title: Maple: simplifying SDN programming using algorithmic policies

abstract: Software-Defined Networking offers the appeal of a simple, centralized programming model for managing complex networks. However, challenges in managing low-level details, such as setting up and maintaining correct and efficient forwarding tables on distributed switches, often compromise this conceptual simplicity. In this pa- per, we present Maple, a system that simplifies SDN programming by (1) allowing a programmer to use a standard programming language to design an arbitrary, centralized algorithm, which we call an algorithmic policy, to decide the behaviors of an entire network, and (2) providing an abstraction that the programmer-defined, centralized policy runs, conceptually, "afresh" on every packet entering a network, and hence is oblivious to the challenge of translating a high-level policy into sets of rules on distributed individual switches. To implement algorithmic policies efficiently, Maple includes not only a highly-efficient multicore scheduler that can scale efficiently to controllers with 40+ cores, but more importantly a novel tracing runtime optimizer that can automatically record reusable policy decisions, offload work to switches when possible, and keep switch flow tables up-to-date by dynamically tracing the dependency of policy decisions on packet contents as well as the environment (system state). Evaluations using real HP switches show that Maple optimizer reduces HTTP connection time by a factor of 100 at high load. During simulated benchmarking, Maple scheduler, when not running the optimizer, achieves a throughput of over 20 million new flow requests per second on a single machine, with 95-percentile latency under 10 ms.


title: Des-acetyl-α-MSH and not α-MSH is the major form of α-MSH in amniotic fluid

abstract: Immunoreactive or-melanocyte stimulating hormone (IR-or-MSH)-like activity was measured by radioimmunoassay (RIA) in at term pregnancy amniotic fluid prior and after adsorption on a Sep-pak C18 cartridge. a-MSH activity was 3—4 times lower after Sep-pak purification but, unlike the levels of IR-α-MSH in the fluid analyzed in toto, increased linearly with the volume of fluid analyzed. Furthermore, fractionation by high pressure liquid chromatography (HPLC) revealed that IR-α-MSH recovered from the Sep-pak was due to several peptides rather than to a single peptide. The most abundant of them (50% of total activity) behaved like authentic des-acetyl-α-MSH. Since des-acetyl-α-MSH is also the most abundant α-MSH-like peptide in the fetal pituitary gland, the present results suggest that the fetal pituitary is a main source of des-acetyl-α-MSH in the amniotic fluid.


title: Large-scale automated synthesis of human functional neuroimaging data

abstract: A framework and web interface for the large-scale and automated synthesis of human neuroimaging data extracted from the literature is presented. It is used to generate a large database of mappings between neural and cognitive states and to address long-standing inferential problems in the neuroimaging literature.


title: Coordinated static and dynamic cache bypassing for GPUs

abstract: The massive parallel architecture enables graphics processing units (GPUs) to boost performance for a wide range of applications. Initially, GPUs only employ scratchpad memory as on-chip memory. Recently, to broaden the scope of applications that can be accelerated by GPUs, GPU vendors have used caches in conjunction with scratchpad memory as on-chip memory in the new generations of GPUs. Unfortunately, GPU caches face many performance challenges that arise due to excessive thread contention for cache resource. Cache bypassing, where memory requests can selectively bypass the cache, is one solution that can help to mitigate the cache resource contention problem. In this paper, we propose coordinated static and dynamic cache bypassing to improve application performance. At compile-time, we identify the global loads that indicate strong preferences for caching or bypassing through profiling. For the rest global loads, our dynamic cache bypassing has the flexibility to cache only a fraction of threads. In CUDA programming model, the threads are divided into work units called thread blocks. Our dynamic bypassing technique modulates the ratio of thread blocks that cache or bypass at run-time. We choose to modulate at thread block level in order to avoid the memory divergence problems. Our approach combines compile-time analysis that determines the cache or bypass preferences for global loads with run-time management that adjusts the ratio of thread blocks that cache or bypass. Our coordinated static and dynamic cache bypassing technique achieves up to 2.28X (average I.32X) performance speedup for a variety of GPU applications.


title: SentiSense: An easily scalable concept-based affective lexicon for sentiment analysis

abstract: This paper presents SentiSense, a concept-based affective lexicon. It is intended to be used in sentiment analysis-related tasks, specially in polarity and intensity classification and emotion identification. SentiSense attaches emotional meanings to concepts from the WordNet lexical database, instead of terms, thus allowing to address the word ambiguity problem using one of the many WordNet-based word sense disambiguation algorithms. SentiSense consists of 5,496 words and 2,190 synsets labeled with an emotion from a set of 14 emotional categories, which are related by an antonym relationship. SentiSense has been developed semi-automatically using several semantic relations between synsets in WordNet. SentiSense is endowed with a set of tools that allow users to visualize the lexicon and some statistics about the distribution of synsets and emotions in SentiSense, as well as to easily expand the lexicon. SentiSense is available for research purposes.


title: Optimizing the SUSAN corner detection algorithm for a high speed FPGA implementation

abstract: In many embedded systems for video surveillance distinctive features are used for the detection of objects. In this contribution a real-time FPGA implementation of a feature detector, namely the SUSAN algorithm is described. As the original SUSAN algorithm performs poorly on non-synthetic images a significant quality improvement of this algorithm is presented. The hardware accelerator outperforms a comparable software version running on an Intel Core2Duo E8400 core at 3.00GHz and delivers almost the same execution time compared to an implementation of the Harris corner detector running on an Nvidia GeForce 8800 GTX GPU.


title: A 77-GHz CMOS Automotive Radar Transceiver With Anti-Interference Function

abstract: This paper presents a 77-GHz long-range automotive radar transceiver with the function of reducing mutual interference. The proposed frequency-hopping random chirp FMCW technique reconfigures the chirp sweep frequency and time every cycle to result in noise-like frequency response for mutual interference after the received signal is down-converted and demodulated. Thus, the false alarm rate can be reduced significantly. The transceiver IC is fully integrated in TSMC 1P9M 65-nm digital CMOS technology. The chip including pads occupies a silicon area of 1.03 mm × 0.94 mm. The transceiver consumes totally 275 mW of power, and the measured transmitting power and receiver noise figure are 6.4 dBm and 14.8 dB, respectively. To the authors' knowledge, this is the first integrated 77-GHz automotive radar transceiver with the feature of anti-interference.


title: STREAMER: A distributed framework for incremental closeness centrality computation

abstract: Networks are commonly used to model the traffic patterns, social interactions, or web pages. The nodes in a network do not possess the same characteristics: some nodes are naturally more connected and some nodes can be more important. Closeness centrality (CC) is a global metric that quantifies how important is a given node in the network. When the network is dynamic and keeps changing, the relative importance of the nodes also changes. The best known algorithm to compute the CC scores makes it impractical to recompute them from scratch after each modification. In this paper, we propose Streamer, a distributed memory framework for incrementally maintaining the closeness centrality scores of a network upon changes. It leverages pipelined and replicated parallelism and takes NUMA effects into account. It speeds up the maintenance of the CC of a real graph with 916K vertices and 4.3M edges by a factor of 497 using a 64 nodes cluster.


title: EasyWay: simplifying and automating enterprise network management with SDN/OpenFlow

abstract: This paper presents new high level abstractions to manage the whole network at once: "names", "groups", "paths" instead of low level traditional IPs, subnets, routes. We called this approach as semantic network management where administrators simply say what they want from the network but not how and a SDN/OpenFlow controller will automatically program the network elements. All low level details are hidden from administrators (e.g., choosing IP addressing scheme). We implemented the proposed approach in a new SDN/OpenFlow-based network management system for enterprise networks called EasyWay.


title: TD-Gammon, a Self-Teaching Backgammon Program, Achieves Master-Level Play

abstract: TD-Gammon is a neural network that is able to teach itself to play backgammon solely by playing against itself and learning from the results, based on the TD(») reinforcement learning algorithm (Sutton 1988). Despite starting from random initial weights (and hence random initial strategy), TD-Gammon achieves a surprisingly strong level of play. With zero knowledge built in at the start of learning (i.e., given only a "raw" description of the board state), the network learns to play at a strong intermediate level. Furthermore, when a set of hand-crafted features is added to the network's input representation, the result is a truly staggering level of performance: the latest version of TD-Gammon is now estimated to play at a strong master level that is extremely close to the world's best human players.


title: DASH fast start using HTTP/2

abstract: Dynamic Adaptive Streaming over HTTP (DASH) is broadly deployed on the Internet for live and on-demand video streaming services. Recently, a new version of HTTP was proposed, named HTTP/2. One of the objectives of HTTP/2 is to improve the end-user perceived latency compared to HTTP/1.1. HTTP/2 introduces the possibility for the server to push resources to the client. This paper focuses on using the HTTP/2 protocol and the server push feature to reduce the start-up delay in a DASH streaming session. In addition, the paper proposes a new approach for video adaptation, which consists in estimating the bandwidth, using WebSocket (WS) over HTTP/2, and in making partial adaptation on the server side. Obtained results show that, using the server push feature and WebSocket layered over HTTP/2 allow faster loading time and faster convergence to the nominal state. Proposed solution is studied in the context of a direct client-server HTTP/2 connection. Intermediate caches are not considered in this study.


title: Commonsense causal reasoning between short texts

abstract: Commonsense causal reasoning is the process of capturing and understanding the causal dependencies amongst events and actions. Such events and actions can be expressed in terms, phrases or sentences in natural language text. Therefore, one possible way of obtaining causal knowledge is by extracting causal relations between terms or phrases from a large text corpus. However, causal relations in text are sparse, ambiguous, and sometimes implicit, and thus difficult to obtain. This paper attacks the problem of commonsense causality reasoning between short texts (phrases and sentences) using a data driven approach. We propose a framework that automatically harvests a network of causal-effect terms from a large web corpus. Backed by this network, we propose a novel and effective metric to properly model the causality strength between terms. We show these signals can be aggregated for causality reasonings between short texts, including sentences and phrases. In particular, our approach outperforms all previously reported results in the standard SE-MEVAL COPA task by substantial margins.


title: Machine-guided Solution to Mathematical Word Problems

abstract: Mathematical word problems (MWP) test crit- ical aspects of reading comprehension in con- junction with generating a solution that agrees with the "story" in the problem. In this paper we design and construct an MWP solver in a systematic manner, as a step towards enabling comprehension in mathematics and teaching problem solving for children in the elemen- tary grades. We do this by (a) identifying the discourse structure of MWPs that will enable comprehension in mathematics, and (b) utiliz- ing the information in the discourse structure towards generatingthe solution ina systematic manner. We build a multistage software proto- type that predicts the problem type, identifies the function of sentences in each problem, and extracts the necessary information from the question to generate the corresponding math- ematical equation. Our prototype has an ac- curacy of 86% on a large corpus of MWPs of three problem types from elementary grade mathematics curriculum.


title: Polycystic Ovary Syndrome, Insulin Resistance, and Obesity: Navigating the Pathophysiologic Labyrinth

abstract: Polycystic ovary syndrome (PCOS) is a highly prevalent endocrine-metabolic disorder that implies various severe consequences to female health, including alarming rates of infertility. Although its exact etiology remains elusive, it is known to feature several hormonal disturbances, including hyperandrogenemia, insulin resistance (IR), and hyperinsulinemia. Insulin appears to disrupt all components of the hypothalamus-hypophysis-ovary axis, and ovarian tissue insulin resistance results in impaired metabolic signaling but intact mitogenic and steroidogenic activity, favoring hyperandrogenemia, which appears to be the main culprit of the clinical picture in PCOS. In turn, androgens may lead back to IR by increasing levels of free fatty acids and modifying muscle tissue composition and functionality, perpetuating this IR-hyperinsulinemia-hyperandrogenemia cycle. Nonobese women with PCOS showcase several differential features, with unique biochemical and hormonal profiles. Nevertheless, lean and obese patients have chronic inflammation mediating the long term cardiometabolic complications and comorbidities observed in women with PCOS, including dyslipidemia, metabolic syndrome, type 2 diabetes mellitus, and cardiovascular disease. Given these severe implications, it is important to thoroughly understand the pathophysiologic interconnections underlying PCOS, in order to provide superior therapeutic strategies and warrant improved quality of life to women with this syndrome.


title: Keystroke-level model for advanced mobile phone interaction

abstract: The design of applications using mobile devices needs a different quality assessment than those known for desktop applications. Of the many aspects that have to be taken into account, one important criterion is the average time users need to complete a task. For interactions with the mouse, keyboard or touch screens, there exist models that predict interaction times like Fitts' law or the Keystroke-Level Model (KLM). This paper shows parallels to these models for advanced interactions with mobile phones targeted at pervasive services, including near field communication as well as built-in cameras and sensors. Applications can be evaluated with respect to user performance time without having a prototype running on the phone. To accomplish that, we extend the known KLM by identifying basic interaction elements for mobile phones and give estimates for expert user performance derived from several user tests.


title: Universal Neural Machine Translation for Extremely Low Resource Languages

abstract: In this paper, we propose a new universal machine translation approach focusing on languages with a limited amount of parallel data. Our proposed approach utilizes a transfer-learning approach to share lexical and sentence level representations across multiple source languages into one target language. The lexical part is shared through a Universal Lexical Representation to support multilingual word-level sharing. The sentence-level sharing is represented by a model of experts from all source languages that share the source encoders with all other languages. This enables the low-resource language to utilize the lexical and sentence representations of the higher resource languages. Our approach is able to achieve 23 BLEU on Romanian-English WMT2016 using a tiny parallel corpus of 6k sentences, compared to the 18 BLEU of strong baseline system which uses multilingual training and back-translation. Furthermore, we show that the proposed approach can achieve almost 20 BLEU on the same dataset through fine-tuning a pre-trained multi-lingual system in a zero-shot setting.


title: Quantum-Inspired Optimization of Echo State Networks Applied to System Identification

abstract: Quantum-Inspired Evolutionary Algorithms (QIEA) represent an efficient alternative to the traditional genetic algorithms, being capable of finding good solutions with smaller populations. Echo State Networks (ESNs) are a simple and efficient implementation of the Reservoir Computing framework. The use of this kind of networks in system identification is advantageous due to its intrinsic dynamic behavior and fast training procedure. However, ESNs have global parameters that should be tuned in order to improve their performance in a determined task. Besides, the random generation of the reservoir weights of these networks may not be ideal in terms of performance. Thus, this work presents a method that automatically defines an ESN for system identification problems by using a real coded QIEA (QIEA-R) in a two-phase optimization procedure. The QIEA-R firstly searches for the best global parameters of an ESN; then, on a second stage, optimizes some of its reservoir weights. In two benchmark problems for system identification, the proposed method overcame the performance of a randomly generated ESN with the same global parameters and has presented comparable and, in most cases, better accuracy results in comparison to some methods which were applied to the same datasets.


title: QoS guided Min-Min heuristic for grid task scheduling

abstract: Task scheduling is an integrated component of computing. With the emergence of Grid and ubiquitous computing, new challenges appear in task scheduling based on properties such as security, quality of service, and lack of central control within distributed administrative domains. A Grid task scheduling framework must be able to deal with these issues. One of the goals of Grid task scheduling is to achieve high system throughput while matching applications with the available computing resources. This matching of resources in a nondeterministically shared heterogeneous environment leads to concerns over Quality of Service (QoS). In this paper a novel QoS guided task scheduling algorithm for Grid computing is introduced. The proposed novel algorithm is based on a general adaptive scheduling heuristics that includes QoS guidance. The algorithm is evaluated within a simulated Grid environment. The experimental results show that the new QoS guided Min-Min heuristic can lead to significant performance gain for a variety of applications. The approach is compared with others based on the quality of the prediction formulated by inaccurate information.


title: Ambient RF Energy Harvesting in Ultra-Dense Small Cell Networks: Performance and Trade-offs

abstract: In order to minimize electric grid power consumption, energy harvesting from ambient RF sources is considered as a promising technique for wireless charging of low-power devices. To illustrate the design considerations of RF-based ambient energy harvesting networks, this article first points out the primary challenges of implementing and operating such networks, including non-deterministic energy arrival patterns, energy harvesting mode selection, energy-aware cooperation among base stations, and so on. A brief overview of recent advances and a summary of their shortcomings are then provided to highlight existing research gaps and possible future research directions. To this end, we investigate the feasibility of implementing RF-based ambient energy harvesting in ultra-dense small cell networks and examine the related trade-offs in terms of the energy efficiency and signal-to-interference-plus-noise ratio outage probability of a typical user in the downlink. Numerical results demonstrate the significance of deploying a mixture of on-grid small base stations (powered by electric grid) and off-grid small base stations (powered by energy harvesting), and optimizing their corresponding proportions as a function of the intensity of active small base stations in the network.


title: Smaller and Faster: Parallel Processing of Compressed Graphs with Ligra+

abstract: We study compression techniques for parallel in-memory graph algorithms, and show that we can achieve reduced space usage while obtaining competitive or improved performance compared to running the algorithms on uncompressed graphs. We integrate the compression techniques into Ligra, a recent shared-memory graph processing system. This system, which we call Ligra+, is able to represent graphs using about half of the space for the uncompressed graphs on average. Furthermore, Ligra+ is slightly faster than Ligra on average on a 40-core machine with hyper-threading. Our experimental study shows that Ligra+ is able to process graphs using less memory, while performing as well as or faster than Ligra.


title: Deep Learning for the Internet of Things

abstract: How can the advantages of deep learning be brought to the emerging world of embedded IoT devices? The authors discuss several core challenges in embedded and mobile deep learning, as well as recent solutions demonstrating the feasibility of building IoT applications that are powered by effective, efficient, and reliable deep learning models.


title: Big database stores a review on various big data datastores

abstract: In this age where there is a massive explosion in the data in our surroundings, the traditional way of actually storing and processing the data is no longer feasible. Moreover, the unstructured data that is present cannot be processed by the conventional ways. With the emergence of this data explosion also known as Big Data, a lot of importance has given to actually process and more importantly analyze the data to get the best results being in terms of decision making or predicting the future evens based on the past data at hand.


title: Vision-based robust road lane detection in urban environments

abstract: Road and lane detection play an important role in autonomous driving and commercial driver-assistance systems. Vision-based road detection is an essential step towards autonomous driving, yet a challenging task due to illumination and complexity of the visual scenery. Urban scenes may present additional challenges such as intersections, multi-lane scenarios, or clutter due to heavy traffic. This paper presents an integrative approach to ego-lane detection that aims to be as simple as possible to enable real-time computation while being able to adapt to a variety of urban and rural traffic scenarios. The approach at hand combines and extends a road segmentation method in an illumination-invariant color image, lane markings detection using a ridge operator, and road geometry estimation using RANdom SAmple Consensus (RANSAC). Employing the segmented road region as a prior for lane markings extraction significantly improves the execution time and success rate of the RANSAC algorithm, and makes the detection of weakly pronounced ridge structures computationally tractable, thus enabling ego-lane detection even in the absence of lane markings. Segmentation performance is shown to increase when moving from a color-based to a histogram correlation-based model. The power and robustness of this algorithm has been demonstrated in a car simulation system as well as in the challenging KITTI data base of real-world urban traffic scenarios.


title: NICE: Non-linear Independent Components Estimation

abstract: We propose a deep learning framework for modeling complex high-dimensional densities called Non-linear Independent Component Estimation (NICE). It is based on the idea that a good representation is one in which the data has a distribution that is easy to model. For this purpose, a non-linear deterministic transformation of the data is learned that maps it to a latent space so as to make the transformed data conform to a factorized distribution, i.e., resulting in independent latent variables. We parametrize this transformation so that computing the Jacobian determinant and inverse transform is trivial, yet we maintain the ability to learn complex non-linear transformations, via a composition of simple building blocks, each based on a deep neural network. The training criterion is simply the exact log-likelihood, which is tractable. Unbiased ancestral sampling is also easy. We show that this approach yields good generative models on four image datasets and can be used for inpainting.


title: An improved simulated annealing andgenetic algorithm for TSP

abstract: In order to improve the evolution efficiency and species diversity of traditional genetic algorithm in solving TSP problems, a modified hybrid simulated annealing genetic algorithm is proposed. This algorithm adopts the elite selection operator to ensure not only the diversity of the algorithm but also that groups are always close to the optimal solution; at the same time, places the simulated annealing algorithm in the evolutionary process of genetic algorithm, and using the hybrid algorithm dual criteria to control algorithm's optimize performance and efficiency simultaneously. The final example shows that the hybrid algorithm is an optimization method with higher optimize performance, efficiency and reliability.


title: Robust solutions for network design under transportation cost and demand uncertainty

abstract: In many applications, the network design problem (NDP) faces significant uncertainty in transportation costs and demand, as it can be difficult to estimate current (and future values) of these quantities. In this paper, we present a robust optimization-based formulation for the NDP under transportation cost and demand uncertainty. We show that solving an approximation to this robust formulation of the NDP can be done efficiently for a network with single origin and destination per commodity and general uncertainty in transportation costs and demand that are independent of each other. For a network with path constraints, we propose an efficient column generation procedure to solve the linear programming relaxation. We also present computational results that show that the approximate robust solution found provides significant savings in the worst case while incurring only minor sub-optimality for specific instances of the uncertainty.


title: Reinforcement Learning for Trading Systems and Portfolios

abstract: We propose to train trading systems by optimizing financial objective functions via reinforcement learning. The performance functions that we consider as value functions are profit or wealth, the Sharpe ratio and our recently proposed differential Sharpe ratio for online learning. In Moody & Wu (1997), we presented empirical results in controlled experiments that demonstrated the advantages of reinforcement learning relative to supervised learning. Here we extend our previous work to compare Q-Learning to a reinforcement learning technique based on real-time recurrent learning (RTRL) that maximizes immediate reward. ::: ::: Our simulation results include a spectacular demonstration of the presence of predictability in the monthly Standard and Poors 500 stock index for the 25 year period 1970 through 1994. Our reinforcement trader achieves a simulated out-of-sample profit of over 4000% for this period, compared to the return for a buy and hold strategy of about 1300% (with dividends reinvested). This superior result is achieved with substantially lower risk.


title: Privacy Preserving Data Sharing With Anonymous ID Assignment

abstract: An algorithm for anonymous sharing of private data among N parties is developed. This technique is used iteratively to assign these nodes ID numbers ranging from 1 to N. This assignment is anonymous in that the identities received are unknown to the other members of the group. Resistance to collusion among other members is verified in an information theoretic sense when private communication channels are used. This assignment of serial numbers allows more complex data to be shared and has applications to other problems in privacy preserving data mining, collision avoidance in communications and distributed database access. The required computations are distributed without using a trusted central authority. Existing and new algorithms for assigning anonymous IDs are examined with respect to trade-offs between communication and computational requirements. The new algorithms are built on top of a secure sum data mining operation using Newton's identities and Sturm's theorem. An algorithm for distributed solution of certain polynomials over finite fields enhances the scalability of the algorithms. Markov chain representations are used to find statistics on the number of iterations required, and computer algebra gives closed form results for the completion rates.


title: Further extensions of Clifford circuits and their classical simulation complexities

abstract: Extended Clifford circuits straddle the boundary between classical and quantum computational power. Whether such circuits are efficiently classically simulable seems to depend delicately on the ingredients of the circuits. While some combinations of ingredients lead to efficiently classically simulable circuits, other combinations that might just be slightly different, lead to circuits which are likely not. We extend the results of Jozsa and Van den Nest (arXiv:1305.6190) by studying two further extensions of Clifford circuits. Firstly, we consider how the classical simulation complexity changes when we allow for more general measurements. Secondly, we investigate different notions of what it means to "classically simulate" a quantum circuit. These further extensions give us 24 new combinations of ingredients compared to Jozsa and Van den Nest, and we give a complete classification of their classical simulation complexities. Our results provide more examples where seemingly modest changes to the ingredients of Clifford circuits lead to large changes in the classical simulation complexity under plausible complexity assumptions.


title: AOGNets: Compositional Grammatical Architectures for Deep Learning

abstract: Neural architectures are the foundation for improving performance of deep neural networks (DNNs). This paper presents deep compositional grammatical architectures which harness the best of two worlds: grammar models and DNNs. The proposed architectures integrate compositionality and reconfigurability of the former and the capability of learning rich features of the latter in a principled way. We utilize AND-OR Grammar (AOG) as network generator in this paper and call the resulting networks AOGNets. An AOGNet consists of a number of stages each of which is composed of a number of AOG building blocks. An AOG building block splits its input feature map into N groups along feature channels and then treat it as a sentence of N words. It then jointly realizes a phrase structure grammar and a dependency grammar in bottom-up parsing the “sentence” for better feature exploration and reuse. It provides a unified framework for the best practices developed in state-of-the-art DNNs. In experiments, AOGNet is tested in the ImageNet-1K classification benchmark and the MS-COCO object detection and segmentation benchmark. In ImageNet-1K, AOGNet obtains better performance than ResNet and most of its variants, ResNeXt and its attention based variants such as SENet, DenseNet and DualPathNet. AOGNet also obtains the best model interpretability score using network dissection. AOGNet further shows better potential in adversarial defense. In MS-COCO, AOGNet obtains better performance than the ResNet and ResNeXt backbones in Mask R-CNN.


Multi-Layer MultiView Classification for Alzheimer ’ s Disease Diagnosis


title: A New Method for Yielding a Database of Location Abstract Fingerprints in WLAN

abstract: Location fingerprinting in wireless LAN positioning has received much attention recently. One of the key issues of this technique is generating the database of fingerprints. The conventional method does not utilise the spatial correlation of measurements sampled at adjacent reference points, and the training process is not an easy task. A new method based on kriging is presented which can not only achieve more accurate estimation, but can also greatly reduce the workload and save training time. This can make the fingerprinting technique more flexible and easier to implement.


title: Issues in Complex Event Processing : Status and Prospects in the Big Data Era

abstract: Research issues in complex event processing (CEP) emphasizing on query optimization.Cover deterministic probabilistic models, centralized distributed settings.Issues for CEP optimization over Big Data enabling cloud computing platforms.Predictive Analytics and CEP in cloud platforms even with dispersed resource pools. Many Big Data technologies were built to enable the processing of human generated data, setting aside the enormous amount of data generated from Machine-to-Machine (M2M) interactions and Internet-of-Things (IoT) platforms. Such interactions create real-time data streams that are much more structured, often in the form of series of event occurrences. In this paper, we provide an overview on the main research issues confronted by existing Complex Event Processing (CEP) techniques, with an emphasis on query optimization aspects. Our study expands on both deterministic and probabilistic event models and spans from centralized to distributed network settings. In that, we cover a wide range of approaches in the CEP domain and review the current status of techniques that tackle efficient query processing. These techniques serve as a starting point for developing Big Data oriented CEP applications. Therefore, we further study the issues that arise upon trying to apply those techniques over Big Data enabling technologies, as is the case with cloud platforms. Furthermore, we expand on the synergies among Predictive Analytics and CEP with an emphasis on scalability and elasticity considerations in cloud platforms with potentially dispersed resource pools.


title: Multi-Task Cross-Lingual Sequence Tagging from Scratch

abstract: We present a deep hierarchical recurrent neural network for sequence tagging. Given a sequence of words, our model employs deep gated recurrent units on both character and word levels to encode morphology and context information, and applies a conditional random field layer to predict the tags. Our model is task independent, language independent, and feature engineering free. We further extend our model to multi-task and cross-lingual joint training by sharing the architecture and parameters. Our model achieves state-of-the-art results in multiple languages on several benchmark tasks including POS tagging, chunking, and NER. We also demonstrate that multi-task and cross-lingual joint training can improve the performance in various cases.


title: Emotion Prediction from Physiological Signals: A Comparison Study Between Visual and Auditory Elicitors

abstract: Unlike visual stimuli, little attention has been paid to auditory stimuli in terms of emotion prediction with physiological signals. This paper aimed to investigate whether auditory stimuli can be used as an effective elicitor as visual stimuli for emotion prediction using physiological channels. For this purpose, a well-controlled experiment was designed, in which standardized visual and auditory stimuli were systematically selected and presented to participants to induce various emotions spontaneously in a laboratory setting. Numerous physiological signals, including facial electromyogram, electroencephalography, skin conductivity and respiration data, were recorded when participants were exposed to the stimulus presentation. Two data mining methods, namely decision rules and k-nearest neighbor based on the rough set technique, were applied to construct emotionpredictionmodelsbasedonthefeaturesextractedfromthephysiologicaldata.Experimental results demonstrated that auditory stimuli were as effective as visual stimuli in eliciting emotions in terms of systematic physiological reactivity. This was evidenced by the best prediction accuracy quantified by the F1 measure (visual: 76.2% vs. auditory: 76.1%) among six emotion categories (excited, happy, neutral, sad, fearful and disgusted). Furthermore, we also constructed culturespecific (Chinese vs. Indian) prediction models. The results showed that model prediction accuracy was not significantly different between culture-specific models. Finally, the implications of affective auditory stimuli in human‐computer interaction, limitations of the study and suggestions for further research are discussed. RESEARCH HIGHLIGHTS • Emotions can be predicted accurately based on physiological data using computational models. • Prediction accuracy was on the same level for auditory and visual stimuli. • Auditory stimuli can thus have potential in affective human–computer interaction applications.


title: Beyond Skip Connections: Top-Down Modulation for Object Detection

abstract: In recent years, we have seen tremendous progress in the field of object detection. Most of the recent improvements have been achieved by targeting deeper feedforward networks. However, many hard object categories such as bottle, remote, etc. require representation of fine details and not just coarse, semantic representations. But most of these fine details are lost in the early convolutional layers. What we need is a way to incorporate finer details from lower layers into the detection architecture. Skip connections have been proposed to combine high-level and low-level features, but we argue that selecting the right features from low-level requires top-down contextual information. Inspired by the human visual pathway, in this paper we propose top-down modulations as a way to incorporate fine details into the detection framework. Our approach supplements the standard bottom-up, feedforward ConvNet with a top-down modulation (TDM) network, connected using lateral connections. These connections are responsible for the modulation of lower layer filters, and the top-down network handles the selection and integration of contextual information and low-level features. The proposed TDM architecture provides a significant boost on the COCO testdev benchmark, achieving 28.6 AP for VGG16, 35.2 AP for ResNet101, and 37.3 for InceptionResNetv2 network, without any bells and whistles (e.g., multi-scale, iterative box refinement, etc.).


title: Image Prediction for Limited-angle Tomography via Deep Learning with Convolutional Neural Network

abstract: Limited angle problem is a challenging issue in x-ray computed tomography (CT) field. Iterative reconstruction methods that utilize the additional prior can suppress artifacts and improve image quality, but unfortunately require increased computation time. An interesting way is to restrain the artifacts in the images reconstructed from the practical filtered back projection (FBP) method. Frikel and Quinto have proved that the streak artifacts in FBP results could be characterized. It indicates that the artifacts created by FBP method have specific and similar characteristics in a stationary limited-angle scanning configuration. Based on this understanding, this work aims at developing a method to extract and suppress specific artifacts of FBP reconstructions for limited-angle tomography. A data-driven learning-based method is proposed based on a deep convolutional neural network. An end-to-end mapping between the FBP and artifact-free images is learned and the implicit features involving artifacts will be extracted and suppressed via nonlinear mapping. The qualitative and quantitative evaluations of experimental results indicate that the proposed method show a stable and prospective performance on artifacts reduction and detail recovery for limited angle tomography. The presented strategy provides a simple and efficient approach for improving image quality of the reconstruction results from limited projection data.


title: Pharmacokinetics of Oral Cyanocobalamin Formulated With Sodium N-(8-(2-hydroxybenzoyl)amino)caprylate (SNAC): An Open-Label, Randomized, Single-Dose, Parallel-Group Study in Healthy Male Subjects

abstract: Abstract Background Vitamin B12 (cobalamin) deficiency may be caused by inadequate dietary intake of B12 or by conditions that result in malabsorption of the vitamin. Crystalline vitamin B12, usually in the form of cyanocobalamin, is administered parenterally (ie, intramuscularly) or orally for treating deficiency states. Intramuscular administration is widely accepted as a treatment method. Oral B12 supplementation is also used, but it is considered to be less reliable. Objective This study was conducted to compare the pharmacokinetics and tolerability of 2 oral formulations of cyanocobalamin—a marketed cyanocobalamin tablet (immediate-release B12 5 mg) and cyanocobalamin formulated with a proprietary carrier, sodium N-[8-(2-hydroxybenzoyl)amino]caprylate (SNAC)—to establish the feasibility of using an absorption enhancer with B12 to improve uptake of the vitamin. This was the first clinical study conducted with the cyanocobalamin/SNAC coformulation. Methods An open-label, randomized, single-dose, parallel-group study was conducted in healthy male subjects. Subjects were randomly assigned to 1 of 4 treatment groups: Treatment A subjects (n = 4) received 2 tablets of 5-mg cyanocobalamin formulated with 100-mg SNAC as part of a dose range–finding arm included to determine a dose to provide a measurable concentration of vitamin B12 at all time points when tested with the available vitamin B12 assay; treatment B subjects (n = 6) received 1 tablet of 5-mg cyanocobalamin formulated with 100-mg SNAC; treatment C subjects (n = 6) received 1 commercially available 5-mg cyanocobalamin tablet; and treatment D subjects (n = 4) received commercially available 1-mg cyanocobalamin IV. Treatment A was completed 3 weeks before treatments B, C, and D were studied. Human serum B12 was analyzed by chemiluminescence assay method. Validation procedures established that samples could be diluted up to 100 times without any effects on accuracy and precision. The pharmacokinetic properties of vitamin B12 were characterized by noncompartmental analysis. Vitamin B12 absolute bioavailability estimates were calculated between the oral (A, B, and C) and IV (D) treatments using non–baseline-adjusted vitamin B12 concentrations as well as baseline-adjusted vitamin B12 concentrations, with or without body weight adjustments. Tolerability was evaluated through review or monitoring of medical history, physical examination findings, concomitant medications, vital signs, laboratory tests (hematology, serum chemistry, and urinalysis values), electrocardiography, adverse events, and serious adverse events. Results Twenty healthy male subjects, aged 20 to 45 years, participated in this study. Based on data from treatment A, a 5-mg cyanocobalamin dose was selected for use with treatments B and C. The oral cyanocobalamin formulation containing SNAC had greater mean absolute bioavailability than the commercial oral formulation (5.09% vs 2.16%, respectively), calculated on AUC 0–last values uncorrected for baseline, weight, or body mass index. It also had a reduced T max compared with the commercial formulation (0.5 hours vs 6.83 hours, respectively). The K e was similar between treatments (0.028 1/h vs 0.025 1/h). Comparable results were achieved using corrected values. The cyanocobalamin/SNAC formulation was well tolerated, and there were no reported adverse events. Conclusions An oral formulation of 5-mg cyanocobalamin containing 100-mg SNAC, an absorption enhancer, provided significantly improved bioavailability and a significant decrease in T max for B12 in a small study of normal healthy subjects compared with a commercially available 5-mg cyanocobalamin oral formulation. Both oral formulations and commercial 1-mg cyanocobalamin IV were well tolerated. ClinicalTrials.gov identifier: NCT01311739 .


title: Adaptive simple pendulum swing-up controller based on the closed-loop fundamental dynamics

abstract: An adaptive energy-based swing-up controller for simple pendulums is presented. A state transformation from cartesian to polar phase space followed by approximation steps leads to the fundamental dynamics of the controlled simple pendulum. Based on the fundamental dynamics, the unknown natural frequency is estimated and a control gain is adjusted such that the system energy follows desired reference dynamics. We prove convergence of the natural frequency estimate to the true value as well as convergence of the system energy to the desired energy level under the fundamental dynamics assumption. The implications of the fundamental dynamics approximation are evaluated in simulation. Noise affected angle measurements simulate a realistic inverted pendulum experiment. The successful swing-up into the highly nonlinear regimes of the simple pendulum support the applicability of the fundamental dynamics.


title: Person Re-identification: Past, Present and Future

abstract: Person re-identification (re-ID) has become increasingly popular in the community due to its application and research significance. It aims at spotting a person of interest in other cameras. In the early days, hand-crafted algorithms and small-scale evaluation were predominantly reported. Recent years have witnessed the emergence of large-scale datasets and deep learning systems which make use of large data volumes. Considering different tasks, we classify most current re-ID methods into two classes, i.e., image-based and video-based; in both tasks, hand-crafted and deep learning systems will be reviewed. Moreover, two new re-ID tasks which are much closer to real-world applications are described and discussed, i.e., end-to-end re-ID and fast re-ID in very large galleries. This paper: 1) introduces the history of person re-ID and its relationship with image classification and instance retrieval; 2) surveys a broad selection of the hand-crafted systems and the large-scale methods in both image- and video-based re-ID; 3) describes critical future directions in end-to-end re-ID and fast retrieval in large galleries; and 4) finally briefs some important yet under-developed issues.


title: IOT-based drone for improvement of crop quality in agricultural field

abstract: Unmanned Aerial Vehicles are becoming more and more popular to meet the demands of increased population and agriculture. Drones equipped with appropriate cameras, sensors and integrating modules will help in achieving easy, efficient, precision agriculture. The proposed solutions related to these drones, if integrated with various Machine Learning and Internet of Things concepts, can help in increasing the scope of further improvement. In this paper, the related work in this field has been highlighted along with proposed solutions that can be integrated into the drone using Raspberry Pi 3 B module.


title: The physiologic basis and clinical applications of cryotherapy and thermotherapy for the pain practitioner.

abstract: Cryotherapy and thermotherapy are useful adjuncts for the treatment of musculoskeletal injuries. Clinicians treating these conditions should be aware of current research findings regarding these modalities, because their choice of modality may affect the ultimate outcome of the patient being treated. Through a better understanding of these modalities, clinicians can optimize their present treatment strategies. Although cold and hot treatment modalities both decrease pain and muscle spasm, they have opposite effects on tissue metabolism, blood flow, inflammation, edema, and connective tissue extensibility. Cryotherapy decreases these effects while thermotherapy increases them. Continuous low-level cryotherapy and thermotherapy are newer concepts in therapeutic modalities. Both modalities provide significant pain relief with a low side-effect profile. Contrast therapy, which alternates between hot and cold treatment modalities, provides no additional therapeutic benefits compared with cryotherapy or thermotherapy alone. Complications of cryotherapy include nerve damage, frostbite, Raynaud's phenomenon, cold-induced urticaria, and slowed wound healing. With thermotherapy, skin burns may occur, especially in patients with diabetes mellitus, multiple sclerosis, poor circulation, and spinal cord injuries. In individuals with rheumatoid arthritis, deep-heating modalities should be used with caution because increased inflammation may occur. Whirlpool and other types of hydrotherapy have caused infections of the skin, urogenital, and pulmonary systems. Additionally, ultrasound should not be used in patients with joint prostheses.


title: 3D-Div: A novel local surface descriptor for feature matching and pairwise range image registration

abstract: This paper presents a novel local surface descriptor, called 3D-Div. The proposed descriptor is based on the concept of 3D vector fields divergence, extensively used in electromagnetic theory. To generate a 3D-Div descriptor of a 3D surface, a keypoint is first extracted on the 3D surface, then a local patch of a certain size is selected around that keypoint. A Local Reference Frame (LRF) is then constructed at the keypoint using all points forming the patch. A normalized 3D vector field is then computed at each point in the patch and referenced with LRF vectors. The 3D-Div descriptors are finally generated as the divergence of the reoriented 3D vector field. We tested our proposed descriptor on the low resolution Washington RGB-D (Kinect) object dataset. Performance was evaluated for the tasks of feature matching and pairwise range image registration. Experimental results showed that the proposed 3D-Div is 88% more computationally efficient and 47% more accurate than commonly used Spin Image (SI) descriptors.


title: A privacy preservation model for Facebook-style social network systems

abstract: Recent years have seen unprecedented growth in the popularity of social network systems, with Facebook being an archetypical example. The access control paradigm behind the privacy preservation mechanism of Facebook is distinctly different from such existing access control paradigms as Discretionary Access Control, Role-Based Access Control, Capability Systems, and TrustManagement Systems. This work takes a first step in deepening the understanding of this access control paradigm, by proposing an access control model that formalizes and generalizes the privacy preservation mechanism of Facebook. The model can be instantiated into a family of Facebook-style social network systems, each with a recognizably different access control mechanism, so that Facebook is but one instantiation of the model. We also demonstrate that the model can be instantiated to express policies that are not currently supported by Facebook but possess rich and natural social significance. This work thus delineates the design space of privacy preservation mechanisms for Facebook-style social network systems, and lays out a formal framework for policy analysis in these systems.


title: A Bayesian Space–Time Hierarchical Model for Remotely Sensed Lattice Data Based on Multiscale Homogeneous Statistical Units

abstract: The Bayesian hierarchical model has the outstanding capacity to combine prior knowledge with observations, as well as to generate rich spatiotemporal patterns. However, this advanced method has limited use in remotely sensed lattice data applications due to the large computational burden and unreasonable statistical inferences based on statistical units with a fixed scale. This paper presents a multiscale spatial homogeneous subdivision method and develops a Bayesian space–time hierarchical model (BSTHM) for remotely sensed lattice data. This can solve the above-mentioned limitations by constructing multiscale spatial homogeneous units with good statistical properties. The quantitative criteria for subdivision are provided. The outcome of the BSTHM is not only more reasonable in theory but also much easier to interpret; meanwhile, the computational efficiency is also considerably improved. This novel approach and its merits are illustrated by a case study that examines the spatiotemporal variation of PM2.5 pollution in Asia from 2000 to 2014 using remotely sensed data describing PM2.5 annual mean concentrations. Overall spatial pattern, common time trend, and local variation trend were decomposed and quantificationally estimated from an intricate spatiotemporal process.


title: Understanding and representing the semantics of large structured documents

abstract: Understanding large, structured documents like scholarly articles, requests for proposals or business reports is a complex and difficult task. It involves discovering a document's overall purpose and subject(s), understanding the function and meaning of its sections and subsections, and extracting low level entities and facts about them. In this research, we present a deep learning based document ontology to capture the general purpose semantic structure and domain specific semantic concepts from a large number of academic articles and business documents. The ontology is able to describe different functional parts of a document, which can be used to enhance semantic indexing for a better understanding by human beings and machines. We evaluate our models through extensive experiments on datasets of scholarly articles from arXiv and Request for Proposal documents.


title: A polynomial-time maximum common subgraph algorithm for outerplanar graphs and its application to chemoinformatics

abstract: Metrics for structured data have received an increasing interest in the machine learning community. Graphs provide a natural representation for structured data, but a lot of operations on graphs are computationally intractable. In this article, we present a polynomial-time algorithm that computes a maximum common subgraph of two outerplanar graphs. The algorithm makes use of the block-and-bridge preserving subgraph isomorphism, which has significant efficiency benefits and is also motivated from a chemical perspective. We focus on the application of learning structure-activity relationships, where the task is to predict the chemical activity of molecules. We show how the algorithm can be used to construct a metric for structured data and we evaluate this metric and more generally also the block-and-bridge preserving matching operator on 60 molecular datasets, obtaining state-of-the-art results in terms of predictive performance and efficiency.


title: Building large urban environments from unstructured point data

abstract: We present a robust method for modeling cities from unstructured point data. Our algorithm provides a more complete description than existing approaches by reconstructing simultaneously buildings, trees and topologically complex grounds. Buildings are modeled by an original approach which guarantees a high generalization level while having semantized and compact representations. Geometric 3D-primitives such as planes, cylinders, spheres or cones describe regular roof sections, and are combined with mesh-patches that represent irregular roof components. The various urban components interact through a non-convex energy minimization problem in which they are propagated under arrangement constraints over a planimetric map. We experimentally validate the approach on complex urban structures and large urban scenes of millions of points.


title: “RFID security issues & challenges”

abstract: The deployment and use of Radio Frequency Identification (RFID) technology is growing rapidly across many different industries. Developers apply the technology not only in traditional applications such as asset or inventory tracking, but also in security services such as electronic passports and RFID-embedded credit cards. Within less than a decade, a large number of research papers dealing with security issues of RFID technology have appeared. In this paper we want to provide some thoughts on security issues concerning RFID systems and to highlight some of the areas that have to be considered regarding this topic. To deal with security and RFID means to deal not only with security aspects of RFID systems but also with security aspects of anything or anyone affected by RFID systems. The widespread dissemination of identification technology and storage devices certainly has side effects and can lead to new threats in other areas and applications.


title: A Novel Adaptive PSO Algorithm on Schaffer's F6 Function

abstract: Analyzing the distance between the location and the new location, we conclude inertia weight method which linearly decreases from 0.9 to 0.4 has the powerful local search ability on Schaffer’s F6 function. In order to improve the balance between local and global search ability, the novel adaptive PSO algorithm which evaluates a reset function to control the inertia weight value is proposed. Once plunged into the local optimum, inertia weight, pbest and gbest should be reset to get away from the local optimum. Compared with the particle’s traces, the novel algorithm has a great potential advantage. Simulation results show that the novel adaptive algorithm is better than the inertia weight algorithm in terms of the successful searching rate on Schaffer’s F6 function.


title: LoRa for the Internet of Things

abstract: New transceiver technologies have emerged which enable power efficient communication over very long distances. Examples of such Low-Power Wide-Area Network (LPWAN) technologies are LoRa, Sigfox and Weightless. A typical application scenario for these technologies is city wide meter reading collection where devices send readings at very low frequency over a long distance to a data concentrator (one-hop networks). We argue that these transceivers are potentially very useful to construct more generic Internet of Things (IoT) networks incorporating multi-hop bidirectional communication enabling sensing and actuation. Furthermore, these transceivers have interesting features not available with more traditional transceivers used for IoT networks which enable construction of novel protocol elements. In this paper we present a performance and capability analysis of a currently available LoRa transceiver. We describe its features and then demonstrate how such transceiver can be put to use efficiently in a wide-area application scenario. In particular we demonstrate how unique features such as concurrent non-destructive transmissions and carrier detection can be employed. Our deployment experiment demonstrates that 6 LoRa nodes can form a network covering 1.5 ha in a built up environment, achieving a potential lifetime of 2 year on 2 AA batteries and delivering data within 5 s and reliability of 80%.


title: Anisotropic material properties of fused deposition modeling ABS

abstract: Rapid Prototyping (RP) technologies provide the ability to fabricate initial prototypes from various model materials. Stratasys Fused Deposition Modeling (FDM) is a typical RP process that can fabricate prototypes out of ABS plastic. To predict the mechanical behavior of FDM parts, it is critical to understand the material properties of the raw FDM process material, and the effect that FDM build parameters have on anisotropic material properties. This paper characterizes the properties of ABS parts fabricated by the FDM 1650. Using a Design of Experiment (DOE) approach, the process parameters of FDM, such as raster orientation, air gap, bead width, color, and model temperature were examined. Tensile strengths and compressive strengths of directionally fabricated specimens were measured and compared with injection molded FDM ABS P400 material. For the FDM parts made with a 0.003 inch overlap between roads, the typical tensile strength ranged between 65 and 72 percent of the strength of injection molded ABS P400. The compressive strength ranged from 80 to 90 percent of the injection molded FDM ABS. Several build rules for designing FDM parts were formulated based on experimental results.


title: NIMS-PL: A Cable-Driven Robot With Self-Calibration Capabilities

abstract: We present the Networked InfoMechanical System for Planar Translation, which is a novel two-degree-of-freedom (2-DOF) cable-driven robot with self-calibration and online drift-correction capabilities. This system is intended for actuated sensing applications in aquatic environments. The actuation redundancy resulting from in-plane translation driven by four cables results in an infinite set of tension distributions, thus requiring real-time computation of optimal tension distributions. To this end, we have implemented a highly efficient, iterative linear programming solver, which requires a very small number of iterations to converge to the optimal value. In addition, two novel self-calibration methods have been developed that leverage the robot's actuation redundancy. The first uses an incremental displacement, or jitter method, whereas the second uses variations in cable tensions to determine end-effector location. We also propose a novel least-squares drift-detection algorithm, which enables the robot to detect long-term drift. Combined with self-calibration capabilities, this drift-monitoring algorithm enables long-term autonomous operation. To verify the performance of our algorithms, we have performed extensive experiments in simulation and on a real system.


The Effects of User Features on Twitter Hate Speech Detection


title: A relationship between the percentage of calcium by mass and the effective atomic number of regions containing bone

abstract: It is shown that, in a mixture such as these in bone where one element is prominent because of its photoelectric effect, a realistic estimate can be made of its mass fraction despite large variations of those of the lower atomic number elements. A relationship has been derived from which the percentage of calcium by mass present can be calculated form the effective atomic number as defined and determined by the dual energy technique. Estimates of error in the experimental determination of effective atomic number Z* show that if the two effective energies are correct to +or-2 keV, with their difference correct to +or-1 keV, and with no special care in the selection of the region of interest, the calcium percentage is calculable to +or-13% for Z* values from about 10 up to 13.5. The error increases with decreasing Z* and is almost 19% at Z*=8, corresponding to a calcium percentage of 2.2+or-0.4. It is believed that these errors could be reduced by careful choice of the region of interest, e.g. by taking large trabecular areas to reduce the standard error of measurement or by selection of small cortical zones having low standard deviations of their CT numbers.


Paraphrase Detection Using Recursive Autoencoder


title: The Volitional Questionnaire: Psychometric Development and Practical Use

abstract: This article describes the Volitional Questionnaire and presents recent empirical efforts to develop and validate this instrument. The Volitional Questionnaire is an observational method of gathering data on motivation that is designed for persons who are unable to self-report their own motives. Studies to date suggest that the instrument can provide a valid measure of volition, though a number of limitations of the tool are noted. Implications for use of the Volitional Questionnaire in practice and for further refinement and future research are discussed.


title: Competitive wireless energy transfer bidding: A game theoretic approach

abstract: Wireless or RF energy transfer will be capable of supplying energy to mobile nodes for packet transmission without physical battery changing or replacement. In this paper, we consider the network in which the access point can transfer wireless energy to the nodes and the nodes use that energy to transmit packets. The nodes can send requests (i.e., bids) to the access point for transferring wireless energy. As the access point adopts auction mechanism for wireless energy transfer, we formulate a noncooperative game to analyze the competitive wireless energy transfer bidding of the nodes. The Nash equilibrium is considered to be a solution of the game. To reach the Nash equilibrium, the nodes as players can adapt their strategies based on stochastic response dynamic. The Markov chain is used to analyze the properties of the game (e.g., convergence). The numerical results clearly show the convergence of the bidding strategy adaptation of the nodes toward the Nash equilibrium.


title: Guidance for the evaluation and treatment of hereditary and acquired thrombophilia

abstract: Thrombophilias are hereditary and/or acquired conditions that predispose patients to thrombosis. Testing for thrombophilia is commonly performed in patients with venous thrombosis and their relatives; however such testing usually does not provide information that impacts management and may result in harm. This manuscript, initiated by the Anticoagulation Forum, provides clinical guidance for thrombophilia testing in five clinical situations: following 1) provoked venous thromboembolism, 2) unprovoked venous thromboembolism; 3) in relatives of patients with thrombosis, 4) in female relatives of patients with thrombosis considering estrogen use; and 5) in female relatives of patients with thrombosis who are considering pregnancy. Additionally, guidance is provided regarding the timing of thrombophilia testing. The role of thrombophilia testing in arterial thrombosis and for evaluation of recurrent pregnancy loss is not addressed. Statements are based on existing guidelines and consensus expert opinion where guidelines are lacking. We recommend that thrombophilia testing not be performed in most situations. When performed, it should be used in a highly selective manner, and only in circumstances where the information obtained will influence a decision important to the patient, and outweigh the potential risks of testing. Testing should not be performed during acute thrombosis or during the initial (3-month) period of anticoagulation.


title: Conception, evolution, and application of functional programming languages

abstract: The foundations of functional programming languages are examined from both historical and technical perspectives. Their evolution is traced through several critical periods: early work on lambda calculus and combinatory calculus, Lisp, Iswim, FP, ML, and modern functional languages such as Miranda1 and Haskell. The fundamental premises on which the functional programming methodology stands are critically analyzed with respect to philosophical, theoretical, and pragmatic concerns. Particular attention is paid to the main features that characterize modern functional languages: higher-order functions, lazy evaluation, equations and pattern matching, strong static typing and type inference, and data abstraction. In addition, current research areas—such as parallelism, nondeterminism, input/output, and state-oriented computations—are examined with the goal of predicting the future development and application of functional languages.


title: Ultra low-power high-speed single-bit hybrid full adder circuit

abstract: In this paper a low power hybrid 1-bit full adder circuit is designed and extended for 4 bit ripple carry adder (RCA). A new XNOR logic is designed using complimentary-metal-oxide semiconductor (CMOS) logic and Pass transistor logic. CMOS weak inverters improve the power consumption while pass transistors overcome voltage degradation problem. Carry logic is designed using transmission gates which reduces the carry propagation path. A 4-Bit RCA is also designed using the proposed single bit full adder. The proposed full adder and RCA are designed in 180nm technology and post layout simulations are done using Cadence Virtuoso. Power, delay, power delay product (PDP) and area are performance parameters of the proposed full adder which are calculated using umc technology. The power and delay parameters of the proposed single-bit full adder are 930.3-nW and 43.07-ps respectively.


title: Ciratefi: An RST-Invariant Template Matching with Extension to Color Images

abstract: Template matching is a technique widely used for finding patterns in digital images. A good template matching should be able to detect template instances that have undergone geometric transformations. In this paper, we proposed a grayscale template matching algorithm named Ciratefi, invariant to rotation, scale, translation, brightness and contrast and its extension to color images. We introduce CSSIM (color structural similarity) for comparing the similarity of two color image patches and use it in our algorithm. We also describe a scheme to determine automatically the appropriate parameters of our algorithm and use pyramidal structure to improve the scale invariance. We conducted several experiments to compare grayscale and color Ciratefis with SIFT, C-color-SIFT and EasyMatch algorithms in many different situations. The results attest that grayscale and color Ciratefis are more accurate than the compared algorithms and that color-Ciratefi outperforms grayscale Ciratefi most of the time. However, Ciratefi is slower than the other algorithms.


title: Fast algorithms for projected clustering

abstract: The clustering problem is well known in the database literature for its numerous applications in problems such as customer segmentation, classification and trend analysis. Unfortunately, all known algorithms tend to break down in high dimensional spaces because of the inherent sparsity of the points. In such high dimensional spaces not all dimensions may be relevant to a given cluster. One way of handling this is to pick the closely correlated dimensions and find clusters in the corresponding subspace. Traditional feature selection algorithms attempt to achieve this. The weakness of this approach is that in typical high dimensional data mining applications different sets of points may cluster better for different subsets of dimensions. The number of dimensions in each such cluster-specific subspace may also vary. Hence, it may be impossible to find a single small subset of dimensions for all the clusters. We therefore discuss a generalization of the clustering problem, referred to as the projected clustering problem , in which the subsets of dimensions selected are specific to the clusters themselves. We develop an algorithmic framework for solving the projected clustering problem, and test its performance on synthetic data.


title: Integrating knowledge management into enterprise environments for the next generation decision support

abstract: Decision support and knowledge management processes are interdependent activities in many organizations. In this paper, we propose an approach for integrating decision support and knowledge management processes using knowledge discovery techniques. Based on the proposed approach, an integrative framework is presented for building enterprise decision support environments using model marts and model warehouses as repositories for knowledge obtained through various conversions. This framework is expected to guide further research on the development of the next generation decision support environments.


title: AnalyticalInk: An Interactive Learning Environment for Math Word Problem Solving

abstract: We present AnalyticalInk, a novel math learning environment prototype that uses a semantic graph as the knowledge representation of algebraic and geometric word problems. The system solves math problems by reasoning upon the semantic graph and automatically generates conceptual and procedural scaffoldings in sequence. We further introduces a step-wise tutoring framework, which can check students' input steps and provide the adaptive scaffolding feedback. Based on the knowledge representation, AnalyticalInk highlights keywords that allow users to further drag them onto the workspace to gather insight into the problem's initial conditions. The system simulates a pen-and-paper environment to let users input both in algebraic and geometric workspaces. We conducted an usability evaluation to measure the effectiveness of AnalyticalInk. We found that keyword highlighting and dragging is useful and effective toward math problem solving. Answer checking in the tutoring component is useful. In general, our prototype shows the promise in helping users to understand geometrical concepts and master algebraic procedures under the problem solving.


title: Lossless Multiway Power Combining and Outphasing for High-Frequency Resonant Inverters

abstract: A lossless multi-way power combining and outphasing system have recently been proposed for high-frequency inverters and power amplifiers that offers major performance advantages over traditional approaches. This paper presents outphasing control strategies for the proposed power combining system that enable output power control through effective load modulation of the inverters. It describes a straightforward power combiner design methodology and enumerates various possible topological combiner implementations. Moreover, this study presents the first-ever experimental demonstration of the proposed outphasing system. The design of a 27.12 MHz, four-way power combining and outphasing system is described and used to experimentally verify the power combiner's characteristics. The proposed outphasing law is shown to be effective in controlling the output power over a 10-100 W (10:1) power range.


title: An Octa-band Monopole Antenna With a Small Nonground Portion Height for LTE/WLAN Mobile Phones

abstract: An octa-band antenna for 5.7-in mobile phones with the size of 80 mm × 6 mm × 5.8 mm is proposed and studied. The proposed antenna is composed of a coupled line, a monopole branch, and a ground branch. By using the 0.25-, 0.5-, and 0.75-wavelength modes, the lower band (704-960 MHz) and the higher band (1710-2690 MHz) are covered. The working mechanism is analyzed based on the S-parameters and the surface current distributions. The attractive merits of the proposed antenna are that the nonground portion height is only 6 mm and any lumped element is not used. A prototype of the proposed antenna is fabricated and measured. The measured -6 dB impedance bandwidths are 350 MHz (0.67-1.02 GHz) and 1.27 GHz (1.65-2.92 GHz) at the lower and higher bands, respectively, which can cover the LTE700, GSM850, GSM900, GSM1800, GSM1900, UMTS, LTE2300, and LTE2500 bands. The measured patterns, gains, and efficiencies are presented.


title: A Maximum Likelihood Approach to Continuous Speech Recognition

abstract: Speech recognition is formulated as a problem of maximum likelihood decoding. This formulation requires statistical models of the speech production process. In this paper, we describe a number of statistical models for use in speech recognition. We give special attention to determining the parameters for such models from sparse data. We also describe two decoding methods, one appropriate for constrained artificial languages and one appropriate for more realistic decoding tasks. To illustrate the usefulness of the methods described, we review a number of decoding results that have been obtained with them.


title: Bug Localization with Combination of Deep Learning and Information Retrieval

abstract: The automated task of locating the potential buggy files in a software project given a bug report is called bug localization. Bug localization helps developers focus on crucial files. However, the existing automated bug localization approaches face a key challenge, called lexical mismatch. Specifically, the terms used in bug reports to describe a bug are different from the terms and code tokens used in source files. To address that, we present a novel approach that uses deep neural network (DNN) in combination with rVSM, an information retrieval (IR) technique. rVSM collects the feature on the textual similarity between bug reports and source files. DNN is used to learn to relate the terms in bug reports to potentially different code tokens and terms in source files. Our empirical evaluation on real-world bug reports in the open-source projects shows that DNN and IR complement well to each other to achieve higher bug localization accuracy than individual models. Importantly, our new model, D nn L oc , with a combination of the features built from DNN, rVSM, and project's bug-fixing history, achieves higher accuracy than the state-of-the-art IR and machine learning techniques. In half of the cases, it is correct with just a single suggested file. In 66% of the time, a correct buggy file is in the list of three suggested files. With 5 suggested files, it is correct in almost 70% of the cases.


title: Using Ontologies with UML Class-based Modeling: The TwoUse Approach

abstract: UML class-based models and OWL ontologies constitute modeling approaches with different strengths and weaknesses that make them appropriate for specifying distinct aspects of software systems. We propose an integrated use of both modeling approaches in a coherent framework - TwoUse. We present a framework involving different concrete syntaxes for developing integrated models and use a SPARQL-like approach for writing query operations. We illustrate TwoUse's applicability with a case study and conclude that TwoUse achieves enhancements of non-functional software requirements like maintainability, reusability and extensibility.


title: Population ecology of a freshwater turtle Kachuga tentoria near Panchnada (Etawah: U.P.) and its role as water purifier.

abstract: The present study deals with biology, ecology and population dynamics of freshwater turtle Kachuga tentoria and its role as water purifier. The study area Panchnada is the site, where five important national rivers meet together and is preserving an appreciable population of nine species of fresh water turtles. Kachuga tentoria was located at all the sampling stations surveyed by the authors, and hence selected for the present study. Different activities (nesting, incubation, predation and other reproductive aspects), climatic conditions, habitat, population density and morphometric features were worked out in detail. A time bound conservation strategy is needed to save this species from extinction. In situ conservation will be more helpful for the recruitment of the population of this species. It will help in the "hatch and release programme" to clean different polluted national rivers.


title: Improving Native Language Identification with TF-IDF Weighting

abstract: This paper presents a Native Language Identification (NLI) system based on TF-IDF weighting schemes and using linear classifiers - support vector machines, logistic regressions and perceptrons. The system was one of the participants of the 2013 NLI Shared Task in the closed-training track, achieving 0.814 overall accuracy for a set of 11 native languages. This accuracy was only 2.2 percentage points lower than the winner’s performance. Furthermore, with subsequent evaluations using 10-fold cross-validation (as given by the organizers) on the combined training and development data, the best average accuracy obtained is 0.8455 and the features that contributed to this accuracy are the TF-IDF of the combined unigrams and bigrams of words.


title: Diagnosis of alzheimers disease from EEG signals: Where are we standing

abstract: This paper reviews recent progress in the diagnosis of Alzheimer's disease (AD) from electroencephalograms (EEG). Three major effects of AD on EEG have been observed: slowing of the EEG, reduced complexity of the EEG signals, and perturbations in EEG synchrony. In recent years, a variety of sophisticated computational approaches has been proposed to detect those subtle perturbations in the EEG of AD patients. The paper ﬁrst describes methods that try to detect slowing of the EEG. Next the paper deals with several measures for EEG complexity, and explains how those measures have been used to study ﬂuctuations in EEG complexity in AD patients. Then various measures of EEG synchrony are considered in the context of AD diagnosis. Also the issue of EEG pre-processing is briefly addressed. Before one can analyze EEG, it is necessary to remove artifacts due to for example head and eye movement or interference from electronic equipment. Pre-processing of EEG has in recent years received much attention. In this paper, several state-of-the-art pre-processing tech- niques are outlined, for example, based on blind source separation and other non-linear ﬁltering paradigms. In addition, the paper outlines opportunities and limitations of computational approaches for diagnosing AD based on EEG. At last, future challenges and open problems are discussed.


title: An Ultralow Power Subthreshold CMOS Voltage Reference Without Requiring Resistors or BJTs

abstract: This brief presents a novel ultralow power CMOS voltage reference (CVR) with only 4.6-nW power consumption. In the proposed CVR circuit, the proportional-to-absolute-temperature voltage is generated by feeding the leakage current of a zero- $V_{\mathrm {gs}}$ nMOS transistor to two diode-connected nMOS transistors in series, both of which are in subthreshold region; while the complementary-to-absolute-temperature voltage is created by using the body diodes of another nMOS transistor. Consequently, low-power operation can be achieved without requiring resistors or bipolar junction transistors, leading to small chip area consumption. The proposed CVR circuit is fabricated in a standard 0.18- $\mu \text{m}$ CMOS process. Measurement results show that the prototype design is capable of providing a 755 mV typical reference voltage with 34 ppm/°C from −15 °C to 140 °C. Moreover, the typical power consumption is only 4.6 nW at room temperature and the active area is only 0.0598 mm2.


title: What to Do on Spring Break? The Role of Predicted, On-Line, and Remembered Experience in Future Choice

abstract: When individuals choose future activities on the basis of their past experiences, what guides those choices? The present study compared students' predicted, on-line, and remembered spring-break experiences, as well as the influence of these factors on students' desire to take a similar vacation in the future. Predicted and remembered experiences were both more positive—and, paradoxically, more negative—than on-line experiences. Of key importance, path analyses revealed that remembered experience, but neither on-line nor anticipated experience, directly predicted the desire to repeat the experience. These results suggest that although on-line measures may be superior to retrospective measures for approximating objective experience, retrospective measures may be superior for predicting choice.


title: Quantitative electroencephalographic measures in homicidal men with antisocial personality disorder

abstract: Many symptoms of antisocial personality disorder have been proposed to be related to decreased daytime vigilance. To explore this hypothesis, quantitative analyses were conducted of the electroencephalographic (EEG) activity of drug-free and detoxified homicidal male offenders with antisocial personality disorder as the primary diagnosis. Subjects comprised 16 men recruited from a forensic psychiatric examination in a special ward of a university psychiatric hospital. Fifteen healthy age- and gender-matched controls with no criminal record or history of physical violence consisted of hospital staff and students. An overall reduction of alpha power was observed in the waking EEG of offenders. A bilateral increase in occipital delta and theta power was also found in these individuals. This study provides further support to the growing evidence of brain dysfunction in severe aggressive behavior. Homicidal offenders with antisocial personality disorder seem to have difficulties in maintaining normal daytime arousal. Decreased vigilance, together with social and psychological variables, may explain their aberrant behavior in everyday life. New studies are, however, needed to specify the vigilance problems of this patient group.


title: Mechanical Design of a Biomimetic Compliant Lower Limb Exoskeleton (BioComEx)

abstract: Exoskeleton robots are wearable electromechanical structures interacting with human limbs. They are used for extending or replacing human performance in power augmentation and rehabilitation applications. The neuro-muscular system of the human body provides flexible and stable movement with minimum energy consumption by means of the compliant actuation of human joints. Similar to human body, compliant actuation can be used to maximize the performance in exoskeleton robots. In the present study, we designed a new biomimetic compliant lower limb exoskeleton robot (BioComEx). Firstly, the current exoskeleton robot designs and biomechanics of the human body joints are reviewed. Then, according to the inferences of human joint biomechanics review, ankle joint is designed as variable stiffness actuator, knee and hip joints are designed as series elastic actuators. Kinetostatic analysis of these joint mechanisms is conducted, and finally the design details of each joint and complete exoskeleton structure adapted to a human dummy model are explained.


Extracting Measures of Active Learning and Student Self-Regulated Learning Strategies from MOOC Data


title: Applying the motivation-opportunity-ability (MOA) model to reveal factors that influence inclusive engagement within local community festivals

abstract: Purpose – The purpose of this paper is to debate on community engagement and participation in local community festivals and events by empirically analysing the factors which either inhibit or facilitate engagement. The application of the motivation-opportunity-ability (MOA) model to the analysis of community festivals is an important contribution. Design/methodology/approach – Recognising the complexity associated with community participation in festivals produced a set of four research questions which are addressed via a mixed methodology research design. The model was then tested using four key data collection methods including the interviews and the questionnaires analysed within this paper. Findings – The MOA model can be seen as an appropriate framework with which to analyse community engagement in a local community festival setting, in the case of “The Utcazene-Fesztival”, a Street Music Festival in Veszprem, Hungary. Community participation is often interpreted quite simply as a way of involving lo...


title: ESD protection design for differential low-noise amplifier with cross-coupled SCR

abstract: A new electrostatic discharge (ESD) protection scheme for differential low-noise amplifier (LNA) was proposed in this paper. The new ESD protection scheme, which evolved from the conventional double-diode ESD protection scheme without adding any extra device, was realized with cross-coupled silicon-controlled rectifier (SCR). With the new ESD protection scheme, the pin-to-pin ESD robustness can be improved, which was the most critical ESD-test pin combination for differential input pads. Experimental results had shown that differential LNA with cross-coupled-SCR ESD protection scheme can achieve excellent ESD robustness and good RF performances.


On Replacing PID Controller with Deep Learning Controller for DC Motor System


title: The small world of the cerebral cortex

abstract: While much information is available on the structural connectivity of the cerebral cortex, especially in the primate, the main organizational principles of the connection patterns linking brain areas, columns and individual cells have remained elusive. We attempt to characterize a wide variety of cortical connectivity data sets using a specific set of graph theory methods. We measure global aspects of cortical graphs including the abundance of small structural motifs such as cycles, the degree of local clustering of connections and the average path length. We examine large-scale cortical connection matrices obtained from neuroanatomical data bases, as well as probabilistic connection matrices at the level of small cortical neuronal populations linked by intra-areal and interareal connections. All cortical connection matrices examined in this study exhibit “small-world” attributes, characterized by the presence of abundant clustering of connections combined with short average distances between neuronal elements. We discuss the significance of these universal organizational features of cortex in light of functional brain anatomy. Supplementary materials are at www.indiana.edu/∼cortex/lab.htm.


title: Acoustic feature selection for automatic emotion recognition from speech

abstract: Emotional expression and understanding are normal instincts of human beings, but automatical emotion recognition from speech without referring any language or linguistic information remains an unclosed problem. The limited size of existing emotional data samples, and the relative higher dimensionality have outstripped many dimensionality reduction and feature selection algorithms. This paper focuses on the data preprocessing techniques which aim to extract the most effective acoustic features to improve the performance of the emotion recognition. A novel algorithm is presented in this paper, which can be applied on a small sized data set with a high number of features. The presented algorithm integrates the advantages from a decision tree method and the random forest ensemble. Experiment results on a series of Chinese emotional speech data sets indicate that the presented algorithm can achieve improved results on emotional recognition, and outperform the commonly used Principle Component Analysis (PCA)/Multi-Dimensional Scaling (MDS) methods, and the more recently developed ISOMap dimensionality reduction method.


title: Android malware development on public malware scanning platforms: A large-scale data-driven study

abstract: Android malware scanning services (e.g., VirusTotal) are websites that users submit suspicious Android programs and get an array of malware detection results. With the growing popularity of such websites, we suspect that, these services are not only used by innocent users, but also, malware writers for testing the evasion capability of their malware samples. May this hypothesis be true, it not only provides interesting insight on Android malware development (AMD), but also provides opportunities for important security applications such as zero-day sample detection. In this work, we first validate this hypothesis with massive data; then design a system AMDHunter to hunt for AMDs on VirusTotal that reveals new threats for Android that has never been revealed before. This is the first systematic study of the malware development phenomenon on VirusTotal, and the first system to automatically detect such malware development cases. AMDHunter has been used in a leading security company for months. Our study is driven by the large amount of data on VirusTotal— We analyzed 153 million submissions collected on VirusTotal during 102 days. Our system identifies 1,623 AMDs with 13,855 samples from 83 countries. We also performed case studies on 890 malware samples selected from the identified AMDs, which revealed lots of new threats, e.g., the development cases of fake system/banking phishing malware, new rooting exploits and etc.


title: Scalable similarity search for SimRank

abstract: SimRank, proposed by Jeh and Widom, provides a good similarity score and has been successfully used in many of the above mentioned applications. While there are many algorithms proposed so far to compute SimRank, but unfortunately, none of them are scalable up to graphs of billions size. Motivated by this fact, we consider the following SimRank-based similarity search problem: given a query vertex u, find top-k vertices v with the k highest SimRank scores s(u,v) with respect to u. We propose a very fast and scalable algorithm for this similarity search problem. Our method consists of the following ingredients: (1) We first introduce a "linear" recursive formula for SimRank. This allows us to formulate a problem that we can propose a very fast algorithm. (2) We establish a Monte-Carlo based algorithm to compute a single pair SimRank score s(u,v), which is based on the random-walk interpretation of our linear recursive formula. (3) We empirically show that SimRank score s(u,v) decreases rapidly as distance d(u,v) increases. Therefore, in order to compute SimRank scores for a query vertex u for our similarity search problem, we only need to look at very "local" area. (4) We can combine two upper bounds for SimRank score s(u,v) (which can be obtained by Monte-Carlo simulation in our preprocess), together with some adaptive sample technique, to prune the similarity search procedure. This results in a much faster algorithm. Once our preprocess is done (which only takes O(n) time), our algorithm finds, given a query vertex u, top-20 similar vertices v with the 20 highest SimRank scores s(u,v) in less than a few seconds even for graphs with billions edges. To the best of our knowledge, this is the first time to scale for graphs with at least billions edges(for the single source case).


title: Bio-inspired predictive orientation decomposition of skeleton trajectories for real-time human activity prediction

abstract: Activity prediction is an essential task in practical human-centered robotics applications, such as security, assisted living, etc., which targets at inferring ongoing human activities based on incomplete observations. To address this challenging problem, we introduce a novel bio-inspired predictive orientation decomposition (BIPOD) approach to construct representations of people from 3D skeleton trajectories. Our approach is inspired by biological research in human anatomy. In order to capture spatio-temporal information of human motions, we spatially decompose 3D human skeleton trajectories and project them onto three anatomical planes (i.e., coronal, transverse and sagittal planes); then, we describe short-term time information of joint motions and encode high-order temporal dependencies. By estimating future skeleton trajectories that are not currently observed, we endow our BIPOD representation with the critical predictive capability. Empirical studies validate that our BIPOD approach obtains promising performance, in terms of accuracy and efficiency, using a physical TurtleBot2 robotic platform to recognize ongoing human activities. Experiments on benchmark datasets further demonstrate that our new BIPOD representation significantly outperforms previous approaches for real-time activity classification and prediction from 3D human skeleton trajectories.


title: Development of portable digital spirometer using NI sbRIO

abstract: One of the effective ways to diagnose various respiratory diseases is using spirometry test. Good spirometer comes with excellent graphical user interface. Spirometer is used to measure lung parameters such as FVC (Forced Vital Capacity) and FEV1 (Forced Expiratory Volume in the first second). The aim of this project is to develop a portable spirometer using NI sbRIO FPGA board and LabVIEW Software. The LabVIEW program consists of two parts: 1) the data acquisition and 2) the disease diagnosis. The data acquisition part will obtain data from user in terms of breathed air. Meanwhile, the disease diagnosis part will use the obtained data to analyze the disease. The system uses National Instrument Single Board Rio 9636 (NI sbRIO) for digital data conversion and data acquisition and breathing hardware from which users will used to flow air into the spirometer. The breathing hardware uses Venturi's principle where the difference of the two pressures with different tube diameters used to measure flow rate and volume of air exhaled over a period of time; to determine the respiratory conditions. Results obtained from three different volunteers with different health performances are also presented in this paper. The FEV1 ratio of a healthy volunteer is 81.1%, an asthma volunteer is 72.04%, while a volunteer with suspected bronchitis is 33.4%. Based on these results, unhealthy users tend to have smaller value of FEV1 with lower area under the curve when compared to a healthy user. The test results are represented in parameters of pulmonary functions which include Volume-time and Flow-volume graphs.