Query: BIDaaS: Blockchain Based ID As a Service

Relevant passages:
Practical byzantine fault tolerance and proactive recovery <=SEP=> Our growing reliance on online services accessible on the Internet demands highly available systems that provide correct service without interruptions. Software bugs, operator mistakes, and malicious attacks are a major cause of service interruptions and they can cause arbitrary behavior, that is, Byzantine faults. This article describes a new replication algorithm, BFT, that can be used to build highly available systems that tolerate Byzantine faults. BFT can be used in practice to implement real services: it performs well, it is safe in asynchronous environments such as the Internet, it incorporates mechanisms to defend against Byzantine-faulty clients, and it recovers replicas proactively. The recovery mechanism allows the algorithm to tolerate any number of faults over the lifetime of the system provided fewer than 1/3 of the replicas become faulty within a small window of vulnerability. BFT has been implemented as a generic program library with a simple interface. We used the library to implement the first Byzantine-fault-tolerant NFS file system, BFS. The BFT library and BFS perform well because the library incorporates several important optimizations, the most important of which is the use of symmetric cryptography to authenticate messages. The performance results show that BFS performs 2% faster to 24% slower than production implementations of the NFS protocol that are not replicated. This supports our claim that the BFT library can be used to build practical systems that tolerate Byzantine faults.

Blockchain-based secure firmware update for embedded devices in an Internet of Things environment <=SEP=> Embedded devices are going to be used extremely in Internet of Things (IoT) environments. The small and tiny IoT devices will operate and communicate each other without involvement of users, while their operations must be correct and protected against various attacks. In this paper, we focus on a secure firmware update issue, which is a fundamental security challenge for the embedded devices in an IoT environment. A new firmware update scheme that utilizes a blockchain technology is proposed to securely check a firmware version, validate the correctness of firmware, and download the latest firmware for the embedded devices. In the proposed scheme, an embedded device requests its firmware update to nodes in a blockchain network and gets a response to determine whether its firmware is up-to-date or not. If not latest, the embedded device downloads the latest firmware from a peer-to-peer firmware sharing network of the nodes. Even in the case that the version of the firmware is up-to-date, its integrity, i.e., correctness of firmware, is checked. The proposed scheme guarantees that the embedded device’s firmware is up-to-date while not tampered. Attacks targeting known vulnerabilities on firmware of embedded devices are thus mitigated.

ETHEREUM: A SECURE DECENTRALISED GENERALISED TRANSACTION LEDGER <=SEP=> The blockchain paradigm when coupled with cryptographically-secured transactions has demonstrated its utility through a number of projects, with Bitcoin being one of the most notable ones. Each such project can be seen as a simple application on a decentralised, but singleton, compute resource. We can call this paradigm a transactional singleton machine with shared-state. Ethereum implements this paradigm in a generalised manner. Furthermore it provides a plurality of such resources, each with a distinct state and operating code but able to interact through a message-passing framework with others. We discuss its design, implementation issues, the opportunities it provides and the future hurdles we envisage.

How the Blockchain Revolution Will Reshape the Consumer Electronics Industry [Future Directions] <=SEP=> ^ IEEE ConsumEr ElECtronICs magazInE 19 By early 2016, Pabellon achieved another major milestone: a first touchfree control panel using solely the body’s electrolytes to control a matrix of magnetic pixels. Unlike conventional passive near-field communication technology that relies on receiver loop antennas for detection and identification, the magnetic pixel needs only our body salts to work. Each pixel represents a discrete sensing element. When arrayed or matrixed, the collection of pixels can be used to position index fingers approaching but not touching the surface. Pabellon’s powertransfer emitter turns into a touch-free pad. Unlike motion and other opticalsensing methods, the position of objects like fingers can be resolved by small changes in the magnetic near field. The ability to finely control the vast amount of things we contact on a daily basis may not require contact alone. The magnetic pixel has a very exciting future in the look-but-don’t-touch world that Pabellon envisions.

Asynchronous Consensus and Broadcast Protocols <=SEP=> A consensus protocol enables a system of n asynchronous processes, some of which are faulty, to reach agreement. There are two kinds of faulty processes: fail-stop processes that can only die and malicious processes that can also send false messages. The class of asynchronous systems with fair schedulers is defined, and consensus protocols that terminate with probability 1 for these systems are investigated. With fail-stop processes, it is shown that ⌈(n + 1)/2⌉ correct processes are necessary and sufficient to reach agreement. In the malicious case, it is shown that ⌈(2n + 1)/3⌉ correct processes are necessary and sufficient to reach agreement. This is contrasted with an earlier result, stating that there is no consensus protocol for the fail-stop case that always terminates within a bounded number of steps, even if only one process can fail. The possibility of reliable broadcast (Byzantine Agreement) in asynchronous systems is also investigated. Asynchronous Byzantine Agreement is defined, and it is shown that ⌈(2n + 1)/3⌉ correct processes are necessary and sufficient to achieve it.

C4.5: Programs for Machine Learning <=SEP=> 

Electromagnetic Vortex Imaging Using Uniform Concentric Circular Arrays <=SEP=> Electromagnetic vortex imaging based on orbital angular momentum (OAM) modulation has the ability of azimuthal resolution without relative motion, which needs various OAM modes. However, the main-lobe directions of different OAM beams differ from each other so that the echo energy is limited. In this letter, several concentric circular arrays are employed to adjust the directivity, each array generating a beam carrying special OAM mode. With this scheme, the target can be illuminated by main lobes of all OAM beams simultaneously. Our proposal can benefit the image reconstruction in noisy environment. Moreover, this scheme can successfully reduce the artifacts caused by high sidelobes at the cost of reduction in azimuthal resolution. Simulation results demonstrate the effectiveness of this scheme.

Supply Chain Risk Management: Literature Review and Future Research <=SEP=> Supply Chain Risk Management has increasingly becoming a more popular research area recently. Various papers, with different focus and approaches, have been published. This paper aims to survey supply chain risk management (SCRM) literature. Paper published in relevant journals from 2000 to 2006 will be analyzed and classified into five categories: conceptual, descriptive, empirical, exploratory cross-sectional, and exploratory longitudinal. The literature review will provide the basis for outlining future research opportunities in this field.

HOW TO IMPROVE FIRM PERFORMANCE USING BIG DATA ANALYTICS CAPABILITY <=SEP=> The recent interest in big data has led many companies to develop big data analytics capability (BDAC) in order to enhance firm performance (FPER). However, BDAC pays off for some companies but not for others. It appears that very few have achieved a big impact through big data. To address this challenge, this study proposes a BDAC model drawing on the resource-based theory (RBT) and the entanglement view of sociomaterialism. The findings show BDAC as a hierarchical model, which consists of three primary dimensions (i.e., management, technology, and talent capability) and 11 subdimensions (i.e., planning, investment, coordination, control, connectivity, compatibility, modularity, technology management knowledge, technical knowledge, business knowledge and relational knowledge). The findings confirm the value of the entanglement conceptualization of the higher-order BDAC model and its impact on FPER. The results also illuminate the significant moderating impact of analytics capability–business strategy alignment on the BDAC FPER relationship.

Towards cloud based mobile ad hoc network simulation <=SEP=> Simulations are considered as the most convenient approach for validating any research proposal. A simulator attempts to model a real-world setting in order to analyze the behavior of any algorithm. A large number of simulation software for ad hoc network has been proposed in literature. These simulators are based on execution of simulation on standalone computers. This paper introduces the notion of `simulation as a service' for mobile ad hoc network's simulation based on cloud computing. Cloud computing is an emerging discipline that enables outsourcing computing task remotely based on pay-as-you-go model. The paper provides a generalized architecture to perform ad hoc network simulations on cloud platforms. Similar to different service models provided by cloud, some service models have been introduced for cloud based simulation. The proposed approach provides different advantages such as dynamic service provisioning to support large scale simulation, sharing of simulation models and reduced cost for simulation etc. The proposed tool has been implemented on Heroku platform that demonstrates the scalability of proposed approach.

Comparison of orthodontic treatment outcomes in adults with skeletal open bite between conventional edgewise treatment and implant-anchored orthodontics. <=SEP=> INTRODUCTION
Open bites are known to be difficult malocclusions to treat. Generally, with conventional edgewise treatment, incisor extrusion rather than molar intrusion is observed. Recently, the use of miniscrews as anchorage has markedly increased. In this study, orthodontic treatment outcomes after conventional edgewise treatment and implant-anchored treatment were investigated by cephalometric analysis and several occlusal indexes. In addition, the stability of these patients 2 years after the retention phase was also analyzed.


METHODS
Thirty adults (15 for nonimplant treatment [non-IA group] and 15 for implant-anchored treatment [IA group]) were our subjects. Cephalometric analysis, peer assessment rating, discrepancy index, and objective grading system were used.


RESULTS
From the cephalometric values in the non-IA group, open-bite patients were generally treated by extrusion of the maxillary and mandibular incisors that resulted in clockwise rotation of the mandibular plane angle. In the IA group, intrusion of the maxillary and mandibular molars that resulted in counterclockwise rotation was noted. Furthermore, in the IA group, the soft-tissue analysis showed decreases in the facial convexity and the inferior labial sulcus angle that resulted in the disappearance of incompetent lips. In the retention phase, extrusion of the mandibular molars was observed in the IA group. From the objective grading system evaluation, significant reductions of overbite in canines and premolars were observed in both groups. Furthermore, less stability was observed in the IA group compared with the non-IA group according to the total objective grading system score.


CONCLUSIONS
Ideal occlusion can be achieved in adults with severe open bite with both conventional edgewise and implant-anchored orthodontic treatment. However, absolute intrusion of the molars and improvement in esthetics might be achieved more effectively by using miniscrews as an anchorage device. In addition, since a significant amount of tooth movement occurs with miniscrews, careful attention is required during the retention phase.

Parametric Optimization for Practical Control Systems Design <=SEP=> In this contribution, calculation machinery and code to compute different performance indices, which are necessary for the parametric optimization, are proposed. Moreover, a short review about parametric optimization is carried out. It is highlighted that normally only the ISE (Integral Square Error) performance index is used for control system design. One reason for that is the existence of several numerical algorithms to compute it along the infinite trajectory at once. Finally, an optimal design of a PI controller is used to illustrate and compare the new algorithms.

Embedding Factorization Models for Jointly Recommending Items and User Generated Lists <=SEP=> Existing recommender algorithms mainly focused on recommending individual items by utilizing user-item interactions. However, little attention has been paid to recommend user generated lists (e.g., playlists and booklists). On one hand, user generated lists contain rich signal about item co-occurrence, as items within a list are usually gathered based on a specific theme. On the other hand, a user's preference over a list also indicate her preference over items within the list. We believe that 1) if the rich relevance signal within user generated lists can be properly leveraged, an enhanced recommendation for individual items can be provided, and 2) if user-item and user-list interactions are properly utilized, and the relationship between a list and its contained items is discovered, the performance of user-item and user-list recommendations can be mutually reinforced.
 Towards this end, we devise embedding factorization models, which extend traditional factorization method by incorporating item-item (item-item-list) co-occurrence with embedding-based algorithms. Specifically, we employ factorization model to capture users' preferences over items and lists, and utilize embedding-based models to discover the co-occurrence information among items and lists. The gap between the two types of models is bridged by sharing items' latent factors. Remarkably, our proposed framework is capable of solving the new-item cold-start problem, where items have never been consumed by users but exist in user generated lists. Overall performance comparisons and micro-level analyses demonstrate the promising performance of our proposed approaches.

Promoting Transfer through Complex Systems Principles <=SEP=> Understanding scientific phenomena in terms of complex systems principles is both scientifically and pedagogically important. Situations from different disciplines of science are often governed by the same principle, and so promoting knowledge transfer across disciplines makes valuable cross-fertilization and scientific unification possible. Although evidence for this kind of transfer has been historically controversial, experiments and observations of students suggest pedagogical methods to promote transfer of complex systems principles. One powerful strategy is for students to actively interpret the elements and interactions of perceptually grounded scenarios. Such interpretation can be facilitated through the presentation of cases alongside general principles, and by students exploring and constructing computational models of cases. The resulting knowledge can be both concretely grounded yet highly perspective-dependent and generalizeable. We discuss methods for coordinating computational and mental models of complex systems, the roles of idealization and concreteness in fostering understanding and generalization, and other complementary theoretical approaches to transfer. Understanding Complex Systems 3 Promoting Transfer through Complex Systems Principles When and how do students transfer what they have learned to new situations? This is one of the most important questions confronting education and cognitive science. Addressing it has crucial practical consequence, while also touching on deep basic research issues related to learning, analogical reasoning, and conceptual representation. Considerable research suggests that students do not spontaneously transfer what they have learned, at least not to superficially dissimilar domains (Detterman, 1993; Gick & Holyoak, 1980; 1983). This is disturbing because teachers choose content with the hope that students will apply what they have learned to relevant new situations. We believe that students can transfer scientific principles across superficially dissimilar domains, and we are not alone in this belief (Bransford & Schwartz, 1999; Jacobson, 2001; Judd, 1908; Simon, 1980). To present our case, we will describe kinds of transfer that are worth “fighting for.” Identifying these turns out not to be only an educational question, but a scientific question as well. Accordingly, we will describe a novel approach toward science that seeks to unite phenomena from disparate domains according to general principles that govern complex systems. This complex systems approach to science offers unique educational opportunities for imparting scientific understanding that is both concretely grounded yet transportable. The notion of a grounded generalization may sound like an oxymoron, but it is key to our account of transfer. The time-honored method for conveying generalizations has been to use symbolic formalisms such as predicate logic or algebra. These formalisms can enable a student to transcend the specifics of a situation, but they also run the risk of disconnecting the resulting abstraction from an intuitive understanding of the situation. Instead, we propose learning and teaching methods that promote situation construals that are concrete insofar as they are perceptually, temporally, and spatially grounded. However, they are still idealizations in that many elements of a situation are ignored or highly simplified. In this paper, we will develop an approach to achieving grounded generalizations through the following steps: 1) Describe the 1 See also the notion of ‘situated abstraction’ in the section “comparison to other approaches to transfer.” Understanding Complex Systems 4 nature of complex systems accounts of science, 2) provide examples of general complex systems principles that appear in several case studies, 3) Describe pedagogical benefits of teaching science through complex systems, 4) Discuss the importance of transfer and generalization in relation to complex systems, 5) present a method for achieving generalization through perceptually grounded yet interpreted simulations, 6) compare generalization from grounded simulations to formalism-centered strategies, and 7) draw parallels between computational and mental models, with the aim of identifying design principles that allow the two kinds of models to mesh. Connecting Science with Complex Systems Principles One way to advance science is to progressively flesh out theories, adding experimental details and elaborating mechanistic accounts. By this account, the “devil is in the details” and the proper occupation of scientists is to pursue these details. This vision of science was most emphatically painted by John Horgan in his 1996 book “The End of Science.” He argued that the age of fundamental scientific theorizing and discoveries has passed, and that all that is left to be done is refining the details of theories already laid down by the likes of Einstein, Darwin, and Newton. The rapid rate of scientific specialization seems to support Horgan’s argument. We have gone from an era when the only major scientific journals were “Nature” and “Science” to an era with specialized journals such as “Journal of Contaminant Hydrology” and “Journal of Shoulder and Elbow Surgery,” each an umbrella outlet for several distinct sub-specializations. Well over half of the respondents to a Science Advisory Board believe that biologists are loosing track of the big picture by specializing in narrowly defined biological fields. Yet, many scientists feel compelled to specialize to still further degrees due to the sheer volume of scholarly output that competes for their eyes and minds. A small subset of scientists have chosen to reverse the trend toward increasing specialization. They have instead pursued principles that apply to many scientific domains, from physics to biology to social sciences. The principles are not applied to these domains in a metaphoric or vague fashion, as the terms “chaos” and “fractal” often were applied to art or interpersonal relations. Rather, the claim of complex system researchers Understanding Complex Systems 5 is that the same specific principle, sometimes expressible as equations or as sets of computational rules, can describe seemingly different phenomena. By complex systems, we refer to systems that contain numerous distinct elements that interact locally, resulting in a system globally changing over time. Over the past few decades, the field of complex systems theory has been rapidly developing (Bar-Yam, 1997; Holland, 1995; Kauffman, 1993; Wolfram, 2002). Complex systems theory and methods are now endemic to the sciences and can be found in just about every academic discipline and profession (Amaral & Ottino, 2004; Barabasi, 2002; Diermeir & Merlo, 2000; Epstein & Axtell 1996; Wolfram, 1986). It may at first seem that complex systems represent a small fraction of natural and social phenomena. But, this would be a misunderstanding of the field. What makes something a complex system or complex phenomenon is a matter of perspective. If you take a complex systems perspective, focusing on the interaction of system elements, then almost every system in nature and in society can be described as a complex system. Seen from this perspective, the system can then be analyzed using complex systems principles and methods. Complex systems theory has described a number of quite general principles that can describe natural and social systems across a wide variety of traditional disciplines. A few examples are in order. A commonly found system architecture in nature is: Pattern 1: An entity causes more entities like itself to be produced. At the same time, it causes another kind of entity to be produced that eliminates the

Wireless Body Sensor Network With Adaptive Low-Power Design for Biometrics and Healthcare Applications <=SEP=> A four-levels hierarchical wireless body sensor network (WBSN) system is designed for biometrics and healthcare applications. It also separates pathways for communication and control. In order to improve performance, a communication cycle is constructed for synchronizing the WBSN system with the pipeline. A low-power adaptive process is a necessity for long-time healthcare monitoring. It includes a data encoder and an adaptive power conserving algorithm within each sensor node along with an accurate control switch system for adaptive power control. The thermal sensor node consists of a micro control unit (MCU), a thermal bipolar junction transistor sensor, an analog-to-digital converter (ADC), a calibrator, a data encoder, a 2.4-GHz radio frequency transceiver, and an antenna. When detecting ten body temperature or 240 electrocardiogram (ECG) signals per second, the power consumption is either 106.3 ¿W or 220.4 ¿W. By switching circuits, multi sharing wireless protocol, and reducing transmission data by data encoder, it achieves a reduction of 99.573% or 99.164% in power consumption compared to those without using adaptive and encoding modules. Compared with published research reports and industrial works, the proposed method is 69.6% or 98% lower than the power consumption in thermal sensor nodes which consist only of a sensor and ADC (without MCU, 2.4-GHz transceiver, modulator, demodulator, and data encoder) or wireless ECG sensor nodes which selected Bluetooth, 2.4-GHz transceiver, and Zigbee as wireless protocols.

Active torque control of electric power steering system using composite nonlinear feedback control <=SEP=> Electric power steering (EPS) system plays an important role in assisting the driver to achieve better handling, steering feel and response in securing vehicle stability. In EPS control system, controlling the torque of the EPS motor is crucial where tracking of the robust command of motor angle to generate desired assist torque is very challenging. In this paper, a reference model is adopted to generate an ideal motor angle that can guarantee the desired performance and composite nonlinear feedback (CNF) control is applied to achieve a faster transient response in tracking the robust reference motor angle. The H2 state optimization is used to obtain the linear control parameters whereas Hooke-Jeeves auto-tuning algorithm is used to fine-tune the nonlinear control parameters to achieve the best transient performance. The proposed control strategy is validated with CarSim Software by interfacing Simulink model with CarSim full nonlinear vehicle model under different test procedures and compared to other controllers based on ITAE performance criteria.

Correlates of Young Women ’ s Interest in Obtaining Cosmetic Surgery <=SEP=> This study examined predictors of young women’s interest in obtaining cosmetic surgery. The sample investigated was comprised of 101 college undergraduates residing in the northeastern U.S. (M age=19.99, SD=4.79). Participants’ weight status (M BMI=24.52, SD=5.69), body dissatisfaction, internalization of media messages, reports of physical appearance teasing, and interest in obtaining cosmetic surgery were assessed. Results indicated that all of the predictors examined were correlated with women’s interest in cosmetic surgery. Further, body dissatisfaction mediated the relations between weight status, internalization of media messages, reports of teasing, and women’s interest in cosmetic surgery. Findings are discussed in terms of the importance of understanding cosmetic surgery trends and young women’s susceptibility to body dissatisfaction.

VMVM: unit test virtualization for Java <=SEP=> As software evolves and grows, its regression test suites tend to grow as well. When these test suites become too large, they can eventually reach a point where they become too length to regularly execute. Previous work in Test Suite Minimization has reduced the number of tests in such suites by attempting to identify those that are redundant (e.g. by a coverage metric). Our approach to ameliorating the runtime of these large test suites is complementary, instead focusing on reducing the overhead of running each test, an approach that we call Unit Test Virtualization. This Tool Demonstration presents our implementation of Unit Test Virtualization, VMVM (pronounced "vroom-vroom") and summarizes an evaluation of our implementation on 20 real-world Java applications, showing that it reduces test suite execution time by up to 97% (on average, 62%). A companion video to this demonstration is available online, at https://www.youtube.com/watch?v=sRpqF3rJERI.

AR-RRNS: Configurable reliable distributed data storage systems for Internet of Things to ensure security <=SEP=> Benefits of Internet of Things and cloud–fog-edge computing are associated with the risks of confidentiality, integrity, and availability related with the loss of information, denial of access for a long time, information leakage, conspiracy and technical failures. In this article, we propose a configurable, reliable, and confidential distributed data storage scheme with the ability to process encrypted data and control results of computations. Our systemutilizes Redundant ResidueNumber System (RRNS)with newmethod of error correction codes and secret sharing schemes. We introduce the concept of an approximate value of a rank of a number (AR), which allows us to reduce the computational complexity of the decoding from RNS to binary representation, and size of the coefficients. Based on the properties of the approximate value and arithmetic properties of RNS,we introduce AR-RRNSmethod for error detection, correction, and controlling computational results. We provide a theoretical basis to configure probability of information loss, data redundancy, speed of encoding and decoding to cope with different objective preferences, workloads, and storage properties. Theoretical analysis shows that by appropriate selection of RRNS parameters, the proposed scheme allows not only increasing safety, reliability, and reducing an overhead of data storage, but also processing of encrypted data. © 2017 Elsevier B.V. All rights reserved.

Toward Meta-cognitive Tutoring: A Model of Help Seeking with a Cognitive Tutor <=SEP=> The research reported in this paper focuses on the hypothesis that an intelligent tutoring system that provides guidance with respect to students' meta-cognitive abilities can help them to become better learners. Our strategy is to extend a Cognitive Tutor (Anderson, Corbett, Koedinger, & Pelletier, 1995) so that it not only helps students acquire domain-specific skills, but also develop better general help-seeking strategies. In developing the Help Tutor, we used the same Cognitive Tutor technology at the metacognitive level that has been proven to be very effective at the cognitive level. A key challenge is to develop a model of how students should use a Cognitive Tutor's help facilities. We created a preliminary model, implemented by 57 production rules that capture both effective and ineffective help-seeking behavior. As a first test of the model's efficacy, we used it off-line to evaluate students' help-seeking behavior in an existing data set of student-tutor interactions. We then refined the model based on the results of this analysis. Finally, we conducted a pilot study with the Help Tutor involving four students. During one session, we saw a statistically significant reduction in students' meta-cognitive error rate, as determined by the Help Tutor's model. These preliminary results inspire confidence as we gear up for a larger-scale controlled experiment to evaluate whether tutoring on help seeking has a positive effect on students' learning outcomes.

Capacity of Neural Networks for Lifelong Learning of Composable Tasks <=SEP=> We investigate neural circuits in the exacting setting that (i) the acquisition of a piece of knowledge can occur from a single interaction, (ii) the result of each such interaction is a rapidly evaluatable subcircuit, (iii) hundreds of thousands of such subcircuits can be acquired in sequence without substantially degrading the earlier ones, and (iv) recall can be in the form of a rapid evaluation of a composition of subcircuits that have been so acquired at arbitrary different earlier times.We develop a complexity theory, in terms of asymptotically matching upper and lower bounds, on the capacity of a neural network for executing, in this setting, the following action, which we call {\it association}: Each action sets up a subcircuit so that the excitation of a chosen set of neurons A will in future cause the excitation of another chosen set B.% As model of computation we consider the neuroidal model, a fully distributed model in which the quantitative resources n, the neuron numbers, d, the number of other neurons each neuron is connected to, and k, the inverse of the maximum synaptic strength, are all accounted for.A succession of experiences, possibly over a lifetime, results in the realization of a complex set of subcircuits. The composability requirement constrains the model to ensure that, for each association as realized by a subcircuit, the excitation in the triggering set of neurons A is quantitatively similar to that in the triggered set B, and also that the unintended excitation in the rest of the system is negligible. These requirements ensure that chains of associations can be triggeredWe first analyze what we call the Basic Mechanism, which uses only direct connections between neurons in the triggering set A and the target set B. We consider random networks of n neurons with expected number d of connections to and from each. We show that in the composable context capacity growth is limited by d^2, a severe limitation if the network is sparse, as it is in cortex. We go on to study the Expansive Mechanism, that additionally uses intermediate relay neurons which have high synaptic weights. For this mechanism we show that the capacity can grow as dn, to within logarithmic factors. From these two results it follows that in the composable regime, for the realistic cortical estimate of d=n^{\frac{1}{2}, superlinear capacity of order n^{\frac{3}{2}} in terms of the neuron numbers can be realized by the Expansive Mechanism, instead of the linear order n to which the Basic Mechanism is limited. More generally, for both mechanisms, we establish matching upper and lower bounds on capacity in terms of the parameters n, d, and the inverse maximum synaptic strength k.The results as stated above assume that in a set of associations, a target B can be triggered by at most one set A. It can be shown that the capacities are similar if the number m of As that can trigger a B is greater than one but small, but become severely constrained if m exceeds a certain threshold.

A survey of unmanned aerial vehicles (UAVs) for traffic monitoring <=SEP=> The focus of this paper is on surveying UAV-based systems for traffic monitoring and management. Although there has been voluminous research on the subject, unmanned aerial vehicles (UAVs) are proven to be a viable and less time-consuming alternative to real-time traffic monitoring and management, providing the eye-in-the-sky solution to the problem.

On Training Classifiers for Linking Event Templates <=SEP=> The paper reports on exploring various machine learning techniques and a range of textual and meta-data features to train classifiers for linking related event templates automatically extracted from online news. With the best model using textual features only we achieved 94.7% (92.9%) F1 score on GOLD (SILVER) dataset. These figures were further improved to 98.6% (GOLD) and 97% (SILVER) F1 score by adding meta-data features, mainly thanks to the strong discriminatory power of automatically extracted geographical information related to events.

Spatial, Structural and Temporal Feature Learning for Human Interaction Prediction <=SEP=> Human interaction prediction, i.e., the recognition of an ongoing interaction activity before it is completely executed, has a wide range of applications such as human-robot interaction and the prevention of dangerous events. Due to the large variations in appearance and the evolution of scenes, interaction prediction at an early stage is a challenging task. In this paper, a novel structural feature is exploited as a complement together with the spatial and temporal information to improve the performance of interaction prediction. The proposed structural feature is captured by Long Short Term Memory (LSTM) networks, which process the global and local features associated to each frame and each optical flow image. A new ranking score fusion method is then introduced to combine the spatial, temporal and structural models. Experimental results demonstrate that the proposed method outperforms state-of-the-art methods for human interaction prediction on the BIT-Interaction, the TV Human Interaction and the UT-Interaction datasets.

BOSS: Building Operating System Services <=SEP=> Commercial buildings are attractive targets for introducing innovative cyber-physical control systems, because they are already highly instrumented distributed systems which consume large quantities of energy. However, they are not currently programmable in a meaningful sense because each building is constructed with vertically integrated, closed subsystems and without uniform abstractions to write applications against. We develop a set of operating system services called BOSS, which supports multiple portable, fault-tolerant applications on top of the distributed physical resources present in large commercial buildings. We evaluate our system based on lessons learned from deployments of many novel applications in our test building, a four-year-old, 140,000sf building with modern digital controls, as well as partial deployments at other sites.

Research on deep neural network's hidden layers in phoneme recognition <=SEP=> In spite of the great success of the deep neural network (DNN) in speech processing, it is still unclear what kind of underlying mechanisms are involved in this achievement. This preliminary study attempts to find an answer by investigating the functions of DNN's hidden layers in representing speech articulations. Two sets of experiments are performed on the hidden layers in speech recognition. The layer removing experiment is conducted on the English TIMIT database, and the layer replacing experiment is to substitute a layer in an English DNN by the corresponding layer in a Japanese DNN. It is found that the different layers seem to be responsible for different phoneme groups according to the place of articulation. The lower layers are responsible for the back vowels, and the higher layers are responsible for the front vowels. The second layer (i.e. the first hidden layer) of the seven-layer network has major responsibility for more than half of the consonants with the constriction located in the front of the vocal tract, while the other consonants rely on the middle and higher layers. The layer replacing experiment demonstrated that the above relation was language independent. It is necessary to design elaborate studies to discover more details in the future.

Mobile application usability testing in quasi-real conditions <=SEP=> The paper presents the setup and results of a mobile application usability test using mobile eye-tracking glasses. Quasi-real conditions of the natural environment were assured for the testing in order to avoid incidental disorders possible in natural conditions and the weaknesses of a fully lab experiment. The experiment methodology was developed and used to carry out a usability test of the mobile part of a Sale Force Automation (SFA) application in terms of its basic functionality. The paper's outlines are the following: the methodology used, arrangement and realisation of the usability testing experiment, test results and answers to the research questions. A list of general conclusions rounds off the report.

Accelerator-centric deep learning systems for enhanced scalability, energy-efficiency, and programmability <=SEP=> Deep learning (DL) has been successfully deployed in various application domains ranging from computer vision, speech recognition, to natural language processing. As the network models and the datasets used to train these models scale, system architects are faced with new challenges in designing a scalable and energy-efficient high-performance computing (HPC) system for training DL algorithms. One of the key obstacles that DL researchers are facing is the memory capacity bottleneck, where the limited physical memory size of the PCIe-attached DL accelerator (whether it be a discrete GPU or ASIC accelerator like Google's Tensor Processing Unit) constrains the algorithm that can be studied. In this paper, and the associated invited special session talk, we first discuss recent research literature geared towards designing scalable HPC systems for DL. In this context, we then discuss the memory capacity wall problem and introduce the work on virtualized deep neural networks, a memory virtualization solution that systematically reduces the memory consumption of DNN training. We conclude by providing projections on future challenges DNN memory virtualization will encounter upon and suggest accelerator-centric DL system as a promising research direction for the development of a scalable and energy-efficient deep learning system architecture.

Topographical and temporal diversity of the human skin microbiome. <=SEP=> Human skin is a large, heterogeneous organ that protects the body from pathogens while sustaining microorganisms that influence human health and disease. Our analysis of 16S ribosomal RNA gene sequences obtained from 20 distinct skin sites of healthy humans revealed that physiologically comparable sites harbor similar bacterial communities. The complexity and stability of the microbial community are dependent on the specific characteristics of the skin site. This topographical and temporal survey provides a baseline for studies that examine the role of bacterial communities in disease states and the microbial interdependencies required to maintain healthy skin.

Adaptive Decentralized Droop Controller to Preserve Power Sharing Stability of Paralleled Inverters in Distributed Generation Microgrids <=SEP=> This paper addresses the low-frequency relative stability problem in paralleled inverter-based distributed generation (DG) units in microgrids. In the sense of the small-signal dynamics of a microgrid, it can be shown that as the demanded power of each inverter changes, the low-frequency modes of the power sharing dynamics drift to new locations and the relative stability is remarkably affected, and eventually, instability can be yielded. To preserve the power sharing stability, an adaptive decentralized droop controller of paralleled inverter-based DG units is presented in this paper. The proposed power sharing strategy is based on the static droop characteristics combined with an adaptive transient droop function. Unlike conventional droop controllers, which yield 1-DOF tunable controller, the proposed droop controller yields 2-DOF tunable controller. Subsequently, the dynamic performance of the power sharing mechanism can be adjusted, without affecting the static droop gain, to damp the oscillatory modes of the power sharing controller. To account for the power modes immigration at different loading conditions, the transient droop gains are adaptively scheduled via small-signal analysis of the power sharing mechanism along the loading trajectory of each DG unit to yield the desired transient and steady-state response. The gain adaptation scheme utilizes the filtered active and reactive powers as indices; therefore, a stable and smooth power injection performance can be obtained at different loading conditions. The adaptive nature of the proposed controller ensures active damping of power oscillations at different operating conditions, and yields a stable and robust performance of the paralleled inverter system.