Evaluating the Effect of Total Quality Management Practices on Business Performance : A Study of Manufacturing Firms of Pakistan

The present study has evaluated the effect of TQM practices on business performance of manufacturing firms in Pakistan. A sample of 65 managers working in the quality assurance department was taken from these manufacturing firms and their responses were collected using a structured questionnaire. Descriptive statistics were used to demonstrate the effect of different levels of TQM practices on business performance in terms of financial performance, product quality performance, customer satisfaction. The study had also considered organizational level variables like size of firm, year of existence, top management commitment as moderating variables, the results have shown that TQM was implemented only at the first three levels i.e. quality control ,quality assurance and continuous improvement in the selected firms and that top management commitment is the most important variable effecting on TQM implementation which then directly effect on business performance in these manufacturing firms.


Food Balance Estimation by Using Personal Dietary Tendencies in a Multimedia Food Log

We have investigated the “FoodLog” multimedia food-recording tool, whereby users upload photographs of their meals and a food diary is constructed using image-processing functions such as food-image detection and food-balance estimation. In this paper, following a brief introduction to FoodLog, we propose a Bayesian framework that makes use of personal dietary tendencies to improve both food-image detection and food-balance estimation. The Bayesian framework facilitates incremental learning. It incorporates three personal dietary tendencies that influence food analysis: likelihood, prior distribution, and mealtime category. In the evaluation of the proposed method using images uploaded to FoodLog, both food-image detection and food-balance estimation are improved. In particular, in the food-balance estimation, the mean absolute error is significantly reduced from 0.69 servings to 0.28 servings on average for two persons using more than 200 personal images, and 0.59 servings to 0.48 servings on average for four persons using 100 personal images. Among the works analyzing food images, this is the first to make use of statistical personal bias to improve the performance of the analysis.


IoT in Agriculture: Designing a Europe-Wide Large-Scale Pilot

The technologies associated with the Internet of Things have great potential for application in the domain of food and agriculture, especially in view of the societal and environmental challenges faced by this sector. From farm to fork, IoT technologies could transform the sector, contributing to food safety, and the reduction of agricultural inputs and food waste. A major step toward greater uptake of these technologies will be the execution of IoT-based large-scale pilots (LSPs) in the entire supply chain. This article outlines the challenges and constraints that an LSP deployment of IoT in this domain must consider. Sectoral and technological challenges are described in order to identify a set of technological and agrifood requirements. An architecture based on a system of systems approach is briefly presented, the importance of addressing the interoperability challenges faced by this sector is highlighted, and we elaborate on requirements for new business models, security, privacy, and data governance. A description of the technologies and solutions involved in designing pilots for four agrifood domains (dairy, fruit, arable, meat and vegetable supply chain) is eventually provided. In conclusion, it is noted that for IoT to be successful in this domain, a significant change of culture is needed.


Tapping the power of text mining

Sifting through vast collections of unstructured or semistructured data beyond the reach of data mining tools, text mining tracks information sources, links isolated concepts in distant documents, maps relationships between activities, and helps answer questions.


Designed Interdependence, Growing Social Capital and Knowledge Sharing in IS Project Teams

This study brings in the task interdependence as the antecedent for growing team social capital and increasing knowledge sharing among team members in IS project context in which heterogeneous members need to interact very closely. Data collected from 203 IS projects largely confirm the proposed research model. Between the task interdependence and team performance, the team social capital as well as the knowledge sharing among members fully mediates the relationship. Interestingly, direct path from task interdependence onto performance is found to be insignificant, rebutting the suggestions of the work design theory which says well designed interdependence directly increase performance in heterogeneous teams. Findings of our study signify the criticality of the mediating roles of social capital and knowledge sharing. Additionally, it is also confirmed that the social capital grows with time. Also, longer term projects revealed a bit different mechanism than shorter ones. Implications are discussed with limitations and further studies.


BAG: Managing GPU as Buffer Cache in Operating Systems

This paper presents the design, implementation and evaluation of BAG, a system that manages GPU as the buffer cache in operating systems. Unlike previous uses of GPUs, which have focused on the computational capabilities of GPUs, BAG is designed to explore a new dimension in managing GPUs in heterogeneous systems where the GPU memory is an exploitable but always ignored resource. With the carefully designed data structures and algorithms, such as concurrent hashtable, log-structured data store for the management of GPU memory, and highly-parallel GPU kernels for garbage collection, BAG achieves good performance under various workloads. In addition, leveraging the existing abstraction of the operating system not only makes the implementation of BAG non-intrusive, but also facilitates the system deployment.


Packaging a $W$ -Band Integrated Module With an Optimized Flip-Chip Interconnect on an Organic Substrate

This paper, for the first time, presents successful integration of a W-band antenna with an organically flip-chip packaged silicon-germanium (SiGe) low-noise amplifier (LNA). The successful integration requires an optimized flip-chip interconnect. The interconnect performance was optimized by modeling and characterizing the flip-chip transition on a low-loss liquid crystal polymer organic substrate. When the loss of coplanar waveguide (CPW) lines is included, an insertion loss of 0.6 dB per flip-chip-interconnect is measured. If the loss of CPW lines is de-embedded, 0.25 dB of insertion loss is observed. This kind of low-loss flip-chip interconnect is essential for good performance of W-band modules. The module, which we present in this paper, consists of an end-fire Yagi-Uda antenna integrated with an SiGe BiCMOS LNA. The module is 3 mm × 1.9 mm and consumes only 19.2 mW of dc power. We present passive and active E- and H-plane radiation pattern measurements at 87, 90, and 94 GHz. Passive and active antennas both showed a 10-dB bandwidth of 10 GHz. The peak gain of passive and active antennas was 5.2 dBi at 90 GHz and 21.2 dBi at 93 GHz, respectively. The measurements match well with the simulated results.


End-to-End Joint Semantic Segmentation of Actors and Actions in Video

Traditional video understanding tasks include human action recognition and actor/object semantic segmentation. However, the combined task of providing semantic segmentation for different actor classes simultaneously with their action class remains a challenging but necessary task for many applications. In this work, we propose a new end-to-end architecture for tackling this task in videos. Our model effectively leverages multiple input modalities, contextual information, and multitask learning in the video to directly output semantic segmentations in a single unified framework. We train and benchmark our model on the Actor-Action Dataset (A2D) for joint actor-action semantic segmentation, and demonstrate state-of-the-art performance for both segmentation and detection. We also perform experiments verifying our approach improves performance for zero-shot recognition, indicating generalizability of our jointly learned feature space.


Comparison of Longley-Rice, ITM and ITWOM propagation models for DTV and FM broadcasting

With the rapid deployment of digital TV, there is an increasing need for accurate point-to-area prediction tools. There is a great deal of propagation models for coverage prediction of DTV. Some of them are pure empirical models, and others are mixed, empirical-analytical models, based on measurement campaigns and electromagnetic theory. The aim of this paper is to compare accurate measurements taken by a Rohde & Schwarz FSH-3 portable spectrum analyzer and precision antennas (biconical and log-periodic), with simulation results derived from coverage prediction models, like the NTIA-ITS Longley-Rice model, the ITM (Irregular Terrain Model) using the 3-arc-second SRTM (Satellite Radar Topography Mission) data that is available freely, and the newer ITWOM (Irregular Terrain with Obstructions Model) model which combines equations from ITU-R P.1546 model with Beer's law and Snell's law. Furthermore, measurements for analog FM broadcasting are compared to predictions from the above mentioned models.


iDocChip: A Configurable Hardware Architecture for Historical Document Image Processing: Percentile Based Binarization

End-to-end Optical Character Recognition (OCR) systems are heavily used to convert document images into machine-readable text. Commercial and open-source OCR systems (like Abbyy, OCRopus, Tesseract etc.) have traditionally been optimized for contemporary documents like books, letters, memos, and other end-user documents. However, these systems are difficult to use equally well for digitizing historical document images, which contain degradations like non-uniform shading, bleed-through, and irregular layout; such degradations usually do not exist in contemporary document images.
 The open-source anyOCR is an end-to-end OCR pipeline, which contains state-of-the-art techniques that are required for digitizing degraded historical archives with high accuracy. However, high accuracy comes at a cost of high computational complexity that results in 1) long runtime that limits digitization of big collection of historical archives and 2) high energy consumption that is the most critical limiting factor for portable devices with constrained energy budget. Therefore, we are targeting energy efficient and high throughput acceleration of the anyOCR pipeline. Generalpurpose computing platforms fail to meet these requirements that makes custom hardware design mandatory. In this paper, we are presenting a new concept named iDocChip. It is a portable hybrid hardware-software FPGA-based accelerator that is characterized by low footprint meaning small size, high power efficiency that will allow using it in portable devices, and high throughput that will make it possible to process big collection of historical archives in real time without effecting the accuracy.
 In this paper, we focus on binarization, which is the second most critical step in the anyOCR pipeline after text-line recognizer that we have already presented in our previous publication [21]. The anyOCR system makes use of a Percentile Based Binarization method that is suitable for overcoming degradations like non-uniform shading and bleed-through. To the best of our knowledge, we propose the first hardware architecture of the PBB technique. Based on the new architecture, we present a hybrid hardware-software FPGA-based accelerator that outperforms the existing anyOCR software implementation running on i7-4790T in terms of runtime by factor of 21, while achieving energy efficiency of 10 Images/J that is higher than that achieved by low power embedded processors with negligible loss of recognition accuracy.


Architecture Technical Debt: Understanding Causes and a Qualitative Model

A known problem in large software companies is to balance the prioritization of short-term with long-term responsiveness. Specifically, architecture violations (Architecture Technical Debt) taken to deliver fast might hinder future feature development, which would hinder agility. We conducted a multiple-case embedded case study in 7 sites at 5 large companies in order to shed light on the current causes for the accumulation of Architectural Technical Debt that causes effort. We provide a taxonomy of the factors and their influence in the accumulation of debt, and we provide a qualitative model of how the debt is accumulated and recovered over time.


E-S-QUAL A Multiple-Item Scale for Assessing Electronic Service Quality

Using the means-end framework as a theoretical foundation, this article conceptualizes, constructs, refines, and tests a multiple-item scale (E-S-QUAL) for measuring the service quality delivered by Web sites on which customers shop online. Two stages of empirical data collection revealed that two different scales were necessary for capturing electronic service quality. The basic E-S-QUAL scale developed in the research is a 22-item scale of four dimensions: efficiency, fulfillment, system availability, and privacy. The second scale, E-RecS-QUAL, is salient only to customers who had nonroutine encounters with the sites and contains 11 items in three dimensions: responsiveness, compensation, and contact. Both scales demonstrate good psychometric properties based on findings from a variety of reliability and validity tests and build on the research already conducted on the topic. Directions for further research on electronic service quality are offered. Managerial implications stemming from the empirical findings about E-S-QUAL are also discussed.


Visual Analysis of Large Graphs

The analysis of large graphs plays a prominent role in various fields of research and is relevant in many important application areas. Effective visual analysis of graphs requires appropriate visual presentations in combination with respective user interaction facilities and algorithmic graph analysis methods. How to design appropriate graph analysis systems depends on many factors, including the type of graph describing the data, the analytical task at hand, and the applicability of graph analysis methods. The most recent surveys of graph visualization and navigation techniques were presented by Herman et al. [HMM00] and Diaz [DPS02]. The first work surveyed the main techniques for visualization of hierarchies and graphs in general that had been introduced until 2000. The second work concentrated on graph layouts introduced until 2002. Recently, new techniques have been developed covering a broader range of graph types, such as time-varying graphs. Also, in accordance with ever growing amounts of graph-structured data becoming available, the inclusion of algorithmic graph analysis and interaction techniques becomes increasingly important. In this State-of-the-Art Report, we survey available techniques for the visual analysis of large graphs. Our review firstly considers graph visualization techniques according to the type of graphs supported. The visualization techniques form the basis for the presentation of interaction approaches suitable for visual graph exploration. As an important component of visual graph analysis, we discuss various graph algorithmic aspects useful for the different stages of the visual graph analysis process.


Internet Banking Adoption Among Young Intellectuals

The aim of this paper is to study technology acceptance of internet banking among undergraduate students in Malaysia. Thus, the theoretical framework of the paper is based on modified version of Technology Acceptance Model (TAM). This paper develops a technology acceptance model for internet banking, a conceptual framework to explain the factors influencing undergraduate students' acceptance of internet banking. The model employs perceived usefulness (PU), perceived ease of use (PEOU), perceived credibility (PC) and computer self-efficacy (CSE). The first two are two initial constructs for TAM model. The rest are new constructs to be included in the model in order to have an extension of TAM model that better reflects the students’ view. The results suggest that PU, PEOU and PC had a significant relationship with behavioral intention. Further, these measures are good determinant for undergraduate acceptance for internet banking. Results also suggest that PU and PEOU had a significant relationship with CSE. On the contrary, CSE did not associate with PC. Also, PEOU had JIBC December 2007, Vol. 12, No. 3 2 a significant relationship with PU and PC that indicate these scales are related to PEOU in explaining undergraduate preference. The paper is useful in providing the understanding of the TAM among undergraduate from Malaysians’ perspective.


Best Practices for Data Centers : Lessons Learned from Benchmarking 22 Data Centers

Over the past few years, the authors benchmarked 22 data center buildings. From this effort, we have determined that data centers can be over 40 times as energy intensive as conventional office buildings. Studying the more efficient of these facilities enabled us to compile a set of “best-practice” technologies for energy efficiency. These best practices include: improved air management, emphasizing control and isolation of hot and cold air streams; rightsizing central plants and ventilation systems to operate efficiently both at inception and as the data center load increases over time; optimized central chiller plants, designed and controlled to maximize overall cooling plant efficiency, central air-handling units, in lieu of distributed units; “free cooling” from either air-side or water-side economizers; alternative humidity control, including elimination of control conflicts and the use of direct evaporative cooling; improved uninterruptible power supplies; high-efficiency computer power supplies; on-site generation combined with special chillers for cooling using the waste heat; direct liquid cooling of racks or computers; and lowering the standby losses of standby generation systems. Other benchmarking findings include power densities from 5 to nearly 100 Watts per square foot; though lower than originally predicted, these densities are growing. A 5:1 variation in cooling effectiveness index (ratio of cooling power to computer power) was found, as well as large variations in power distribution efficiency and overall center performance (ratio of computer power to total building power). These observed variations indicate the potential of energy savings achievable through the implementation of best practices in the design and operation of data centers. The Data Center Challenge The energy used by a typical rack of state-of-the art servers, drawing 20 kilowatts of power at 10 cents per kWh, uses more that $17,000 per year in electricity. Given that data centers can hold hundreds of such racks, they constitute a very energy-intensive building type. Clearly, efforts to improve energy efficiency in data centers can pay big dividends. But, where to start? “You can’t manage what you don’t measure” is a mantra often recited by corporate leaders (Tschudi et al 2005). Similarly, the high-availability needs of data center facilities can be efficiently met using this philosophy. This paper presents a selected distillation of the extensive benchmarking in 22 data centers which formed the basis for the development of 10 best-practice guides for design and operation that summarized how better energy performance was obtained in these actual facilities (LBNL 2006). With typical annual energy costs per square foot 15 times (and in some cases over 40 times) that of typical office buildings, data centers are an important target for energy savings. They operate continuously, which means that their electricity demand is always contributing to 3-76 © 2006 ACEEE Summer Study on Energy Efficiency in Buildings peak utility system demands, an important fact given that utility pricing increasingly reflects time-dependent tariffs. Energy-efficiency best practices can realize significant energy and peakpower savings while maintaining or improving reliability, and yield other non-energy benefits. Data centers ideally are buildings built primarily for computer equipment: to house it and provide power and cooling to it. They can also be embedded in other buildings including highrise office buildings. In any event, a useful metric to help gauge the energy efficiency of the data center is the computer power consumption index, or the fraction of the total data center power (including computers, power conditioning, HVAC, lighting, and miscellaneous) to that used by the computers (higher is better; a realistic maximum is in the 0.8 to 0.9 range, depending on climate). As shown in Figure 1, this number varies by more than a factor of 2 from the worst centers to the best we’ve benchmarked to date. Even the best have room for improvement, suggesting that many centers have tremendous opportunities for greater energy efficiency. Figure 1. Computer Power Consumption Index Computer Power Consumption Index


Mesofluidic actuation for articulated finger and hand prosthetics

The loss of fingers and hands severely limits career and lifestyle options for the amputee. Unfortunately, while there have been strides made in advancements of upper arm and leg prosthetics, the state of the art in prosthetic hands is lagging far behind. Options are generally limited to claw like devices that provide limited gripping capacity. The overall objective of this paper is to demonstrate a path towards a low-cost prosthetic hand with multiple articulated fingers and a thumb that rivals the human hand in terms of weight, size, dexterity, range of motion, force carrying capacity and speed. We begin with a description of the functional requirements for a human hand. When comparing requirements with actuation technologies, the fluid power approach has the potential to realize a prosthetic hand that rivals a human hand in size, strength and dexterity. We introduce a new actuation technology, mesofluidics, that focuses on miniaturization of fluid power to the meso-scale (mm to cm). As a novel demonstration of the potential for this technology, we describe a proof-of-principle mesofluidic finger that has intrinsic actuation and control (actuators and control valves within the volume of the finger). This finger weighs 63 grams, is sized to the 50th percentile male finger, has a total of 25 mechanical parts and is capable of providing 10 kg (22 lbs) of pinch force.


A Learning-Based Framework for Velocity Control in Autonomous Driving

We present a framework for autonomous driving which can learn from human demonstrations, and we apply it to the longitudinal control of an autonomous car. Offline, we model car-following strategies from a set of example driving sequences. Online, the model is used to compute accelerations which replicate what a human driver would do in the same situation. This reference acceleration is tracked by a predictive controller which enforces a set of comfort and safety constraints before applying the final acceleration. The controller is designed to be robust to the uncertainty in the predicted motion of the preceding vehicle. In addition, we estimate the confidence of the driver model predictions and use it in the cost function of the predictive controller. As a result, we can handle cases where the training data used to learn the driver model does not provide sufficient information about how a human driver would handle the current driving situation. The approach is validated using a combination of simulations and experiments on our autonomous vehicle.


Challenges in Autonomous Vehicle Testing and Validation

Software testing is all too often simply a bug hunt rather than a wellconsidered exercise in ensuring quality. A more methodical approach than a simple cycle of system-level test-fail-patch-test will be required to deploy safe autonomous vehicles at scale. The ISO 26262 development V process sets up a framework that ties each type of testing to a corresponding design or requirement document, but presents challenges when adapted to deal with the sorts of novel testing problems that face autonomous vehicles. This paper identifies five major challenge areas in testing according to the V model for autonomous vehicles: driver out of the loop, complex requirements, non-deterministic algorithms, inductive learning algorithms, and failoperational systems. General solution approaches that seem promising across these different challenge areas include: phased deployment using successively relaxed operational scenarios, use of a monitor/actuator pair architecture to separate the most complex autonomy functions from simpler safety functions, and fault injection as a way to perform more efficient edge case testing. While significant challenges remain in safety-certifying the type of algorithms that provide high-level autonomy themselves, it seems within reach to instead architect the system and its accompanying design process to be able to employ existing software safety approaches.


A machine learning approach for personalized autonomous lane change initiation and control

We study an algorithm that allows a vehicle to autonomously change lanes in a safe but personalized fashion without the driver's explicit initiation (e.g. activating the turn signals). Lane change initiation in autonomous driving is typically based on subjective rules, functions of the positions and relative velocities of surrounding vehicles. This approach is often arbitrary, and not easily adapted to the driving style preferences of an individual driver. Here we propose a data-driven modeling approach to capture the lane change decision behavior of human drivers. We collect data with a test vehicle in typical lane change situations and train classifiers to predict the instant of lane change initiation with respect to the preferences of a particular driver. We integrate this decision logic into a model predictive control (MPC) framework to create a more personalized autonomous lane change experience that satisfies safety and comfort constraints. We show the ability of the decision logic to reproduce and differentiate between two lane changing styles, and demonstrate the safety and effectiveness of the control framework through simulations.


Kinematic and dynamic vehicle models for autonomous driving control design

We study the use of kinematic and dynamic vehicle models for model-based control design used in autonomous driving. In particular, we analyze the statistics of the forecast error of these two models by using experimental data. In addition, we study the effect of discretization on forecast error. We use the results of the first part to motivate the design of a controller for an autonomous vehicle using model predictive control (MPC) and a simple kinematic bicycle model. The proposed approach is less computationally expensive than existing methods which use vehicle tire models. Moreover it can be implemented at low vehicle speeds where tire models become singular. Experimental results show the effectiveness of the proposed approach at various speeds on windy roads.


Apprenticeship learning for motion planning with application to parking lot navigation

Motion and path-planning algorithms often use complex cost functions for both global navigation and local smoothing of trajectories. Obtaining good results typically requires carefully hand-engineering the trade-offs between different terms in the cost function. In practice, it is often much easier to demonstrate a few good trajectories. In this paper, we describe an efficient algorithm which - when given access to a few trajectory demonstrations - can automatically infer good trade-offs between the different costs. In our experiments, we apply our algorithm to the problem of navigating a robotic car in a parking lot.


Deep or shallow, NLP is breaking out

Neural net advances improve computers' language ability in many fields.


Long-Term Amnesia: A Review and Detailed Illustrative Case Study

Long-term amnesia is a slowly developing form of anterograde amnesia accompanied by retrograde amnesia of variable severity (Kapur, 1996; 1997) often associated with damage to the anterior temporal neocortex and epileptic seizures. The precise neural and functional deficits that underlie this condition are unknown. A patient, JL, who has this condition following a closed-head injury, is described in detail. Her injury caused bilateral anterior temporal neocortex damage that was more extensive on the left and right-sided damage to the perirhinal and orbitofrontal cortices. The hippocampus appeared to be intact bilaterally. Epilepsy developed within two years of JL's injury. Apart from her memory impairments, JL's cognitive functions, including high-level visual perception, attention, semantic memory and executive functions were well preserved. Her memory also seemed well preserved for at least 30 minutes following encoding. The one exception was the patient's relatively greater impairment at difficult visual recognition tests for which verbalization may not have been an effective strategy. This problem may have been caused by JL's right-sided perirhinal and orbitofrontal cortex damage. Her recall and recognition was clearly impaired after a three-week delay. She also showed a retrograde amnesia, which appeared to be milder than her remote post-morbid memory deficit. JL's remote memory was preserved for information first encountered in either the pre- or post-morbid period provided the information had received sufficient rehearsal over long periods of time. Her long-term amnesia may have been caused by anterior temporal neocortex damage, possibly in association with her epileptic seizures. Whether the condition is heterogeneous, involves a deficit in slow consolidation, disruption of unconsolidated memories, or blockage of maintenance or disruption of insufficiently rehearsed memories whether or not these have been slowly consolidated is discussed.


met*: A Method for Discriminating Matonymy and Metaphor by Computer

The met* method distinguishes selected examples of metonymy from metaphor and from literalness and anomaly in short English sentences. In the met* method, literalness is distinguished because it satisfies contextual constraints that the nonliteral others all violate. Metonymy is discriminated from metaphor and anomaly in a way that [1] supports Lakoff and Johnson's (1980) view that in metonymy one entity stands for another whereas in metaphor one entity is viewed as another, [2] permits chains of metonymies (Reddy 1979), and [3] allows metonymies to co-occur with instances of either literalness, metaphor, or anomaly. Metaphor is distinguished from anomaly because the former contains a relevant analogy, unlike the latter. The met* method is part of Collative Semantics, a semantics for natural language processing, and has been implemented in a computer program called meta5. Some examples of meta5's analysis of metaphor and metonymy are given. The met* method is compared with approaches from artificial intelligence, linguistics, philosophy, and psychology.


Genetic engineering of algae for enhanced biofuel production.

There are currently intensive global research efforts aimed at increasing and modifying the accumulation of lipids, alcohols, hydrocarbons, polysaccharides, and other energy storage compounds in photosynthetic organisms, yeast, and bacteria through genetic engineering. Many improvements have been realized, including increased lipid and carbohydrate production, improved H(2) yields, and the diversion of central metabolic intermediates into fungible biofuels. Photosynthetic microorganisms are attracting considerable interest within these efforts due to their relatively high photosynthetic conversion efficiencies, diverse metabolic capabilities, superior growth rates, and ability to store or secrete energy-rich hydrocarbons. Relative to cyanobacteria, eukaryotic microalgae possess several unique metabolic attributes of relevance to biofuel production, including the accumulation of significant quantities of triacylglycerol; the synthesis of storage starch (amylopectin and amylose), which is similar to that found in higher plants; and the ability to efficiently couple photosynthetic electron transport to H(2) production. Although the application of genetic engineering to improve energy production phenotypes in eukaryotic microalgae is in its infancy, significant advances in the development of genetic manipulation tools have recently been achieved with microalgal model systems and are being used to manipulate central carbon metabolism in these organisms. It is likely that many of these advances can be extended to industrially relevant organisms. This review is focused on potential avenues of genetic engineering that may be undertaken in order to improve microalgae as a biofuel platform for the production of biohydrogen, starch-derived alcohols, diesel fuel surrogates, and/or alkanes.


The use of time-variant EEG Granger causality for inspecting directed interdependencies of neural assemblies

Understanding of brain functioning requires the investigation of activated cortical networks, in particular the detection of interactions between different cortical sites. Commonly, coherence and correlation are used to describe interrelations between EEG signals. However, on this basis, no statements on causality or the direction of their interrelations are possible. Causality between two signals may be expressed in terms of upgrading the predictability of one signal by the knowledge of the immediate past of the other signal. The best-established approach in this context is the so-called Granger causality. The classical estimation of Granger causality requires the stationarity of the signals. In this way, transient pathways of information transfer stay hidden. The study presents an adaptive estimation of Granger causality. Simulations demonstrate the usefulness of the time-variant Granger causality for detecting dynamic causal relations within time intervals of less than 100 ms. The time-variant Granger causality is applied to EEG data from the Stroop task. It was shown that conflict situations generate dense webs of interactions directed from posterior to anterior cortical sites. The web of directed interactions occurs mainly 400 ms after the stimulus onset and lasts up to the end of the task.


An application-specific protocol architecture for wireless microsensor networks

In recent years advances in energy e cient design and wireless technologies have enabled exciting new applications for wireless devices These applications span a wide range including real time and streaming video and audio delivery remote monitoring using networked microsensors personal medical monitoring and home networking of everyday appliances While these applications require high performance from the network they su er from resource constraints that do not appear in more traditional wired computing environments In particular wireless spectrum is scarce often limiting the bandwidth available to applications and making the channel error prone and the nodes are battery operated often limiting available energy My thesis is that this harsh environment with severe resource constraints requires an application speci c protocol architecture rather than the traditional layered approach to obtain the best pos sible performance This dissertation supports this claim using detailed case studies on microsensor networks and wireless video delivery The rst study develops LEACH Low Energy Adaptive Clustering Hierarchy an architecture for remote microsensor networks that combines the ideas of energy e cient cluster based routing and media access together with application speci c data aggregation to achieve good performance in terms of system lifetime latency and application perceived quality This approach improves system lifetime by an order of magnitude compared to general purpose approaches when the node energy is limited The second study develops an unequal error protection scheme for MPEG compressed video delivery that adapts the level of protection applied to portions of a packet to the degree of importance of the corresponding bits This approach obtains better application perceived performance than current approaches for the same amount of transmission bandwidth These two systems show that application speci c protocol architectures achieve the energy and latency e ciency and error robustness needed for wireless networks Thesis Supervisor Anantha P Chandrakasan Title Associate Professor Thesis Supervisor Hari Balakrishnan Title Assistant Professor This thesis is dedicated to the memory of Robert H Rabiner The rst Rabiner at the Tute


'Strong is the new skinny': A content analysis of #fitspiration images on Instagram.

'Fitspiration' is an online trend designed to inspire viewers towards a healthier lifestyle by promoting exercise and healthy food. This study provides a content analysis of fitspiration imagery on the social networking site Instagram. A set of 600 images were coded for body type, activity, objectification and textual elements. Results showed that the majority of images of women contained only one body type: thin and toned. In addition, most images contained objectifying elements. Accordingly, while fitspiration images may be inspirational for viewers, they also contain a number of elements likely to have negative effects on the viewer's body image.


Service Science

This paper is a first exploration of the relationship between service science and Grid computing. Service science is the study of value co-creation interactions among entities, known as service systems. Within the emerging service science community, service is often defined as the application of competences (resources) for the benefit of another. Grid computing is the study of resource sharing among entities, known as virtual organizations, which solve complex business, societal, scientific, and engineering problems. Within the Grid computing community, service is sometimes defined as protocols plus behavior. Both Grid computing and service science are connecting academic, industry, government, and volunteer sector collaborators on a range of projects including eScience, healthcare, environmental sustainability, and more. This paper compares and contrasts the notions of resource, entity, service, interaction, and success criteria for the two areas of study. In conclusion, new areas for collaborative inquiry are proposed.


Global Changes and Drivers of the Water Footprint of Food Consumption : A Historical Analysis

Water is one of the most important limiting resources for food production. How much water is needed for food depends on the size of the population, average food consumption patterns and food production per unit of water. These factors show large differences around the world. This paper analyzes sub-continental dynamics of the water footprint of consumption (WFcons) for the prevailing diets from 1961 to 2009 using data from the Food and Agriculture Organization (FAO). The findings show that, in most regions, the water needed to feed one person decreased even if diets became richer, because of the increase in water use efficiency in food production during the past half-century. The logarithmic mean Divisia index (LMDI) decomposition approach is used to analyze the contributions of the major drivers of WFcons for food: population, diet and agricultural practices (output per unit of water). We compare the contributions of these drivers through different subcontinents, and find that population growth still was the major driver behind increasing WFcons for food until now and that potential water savings through agricultural practice improvements were offset by population growth and diet change. The changes of the factors mentioned above were the largest in most developing areas with rapid economic development. With the development of globalization, the international food trade has brought more and more water savings in global water use over time. The results indicate that, in the near future and in many regions, diet change is likely to override population growth as the major driver behind WFcons for food. OPEN ACCESS


Optimal Sink Deployment for Distributed Sensing of Spatially Nonstationary Phenomena

The optimal deployment of sinks in a sensor region for power efficient data gathering of a physical phenomenon is investigated in this work. In the system of consideration, nodes perform lossless distributed coding of the sensed data and the spatial statistics of the monitored phenomenon are possibly nonstationary due to heterogeneity in the sensing environment (e.g. variations in the altimetric profile). Non-stationary spatial statistics lead to uneven spatial profiles of the bit rates, unlike stationary statistics. The properties of rate profiles and the consequent optimal sink locations for a broad class of spatially non-stationary covariance models are studied by mathematical analysis and numerical examples.


3D convolutional neural networks by modal fusion

We propose multi-view and volumetric convolutional neural networks (ConvNets) for 3D shape recognition, which combines surface normal and height fields to capture local geometry and physical size of an object. This strategy helps distinguishing between objects with similar geometries but different sizes. This is especially useful for enhancing volumetric ConvNets and classifying 3D scans with insufficient surface details. Experimental results on CAD and real-world scan datasets showed that our technique outperforms previous approaches.


Using ultra-low parasitic hybrid packaging method to reduce high frequency EMI noise for SiC power module

In order to reduce high frequency electromagnetic interference (EMI) noise for SiC devices, this paper presents a hybrid structure SiC power module with low parasitic inductance and low ground capacitance that can suppress EMI efficiently. To certify the improvement on EMI, two buck converters, using the same power loop and gate drive, are built for experiment. Two converters use the proposed power module and TO-247 packaged commercial devices respectively. The experimental results show that low parasitic inductance can increase switching speed considerably while have only slight impact on EMI. And low parasitic ground capacitance can decrease the ground current effectively. Considering soft switching can also reduce EMI, this paper further compares EMI noise of two converters under two conditions: continuous current modulation (CCM) and triangle current modulation (TCM). The result shows that the proposed power module can suppress the common mode (CM) noise effectively at both situations. Furthermore, this paper verifies the noise mode transformation from differential mode (DM) EMI to CM EMI.


MVR: An Architecture for Computation Offloading in Mobile Edge Computing

As communication and sensing capabilities of mobile devices increase, mobile applications are becoming increasingly complex. The ability of computation offloading, which is one of the main features of mobile edge computing gains relevance as a technique to improve the battery lifetime of mobile devices and increase the performance of applications. In this paper, we describe the offloading system model and present an innovative architecture, called "MVR", contributing to computation offloading in mobile edge computing.


A Better Method to Analyze Blockchain Consistency

The celebrated Nakamoto consensus protocol [16] ushered in several new consensus applications including cryptocurrencies. A few recent works [7, 17] have analyzed important properties of blockchains, including most significantly, consistency, which is a guarantee that all honest parties output the same sequence of blocks throughout the execution of the protocol. To establish consistency, the prior analysis of Pass, Seeman and Shelat [17] required a careful counting of certain combinatorial events that was difficult to apply to variations of Nakamoto. The work of Garay, Kiayas, and Leonardas [7] provides another method of analyzing the blockchain under the simplifying assumption that the network was synchronous. The contribution of this paper is the development of a simple Markov-chain based method for analyzing consistency properties of blockchain protocols. The method includes a formal way of stating strong concentration bounds as well as easy ways to concretely compute the bounds. We use our new method to answer a number of basic questions about consistency of blockchains: Our new analysis provides a tighter guarantee on the consistency property of Nakamoto's protocol, including for parameter regimes which [17] could not consider; We analyze a family of delaying attacks first presented in [17], and extend them to other protocols; We analyze how long a participant should wait before considering a high-value transaction "confirmed"; We analyze the consistency of CliqueChain, a variation of the Chainweb [14] system; We provide the first rigorous consistency analysis of GHOST [20] and also analyze a folklore "balancing"-attack. In each case, we use our framework to experimentally analyze the consensus bounds for various network delay parameters and adversarial computing percentages. We hope our techniques enable authors of future blockchain proposals to provide a more rigorous analysis of their schemes.


Cloaking Malware with the Trusted Platform Module

The Trusted Platform Module (TPM) is commonly thought of as hardware that can increase platform security. However, it can also be used for malicious purposes. The TPM, along with other hardware, can implement acloaked computation , whose memory state cannot be observed by any other software, including the operating system and hypervisor. We show that malware can use cloaked computations to hide essential secrets (like the target of an attack) from a malware analyst. We describe and implement a protocol that establishes an encryption key under control of the TPM that can only be used by a specific infection program. An infected host then proves the legitimacy of this key to a remote malware distribution platform, and receives and executes an encrypted payload in a way that prevents software visibility of the decrypted payload. We detail how malware can benefit from cloaked computations and discuss defenses against our protocol. Hardening legitimate uses of the TPM against attack improves the resilience of our malware, creating a Catch-22 for secure computing technology.


Question Generation Shared Task and Evaluation Challenge - Status Report

The First Shared Task Evaluation Challenge on Question Generation took place in 2010 as part of the 3 workshop on Question Generation. The campaign included two tasks: Question Generation from Sentences and Question Generation from Paragraphs. This status report briefly summarizes the motivation, tasks and results. Lessons learned relevant to future QG-STECs are also offered.


Sensor observation streams within cloud-based IoT platforms: Challenges and directions

Observation streams can be considered as a special case of data streams produced by sensors. With the growth of the Internet of Things (IoT), more and more connected sensors will produce unbounded observation streams. In order to bridge the gap between sensors and observation consumers, we have witnessed the design and the development of Cloud-based IoT platforms. Such systems raise new research challenges, in particular regarding observation collection, processing and consumption. These new research challenges are related to observation streams and should be addressed from the implementation phase by developers to build platforms able to meet other non-functional requirements later. Unlike existing surveys, this paper is intended for developers that would like to design and implement a Cloud-based IoT platform capable of handling sensor observation streams. It provides a comprehensive way to understand main observation-related challenges, as well as non-functional requirements of IoT platforms such as platform adaptation, scalability and availability. Last but not the least, it gives recommendations and compares some relevant open-source software that can speed up the development process.


The Effects of Perceived Corporate Social Responsibility on Employee Attitudes

We explore the impact on employee attitudes of their perceptions of how others outside the organization are treated (i.e., corporate social responsibility) above and beyond the impact of how employees are directly treated by the organization. Results of a study of 827 employees in eighteen organizations show that employee perceptions of corporate social responsibility (CSR) are positively related to (a) organizational commitment with the relationship being partially mediated by work meaningfulness and perceived organizational support (POS) and (b) job satisfaction with work meaningfulness partially mediating the relationship but not POS. Moreover, in order to address limited micro-level research in CSR, we develop a measure of employee perceptions of CSR through four pilot studies. Employing a bifactor model, we find that social responsibility has an additional effect on employee attitudes beyond environmental responsibility, which we posit is due to the relational component of social responsibility (e.g., relationships with community).


Steady-State Analysis of the Series Resonant Converter

The series resonant converter is analyzed in steady state, and for constant switching frequency the output current and voltage characteristics are found to be ellipses. The converter operating point can then be easily obtained by superimposing a load line on these elliptical characteristics. Peak resonant capacitor voltage and inductor current are also plotted in the output plane and are dependent to first order only on output current. When peak voltage and current are plotted in this manner, the dependence of component stresses on operating point is clearly revealed. The output characteristics are modified to include the effect of transistor and diode voltage drops, and experimental verification is presented.


CSIFT based locality-constrained linear coding for image classification

In the past decade, SIFT descriptor has been witnessed as one of the most robust local invariant feature descriptors and widely used in various vision tasks. Most traditional image-classification systems depend on the gray-based SIFT descriptors, which only analyze the gray level variations of the images. Misclassification may happen since their color contents are ignored. In this article, we concentrate on improving the performance of existing image-classification algorithms by adding color information. To achieve this purpose, different kinds of colored SIFT descriptors are introduced and implemented. locality-constrained linear coding (LLC), a state-of-the-art sparse coding technology, is employed to construct the image-classification system for the evaluation. Moreover, we propose a simple $$\ell _2$$ ℓ 2 -norm regularized local distance to improve the traditional LLC method. The real experiments are carried out on several benchmarks. With the enhancements to color SIFT and $$\ell _2$$ ℓ 2 -norm regularization, the proposed image-classification system obtains approximately $$2\,\%$$ 2 % improvement of classification accuracy on the Caltech-101 dataset and approximately $$5\,\%$$ 5 % improvement of classification accuracy on the Caltech-256 dataset.


GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wild

In this work, we introduce a large high-diversity database for generic object tracking, called GOT-10k. GOT-10k is backboned by the semantic hierarchy of WordNet [1]. It populates a majority of 563 object classes and 87 motion patterns in real-world, resulting in a scale of over 10 thousand video segments and 1.5 million bounding boxes. To our knowledge, GOT-10k is by far the richest motion trajectory dataset, and its coverage of object classes is more than a magnitude wider than similar scale counterparts [20], [23]. By publishing GOT-10k, we hope to encourage the development of generic purposed trackers that work for a wide range of moving objects and under diverse real-world scenarios. To promote generalization and avoid the evaluation results biased to seen classes, we follow the one-shot principle [35] in dataset splitting where training and testing classes are zero-overlapped. We also carry out a series of analytical experiments to select a compact while highly representative testing subset – it embodies 84 object classes and 32 motion patterns with only 180 video segments, allowing for efficient evaluation. Finally, we train and evaluate a number of representative trackers on GOT-10k and analyze their performance. The evaluation results suggest that tracking in real-world unconstrained videos is far from being solved, and only 40% of frames are successfully tracked using top ranking trackers. The database and toolkits are publicly available at https://got-10k.github.io.


Thermal Facial Analysis for Deception Detection

Thermal imaging technology can be used to detect stress levels in humans based on the radiated heat from their face. In this paper, we use thermal imaging to monitor the periorbital region's thermal variations and test whether it can offer a discriminative signature for detecting deception. We start by presenting an overview on automated deception detection and propose a novel methodology, which we validate experimentally on 492 thermal responses (249 lies and 243 truths) extracted from 25 participants. The novelty of this paper lies in scoring a larger number of questions per subject, emphasizing a within-person approach for learning from data, proposing a framework for validating the decision making process, and correct evaluation of the generalization performance. A $k$ -nearest neighbor classifier was used to classify the thermal responses using different strategies for data representation. We report an 87% ability to predict the lie/truth responses based on a within-person methodology and fivefold cross validation. Our results also show that the between-person approach for modeling deception does not generalize very well across the training data.


Deception detection using a multimodal approach

In this paper we address the automatic identification of deceit by using a multimodal approach. We collect deceptive and truthful responses using a multimodal setting where we acquire data using a microphone, a thermal camera, as well as physiological sensors. Among all available modalities, we focus on three modalities namely, language use, physiological response, and thermal sensing. To our knowledge, this is the first work to integrate these specific modalities to detect deceit. Several experiments are carried out in which we first select representative features for each modality, and then we analyze joint models that integrate several modalities. The experimental results show that the combination of features from different modalities significantly improves the detection of deceptive behaviors as compared to the use of one modality at a time. Moreover, the use of non-contact modalities proved to be comparable with and sometimes better than existing contact-based methods. The proposed method increases the efficiency of detecting deceit by avoiding human involvement in an attempt to move towards a completely automated non-invasive deception detection process.


Multiple View Geometry in Computer Vision


The quintessence of the diagnosis in traditional Chinese medicine is syndrome differentiation and treatment. Syndrome differentiation consists of four methods: observing, hearing as well as smelling, asking, and touching. The examination of the observing is the most important procedure in the method of "tongue." In recent years, numerous medical studies have identified the close relations between sublingual veins and human organs. Therefore, sublingual pathological symptoms, as well as demographical information of patients, imply pathological changes in the organs, and early diagnosis is beneficial for early treatment. However, the diagnosis of sublingual pathological symptoms is usually influenced by the doctor's subjective interpretation, experience, and environmental factors. The results can easily be limited by subjective factors such as knowledge, experience, mentality, diagnostic techniques, color perception and interpretation. Different doctors may make different judgments on the same tongue, presenting less than desirable repeatability. Therefore, assisting doctors' diagnoses with scientific methods and standardizing the differentiating process to obtain reliable diagnoses and enhance the clinical applicability of Chinese medicine is an important issue. In its wake, this study aims to construct an Automatic Sublingual Vein Feature Extraction System based on image processing technologies to allow objective and quantified computer readings. The extraction of sublingual vein features mainly captures the back of the tongue and extract the sublingual vein area for feature expression analysis. Firstly, the patient's back of the tongue is photographed and color-graded to compensate for color distortion, and then the tongue-back area is extracted. This study extracts tongue-back imagery by analyzing the RGB color expression of the back of the tongue, lips, teeth and skin, translating it into the HSI color space easily perceived by the human eye, along with skin area removal, rectangle detection, teeth area removal, black area removal and control point detection. The captured tongue-back image goes through histogram equalization and hue shift to enhance color contrast. Sublingual veins are extracted through analyzing RGB color component shift, hues, saturation and brightness. Then the sublingual vein color information and positioning are used to differentiate hues, lengths and branches. Thinning analysis is used to determine the presence of varicose veins. At the same time, the surrounding features of sublingual veins, such as columnar vein, bubbly vein, petechiae and bloodshot, are extracted. The information regarding features and lingual vein conditions are integrated and analyzed for doctors' clinical reference. This study utilizes 199 lingual images for statistic testing and three lingual diagnostic experts in Chinese medicine for lingual reading. The accuracy for the extractions are: tongue back 86%, sublingual vein 80%, varicose veins 90%, branches 87%, and the accuracy rates for columnar veins and bubbly veins are 87%, 88% and 73% respectively.


Robust camera calibration of soccer video using genetic algorithm

This paper proposes an exact and robust genetic algorithm-based method for the calibration of soccer camera. According to the FIFA official soccer field layout we defined a field model for the soccer court. Camera calibration is done through finding the homography transform between the field model and the input frame which is followed with DLT decomposition. The intersection of lines in the field model and input frame form feature points and by means of a genetic algorithm, we found the correspondence between those points. Our algorithm was applied to a couple of soccer video frames and the achieved results demonstrate its robustness, accuracy and high performance.


You are what you include: large-scale evaluation of remote javascript inclusions

JavaScript is used by web developers to enhance the interactivity of their sites, offload work to the users' browsers and improve their sites' responsiveness and user-friendliness, making web pages feel and behave like traditional desktop applications. An important feature of JavaScript, is the ability to combine multiple libraries from local and remote sources into the same page, under the same namespace. While this enables the creation of more advanced web applications, it also allows for a malicious JavaScript provider to steal data from other scripts and from the page itself. Today, when developers include remote JavaScript libraries, they trust that the remote providers will not abuse the power bestowed upon them.
 In this paper, we report on a large-scale crawl of more than three million pages of the top 10,000 Alexa sites, and identify the trust relationships of these sites with their library providers. We show the evolution of JavaScript inclusions over time and develop a set of metrics in order to assess the maintenance-quality of each JavaScript provider, showing that in some cases, top Internet sites trust remote providers that could be successfully compromised by determined attackers and subsequently serve malicious JavaScript. In this process, we identify four, previously unknown, types of vulnerabilities that attackers could use to attack popular web sites. Lastly, we review some proposed ways of protecting a web application from malicious remote scripts and show that some of them may not be as effective as previously thought.


Memory engram storage and retrieval

A great deal of experimental investment is directed towards questions regarding the mechanisms of memory storage. Such studies have traditionally been restricted to investigation of the anatomical structures, physiological processes, and molecular pathways necessary for the capacity of memory storage, and have avoided the question of how individual memories are stored in the brain. Memory engram technology allows the labeling and subsequent manipulation of components of specific memory engrams in particular brain regions, and it has been established that cell ensembles labeled by this method are both sufficient and necessary for memory recall. Recent research has employed this technology to probe fundamental questions of memory consolidation, differentiating between mechanisms of memory retrieval from the true neurobiology of memory storage.


Dimension Reduction and Data Visualization Using Neural Networks

The problem of visual presentation of multidimensional data is discussed. The projection methods for dimension reduction are reviewed. The chapter deals with the artificial neural networks that may be used for reducing dimension and data visualization, too. The stress is put on combining the selforganizing map (SOM) and Sammon mapping and on the neural network for Sammon’s mapping SAMANN. Large scale applications are discussed: environmental data analysis, statistical analysis of curricula, comparison of schools, analysis of the economic and social conditions of countries, analysis of data on the fundus of eyes and analysis of physiological data on men’s health.


Stochastic Language Generation in Dialogue using Recurrent Neural Networks with Convolutional Sentence Reranking

The natural language generation (NLG) component of a spoken dialogue system (SDS) usually needs a substantial amount of handcrafting or a well-labeled dataset to be trained on. These limitations add significantly to development costs and make cross-domain, multi-lingual dialogue systems intractable. Moreover, human languages are context-aware. The most natural response should be directly learned from data rather than depending on predefined syntaxes or rules. This paper presents a statistical language generator based on a joint recurrent and convolutional neural network structure which can be trained on dialogue act-utterance pairs without any semantic alignments or predefined grammar trees. Objective metrics suggest that this new model outperforms previous methods under the same experimental conditions. Results of an evaluation by human judges indicate that it produces not only high quality but linguistically varied utterances which are preferred compared to n-gram and rule-based systems.


Adaptive bootstrapping of recommender systems using decision trees

Recommender systems perform much better on users for which they have more information. This gives rise to a problem of satisfying users new to a system. The problem is even more acute considering that some of these hard to profile new users judge the unfamiliar system by its ability to immediately provide them with satisfying recommendations, and may quickly abandon the system when disappointed. Rapid profiling of new users by a recommender system is often achieved through a bootstrapping process - a kind of an initial interview - that elicits users to provide their opinions on certain carefully chosen items or categories. The elicitation process becomes particularly effective when adapted to users' responses, making best use of users' time by dynamically modifying the questions to improve the evolving profile. In particular, we advocate a specialized version of decision trees as the most appropriate tool for this task. We detail an efficient tree learning algorithm, specifically tailored to the unique properties of the problem. Several extensions to the tree construction are also introduced, which enhance the efficiency and utility of the method. We implemented our methods within a movie recommendation service. The experimental study delivered encouraging results, with the tree-based bootstrapping process significantly outperforming previous approaches.


Segmenting Unknown 3D Objects from Real Depth Images using Mask R-CNN Trained on Synthetic Point Clouds

The ability to segment unknown objects in depth images has potential to enhance robot skills in grasping and object tracking. Recent computer vision research has demonstrated that Mask R-CNN can be trained to segment specific categories of objects in RGB images when massive handlabeled datasets are available. As generating these datasets is time-consuming, we instead train with synthetic depth images. Many robots now use depth sensors, and recent results suggest training on synthetic depth data can generalize well to the real world. We present a method for automated dataset generation and rapidly generate a training dataset of 50k depth images and 320k object masks synthetically using simulated scenes of 3D CAD models. We train a variant of Mask R-CNN on the generated dataset to perform category-agnostic instance segmentation without hand-labeled data. We evaluate the trained network, which we refer to as Synthetic Depth (SD) Mask R-CNN, on a set of real, high-resolution images of challenging, denselycluttered bins containing objects with highly-varied geometry. SD Mask R-CNN outperforms point cloud clustering baselines by an absolute 15% in Average Precision and 20% in Average Recall, and achieves performance levels similar to a Mask RCNN trained on a massive, hand-labeled RGB dataset and fine-tuned on real images from the experimental setup. The network also generalizes well to a lower-resolution depth sensor. We deploy the model in an instance-specific grasping pipeline to demonstrate its usefulness in a robotics application. Code, the synthetic training dataset, and supplementary material are available at https://bit.ly/2letCuE.


Disruption of Large-Scale Brain Systems in Advanced Aging

Cognitive decline is commonly observed in advanced aging even in the absence of disease. Here we explore the possibility that normal aging is accompanied by disruptive alterations in the coordination of large-scale brain systems that support high-level cognition. In 93 adults aged 18 to 93, we demonstrate that aging is characterized by marked reductions in normally present functional correlations within two higher-order brain systems. Anterior to posterior components within the default network were most severely disrupted with age. Furthermore, correlation reductions were severe in older adults free from Alzheimer's disease (AD) pathology as determined by amyloid imaging, suggesting that functional disruptions were not the result of AD. Instead, reduced correlations were associated with disruptions in white matter integrity and poor cognitive performance across a range of domains. These results suggest that cognitive decline in normal aging arises from functional disruption in the coordination of large-scale brain systems that support cognition.


Use of physiological signals to predict cybersickness

http://dx.doi.org/10.1016/j.displa.2016.07.002 0141-9382/ 2016 Elsevier B.V. All rights reserved. q This paper was recommended for publication by Richard H.Y. So. ⇑ Corresponding author at: University of California, Irvine, 2201 Social & Behavioral Sciences Gateway Building, Irvine, CA 92697-5100, USA. E-mail addresses: mdenniso@uci.edu (M.S. Dennison), awisti@uci.edu (A.Z. Wisti), mdzmura@uci.edu (M. D’Zmura). Mark S. Dennison ⇑, A. Zachary Wisti, Michael D’Zmura


Secure Fine-Grained Access Control and Data Sharing for Dynamic Groups in the Cloud

Cloud computing is an emerging computing paradigm that enables users to store their data in a cloud server to enjoy scalable and on-demand services. Nevertheless, it also brings many security issues, since cloud service providers (CSPs) are not in the same trusted domain as users. To protect data privacy against untrusted CSPs, existing solutions apply cryptographic methods (e.g., encryption mechanisms) and provide decryption keys only to authorized users. However, sharing cloud data among authorized users at a fine-grained level is still a challenging issue, especially when dealing with dynamic user groups. In this paper, we propose a secure and efficient fine-grained access control and data sharing scheme for dynamic user groups by: 1) defining and enforcing access policies based on the attributes of the data; 2) permitting the key generation center to efficiently update user credentials for dynamic user groups; and 3) allowing some expensive computation tasks to be performed by untrusted CSPs without requiring any delegation key. Specifically, we first design an efficient revocable attribute-based encryption (ABE) scheme with the property of ciphertext delegation by exploiting and uniquely combining techniques of identity-based encryption, ABE, subset-cover framework, and ciphertext encoding mechanism. We then present a fine-grained access control and data sharing system for on-demand services with dynamic user groups in the cloud. The experimental data show that our proposed scheme is more efficient and scalable than the state-of-the-art solution.


Machine Learning-Based Software Quality Prediction Models: State of the Art

Quantification of parameters affecting the software quality is one of the important aspects of research in the field of software engineering. In this paper, we present a comprehensive literature survey of prominent quality molding studies. The survey addresses two views: (1) quantification of parameters affecting the software quality; and (2) using machine learning techniques in predicting the software quality. The paper concludes that, model transparency is a common shortcoming to all the surveyed studies.


Design and Implementation of Energy Management System With Fuzzy Control for DC Microgrid Systems

This paper presents the design and implementation of an energy management system (EMS) with fuzzy control for a dc microgrid system. Modeling, analysis, and control of distributed power sources and energy storage devices with MATLAB/Simulink are proposed, and the integrated monitoring EMS is implemented with LabVIEW. To improve the life cycle of the battery, fuzzy control manages the desired state of charge. The RS-485/ZigBee network has been designed to control the operating mode and to monitor the values of all subsystems in the dc microgrid system.


3-Level high power converter with press pack IGBT

Converteam recently has introduced the MV7000 series of medium voltage drives, designed with Press Pack IGBT (PPI) in 3-level NPC topology in the power range from 4 MW to 32 MW [3]. PPI combines the benefits of disk type device technology with state-of-the-art semiconductor technology. This paper explains relevant technical features and design issues related to MV7000 including direct series connection of devices.


Bayesian Unsupervised Topic Segmentation

This paper describes a novel Bayesian approach to unsupervised topic segmentation. Unsupervised systems for this task are driven by lexical cohesion: the tendency of wellformed segments to induce a compact and consistent lexical distribution. We show that lexical cohesion can be placed in a Bayesian context by modeling the words in each topic segment as draws from a multinomial language model associated with the segment; maximizing the observation likelihood in such a model yields a lexically-cohesive segmentation. This contrasts with previous approaches, which relied on hand-crafted cohesion metrics. The Bayesian framework provides a principled way to incorporate additional features such as cue phrases, a powerful indicator of discourse structure that has not been previously used in unsupervised segmentation systems. Our model yields consistent improvements over an array of state-of-the-art systems on both text and speech datasets. We also show that both an entropy-based analysis and a well-known previous technique can be derived as special cases of the Bayesian framework.1


Block diagonalization for multi-user MIMO with other-cell interference

Block diagonalization is one approach for linear preceding in the multiple-input multiple-output broadcast channel that sends multiple interference free data streams to different users in the same cell. Unfortunately, block diagonalization neglects other-cell interference (OCI), which limits the performance of users at the edge of the cell. This paper presents an OCI-aware enhancement to block diagonalization that uses a whitening filter for interference suppression at the receiver and a novel precoder using the interference-plus-noise covariance matrix for each user at the transmitter. For complex Gaussian matrix channels, the asymptotic sum rate of the proposed system is analyzed under a large antenna assumption for isotropic inputs and compared to conventional block diagonalization. The capacity loss due to OCI is quantified in terms of results from single-user MIMO capacity. Several numerical examples compare achievable sum rates, the proposed asymptotic rates, and the capacity loss, in low and high interference regimes.


Design and deployment of Aqua monitoring system using wireless sensor networks and IAR-Kick

In Aquaculture, the yields (shrimp, fish etc.) depend on the water characteristics of the aquaculture pond. For maximizing fish yields, the parameters which are to be kept at certain optimal levels in water. The parameters can vary a lot during the period of a day and can rapidly change depending on the external environmental conditions. Hence it is necessary to monitor these parameters with high frequency . Wireless sensor networks are used to monitor aqua farms for relevant parameters this system consists of two modules which are transmitter station and receiver station. The data transmits through GSM to the Database at receiver station. The graphical user interface was designed, to convey the data in the form of a message to the farmers in their respective local languages to their Mobile Phones and alerts them in unhygienic environmental conditions, in order to take suitable actions. Keywords; aquaculture; wireless sensor networks; IAR-Kick;pH;


A Comparison over Focused Web Crawling Strategies

In this paper we review and compare focused crawling strategies, studied and published during the past decade. Despite giant leaps in communication, storage and computing power in recent years, crawlers have always struggled to keep up with Web content generation and modification. Focused crawlers attempt to i) accelerate the crawling process, ii) maximize the harvest of high quality pages, iii) assign appropriate credit to different documents along a crawling path, such that short-term gains are not pursued at the expense of less obvious paths that ultimately yield larger sets of valuable pages. Beyond the review and comparison of the focused crawling strategies, we additionally propose additions to the corresponding architectures for further research.


Penetration Testing with Improved Input Vector Identification

Penetration testing is widely used to help ensure the security of web applications. It discovers vulnerabilities by simulating attacks from malicious users on a target application. Identifying the input vectors of a web application and checking the results of an attack are important parts of penetration testing, as they indicate where an attack could be introduced and whether an attempted attack was successful. Current techniques for identifying input vectors and checking attack results are typically ad-hoc and incomplete, which can cause parts of an application to be untested and leave vulnerabilities undiscovered. In this paper, we propose a new approach to penetration testing that addresses these limitations by leveraging two recently-developed analysis techniques. The first is used to identify a web application's possible input vectors, and the second is used to automatically check whether an attack resulted in an injection. To empirically evaluate our approach, we compare it against a state-of-the-art, alternative technique. Our results show that our approach performs a more thorough penetration testing and leads to the discovery of more vulnerabilities.


Crawling AJAX by Inferring User Interface State Changes

AJAX is a very promising approach for improving rich interactivity and responsiveness of web applications. At the same time, AJAX techniques shatter the metaphor of a web "page" upon which general search crawlers are based. This paper describes a novel technique for crawling AJAX applications through dynamic analysis and reconstruction of user interface state changes. Our method dynamically infers a state-flow graph modeling the various navigation paths and states within an AJAX application. This reconstructed model can be used to generate linked static pages. These pages could be used to expose AJAX sites to general search engines. Moreover, we believe that the crawling techniques that are part of our solution have other applications, such as within general search engines, accessibility improvements, or in automatically exercising all user interface elements and conducting state-based testing of AJAX applications. We present our open source tool called CRAWLJAX which implements the concepts discussed in this paper. Additionally, we report a case study in which we apply our approach to a number of representative AJAX applications and elaborate on the obtained results.


Flow-Sensitive Type Qualifiers

We present a system for extending standard type systems with flow-sensitive type qualifiers. Users annotate their programs with type qualifiers, and inference checks that the annotations are correct. In our system only the type qualifiers are modeled flow-sensitively---the underlying standard types are unchanged, which allows us to obtain an efficient constraint-based inference algorithm that integrates flow-insensitive alias analysis, effect inference, and ideas from linear type systems to support strong updates. We demonstrate the usefulness of flow-sensitive type qualifiers by finding a number of new locking bugs in the Linux kernel.


A Comparison of Push and Pull Techniques for AJAX

AJAX applications are designed to have high user interactivity and low user-perceived latency. Real-time dynamic Web data such as news headlines, stock tickers, and auction updates need to be propagated to the users as soon as possible. However, AJAX still suffers from the limitations of the Web's request/response architecture which prevents servers from pushing real-time dynamic web data. Such applications usually use a pull style to obtain the latest updates, where the client actively requests the changes based on a predefined interval. It is possible to overcome this limitation by adopting a push style of interaction where the server broadcasts data when a change occurs on the server side. Both these options have their own trade-offs. This paper explores the fundamental limits of browser-based applications and analyzes push solutions for AJAX technology. It also shows the results of an empirical study comparing push and pull.


Tractable Queries for Lightweight Description Logics

It is a classic result in database theory that conjunctive query (CQ) answering, which is NP-complete in general, is feasible in polynomial time when restricted to acyclic queries. Subsequent results identified more general structural properties of CQs (like bounded treewidth) which ensure tractable query evaluation. In this paper, we lift these tractability results to knowledge bases formulated in the lightweight description logics DL-Lite and ELH. The proof exploits known properties of query matches in these logics and involves a querydependent modification of the data. To obtain a more practical approach, we propose a concrete polynomial-time algorithm for answering acyclic CQs based on rewriting queries into datalog programs. A preliminary evaluation suggests the interest of our approach for handling large acyclic CQs.


Twitter Sentiment Classification on Sanders Data using Hybrid Approach

Sentiment analysis is very perplexing and massive issue in the field of social data mining. Twitter is one of the mostly used social media where people discuss on various issues in a dense way. The tweets about a particular topic give peoples’ views, opinions, orientations, inclinations about that topic. In this work, we have used pre-labeled (with positive, negative and neutral opinion) tweets on particular topics for sentiment classification. Opinion score of each tweet is calculated using feature vectors. These opinion score is used to classify the tweets into positive, negative and neutral classes. Then using various machine learning classifiers the accuracy of predicted classification with respect to actual classification is being calculated and compared using supervised learning model. Along with building a sentiment classification model, analysis of tweets is being carried out by visualizing the wordcloud of tweets using R.


Multiple Kernel Fuzzy Clustering

While fuzzy c-means is a popular soft-clustering method, its effectiveness is largely limited to spherical clusters. By applying kernel tricks, the kernel fuzzy c-means algorithm attempts to address this problem by mapping data with nonlinear relationships to appropriate feature spaces. Kernel combination, or selection, is crucial for effective kernel clustering. Unfortunately, for most applications, it is uneasy to find the right combination. We propose a multiple kernel fuzzy c-means (MKFC) algorithm that extends the fuzzy c-means algorithm with a multiple kernel-learning setting. By incorporating multiple kernels and automatically adjusting the kernel weights, MKFC is more immune to ineffective kernels and irrelevant features. This makes the choice of kernels less crucial. In addition, we show multiple kernel k-means to be a special case of MKFC. Experiments on both synthetic and real-world data demonstrate the effectiveness of the proposed MKFC algorithm.


Helicopter induced propeller injuries.

Following routine maintenance a military helicopter was returned to service. Due to negligence some hoses had not been connected correctly. The wrongly connected hoses resulted in a malfunction, causing the rotor blades of the helicopter to smash through the windows of the helicopter cockpit. Both the pilot and the co-pilot were hit by the rotor blades and decapitated. Other passengers in the helicopter were injured. Due to vibrations the helicopter fell to the ground and its tail partly broke away (Figs. 1, 2). The bodies and body parts of the pilot and co-pilot were found on the ground outside the helicopter cockpit.


Sonoliards: Rendering Audible Sound Spots by Reflecting the Ultrasound Beams

This paper proposes a dynamic acoustic field generation system for a spot audio towards particular person indoors. Spot audio techniques have been explored by generating the ultrasound beams toward the target person in certain area however everyone in this area can hear the sound. Our system recognizes the positions of each person indoor using motion capture and 3D model data of the room. After that we control direction of parametric speaker in real-time so that sound reach only particular person by calculating the reflection of sound on surfaces such as wall and ceiling. We calculate direction of parametric speaker using a beam tracing method. We present generating methods of dynamic acoustic field in our system and conducted the experiments on human factor to evaluate performance of proposed system.


Emotions , Partisanship , and Misperceptions : How Anger and Anxiety Moderate the Effect of Partisan Bias on Susceptibility to Political Misinformation

Citizens are frequently misinformed about political issues and candidates but the circumstances under which inaccurate beliefs emerge are not fully understood. This experimental study demonstrates that the independent experience of two emotions, anger and anxiety, in part determines whether citizens consider misinformation in a partisan or open-minded fashion. Anger encourages partisan, motivated evaluation of uncorrected misinformation that results in beliefs consistent with the supported political party, while anxiety at times promotes initial beliefs based less on partisanship and more on the information environment. However, exposure to corrections improves belief accuracy, regardless of emotion or partisanship. The results indicate that the unique experience of anger and anxiety can affect the accuracy of political beliefs by strengthening or attenuating the influence of partisanship.


Sensation seeking in England and America: cross-cultural, age, and sex comparisons.

This study compared the factor structure of the Sensation-Seeking Scale (SSS) in English and American samples, and a new form of the SSS, applicable to both samples, was constructed. Three of the four factors showed good crossnational and cross-sex reliability. English and American males did not differ on the total SSS score, but American females scored higher than English females. Males in both countries scored higher than females on the total SSS score and on the Thrill and Adventure-Seeking and Disinhibition subscales. Significant age declines occurred for both sexes, particularly on Thrill and Adventure Seeking and Disinhibition.


A Better Baseline for AVA

We introduce a simple baseline for action localization on the AVA dataset. The model builds upon the Faster R-CNN bounding box detection framework, adapted to operate on pure spatiotemporal features – in our case produced exclusively by an I3D model pretrained on Kinetics. This model obtains 21.9% average AP on the validation set of AVA v2.1, up from 14.5% for the best RGB spatiotemporal model used in the original AVA paper (which was pretrained on Kinetics and ImageNet), and up from 11.3% of the publicly available baseline using a ResNet101 image feature extractor, that was pretrained on ImageNet. Our final model obtains 22.8%/21.9% mAP on the val/test sets and outperforms all submissions to the AVA challenge at CVPR 2018.


Deep predictive policy training using reinforcement learning

Skilled robot task learning is best implemented by predictive action policies due to the inherent latency of sensorimotor processes. However, training such predictive policies is challenging as it involves finding a trajectory of motor activations for the full duration of the action. We propose a data-efficient deep predictive policy training (DPPT) framework with a deep neural network policy architecture which maps an image observation to a sequence of motor activations. The architecture consists of three sub-networks referred to as the perception, policy and behavior super-layers. The perception and behavior super-layers force an abstraction of visual and motor data trained with synthetic and simulated training samples, respectively. The policy super-layer is a small subnetwork with fewer parameters that maps data in-between the abstracted manifolds. It is trained for each task using methods for policy search reinforcement learning. We demonstrate the suitability of the proposed architecture and learning framework by training predictive policies for skilled object grasping and ball throwing on a PR2 robot. The effectiveness of the method is illustrated by the fact that these tasks are trained using only about 180 real robot attempts with qualitative terminal rewards.


Piriformis syndrome, diagnosis and treatment.

Piriformis syndrome (PS) is an uncommon cause of sciatica that involves buttock pain referred to the leg. Diagnosis is often difficult, and it is one of exclusion due to few validated and standardized diagnostic tests. Treatment for PS has historically focused on stretching and physical therapy modalities, with refractory patients also receiving anesthetic and corticosteroid injections into the piriformis muscle origin, belly, muscle sheath, or sciatic nerve sheath. Recently, the use of botulinum toxin (BTX) to treat PS has gained popularity. Its use is aimed at relieving sciatic nerve compression and inherent muscle pain from a tight piriformis. BTX is being used increasingly for myofascial pain syndromes, and some studies have demonstrated superior efficacy to corticosteroid injection. The success of BTX in treating PS supports the prevailing pathoanatomic etiology of the condition and suggests a promising future for BTX in the treatment of other myofascial pain syndromes.


A high-level 3D visualization API for Java and ImageJ

Current imaging methods such as Magnetic Resonance Imaging (MRI), Confocal microscopy, Electron Microscopy (EM) or Selective Plane Illumination Microscopy (SPIM) yield three-dimensional (3D) data sets in need of appropriate computational methods for their analysis. The reconstruction, segmentation and registration are best approached from the 3D representation of the data set. Here we present a platform-independent framework based on Java and Java 3D for accelerated rendering of biological images. Our framework is seamlessly integrated into ImageJ, a free image processing package with a vast collection of community-developed biological image analysis tools. Our framework enriches the ImageJ software libraries with methods that greatly reduce the complexity of developing image analysis tools in an interactive 3D visualization environment. In particular, we provide high-level access to volume rendering, volume editing, surface extraction, and image annotation. The ability to rely on a library that removes the low-level details enables concentrating software development efforts on the algorithm implementation parts. Our framework enables biomedical image software development to be built with 3D visualization capabilities with very little effort. We offer the source code and convenient binary packages along with extensive documentation at http://3dviewer.neurofly.de .


Psychological first-aid: a practical aide-memoire.

Despite advances made in recent years in medical first aid, psychiatric intervention, survival training and equipment design, many people still perish quickly during and immediately following a disastrous event. In this study, individuals and groups of survivors of life-threatening events were debriefed and the behavior of those who coped well during such a threat to life were compared with those who did not. The behaviors of those who coped well were distilled into a set of principles for psychological first aid; that is, a series of simple actions for use within a disaster which serves to recover victims to functional behavior as quickly as possible, thus increasing their chance for survival. These principles of psychological first aid have recently been introduced into basic first aid and survival training courses for both military and civilian units.


Iterative Thresholding Algorithm for Sparse Inverse Covariance Estimation

The `1-regularized maximum likelihood estimation problem has recently become a topic of great interest within the machine learning, statistics, and optimization communities as a method for producing sparse inverse covariance estimators. In this paper, a proximal gradient method (G-ISTA) for performing `1-regularized covariance matrix estimation is presented. Although numerous algorithms have been proposed for solving this problem, this simple proximal gradient method is found to have attractive theoretical and numerical properties. G-ISTA has a linear rate of convergence, resulting in an O(log ε) iteration complexity to reach a tolerance of ε. This paper gives eigenvalue bounds for the G-ISTA iterates, providing a closed-form linear convergence rate. The rate is shown to be closely related to the condition number of the optimal point. Numerical convergence results and timing comparisons for the proposed method are presented. G-ISTA is shown to perform very well, especially when the optimal point is well-conditioned.


A gene-coexpression network for global discovery of conserved genetic modules.

To elucidate gene function on a global scale, we identified pairs of genes that are coexpressed over 3182 DNA microarrays from humans, flies, worms, and yeast. We found 22,163 such coexpression relationships, each of which has been conserved across evolution. This conservation implies that the coexpression of these gene pairs confers a selective advantage and therefore that these genes are functionally related. Many of these relationships provide strong evidence for the involvement of new genes in core biological functions such as the cell cycle, secretion, and protein expression. We experimentally confirmed the predictions implied by some of these links and identified cell proliferation functions for several genes. By assembling these links into a gene-coexpression network, we found several components that were animal-specific as well as interrelationships between newly evolved and ancient modules.


Sparse inverse covariance estimation with the graphical lasso.

We consider the problem of estimating sparse graphs by a lasso penalty applied to the inverse covariance matrix. Using a coordinate descent procedure for the lasso, we develop a simple algorithm--the graphical lasso--that is remarkably fast: It solves a 1000-node problem ( approximately 500,000 parameters) in at most a minute and is 30-4000 times faster than competing methods. It also provides a conceptual link between the exact problem and the approximation suggested by Meinshausen and Bühlmann (2006). We illustrate the method on some cell-signaling data from proteomics.


Anatomical and biomechanical mechanisms of subacromial impingement syndrome.

Subacromial impingement syndrome is the most common disorder of the shoulder, resulting in functional loss and disability in the patients that it affects. This musculoskeletal disorder affects the structures of the subacromial space, which are the tendons of the rotator cuff and the subacromial bursa. Subacromial impingement syndrome appears to result from a variety of factors. Evidence exists to support the presence of the anatomical factors of inflammation of the tendons and bursa, degeneration of the tendons, weak or dysfunctional rotator cuff musculature, weak or dysfunctional scapular musculature, posterior glenohumeral capsule tightness, postural dysfunctions of the spinal column and scapula and bony or soft tissue abnormalities of the borders of the subacromial outlet. These entities may lead to or cause dysfunctional glenohumeral and scapulothoracic movement patterns. These various mechanisms, singularly or in combination may cause subacromial impingement syndrome.


Artificial Intelligence in Education

In a life-long learning society, learning scenarios can be categorized into five types, which are “classroom learning”, “self-learning”, “inquiry learning”, “learning in doing” and “learning in working”. From a life-wide learning perspective, all these scenarios play vital roles for personal development. How to recognize these learning scenarios (including learning time, learning place, learning peers, learning activities, etc.) and provide the matched learning ways (including learning path, resources, peers, teachers, etc.) are the basis for smart learning environments, however few research could be found to address this problem. In order to solve this problem, we propose a conceptual framework of smart learning engine that is the core of integrated, interactive and intelligent (i) learning environments. The smart learning engine consists of three main functions. The first function is to identify data from student, teacher, subject area, and the environment using wireless sensors, the established learning resources and scenarios, and a learner modeling technology. The acquired data includes prior knowledge, theme-based context, leaner/teacher profile, physical environments, etc. The second function is to compute the best ways of learning based on the learning scenario and learning preference. In detail, this function includes modeling learner’s affective data, building knowledge structure, optimizing knowledge module, and connecting learners. The third function is to deploy personalized and adaptive strategy, resources and tools for students and teachers based on the computed results in the second function. Deploy interactive strategies, learning paces, learning resources, and delivery approaches are the core elements for this function.


Evolutionary reinforcement learning in FX order book and order flow analysis

As macroeconomic fundamentals based modelling of FX timeseries have been shown not to fit the empirical evidence at horizons of less than one year, interest has moved towards microstructure-based approaches. Order flow data has recently been receiving an increasing amount of attention in equity market analyses and thus increasingly in foreign exchange as well. In this paper, order flow data is coupled with order book derived indicators and we explore whether pattern recognition techniques derived from computational learning can be applied to successfully infer trading strategies on the underlying timeseries. Due to the limited amount of data available the results are preliminary. However, the approach demonstrates promise and it is shown that using order flow and order book data is usually superior to trading on technical signals alone.