximum-a-Posteriori formulation that synthesizes an estimate of the high resolution image and scene depth. We conclude with some examples of our array capabilities such as postcapture (still) refocus, video refocus, view synthesis to demonstrate motion parallax, 3D range images, and briefly address future work.


title: Review of microgrid technology

abstract: The use of renewable energy source (RES) in meet the demand of electrical energy is getting into attention as solution of the problem a deficit of electrical energy. Application of RES in electricity generation system is done in a variety of configurations, among others in microgrid system. Implementation of microgrid systems provide many advantages both from the user and from the electric utility provider. Many microgrid development carried out in several countries, because microgrid offers many advantages, including better power quality and more environmentally friendly. Microgrid development concern in technology generation, microgrid architecture, power electronics, control systems, protection systems. This paper reviewing various technological developments related to microgrid system and case study about microgrid system development using grid tie inverter (GTI). Microgrid system can implemented using GTI, power transfer can occur from GTI to grid when GTI has power excess and grid supplying power to GTI when GTI power shortage.


title: Developing fully functional E-government: A four stage model

abstract: Literature reports the experiences with e-government initiatives as chaotic and unmanageable, despite recent numerous initiatives at different levels of government and academic and practitioners’ conferences on e-government. E-government presents a number of challenges for public administrators. To help public administrators think about e-government and their organizations, this article describes different stages of e-government development and proposes a ‘stages of growth’ model for fully functional e-government. Various government websites and related e-government initiatives help to ground and explain this model. These stages outline the multi-perspective transformation within government structures and functions as they make transitions to e-government through each stage. Technological and organizational challenges for each stage accompany these descriptions. At the same time, this paper describes how the e-government becomes amalgamated with traditional public administrative structure. © 2001 Elsevier Science Inc. All rights reserved.


A Prototype Experience Management System for a Software Consulting Organization


title: At-a-glance - Traumatic brain injury management in Canada: changing patterns of care.

abstract: INTRODUCTION ::: With growing awareness about traumatic brain injuries (TBI), there is limited information about population level patterns of TBI care in Canada. ::: ::: ::: METHODS ::: We examined data from the Canadian Community Health Survey (years 2004, 2009, and 2014) among all respondents ages 12 years and older. TBI management characteristics examined included access to care within 48 hours of injury, point of care, hospital admission, and follow-up. ::: ::: ::: RESULTS ::: We observed that many Canadians sought care within 48 hours of their injury, with no changes over time. We found a significant decline in the proportion of Canadians opting to visit an emergency department (p = 0.03, all ages), and a significant increase in youth opting to visit a doctor's office (p < 0.01). ::: ::: ::: CONCLUSION ::: TBIs are an important and growing health concern in Canada. Care for such injuries appears to have shifted towards the use of health care professionals outside the hospital environment, including primary care doctors.


I Will Not Eat ! A Review of the Online Pro-Ana Movement


title: Evidence-Based Systematic Review: Effects of Oral Motor Interventions on Feeding and Swallowing in Preterm Infants

abstract: PURPOSE ::: To conduct an evidence-based systematic review and provide an estimate of the effects of oral motor interventions (OMIs) on feeding/swallowing outcomes (both physiological and functional) and pulmonary health in preterm infants. ::: ::: ::: METHOD ::: A systematic search of the literature published from 1960 to 2007 was conducted. Articles meeting the selection criteria were appraised by 2 reviewers and vetted by a 3rd for methodological quality. ::: ::: ::: RESULTS ::: Twelve studies were included and focused on 3 OMIs-nonnutritive sucking (NNS), oral/perioral stimulation, and NNS plus oral/perioral stimulation. Six studies addressed the effects of OMI on the feeding/swallowing physiology outcomes of feeding efficiency or sucking pressures. Ten studies addressed the functional feeding/swallowing outcomes of oral feeding or weight gain/growth. No studies reported data on pulmonary health. Methodological quality varied greatly. NNS alone and with oral/perioral stimulation showed strong positive findings for improvement in some feeding/swallowing physiology variables and for reducing transition time to oral feeding. Prefeeding stimulation showed equivocal results across the targeted outcomes. None of the OMIs provided consistent positive results on weight gain/growth. ::: ::: ::: CONCLUSIONS ::: Although some OMIs show promise for enhancing feeding/swallowing in preterm infants, methodological limitations and variations in results across studies warrant careful consideration of their clinical use.


ANALYSIS OF SEGMENTATION PARAMETERS IN ECOGNITION SOFTWARE USING HIGH RESOLUTION QUICKBIRD MS IMAGERY


The practice of computer selection


title: MaLoc: a practical magnetic fingerprinting approach to indoor localization using smartphones

abstract: Using magnetic field data as fingerprints for localization in indoor environment has become popular in recent years. Particle filter is often used to improve accuracy. However, most of existing particle filter based approaches either are heavily affected by motion estimation errors, which makes the system unreliable, or impose strong restrictions on smartphone such as fixed phone orientation, which is not practical for real-life use. In this paper, we present an indoor localization system named MaLoc, built on our proposed augmented particle filter. We create several innovations on the motion model, the measurement model and the resampling model to enhance the traditional particle filter. To minimize errors in motion estimation and improve the robustness of particle filter, we augment the particle filter with a dynamic step length estimation algorithm and a heuristic particle resampling algorithm. We use a hybrid measurement model which combines a new magnetic fingerprinting model and the existing magnitude fingerprinting model to improve the system performance and avoid calibrating different smartphone magnetometers. In addition, we present a novel localization quality estimation method and a localization failure detection method to address the "Kidnapped Robot Problem" and improve the overall usability. Our experimental studies show that MaLoc achieves a localization accuracy of 1~2.8m on average in a large building.


title: Weka meets TraceLab: Toward convenient classification: Machine learning for requirements engineering problems: A position paper

abstract: Requirements engineering encompasses many difficult, overarching problems inherent to its subareas of process, elicitation, specification, analysis, and validation. Requirements engineering researchers seek innovative, effective means of addressing these problems. One powerful tool that can be added to the researcher toolkit is that of machine learning. Some researchers have been experimenting with their own implementations of machine learning algorithms or with those available as part of the Weka machine learning software suite. There are some shortcomings to using “one off” solutions. It is the position of the authors that many problems exist in requirements engineering that can be supported by Weka's machine learning algorithms, specifically by classification trees. Further, the authors posit that adoption will be boosted if machine learning is easy to use and is integrated into requirements research tools, such as TraceLab. Toward that end, an initial concept validation of a component in TraceLab is presented that applies the Weka classification trees. The component is demonstrated on two different requirements engineering problems. Finally, insights gained on using the TraceLab Weka component on these two problems are offered.


title: Workflow mining: discovering process models from event logs

abstract: Contemporary workflow management systems are driven by explicit process models, i.e., a completely specified workflow design is required in order to enact a given workflow process. Creating a workflow design is a complicated time-consuming process and, typically, there are discrepancies between the actual workflow processes and the processes as perceived by the management. Therefore, we have developed techniques for discovering workflow models. The starting point for such techniques is a so-called "workflow log" containing information about the workflow process as it is actually being executed. We present a new algorithm to extract a process model from such a log and represent it in terms of a Petri net. However, we also demonstrate that it is not possible to discover arbitrary workflow processes. We explore a class of workflow processes that can be discovered. We show that the /spl alpha/-algorithm can successfully mine any workflow represented by a so-called SWF-net.


title: Wrist Player: A Smartwatch Gesture Controller for Smart TVs

abstract: Emerging technology on mobile and wearable market, smartwatches have embedded movement sensors whose potential is yet to be fully explored. This paper proposes an interaction method with smart TVs via gestures performed by person's wrist using a smartwatch. Detailed architecture and implementation for a complete prototype, named Wrist Player, is presented. A user study is also conducted, in order to evaluate the prototype performance and the user's interest on the proposal. Results show that the method works very well, with participants reporting having a good experience with the prototype. We present our insights on the concept, challenges faced in our research and ideas for future studies.


title: Beyond Compare: Metaphor In Organization Theory

abstract: Despite the increased salience of metaphor in organization theory, current perspectives are flawed and misguided in assuming that metaphor can be explained with the so-called comparison model. I therefore outline an alternative model of metaphor understanding—the domains-interaction model—which suggests that metaphor involves the conjunction of whole semantic domains in which a correspondence between terms or concepts is constructed rather than deciphered and where the resulting image and meaning is creative. I also discuss implications of this model for organizational theorizing and research.


title: Mindfulness, by any other name…: trials and tribulations ofsatiin western psychology and science

abstract: The Buddhist construct of mindfulness is a central element of mindfulness-based interventions and derives from a systematic phenomenological programme developed over several millennia to investigate subjective experience. Enthusiasm for ‘mindfulness’ in Western psychological and other science has resulted in proliferation of definitions, operationalizations and self-report inventories that purport to measure mindful awareness as a trait. This paper addresses a number of seemingly intractable issues regarding current attempts to characterize mindfulness and also highlights a number of vulnerabilities in this domain that may lead to denaturing, distortion, dilution or reification of Buddhist constructs related to mindfulness. Enriching positivist Western psychological paradigms with a detailed and complex Buddhist phenomenology of the mind may require greater study and long-term direct practice of insight meditation than is currently common among psychologists and other scientists. Pursuit of such an approach would seem a necessary precondition for attempts to characterize and quantify mindfulness.


title: Reputation Schemes for Pervasive Social Networks with Anonymity (Short Paper)

abstract: Pervasive Social Networks of Strangers are available constantly, connecting users directly with no preexisting relationships. Such networks demand a high level of privacy, as users should be anonymous and their behaviour unlinkable. However, the anonymity of users could lead to abusive messages, spam, or fraud. A possible solution is to keep track of users' reputations, based on feedback from other users. However, the need to collate feedback on user behaviour to form reputations is at odds with the need for privacy. Anonymous and unlinkable feedback is also required, whilst multiple feedbacks given on the same item should be detected. To resolve this, we use group signatures and direct anonymous attestation, to give a reputation-based messaging scheme.


title: Gesture Recognition with a 3-D Accelerometer

abstract: Gesture-based interaction, as a natural way for human-computer interaction, has a wide range of applications in ubiquitous computing environment. This paper presents an acceleration-based gesture recognition approach, called FDSVM ( Frame-based Descriptor and multi-class SVM), which needs only a wearable 3-dimensional accelerometer. With FDSVM, firstly, the acceleration data of a gesture is collected and represented by a frame-based descriptor, to extract the discriminative information. Then a SVM-based multi-class gesture classifier is built for recognition in the nonlinear gesture feature space. Extensive experimental results on a data set with 3360 gesture samples of 12 gestures over weeks demonstrate that the proposed FDSVM approach significantly outperforms other four methods: DTW, Naive Bayes, C4.5 and HMM. In the user-dependent case, FDSVM achieves the recognition rate of 99.38% for the 4 direction gestures and 95.21% for all the 12 gestures. In the user-independent case, it obtains the recognition rate of 98.93% for 4 gestures and 89.29% for 12 gestures. Compared to other accelerometer-based gesture recognition approaches reported in literature FDSVM gives the best resulrs for both user-dependent and user-independent cases.


title: Efficient Summarization with Read-Again and Copy Mechanism

abstract: Encoder-decoder models have been widely used to solve sequence to sequence prediction tasks. However current approaches suffer from two shortcomings. First, the encoders compute a representation of each word taking into account only the history of the words it has read so far, yielding suboptimal representations. Second, current models utilize large vocabularies in order to minimize the problem of unknown words, resulting in slow decoding times and large storage costs. In this paper we address both shortcomings. Towards this goal, we first introduce a simple mechanism that first reads the input sequence before committing to a representation of each word. Furthermore, we propose a simple copy mechanism that is able to exploit very small vocabularies and handle out-of-vocabulary words. We demonstrate the effectiveness of our approach on the Gigaword dataset and DUC competition outperforming the state-of-the-art.


Efficient Classification of Data Using Decision Tree


title: A novel algorithm for community detection and influence ranking in social networks

abstract: Community detection and influence analysis are significant notions in social networks. We exploit the implicit knowledge of influence-based connectivity and proximity encoded in the network topology, and propose a novel algorithm for both community detection and influence ranking. Using a new influence cascade model, the algorithm generates an influence vector for each node, which captures in detail how the node's influence is distributed through the network. Similarity in this influence space defines a new, meaningful and refined connectivity measure for the closeness of any pair of nodes. Our approach not only differentiates the influence ranking but also effectively finds communities in both undirected and directed networks, and incorporates these two important tasks into one integrated framework. We demonstrate its superior performance with extensive tests on a set of real-world networks and synthetic benchmarks.


title: Size Reduced Multi-Band Printed Quadrifilar Helical Antenna

abstract: A simple and innovative method for designing a spiral folded printed quadrifilar helix antenna (S-FPQHA) for dual-band operations is presented. The axial length of a conventional PQHA is miniaturized of about 43% by meandering and turning the helix arms into the form of square spirals. Parametric studies are performed to explore the performance improvements. Based on the studies a dual band antenna working in L1/L5 GPS application is realized with a good gain and a good circular polarization. Measured results are presented to validate the concept.


title: The research on automatic translation of Chinese-Mongolian numerals

abstract: In Chinese-Mongolian hybrid machine translation, for numerals that including the open words of infinite vocabulary we can not use statistical method to translate all numerals in real text, so we proposed rule-based method to translate the numerals terms. This paper used rule-based method to complete translation of Chinese-Mongolian numerals. In addition, we also completed the translation of Chinese-Mongolian temporal words. It is proved by experiments that the performance of the Chinese-Mongolian machine translation system can be improved by this method.


title: Development and Application of Controller for Transition Flight of Tail-Sitter UAV

abstract: There is renewed interest in tail-sitter airplanes on account of their vertical takeoff and landing capability as well as their efficient horizontal flight capabilities. The transition from a vertical near-hover mode to a horizontal cruise mode is a critical component of the tail-sitter flight profile. In practice, this transition is often achieved by a stall-and-tumble maneuver, which is somewhat risky and therefore not desirable, so alternative maneuvering strategies along controlled trajectories are sought. Accordingly, this paper presents the synthesis and application of a transition controller to a tail-sitter UAV for the first time. For practical reasons, linear controllers are designed using the PID technique and linked by gain scheduling. The limits of the PID controller are complemented by a so-called $\mathcal{L}_{1}$ adaptive controller that considers the coupling effect, reduces the effort for appropriate gain selection, and improves the tracking performance at different points during operation. Each transition trajectory is controlled by the flight velocity and path angle using dynamic inversion. The transition control law is tested on a tail-sitter UAV, an 18-kg vehicle that has a 2-m wingspan with an aspect ratio of 4.71 and is powered by a 100-cm3 gasoline engine driving an aft-mounted ducted fan. This paper describes not only the synthesis and the onboard implementation of the control law but also the flight testing of the fixed-wing UAV in hover, transition, and cruise modes.


title: A step-by-step guide to extracting winding resistance from an impedance measurement

abstract: Impedance analyzer measurements can be helpful in assessing inductor and transformer winding resistance and predicting winding loss, but the measured ESR does not directly correspond to winding resistance. Neglecting the effects of core loss and winding capacitance can yield significant errors in the prediction. A step-by-step method to account for such effects and extract winding resistance from an impedance measurement is described. The proposed methodology is applicable to both inductors and multi-winding transformers. Several measurements are needed in this method; one is to determine the effects of core loss and the others yield the impedance from which winding resistance is extracted to form a resistance matrix. The winding resistance of a transformer was determined experimentally and the interactions between the winding resistance, effects of core loss, winding capacitance and inductance and their contributions to the measured impedance are demonstrated.


title: Design of a microcontroller-based artificial pacemaker: An internal pacing device

abstract: Heart arrhythmia is a serious health problem that is commonplace among patients of different ages, from new born infants to the elderly. For such patients, an artificial pacemaker may be implanted for hear regulation by sending electrical signals to the patient's heart chambers. The advances in the electronics technology, has the potential to improve the artificial pacemakers in terms of price, energy consumption, and miniaturization to eliminate the leads. A microcontroller pacemaker, along with a PC-based programmer is proposed for bradycardia pacing. The algorithm is implemented in Arduino microcontrollers, which is programmed using PC and MATLAB. A ventricular signal with random or frequent lost beats is studied, and a pacing system is proposed for regulation of the simulated heart signal as in VVI mode. The experimental results show the ability of the system in detecting absence of the heartbeat in the expected times, and composing the required pacing pulses.


title: Piezoelectric Bimorphs' Characteristics as In-Socket Sensors for Transfemoral Amputees

abstract: Alternative sensory systems for the development of prosthetic knees are being increasingly highlighted nowadays, due to the rapid advancements in the field of lower limb prosthetics. This study presents the use of piezoelectric bimorphs as in-socket sensors for transfemoral amputees. An Instron machine was used in the calibration procedure and the corresponding output data were further analyzed to determine the static and dynamic characteristics of the piezoelectric bimorph. The piezoelectric bimorph showed appropriate static operating range, repeatability, hysteresis, and frequency response for application in lower prosthesis, with a force range of 0–100 N. To further validate this finding, an experiment was conducted with a single transfemoral amputee subject to measure the stump/socket pressure using the piezoelectric bimorph embedded inside the socket. The results showed that a maximum interface pressure of about 27 kPa occurred at the anterior proximal site compared to the anterior distal and posterior sites, consistent with values published in other studies. This paper highlighted the capacity of piezoelectric bimorphs to perform as in-socket sensors for transfemoral amputees. However, further experiments are recommended to be conducted with different amputees with different socket types.


title: Pick-and-drop: a direct manipulation technique for multiple computer environments

abstract: This paper proposes a new field of user interfaces called multi-computer direct manipulation and presents a penbased direct manipulation technique that can be used for data transfer between different computers as well as within the same computer. The proposed Pick-andDrop allows a user to pick up an object on a display and drop it on another display as if he/she were manipulating a physical object. Even though the pen itself does not have storage capabilities, a combination of Pen-ID and the pen manager on the network provides the illusion that the pen can physically pick up and move a computer object. Based on this concept, we have built several experimental applications using palm-sized, desk-top, and wall-sized pen computers. We also considered the importance of physical artifacts in designing user interfaces in a future computing environment.


title: GIDL in Doped and Undoped FinFET Devices for Low-Leakage Applications

abstract: Investigation of gate-induced drain leakage (GIDL) in thick-oxide dual-gate doped- and undoped-channel FinFET devices through 3-D process and device simulations is presented. For a given gate length (LG) and gate dielectric thickness, the placement and grading of the drain junction and the channel doping are shown to have a tremendous impact on GIDL. Suppression of GIDL by as much as two orders of magnitude can be realized by formation of steep underlapped junctions for both doped- and undoped-channel devices. The prospect of low leakage levels in doped-channel high- VT FinFETs makes them suitable for memory cell applications.


title: Towards Conceptual Compression

abstract: We introduce a simple recurrent variational auto-encoder architecture that significantly improves image modeling. The system represents the state-of-the-art in latent variable models for both the ImageNet and Omniglot datasets. We show that it naturally separates global conceptual information from lower level details, thus addressing one of the fundamentally desired properties of unsupervised learning. Furthermore, the possibility of restricting ourselves to storing only global information about an image allows us to achieve high quality 'conceptual compression'.


title: Lane marking detection based on adaptive threshold segmentation and road classification

abstract: A new robust lane marking detection algorithm for monocular vision is proposed. It is designed for the urban roads with disturbances and with the weak lane markings. The primary contribution of the paper is that it supplies a robust adaptive method of image segmentation, which employs jointly prior knowledge, statistical information and the special geometrical features of lane markings in the bird's-eye view. This method can eliminate many disturbances while keep points of lane markings effectively. Road classification can help us extract more accurate and simple characteristics of lane markings, so the second contribution of the paper is that it uses the row information of image to classify road conditions into three kinds and uses different strategies to complete lane marking detection. The experimental results have shown the high performance of our algorithm in various road scenes.


Potential Ambient Energy-Harvesting Sources and Techniques.


title: Water wave optimization: A new nature-inspired metaheuristic

abstract: Nature-inspired computing has been a hot topic in scientific and engineering fields in recent years. Inspired by the shallow water wave theory, the paper presents a novel metaheuristic method, named water wave optimization (WWO), for global optimization problems. We show how the beautiful phenomena of water waves, such as propagation, refraction, and breaking, can be used to derive effective mechanisms for searching in a high-dimensional solution space. In general, the algorithmic framework of WWO is simple, and easy to implement with a small-size population and only a few control parameters. We have tested WWO on a diverse set of benchmark problems, and applied WWO to a real-world high-speed train scheduling problem in China. The computational results demonstrate that WWO is very competitive with state-of-the-art evolutionary algorithms including invasive weed optimization (IWO), biogeography-based optimization (BBO), bat algorithm (BA), etc. The new metaheuristic is expected to have wide applications in real-world engineering optimization problems.


title: Fast conversion of scFv to Fab antibodies using type IIs restriction enzymes.

abstract: Single chain variable fragment (scFv) antibody libraries are widely used for developing novel bioaffinity reagents, although Fab or IgG molecules are the preferred antibody formats in many final applications. Therefore, rapid conversion methods for combining multiple DNA fragments are needed to attach constant domains to the scFv derived variable domains. In this study we describe a fast and easy cloning method for the conversion of single framework scFv fragments to Fab fragments using type IIS restriction enzymes. All cloning steps excluding plating of the Fab transformants can be done in 96 well plates and the procedure can be completed in one working day. The concept was tested by converting 69 scFv clones into Fab format on 96 well plates, which resulted in 93% success rate. The method is particularly useful as a high-throughput tool for the conversion of the chosen scFv clones into Fab molecules in order to analyze them as early as possible, as the conversion can significantly affect the binding properties of the chosen clones.


title: Classical grasp quality evaluation: New algorithms and theory

abstract: This paper investigates theoretical properties of a well-known L1 grasp quality measure Q whose approximation Q-l is commonly used for the evaluation of grasps and where the precision of Q-l depends on an approximation of a cone by a convex polyhedral cone with l edges. We prove the Lipschitz continuity of Q and provide an explicit Lipschitz bound that can be used to infer the stability of grasps lying in a neighbourhood of a known grasp. We think of Q-l as a lower bound estimate to Q and describe an algorithm for computing an upper bound Q+. We provide worst-case error bounds relating Q and Q-l. Furthermore, we develop a novel grasp hypothesis rejection algorithm which can exclude unstable grasps much faster than current implementations. Our algorithm is based on a formulation of the grasp quality evaluation problem as an optimization problem, and we show how our algorithm can be used to improve the efficiency of sampling based grasp hypotheses generation methods.


title: Dynamical Systems in Neuroscience: The Geometry of Excitability and Bursting

abstract: This book explains the relationship of electrophysiology, nonlinear dynamics, and the computational properties of neurons, with each concept presented in terms of both neuroscience and mathematics and illustrated using geometrical intuition. In order to model neuronal behavior or to interpret the results of modeling studies, neuroscientists must call upon methods of nonlinear dynamics. This book offers an introduction to nonlinear dynamical systems theory for researchers and graduate students in neuroscience. It also provides an overview of neuroscience for mathematicians who want to learn the basic facts of electrophysiology. "Dynamical Systems in Neuroscience" presents a systematic study of the relationship of electrophysiology, nonlinear dynamics, and computational properties of neurons. It emphasizes that information processing in the brain depends not only on the electrophysiological properties of neurons but also on their dynamical properties. The book introduces dynamical systems starting with one- and two-dimensional Hodgkin-Huxley-type models and continuing to a description of bursting systems. Each chapter proceeds from the simple to the complex, and provides sample problems at the end. The book explains all necessary mathematical concepts using geometrical intuition; it includes many figures and few equations, making it especially suitable for non-mathematicians. Each concept is presented in terms of both neuroscience and mathematics, providing a link between the two disciplines. Nonlinear dynamical systems theory is at the core of computational neuroscience research, but it is not a standard part of the graduate neuroscience curriculum - or taught by math or physics department in a way that is suitable for students of biology. This book offers neuroscience students and researchers a comprehensive account of concepts and methods increasingly used in computational neuroscience.


title: Bit-Cell Level Optimization for Non-volatile Memories Using Magnetic Tunnel Junctions and Spin-Transfer Torque Switching

abstract: Spin-transfer torque magnetic random access memories (STT-MRAM), using magnetic tunnel junctions (MTJ), is a resistive memory technology that has spurred significant research interest due to its potential for on-chip, high-density, high-speed, low-power, and non-volatile memory. However, due to conflicting read and write requirements, there is a need to develop optimization techniques for designing STT-MRAM bit-cells to minimize read and write failures. We propose an optimization technique that minimizes read and write failures by proper selection of bit-cell configuration and by proper access transistor sizing. A mixed-mode simulation framework was developed to evaluate the effectiveness of our optimization technique. Our simulation framework captures the transport physics in the MTJ using Non-Equilibrium Green's Function method and self-consistently solves the MTJ magnetization dynamics using Landau-Lifshitz-Gilbert equation augmented with the full Slonczewski spin-torque term. The electrical parameters of the MTJ are then encapsulated in a Verilog-A model and used in HSPICE to perform bit-cell level optimization. The optimization technique is applied to STT-MRAM bit-cells designed using 45 nm bulk and 45 nm silicon-on-insulator CMOS technologies. Finally, predictions are made for optimized STT-MRAM bit-cells designed in 16 nm predictive technology.


title: Using NLU in Context for Question Answering: Improving on Facebook's bAbI Tasks

abstract: For the next step in human to machine interaction, Artificial Intelligence (AI) should interact predominantly using natural language because, if it worked, it would be the fastest way to communicate. Facebook's toy tasks (bAbI) provide a useful benchmark to compare implementations for conversational AI. While the published experiments so far have been based on exploiting the distributional hypothesis with machine learning, our model exploits natural language understanding (NLU) with the decomposition of language based on Role and Reference Grammar (RRG) and the brain-based Patom theory. Our combinatorial system for conversational AI based on linguistics has many advantages: passing bAbI task tests without parsing or statistics while increasing scalability. Our model validates both the training and test data to find 'garbage' input and output (GIGO). It is not rules-based, nor does it use parts of speech, but instead relies on meaning. While Deep Learning is difficult to debug and fix, every step in our model can be understood and changed like any non-statistical computer program. Deep Learning's lack of explicable reasoning has raised opposition to AI, partly due to fear of the unknown. To support the goals of AI, we propose extended tasks to use human-level statements with tense, aspect and voice, and embedded clauses with junctures: and answers to be natural language generation (NLG) instead of keywords. While machine learning permits invalid training data to produce incorrect test responses, our system cannot because the context tracking would need to be intentionally broken. We believe no existing learning systems can currently solve these extended natural language tests. There appears to be a knowledge gap between NLP researchers and linguists, but ongoing competitive results such as these promise to narrow that gap.


title: Incremental Collaborative Filtering for Binary Ratings

abstract: The use of collaborative filtering (CF) recommenders on the Web is typically done in environments where data is constantly flowing. In this paper we propose an incremental version of item-based CF for implicit binary ratings, and compare it with a non-incremental one, as well as with an incremental user-based approach. We also study the usage of sparse matrices in these algorithms. We observe that recall and precision tend to improve when we continuously add information to the recommender model, and that the time spent for recommendation does not degrade. Time for updating the similarity matrix is relatively low and motivates the use of the item-based incremental approach.


title: Boosting recombined weak classifiers q

abstract: Boosting is a set of methods for the construction of classifier ensembles. The differential feature of these methods is that they allow to obtain a strong classifier from the combination of weak classifiers. Therefore, it is possible to use boosting methods with very simple base classifiers. One of the most simple classifiers are decision stumps, decision trees with only one decision node. This work proposes a variant of the most well-known boosting method, AdaBoost. It is based on considering, as the base classifiers for boosting, not only the last weak classifier, but a classifier formed by the last r selected weak classifiers (r is a parameter of the method). If the weak classifiers are decision stumps, the combination of r weak classifiers is a decision tree. The ensembles obtained with the variant are formed by the same number of decision stumps than the original AdaBoost. Hence, the original version and the variant produce classifiers with very similar sizes and computational complexities (for training and classification). The experimental study shows that the variant is clearly beneficial.


title: An Empirical Study of Rich Subgroup Fairness for Machine Learning

abstract: Kearns, Neel, Roth, and Wu [ICML 2018] recently proposed a notion of rich subgroup fairness intended to bridge the gap between statistical and individual notions of fairness. Rich subgroup fairness picks a statistical fairness constraint (say, equalizing false positive rates across protected groups), but then asks that this constraint hold over an exponentially or infinitely large collection of subgroups defined by a class of functions with bounded VC dimension. They give an algorithm guaranteed to learn subject to this constraint, under the condition that it has access to oracles for perfectly learning absent a fairness constraint. In this paper, we undertake an extensive empirical evaluation of the algorithm of Kearns et al. On four real datasets for which fairness is a concern, we investigate the basic convergence of the algorithm when instantiated with fast heuristics in place of learning oracles, measure the tradeoffs between fairness and accuracy, and compare this approach with the recent algorithm of Agarwal, Beygelzeimer, Dudik, Langford, and Wallach [ICML 2018], which implements weaker and more traditional marginal fairness constraints defined by individual protected attributes. We find that in general, the Kearns et al. algorithm converges quickly, large gains in fairness can be obtained with mild costs to accuracy, and that optimizing accuracy subject only to marginal fairness leads to classifiers with substantial subgroup unfairness. We also provide a number of analyses and visualizations of the dynamics and behavior of the Kearns et al. algorithm. Overall we find this algorithm to be effective on real data, and rich subgroup fairness to be a viable notion in practice.


title: Multi-scale CNN Stereo and Pattern Removal Technique for Underwater Active Stereo System

abstract: Demands on capturing dynamic scenes of underwater environments are rapidly growing. Passive stereo is applicable to capture dynamic scenes, however the shape with textureless surfaces or irregular reflections cannot be recovered by the technique. In our system, we add a pattern projector to the stereo camera pair so that artificial textures are augmented on the objects. To use the system at underwater environments, several problems should be compensated, i.e., refraction, disturbance by fluctuation and bubbles. Further, since surface of the objects are interfered by the bubbles, projected patterns, etc., those noises and patterns should be removed from captured images to recover original texture. To solve these problems, we propose three approaches; a depth-dependent calibration, Convolutional Neural Network(CNN)-stereo method and CNN-based texture recovery method. A depth-dependent calibration is our analysis to find the acceptable depth range for approximation by center projection to find the certain target depth for calibration. In terms of CNN stereo, unlike common CNNbased stereo methods which do not consider strong disturbances like refraction or bubbles, we designed a novel CNN architecture for stereo matching using multi-scale information, which is intended to be robust against such disturbances. Finally, we propose a multi-scale method for bubble and a projected-pattern removal method using CNNs to recover original textures. Experimental results are shown to prove the effectiveness of our method compared with the state of the art techniques. Furthermore, reconstruction of a live swimming fish is demonstrated to confirm the feasibility of our techniques.


title: TreeSegNet: Adaptive Tree CNNs for Subdecimeter Aerial Image Segmentation

abstract: For the task of subdecimeter aerial imagery segmentation, fine-grained semantic segmentation results are usually difficult to obtain because of complex remote sensing content and optical conditions. Recently, convolutional neural networks (CNNs) have shown outstanding performance on this task. Although many deep neural network structures and techniques have been applied to improve the accuracy, few have paid attention to better differentiating the easily confused classes. In this paper, we propose TreeSegNet which adopts an adaptive network to increase the classification rate at the pixelwise level. Specifically, based on the infrastructure of DeepUNet, a Tree-CNN block in which each node represents a ResNeXt unit is constructed adaptively according to the confusion matrix and the proposed TreeCutting algorithm. By transporting feature maps through concatenating connections, the Tree-CNN block fuses multiscale features and learns best weights for the model. In experiments on the ISPRS 2D semantic labeling Potsdam dataset, the results obtained by TreeSegNet are better than those of other published state-of-the-art methods. Detailed comparison and analysis show that the improvement brought by the adaptive Tree-CNN block is significant.


title: Automatic sleep stage scoring based on waveform recognition method and decision‐tree learning

abstract: There have been many trials in which the waveform recognition method, which is intended to replace expert observation by computer processing, has been used to extract the features of biological signals during sleep, and to automatically score sleep stages based on feature parameters. This paper proposes a waveform recognition method which extracts the feature parameters based on the characteristics of the biological signal during sleep, and a method of automatic sleep stage scoring by decision-tree learning, which is currently considered to be one of the most successful machine learning methods in practice. As the first step in the method, the features corresponding to the state of the EEG, the EOG, and the EMG during sleep are compared to the features of characteristic waves such as the α-wave, δ-wave, sleep spindle, K-complex, and REM, and the feature parameters needed in order to judge the sleep stage are extracted. Using canonical discriminant analysis and discretization method RWS based on the random walk, the feature parameters are converted to a small number of discrete variables. Based on training instances, obtained by the bootstrap method, a set of multiple small decision trees (a committee) is formed, and the sleep stage is scored by majority decision in the classification results. The method is applied to the PSG chart digital data provided by the Japan Sleep Society, and the performance of the system is evaluated experimentally. It is verified that the proposed method is promising as a method of automatic sleep stage scoring with high accuracy, requiring little expenditure of time in learning and classification. © 2002 Wiley Periodicals, Inc. Syst Comp Jpn, 33(11): 1–13, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/scj.10248


title: An Architecture for Generating Interactive Feedback in Probabilistic User Interfaces

abstract: Increasingly natural, sensed, and touch-based input is being integrated into devices. Along the way, both custom and more general solutions have been developed for dealing with the uncertainty that is associated with these forms of input. However, it is difficult to provide dynamic, flexible, and continuous feedback about uncertainty using traditional interactive infrastructure. Our contribution is a general architecture with the goal of providing support for continual feedback about uncertainty. Our architecture is based on prior work in modeling uncertainty using Monte Carlo sampling, and tracks multiple interfaces -- one for each plausible and differentiable sequence of input that the user may have intended. Importantly, it considers how the presentation of uncertainty can be organized and implemented in a general way. Our primary contribution is a method for reducing the number of alternative interfaces and fusing possible interfaces into a single interface that both communicates uncertainty and allows for disambiguation. We demonstrate the value of this result through a collection of 11 new and existing feedback techniques along with two applications demonstrating the use of the feedback architecture.


title: What, where and who? Classifying events by scene and object recognition

abstract: We propose a first attempt to classify events in static images by integrating scene and object categorizations. We define an event in a static image as a human activity taking place in a specific environment. In this paper, we use a number of sport games such as snow boarding, rock climbing or badminton to demonstrate event classification. Our goal is to classify the event in the image as well as to provide a number of semantic labels to the objects and scene environment within the image. For example, given a rowing scene, our algorithm recognizes the event as rowing by classifying the environment as a lake and recognizing the critical objects in the image as athletes, rowing boat, water, etc. We achieve this integrative and holistic recognition through a generative graphical model. We have assembled a highly challenging database of 8 widely varied sport events. We show that our system is capable of classifying these event classes at 73.4% accuracy. While each component of the model contributes to the final recognition, using scene or objects alone cannot achieve this performance.


title: CAMSHIFT Tracker Design Experiments with Intel OpenCV and SAI

abstract: Abstract : When humans interact with computer systems, they expect the experience to meet human standards of reactiveness, robustness and, if possible, non-intrusiveness. In order for computer vision techniques to have a significant impact in human-computer interaction, the development of efficient and robust algorithms, as well as their integration and operation as part of complex (including multi-modal) systems, must be specifically addressed. This report describes design and implementation experiments for CAMSHIFT-based tracking systems using Intel's Open Computer Vision library and SAI (Software Architecture for Immersipresence), a software architecture model created specifically to address the integration of different solutions to technical challenges, developed independently in separate fields, into working systems, that operate under hard performance constraints. Results show that the SAI formalism is an enabling tool for designing, describing and implementing robust systems of efficient algorithms.


title: Hyperkernel: Push-Button Verification of an OS Kernel

abstract: This paper describes an approach to designing, implementing, and formally verifying the functional correctness of an OS kernel, named Hyperkernel, with a high degree of proof automation and low proof burden. We base the design of Hyperkernel's interface on xv6, a Unix-like teaching operating system. Hyperkernel introduces three key ideas to achieve proof automation: it finitizes the kernel interface to avoid unbounded loops or recursion; it separates kernel and user address spaces to simplify reasoning about virtual memory; and it performs verification at the LLVM intermediate representation level to avoid modeling complicated C semantics. We have verified the implementation of Hyperkernel with the Z3 SMT solver, checking a total of 50 system calls and other trap handlers. Experience shows that Hyperkernel can avoid bugs similar to those found in xv6, and that the verification of Hyperkernel can be achieved with a low proof burden.


title: The Role of Lactic Acid Bacteria in Milk Fermentation

abstract: Species of lactic ::: acid bacteria (LAB) represent as potential microorganisms and have been widely ::: applied in food fermentation worldwide. Milk fermentation process has been ::: relied on the activity of LAB, where transformation ::: of milk to good quality of fermented milk products made possible. The presence of LAB in milk fermentation can be ::: either as spontaneous or inoculated starter cultures. Both of them are ::: promising cultures to be explored in fermented milk manufacture. LAB have a role in milk ::: fermentation to produce acid which is important as preservative agents and generating flavour of the products. They also produce exopolysaccharides ::: which are essential as texture formation. Considering the existing reports on several health-promoting properties as ::: well as their generally recognized as safe ::: (GRAS) status of LAB, they can be widely used in the developing of new ::: fermented milk products.


title: Multi-view local discrimination and canonical correlation analysis for image classification

abstract: Abstract Multi-view subspace learning has been aroused much concern recently. Although there exist a few multi-view subspace learning methods taking both the discrimination information and the correlation information into consideration, they always ignore the use of the inter-view discriminant information. In view of this, we propose an approach called multi-view local discrimination and canonical correlation analysis (MLDC 2 A) for image classification. MLDC 2 A aims to learn a common multi-view subspace from multi-view data, by making use of not only the discriminant information from both intra-view and inter-view but also the correlation information between paired view data. Furthermore, in the learned subspace, the local geometric structure of multi-view data is preserved. We conduct experiments on MNIST, COIL-20, Multi-PIE, Caltech-101, and COCO datasets and the results indicate the effectiveness of the proposed approach.


title: Identity and search in social networks

abstract: Social networks have the surprising property of being “searchable”: Ordinary people are capable of directing messages through their network of acquaintances to reach a specific but distant target person in only a few steps. We present a model that offers an explanation of social network searchability in terms of recognizable personal identities: sets of characteristics measured along a number of social dimensions. Our model defines a class of searchable networks and a method for searching them that may be applicable to many network search problems, including the location of data files in peer-to-peer networks, pages on the World Wide Web, and information in distributed databases.


title: Smartphone Addiction among University Undergraduates: A Literature Review

abstract: ABSTRACT Purpose : This paper reviews the growing literature on Smartphone addiction among university undergraduates to identify trends. Design/Methodological Approach: It is based on literature published during the period 1996-2013. Only original research papers have been included in this literature review. The thematic structure has been adopted. In the beginning, such concepts as the symptoms and levels of this addiction will be clarified. Afterwards, the underlying problems and methodological issues raised in the literature will be touched upon. The paper also reviews the relationship between Smartphone addiction among undergraduates and their academic achievement. Finally, significant differences in addiction among undergraduates according to their gender, field of study, parents educational level and family income level will be examined. Findings: While some studies have shown gender differences in Smartphone addictive use, others have proved that gender and Smartphone use are not significantly related. A few studies have examined the relationship between addiction and students' field of study. Some of these Review Article


title: A CMOS centric 77GHz automotive radar architecture

abstract: A CMOS centric phase array radar architecture is proposed for long range detection with a high angular resolution and short range with a large field of view at once. And one channel transceiver is implemented in a 65nm CMOS technology and patch array antenna also fabricated on LTCC (Low Temperature Co-fired Ceramic) substrate for small form factor, low power radar. Measured 77GHz I/Q receiver showed a 22dB conversion gain with dynamic gain range of 76dB. Two kinds of VCO showed 69.6∼81GHz and 75.2∼79.2GHz tuning range. A gain of 14.3 dB and P1 dB of 10dBm is obtained at transformer coupled two-stage cascade power amplifier. All the measured results showed a good agreement with simulated one up to 110 GHz by modeling of passive/active test devices and EM (Electro Magnetic) simulations, and showed a the promising candidate for automotive radar applications.


title: Organizational Culture and Job Satisfaction: A Review

abstract: The purpose of the present study is to provide a critical review of the relation between organizational culture and the levels of job satisfaction experienced by employees. Organizational culture refers to a series of attitudes and behaviors adopted by employees of a certain organization, which affect its function and total well-being. Job satisfaction refers to the employees’ perceptions of their working environment, relations among colleagues, earnings and promotion opportunities. The review shows that contemporary job-related phenomena like job satisfaction are related to their perceptions of their working environment, relations with colleagues, institution aims and strategies and success criteria. In addition, the employees’ preference of organizational culture is likely to be affected by demographic characteristics, especially gender. It can be supported, therefore, that measuring and analyzing an institution’s organizational culture in combination with its employees’ demographic and individual characteristics may lead to valuable conclusions, so that job satisfaction is promoted.


title: Spacecraft telemetry data monitoring by dimensionality reduction techniques

abstract: In this paper, we consider a "data-driven" anomaly detection framework for spacecraft systems using dimensionality reduction and reconstruction techniques. This method first learns a mapping from the original data space to a low dimensional space and its reverse mapping by applying linear or nonlinear dimensionality reduction algorithms to a normal training data set. After the training, it applies the learned pair of mappings to a test data set to obtain a reconstructed data set, and then evaluate the reconstruction errors. We will show the results of applying several representative linear and nonlinear dimensionality reduction algorithms with this framework to the electrical power subsystem (EPS) data of actual artificial satellites.


title: Supporting action research with learning analytics

abstract: Learning analytics tools should be useful, i.e., they should be usable and provide the functionality for reaching the goals attributed to learning analytics. This paper seeks to unite learning analytics and action research. Based on this, we investigate how the multitude of questions that arise during technology-enhanced teaching and learning systematically can be mapped to sets of indicators. We examine, which questions are not yet supported and propose concepts of indicators that have a high potential of positively influencing teachers' didactical considerations. Our investigation shows that many questions of teachers cannot be answered with currently available research tools. Furthermore, few learning analytics studies report about measuring impact. We describe which effects learning analytics should have on teaching and discuss how this could be evaluated.