title: Multi-scale CNNs for 3D model retrieval

abstract: Recent advancements in low-cost 3D sensors and mobile devices of virtual 3D models have additionally facilitated the accessibility of 3D data. 3D model retrieval is becoming an indispensable function for modern search engines. An effective retrieval model is at the core of computer vision. With the continuous improvement of 3D data, there are large number of methods to solve this problem. Existing works proposed numerous works to deal with feature extraction and object matching. Most of them are unable to fully exploit the information of 3D representations. To address this problem, we propose a novel multi-layer deep network in this paper. First, multiple rendered images are extracted from a 3D object, and combined into one representative view, which is the actual input of the network. Then, the novel multi-layer network structure is trained and tested on these representative views, generating the feature leaning model, which owns the local and global information of a 3D object. Finally, simple Euclidean metric is used to compute the similarity between two different 3D models to complete the retrieval problem. Extensive experiments and corresponding experimental results have demonstrated the superiority of our approach.


title: A Discriminative Framework for Anomaly Detection in Large Videos

abstract: We address an anomaly detection setting in which training sequences are unavailable and anomalies are scored independently of temporal ordering. Current algorithms in anomaly detection are based on the classical density estimation approach of learning high-dimensional models and finding low-probability events. These algorithms are sensitive to the order in which anomalies appear and require either training data or early context assumptions that do not hold for longer, more complex videos. By defining anomalies as examples that can be distinguished from other examples in the same video, our definition inspires a shift in approaches from classical density estimation to simple discriminative learning. Our contributions include a novel framework for anomaly detection that is (1) independent of temporal ordering of anomalies, and (2) unsupervised, requiring no separate training sequences. We show that our algorithm can achieve state-of-the-art results even when we adjust the setting by removing training sequences from standard datasets.


title: A Three-Phase Integrated Onboard Charger for Plug-In Electric Vehicles

abstract: A three-phase onboard charger, integrated with the propulsion system of a plug-in electric vehicle, is presented. It is constructed by connecting an add-on three-phase power electronics interface to the propulsion system. The propulsion motor is utilized as a coupled DC inductor for the charger. The charging power level of the proposed charger could be as high as that of the propulsion system. The charger topology is capable of three-phase power factor (PF) correction and battery voltage/current regulation. Detailed analyses of the circuit operation and modeling of the circuit are presented. Experimental results of a 3.3-kW three-phase integrated charger, using a permanent magnet synchronous machine (PMSM), are provided. A nearly unity PF and 4.77% total harmonic distortion are obtained with a maximum efficiency of 92.6%.


Recommender system techniques applied to Netflix movie data Research Paper Business Analytics


title: Architectural Support for Mitigating Row Hammering in DRAM Memories

abstract: DRAM scaling has been the prime driver of increasing capacity of main memory systems. Unfortunately, lower technology nodes worsen the cell reliability as it increases the coupling between adjacent DRAM cells, thereby exacerbating different failure modes. This paper investigates the reliability problem due to Row Hammering , whereby frequent activations of a given row can cause data loss for its neighboring rows. As DRAM scales to lower technology nodes, the threshold for the number of row activations that causes data loss for the neighboring rows reduces, making Row Hammering a challenging problem for future DRAM chips. To overcome Row Hammering, we propose two architectural solutions: First, Counter-Based Row Activation (CRA) , which uses a counter with each row to count the number of row activations. If the count exceeds the row hammering threshold, a dummy activation is sent to neighboring rows proactively to refresh the data. Second, Probabilistic Row Activation (PRA) , which obviates storage overhead of tracking and simply allows the memory controller to proactively issue dummy activations to neighboring rows with a small probability for all memory access. Our evaluations show that these solutions are effective at mitigating Row hammering while causing negligible performance loss ( $ 1 percent).


title: Copysets: Reducing the Frequency of Data Loss in Cloud Storage

abstract: Random replication is widely used in data center storage systems to prevent data loss. However, random replication is almost guaranteed to lose data in the common scenario of simultaneous node failures due to cluster-wide power outages. Due to the high fixed cost of each incident of data loss, many data center operators prefer to minimize the frequency of such events at the expense of losing more data in each event. ::: ::: We present Copyset Replication, a novel general-purpose replication technique that significantly reduces the frequency of data loss events. We implemented and evaluated Copyset Replication on two open source data center storage systems, HDFS and RAMCloud, and show it incurs a low overhead on all operations. Such systems require that each node's data be scattered across several nodes for parallel data recovery and access. Copyset Replication presents a near optimal tradeoff between the number of nodes on which the data is scattered and the probability of data loss. For example, in a 5000-node RAMCloud cluster under a power outage, Copyset Replication reduces the probability of data loss from 99.99% to 0.15%. For Facebook's HDFS cluster, it reduces the probability from 22.8% to 0.78%.


title: MARS: Memory Attention-Aware Recommender System

abstract: In this paper, we study the problem of modeling users' diverse interests. Previous methods usually learn a fixed user representation, which has a limited ability to represent distinct interests of a user. In order to model users' various interests, we propose a Memory Attention-aware Recommender System (MARS). MARS utilizes a memory component and a novel attentional mechanism to learn deep adaptive user representations. Trained in an end-to-end fashion, MARS adaptively summarizes users' interests. In the experiments, MARS outperforms seven state-of-the-art methods on three real-world datasets in terms of recall and mean average precision. We also demonstrate that MARS has a great interpretability to explain its recommendation results, which is important in many recommendation scenarios.


title: Power signatures of high-performance computing workloads

abstract: Workload-aware power management and scheduling techniques have the potential to save energy while minimizing negative impact on performance. The effectiveness of these techniques depends on the stability of a workload's power consumption pattern across different input data, resource allocations (e.g. number of cores), and hardware platforms. In this paper, we show that the power consumption behavior of HPC workloads can be accurately captured by concise signatures built from their power traces. We validate this approach using 255 traces collected from 13 high-performance computing workloads on 4 different hardware platforms. First, we use both feature-based and time-series-based distance metrics to cluster our traces, and we quantitatively show that feature-based clusterings segregate traces by workload just as effectively as the more compute- and space-intensive time-series-based clusterings. Second, we demonstrate that unlabeled traces can be classified by workload with over 85% accuracy, based only on these concise statistical signatures.


title: Cognitive programs: software for attention's executive

abstract: What are the computational tasks that an executive controller for visual attention must solve? This question is posed in the context of the Selective Tuning model of attention. The range of required computations go beyond top-down bias signals or region-of-interest determinations, and must deal with overt and covert fixations, process timing and synchronization, information routing, memory, matching control to task, spatial localization, priming, and coordination of bottom-up with top-down information. During task execution, results must be monitored to ensure the expected results. This description includes the kinds of elements that are common in the control of any kind of complex machine or system. We seek a mechanistic integration of the above, in other words, algorithms that accomplish control. Such algorithms operate on representations, transforming a representation of one kind into another, which then forms the input to yet another algorithm. Cognitive Programs (CPs) are hypothesized to capture exactly such representational transformations via stepwise sequences of operations. CPs, an updated and modernized offspring of Ullman's Visual Routines, impose an algorithmic structure to the set of attentional functions and play a role in the overall shaping of attentional modulation of the visual system so that it provides its best performance. This requires that we consider the visual system as a dynamic, yet general-purpose processor tuned to the task and input of the moment. This differs dramatically from the almost universal cognitive and computational views, which regard vision as a passively observing module to which simple questions about percepts can be posed, regardless of task. Differing from Visual Routines, CPs explicitly involve the critical elements of Visual Task Executive (vTE), Visual Attention Executive (vAE), and Visual Working Memory (vWM). Cognitive Programs provide the software that directs the actions of the Selective Tuning model of visual attention.


title: Design of a High Efficiency DC–DC Buck Converter With Two-Step Digital PWM and Low Power Self-Tracking Zero Current Detector for IoT Applications

abstract: In this paper, a high efficiency dc–dc buck converter with two-step digital pulse width modulation (DPWM) and low power self-tracking zero current detector (ST-ZCD) is proposed for Internet of Things (IoT) and ultralow power applications. The hybrid DPWM core with high linearity and low power consumption is proposed to implement the high efficiency DPWM dc–dc converter. It is composed of a two-step delay control using the counter and delay line. An adaptive window analog to digital converter is proposed to reduce the output voltage ripple within 20 mV. A dead time generator is implemented with the proposed ST-ZCD to minimize the reverse current. The ST-ZCD can improve efficiency by reducing the control loss that accounts for a large proportion of the dc–dc converter. Also, all digital type-III compensator is implemented for the low power and small die area. This chip is fabricated with a 55 nm CMOS process, which uses the standard supply voltage of 1.5–3 V to generate the output voltage of 1.2 V. The total active area is 500 μm × 300 μm. The measured peak efficiency of the DPWM dc–dc buck converter is 91.5% with a quiescent current consuming only 130 μA.


title: Multilayer Substrate Integrated Waveguide Four-Way Out-of-Phase Power Divider

abstract: In this paper, a multilayer substrate integrated waveguide (SIW) four-way out-of-phase power divider is investigated. In a previous research (Eom , 2009), the four-way power division was studied by a 3-D mode coupling method, which was achieved by a vertical Y-junction as well as a lateral Y-junction of half mode substrate integrated waveguides (HMSIW) using a multilayer substrate. In this research, resistive coupling slots, for obtaining good isolation between four output ports and impedance matching at all the ports, are realized on the lateral HMSIW Y-junctions and the vertical HMSIW Y-junctions using isolation resistors. From the measurement results, excellent performances of insertion loss, isolation, impedance matching, and amplitude balancing were simultaneously achieved for the X-band range. It is expected that the proposed design of a multilayer substrate integrated waveguide (ML-SIW) power divider will play an important role in the future integration of compact multiway SIW circuits and systems.


title: Learning Strict Identity Mappings in Deep Residual Networks

abstract: A family of super deep networks, referred to as residual networks or ResNet [14], achieved record-beating performance in various visual tasks such as image recognition, object detection, and semantic segmentation. The ability to train very deep networks naturally pushed the researchers to use enormous resources to achieve the best performance. Consequently, in many applications super deep residual networks were employed for just a marginal improvement in performance. In this paper, we propose âˆS-ResNet that allows us to automatically discard redundant layers, which produces responses that are smaller than a threshold âˆS, without any loss in performance. The âˆS-ResNet architecture can be achieved using a few additional rectified linear units in the original ResNet. Our method does not use any additional variables nor numerous trials like other hyperparameter optimization techniques. The layer selection is achieved using a single training process and the evaluation is performed on CIFAR-10, CIFAR-100, SVHN, and ImageNet datasets. In some instances, we achieve about 80% reduction in the number of parameters.


title: An Embedded All-Digital Circuit to Measure PLL Response

abstract: We present an all-digital measurement circuit that enables wafer-level test and characterization of phase-locked loop (PLL) response. Through modifications only in the PLL feedback divider state machine, this technique facilitates accurate estimation of PLL frequency-domain closed-loop bandwidth and gain peaking by respectively measuring the time-domain crossover time and maximum overshoot of phase error to a self-induced phase step in the feedback clock. These transient measurements are related back to bandwidth and peaking through the proportionality relationships of crossover time to reciprocal bandwidth and maximum overshoot to peaking. The design-for-test circuit can be used to generate a transient plot of step response, measure static phase error, and observe phase-lock status. We report silicon results from two demonstration vehicles built in a 45-nm SOI-CMOS logic technology for high-performance microprocessors.


title: Investigating Bidirectional Recurrent Neural Network Language Models for Speech Recognition

abstract: Copyright © 2017 ISCA. Recurrent neural network language models (RNNLMs) are powerful language modeling techniques. Significant performance improvements have been reported in a range of tasks including speech recognition compared to n-gram language models. Conventional n-gram and neural network language models are trained to predict the probability of the next word given its preceding context history. In contrast, bidirectional recurrent neural network based language models consider the context from future words as well. This complicates the inference process, but has theoretical benefits for tasks such as speech recognition as additional context information can be used. However to date, very limited or no gains in speech recognition performance have been reported with this form of model. This paper examines the issues of training bidirectional recurrent neural network language models (bi-RNNLMs) for speech recognition. A bi-RNNLM probability smoothing technique is proposed, that addresses the very sharp posteriors that are often observed in these models. The performance of the bi-RNNLMs is evaluated on three speech recognition tasks: broadcast news; meeting transcription (AMI); and low-resource systems (Babel data). On all tasks gains are observed by applying the smoothing technique to the bi-RNNLM. In addition consistent performance gains can be obtained by combining bi-RNNLMs with n-gram and uni-directional RNNLMs.


title: Unpacking virtualization obfuscators

abstract: Nearly every malware sample is sheathed in an executable protection which must be removed before static analyses can proceed. Existing research has studied automatically unpacking certain protections, but has not yet caught up with many modern techniques. Contrary to prior assumptions, protected programs do not always have the property that they are reverted to a fully unprotected state at some point during the course of their execution. This work provides a novel technique for circumventing one of the most problematic features of modern software protections, so-called virtualization obfuscation. The technique enables analysis of heretofore impenetrable malware.


abits and beliefs that guide self-regulated learning : Do they vary ith mindset ?


title: Horizontal Pyramid Matching for Person Re-identification

abstract: Despite the remarkable progress in person re-identification (Re-ID), such approaches still suffer from the failure cases where the discriminative body parts are missing. To mitigate this type of failure, we propose a simple yet effective Horizontal Pyramid Matching (HPM) approach to fully exploit various partial information of a given person, so that correct person candidates can be identified even if some key parts are missing. With HPM, we make the following contributions to produce more robust feature representations for the Re-ID task: 1) we learn to classify using partial feature representations at different horizontal pyramid scales, which successfully enhance the discriminative capabilities of various person parts; 2) we exploit average and max pooling strategies to account for person-specific discriminative information in a global-local manner. To validate the effectiveness of our proposed HPM method, extensive experiments are conducted on three popular datasets including Market-1501, DukeMTMCReID and CUHK03. Respectively, we achieve mAP scores of 83.1%, 74.5% and 59.7% on these challenging benchmarks, which are the new state-of-the-arts.


title: BinarEye: An always-on energy-accuracy-scalable binary CNN processor with all memory on chip in 28nm CMOS

abstract: This paper introduces BinarEye: the first digital processor for always-on Binary Convolutional Neural Networks. The chip maximizes data reuse through a Neuron Array exploiting local weight Flip-Flops. It stores full network models and feature maps and hence requires no off-chip bandwidth, which leads to a 230 lb-TOPS/W peak efficiency. Its 3-levels of flexibility — (a) weight reconfiguration, (b) a programmable network depth and (c) a programmable network width — allow trading energy for accuracy depending on the task's requirements. BinarEye's full-system input-to-label energy consumption ranges from 14.4uJ/f for 86%/CIFAR-10 and 98%/owner recognition down to 0.92uJ/f for 94%/face detection at up to 1700 frames per second. This is 3-12-70× more efficient than the state-of-the-art at on par accuracy.


title: Improving bug triage with bug tossing graphs

abstract: bug report is typically assigned to a single developer who is then responsible for fixing the bug. In Mozilla and Eclipse, between 37%-44% of bug reports are "tossed" (reassigned) to other developers, for example because the bug has been assigned by accident or another developer with additional expertise is needed. In any case, tossing increases the time-to-correction for a bug. In this paper, we introduce a graph model based on Markov chains, which captures bug tossing history. This model has several desirable qualities. First, it reveals developer networks which can be used to discover team structures and to find suitable experts for a new task. Second, it helps to better assign developers to bug reports. In our experiments with 445,000 bug reports, our model reduced tossing events, by up to 72%. In addition, the model increased the prediction accuracy by up to 23 percentage points compared to traditional bug triaging approaches.


title: Rapid Prototyping for Control Education using Arduino and Open-Source Technologies

abstract: Abstract In this article, we introduce our recent development of small-sized experimental devices for control research and education, with a special focus on the use of open-source technologies such as Arduino and Processing. Arduino is a pronominal open-source hardware whose architecture, implementation and other necessary resources are accessible to every users, while Processing is its software counterpart which supports rapid development of controller/interface programs without much expertise. We demonstrate their advantage with a kinematic nonholonomic system called the trident snake robot and an under-actuated mechanical system called the inverted unicycle robot.


title: Clinical Utility of EEG in Attention-Deficit/Hyperactivity Disorder: A Research Update

abstract: Psychiatric research applications of electroencephalography (EEG), the earliest approach to imaging human cortical brain activity, are attracting increasing scientific and clinical interest. For more than 40 years, EEG research has attempted to characterize and quantify the neurophysiology of attention-deficit/hyperactivity disorder (ADHD), most consistently associating it with increased frontocentral theta band activity and increased theta to beta (θ/β) power ratio during rest compared to non-ADHD controls. Recent reports suggest that while these EEG measures demonstrate strong discriminant validity for ADHD, significant EEG heterogeneity also exists across ADHD-diagnosed individuals. In particular, additional studies validating the use of the θ/β power ratio measure appear to be needed before it can be used for clinical diagnosis. In recent years, the number and the scientific quality of research reports on EEG-based neurofeedback (NF) for ADHD have grown considerably, although the studies reviewed here do not yet support NF training as a first-line, stand-alone treatment modality. In particular, more research is needed comparing NF to placebo control and other effective treatments for ADHD. Currently, after a long period of relative stasis, the neurophysiological specificity of measures used in EEG research is rapidly increasing. It is likely, therefore, that new EEG studies of ADHD using higher density recordings and new measures drawn from viewing EEG as a 3-dimensional functional imaging modality, as well as intensive re-analyses of existing EEG study data, can better characterize the neurophysiological differences between and within ADHD and non-ADHD subjects, and lead to more precise diagnostic measures and effective NF approaches.


title: STransE: a novel embedding model of entities and relationships in knowledge bases

abstract: Knowledge bases of real-world facts about entities and their relationships are useful resources for a variety of natural language processing tasks. However, because knowledge bases are typically incomplete, it is useful to be able to perform link prediction, i.e., predict whether a relationship not in the knowledge base is likely to be true. This paper combines insights from several previous link prediction models into a new embedding model STransE that represents each entity as a lowdimensional vector, and each relation by two matrices and a translation vector. STransE is a simple combination of the SE and TransE models, but it obtains better link prediction performance on two benchmark datasets than previous embedding models. Thus, STransE can serve as a new baseline for the more complex models in the link prediction task.


title: Dynamic Pricing in Management Science

abstract: This paper presents a review of the literature on dynamic pricing models in management science. We discuss monopolistic and competitive situations, with an emphasis on applications in different contexts. We also describe the main development perspectives of this literature.


title: Pressure Sensitive Stylus and Algorithm for Touchscreen Panel

abstract: This work presents a new simple passive 3D stylus and its associated stroke reconstruction algorithm for capacitive touch panels that detects the x,y -directional position and z -directional strength without the help of a pressure sensor. The proposed algorithm can successfully detect touch information while eliminating noise. Moreover, unintended touch will be recognized and its effects are eliminated while the desired signal will be accessed by the touch system to enhance the handwriting experience. The results of experiments on a touch panel system were analyzed, demonstrating the effectiveness of the proposed stylus and its stroke reconstruction algorithm.


title: DropBack: Continuous Pruning During Training

abstract: We introduce a technique that compresses deep neural networks both during and after training by constraining the total number of weights updated during backpropagation to those with the highest total gradients. The remaining weights are forgotten and their initial value is regenerated at every access to avoid storing them in memory. This dramatically reduces the number of off-chip memory accesses during both training and inference, a key component of the energy needs of DNN accelerators. ::: By ensuring that the total weight diffusion remains close to that of baseline unpruned SGD, networks pruned using DropBack are able to maintain high accuracy across network architectures. We observe weight compression of 25x with LeNet-300-100 on MNIST while maintaining accuracy. On CIFAR-10, we see an approximately 5x weight compression on 3 models: an already 9x-reduced VGG-16, Densenet, and WRN-28-10 - all with zero or negligible accuracy loss. On Densenet and WRN, which are particularly challenging to compress, Both Densenet and WRN improve on the state of the art, achieving higher compression with better accuracy than prior pruning techniques.


title: Pilot-Streaming: A Stream Processing Framework for High-Performance Computing

abstract: An increasing number of scientific applications utilize stream processing to analyze data feeds of scientific instruments, sensors, and simulations. In this paper, we study the streaming and data processing requirements of light source experiments, which are projected to generate data at 20 GB/sec in the near future. As beamtimes available to users are typically short, it is essential that processing and analysis can be conducted in a streaming mode. The development and deployment of streaming applications is a complex task and requires the integration of heterogeneous, distributed infrastructure, frameworks, middleware and application components written in different languages and abstractions. Streaming applications may be extremely dynamic due to factors, such as variable data rates, network congestions, and application-specific characteristics, such as adaptive sampling techniques and the different processing techniques. Consequently, streaming system are often subject to back-pressures and instabilities requiring additional infrastructure to mitigate these issues. We propose Pilot-Streaming, a framework for supporting streaming applications and their resource management needs on HPC infrastructure. Underlying Pilot-Streaming is a unifying architecture that decouples important concerns and functions, such as message brokering, transport and communication, and processing. Pilot-Streaming simplifies the deployment of stream processing frameworks, such as Kafka and Spark Streaming, while providing a high-level abstraction for managing streaming infrastructure, e. g. adding/removing resources as required by the application at runtime. This capability is critical for balancing complex streaming pipelines. To address the complexity in the development of streaming applications, we present the Streaming Mini-Apps, which supports different plug-able algorithms for data generation and processing, e. g., for reconstructing light source images using different techniques. We use the streaming Mini-Apps to evaluate the Pilot-Streaming framework demonstrating its suitability for different use cases and workloads.


title: Credit card fraud and detection techniques : a review

abstract: Fraud is one of the major ethical issues in the credit card industry. The main aims are, firstly, to identify the different types of credit card fraud, and, secondly, to review alternative techniques that have been used in fraud detection. The sub-aim is to present, compare and analyze recently published findings in credit card fraud detection. This article defines common terms in credit card fraud and highlights key statistics and figures in this field. Depending on the type of fraud faced by banks or credit card companies, various measures can be adopted and implemented. The proposals made in this paper are likely to have beneficial attributes in terms of cost savings and time efficiency. The significance of the application of the techniques reviewed here is in the minimization of credit card fraud. Yet there are still ethical issues when genuine credit card customers are misclassified as fraudulent.


title: A vibrotactile feedback approach to posture guidance

abstract: In this paper, we explore the use of vibrotactile haptic feedback to improve performance of a sensory-motor task. Our specific application is seated posture guidance. Poor sitting postures, over time, can lead to health concerns such as low back pain. We propose a real-time haptic feedback system that actively senses and guides a person to a desired posture. We have instrumented an ergonomic office chair with a simple system using 7 force-sensitive resistors (FSRs) for posture detection and 6 vibrotactile actuators (“tactors”) for haptic feedback. When the chair detects that the subject is not sitting in the desired posture, one or more tactors will vibrate, directing the subject towards or away from a certain position. In a pilot study of 10 subjects (age 23.9±1.45, weight 71.1±13.6kg, height 175±11.2cm), 100% sat in the reference posture (upright) more often when there was haptic feedback than when there was not. Additionally, when haptic feedback was temporarily disabled without the subject's knowledge, all of the subjects continued to sit in upright or near-upright postures. These findings suggest that vibrotactile feedback can be an effective means of communicating musculoskeletal commands to the human sensory-motor system, especially for motor training and rehabilitation.


title: Blockchain Versus Database: A Critical Analysis

abstract: In recent times, Bitcoin has gained special attention both from industry and academia. The underlying technology that enables Bitcoin (or more generally crypto-currency) is called blockchain. At the core of the blockchain technology is a data structure that keeps record of the transactions in the network. The special feature that distinguishes it from existing technology is its immutability of the stored records. To achieve immutability, it uses consensus and cryptographic mechanisms. As the data is stored in distributed nodes this technology is also termed as "Distributed Ledger Technology (DLT)". As many researchers and practitioners are joining the hype of blockchain, some of them are raising the question about the fundamental difference between blockchain and traditional database and its real value or potential. In this paper, we present a critical analysis of both technologies based on a survey of the research literature where blockchain solutions are applied to various scenarios. Based on this analysis, we further develop a decision tree diagram that will help both practitioners and researchers to choose the appropriate technology for their use cases. Using our proposed decision tree we evaluate a sample of the existing works to see to what extent the blockchain solutions have been used appropriately in the relevant problem domains.


Quantum Computation and Shor's Factoring Algorithm


title: Function-Hiding Inner Product Encryption

abstract: We extend the reach of functional encryption schemes that are provably secure under simple assumptions against unbounded collusion to include function-hiding inner product schemes. Our scheme is a private key functional encryption scheme, where ciphertexts correspond to vectors $$\vec {x}$$, secret keys correspond to vectors $$\vec {y}$$, and a decryptor learns $$\langle \vec {x}, \vec {y} \rangle $$. Our scheme employs asymmetric bilinear maps and relies only on the SXDH assumption to satisfy a natural indistinguishability-based security notion where arbitrarily many key and ciphertext vectors can be simultaneously changed as long as the key-ciphertext dot product relationships are all preserved.


title: Real-time Image Processing on Low Cost Embedded Computers

abstract: In 2012 a federal mandate was imposed that required the FAA to integrate unmanned aerial systems (UAS) into the national airspace (NAS) by 2015 for civilian and commercial use. A significant driver for the increasing popularity of these systems is the rise in open hardware and open software solutions which allow hobbyists to build small UAS at low cost and without specialist equipment. This paper describes our work building, evaluating and improving performance of a vision-based system running on an embedded computer onboard such a small UAS. This system utilises open source software and open hardware to automatically land a multi-rotor UAS with high accuracy. Using parallel computing techniques, our final implementation runs at the maximum possible rate of 30 frames per second. This demonstrates a valid approach for implementing other real-time vision based systems onboard UAS using low power, small and economical embedded computers.


title: Improving the Efficiency of Time-Optimal Path-Following Algorithms

abstract: A method is presented which significantly improves the computational efficiency of time-optimal path-following planning algorithms for robot manipulators with limited actuator torques. Characteristics switching points are identified and characterized analytically as functions of the single parameter defining the position along the path. Limit curves are then constructed from the characteristic switching points, in contrast with the so-called maximum velocity curve approach of existing methods. An efficient algorithm is proposed which utilizes the characteristics of the defined concepts. The algorithm can also account for viscous friction effects and smooth state-dependent actuator bounds. A numerical example, while showing the consistency of the algorithm with the existing techniques, demonstrates its potential for increasing computational efficiency by several orders of magnitude. >


title: Exploring specialized near-memory processing for data intensive operations

abstract: Emerging 3D stacked memory systems provide significantly more bandwidth than current DDR modules. However, general purpose processors do not take full advantage of these resources offered by the memory modules. Taking advantage of the increased bandwidth requires the use of specialized processing units. In this paper, we evaluate the benefits of placing hardware accelerators at the bottom layer of a 3D stacked memory system compared to accelerators that are placed external to the memory stack. Our evaluation of the design using cycle-accurate simulation and RTL synthesis shows that, for important data intensive kernels, near-memory accelerators inside a single 3D memory package provide 3x-13x speedup over a Quad-core Xeon processor. Most of the benefits are from the application of accelerators, as the near-memory configurations provide marginal benefits compared to the same number of accelerators placed on a die external to the memory package. This comparable performance for external accelerators is due to the high bandwidth afforded by the high-speed off-chip links. On the other hand, near-memory accelerators consume 7%–39% less energy than the external accelerators.


title: Finding Influential Training Samples for Gradient Boosted Decision Trees

abstract: We address the problem of finding influential training samples for a particular case of tree ensemble-based models, e.g., Random Forest (RF) or Gradient Boosted Decision Trees (GBDT). A natural way of formalizing this problem is studying how the model's predictions change upon leave-one-out retraining, leaving out each individual training sample. Recent work has shown that, for parametric models, this analysis can be conducted in a computationally efficient way. We propose several ways of extending this framework to non-parametric GBDT ensembles under the assumption that tree structures remain fixed. Furthermore, we introduce a general scheme of obtaining further approximations to our method that balance the trade-off between performance and computational complexity. We evaluate our approaches on various experimental setups and use-case scenarios and demonstrate both the quality of our approach to finding influential training samples in comparison to the baselines and its computational efficiency.


title: Progressive Reinforcement Learning with Distillation for Multi-Skilled Motion Control

abstract: Deep reinforcement learning has demonstrated increasing capabilities for continuous control problems, including agents that can move with skill and agility through their environment. An open problem in this setting is that of developing good strategies for integrating or merging policies for multiple skills, where each individual skill is a specialist in a specific skill and its associated state distribution. We extend policy distillation methods to the continuous action setting and leverage this technique to combine expert policies, as evaluated in the domain of simulated bipedal locomotion across different classes of terrain. We also introduce an input injection method for augmenting an existing policy network to exploit new input features. Lastly, our method uses transfer learning to assist in the efficient acquisition of new skills. The combination of these methods allows a policy to be incrementally augmented with new skills. We compare our progressive learning and integration via distillation (PLAID) method against three alternative baselines.


title: Broadband Radial Waveguide Spatial Combiner

abstract: A broadband eight-way spatial combiner using coaxial probes and radial waveguides has been proposed and designed. The simple electromagnetic modeling for the radial waveguide power divider/combiner has been developed using equivalent-circuit method. The measured 10-dB return loss and 1-dB insertion loss bandwidth of this waveguide spatial combiner are all demonstrated to be about 8 GHz.


title: Antineutrophil autoantibodies in Graves' disease. Implications of thyrotropin binding to neutrophils.

abstract: The hyperthyroidism of Graves' disease may be caused by autoantibodies to thyrotropin (TSH) receptors. We have found that patients with this disease have autoantibodies to neutrophils as well, which can be displaced by TSH. Using a radiochemical opsonic assay, we found serum antibodies against homologous neutrophils in 6 of 11 Graves' patients. With a staphylococcal protein A-binding assay, we detected circulating antibodies to homologous neutrophils in 10 of 20 patients, while finding cell-bound antibody on autologous neutrophils in 7 of 8 (including 2 with negative serum tests). Use of human 125I-TSH in a radioligand binding assay revealed that TSH bound to neutrophils rapidly (maximum binding within 10 min at 22 degrees C, pH 7.4), specifically (less than 20% nonspecific binding), and reversibly. Adding TSH to the radiochemical assay resulted in a dose-dependent inhibition of opsonic antibody activity in serum from patients with Graves' disease. In contrast, TSH did not inhibit antibody activity of serum from patients with immune neutropenia not associated with thyroid disease. Our findings suggest a basis for the association of Graves' disease with neutropenia. Furthermore, the discovery of such antineutrophil antibodies in Graves' disease permits detection of cell-bound antibody when free antibody is not present.


title: Blockchain Challenges and Security Schemes: A Survey

abstract: With the increasing number of connected devices and the number of online transactions today, managing all these transactions and devices and maintaining network security is a research issue. Current solutions are mainly based on cloud computing infrastructures, which require servers high-end and broadband networks to provide data storage and computing services. These solutions have a number of significant disadvantages, such as high maintenance costs of centralized servers, critical weakness of Internet Of Things applications, security and trust issues, etc. The blockchain is seen as a promising technique for addressing the mentioned security issues and design new decentralization frameworks. However, this new technology has a great potential in the most diverse technological fields. In this paper, we focus on presenting an overview of blockchain technology, highlighting its advantages, limitations and areas of application.The originality of this work resides in the comparison between the different blockchain systems and their security schemes and the perspective of integrating this technology into secured systems models for our comfort and our private life.


title: Meek-Based Tor Traffic Identification with Hidden Markov Model

abstract: Tor is one of the major technologies of censorship circumvention systems. To protect the privacy of users and the information of first hop to access Tor networks, Tor Browser introduces an obfuscation technology called Meek. Tor traffic is obfuscated by Meek to behave as ordinary cloud service traffic. In order to advance the capability of network monitoring systems, this paper proposes a Mixture of Gaussians based Hidden Markov Model (MGHMM), a new model for identifying Meek-based Tor traffic. The proposed MGHMM has two components: 1) Mixture of Gaussians (MOG) is used to characterize the Inter-Packet Time (IPT) distribution and the Packet Size (PS) density distribution; 2) HMM is used to compute the probability of a traffic observation sequence and identify Meek-based Tor traffic by using two-dimensional observations composed by IPT and PS. The effectiveness of the proposed model is evaluated with real-world traffic. Extensive experiments show that the proposed MGHMM is able to identify Meek-based Tor traffic effectively.


title: Design and modeling of a crowdsource-enabled system for urban parcel relay and delivery

abstract: This paper proposes a crowdsource-enabled system for urban parcel relay and delivery. We consider cyclists and pedestrians as crowdsources who are close to customers and interested in relaying parcels with a truck carrier and undertaking jobs for the last-leg parcel delivery and the first-leg parcel pickup. The crowdsources express their interests in doing so by submitting bids to the truck carrier. The truck carrier then selects bids and coordinates crowdsources’ last-leg delivery (first-leg pickup) with its truck operations. The truck carrier's problem is formulated as a mixed integer non-linear program which simultaneously i) selects crowdsources to complete the last-leg delivery (first-leg pickup) between customers and selected points for crowdsource-truck relay; and ii) determines the relay points and truck routes and schedule. To solve the truck carrier problem, we first decompose the problem into a winner determination problem and a simultaneous pickup and delivery problem with soft time windows, and propose a Tabu Search based algorithm to iteratively solve the two subproblems. Numerical results show that this solution approach is able to yield close-to-optimum solutions with much less time than using off-the-shelf solvers. By adopting this new system, truck vehicle miles traveled (VMT) and total cost can be reduced compared to pure-truck delivery. The advantage of the system over pure-truck delivery is sensitive to factors such as penalty for servicing outside customers’ desired time windows, truck unit operating cost, time value of crowdsources, and the crowdsource mode.


title: Human emotion and the uncanny valley: a GLM, MDS, and Isomap analysis of robot video ratings

abstract: The eerie feeling attributed to human-looking robots and animated characters may be a key factor in our perceptual and cognitive discrimination of the human and humanlike. This study applies regression, the generalized linear model (GLM), factor analysis, multidimensional scaling (MDS), and kernel isometric mapping (Isomap) to analyze ratings of 27 emotions of 18 moving figures whose appearance varies along a human likeness continuum. The results indicate (1) Attributions of eerie and creepy better capture our visceral reaction to an uncanny robot than strange. (2) Eerie and creepy are mainly associated with fear but also shocked, disgusted, and nervous. Strange is less strongly associated with emotion. (3) Thus, strange may be more cognitive, while eerie and creepy are more perceptual/emotional. (4) Human features increase ratings of human likeness. (5) Women are slightly more sensitive to eerie and creepy than men; and older people may be more willing to attribute human likeness to a robot despite its eeriness.


title: Localization of drug-metabolizing enzyme activities to blood-brain interfaces and circumventricular organs.

abstract: : The brain, with the exception of the choroid plexuses and Circumventricular organs, is partially protected from the invasion of blood-borne chemicals by the specific morphological properties of the cerebral micro-vessels, namely, the tight junctions of the blood-brain barrier. Recently, several enzymes that are primarily involved in hepatic drug metabolism have been shown to exist in the brain, albeit at relatively low specific activities. In the present study, the hypothesis that these enzymes are located primarily at blood-brain interfaces, where they form an “enzymatic barrier,” is tested. By using microdissection techniques or a gradient-centrifugation isolation procedure, the activities of seven drug-metabolizing enzymes in isolated microvessels, choroid plexuses, meningeal membranes, and tissue from three Circumventricular organs (the neural lobe of the hypophysis, pineal gland, and median eminence) were assayed. With two exceptions, the activities of these enzymes were higher in the three Circumventricular organs and cerebral microvessel than in the cortex. Very high membrane-bound epoxide hydrolase and UDP-glucuronosyltransferase activities (approaching those in liver) and somewhat high 7-benzoxyre-sorufin-O-dealkylase and NADPH-cytochrome P-450 reductase activities were determined in the choroid plexuses. The pia-arachnoid membranes, but not the dura matter, displayed drug-metabolizing enzyme activities, notably that of epoxide hydrolase: The drug-metabolizing enzymes located at these nonparenchymal sites may function to protect brain tissue from harmful compounds.


title: Nursing Management of Haemorrhagic Cystitis in Patients Undergoing Haematopoietic Stem Cell Transplantation: a Multicentre Italian Survey

abstract: Background ::: Haemorrhagic cystitis (HC) is a severe complication occurring after haematopoietic stem cell transplantation (HSCT) in 13–40% of patients, caused by infectious and/or non-infectious factors that increase the in-hospital length of stay and the risk of mortality of transplanted recipients. Although different management interventions have been suggested in the literature, available knowledge on interventions performed by Italian nurses in their daily practices has not been documented to date.


title: Initialization and Optimization of Multilayered Perceptrons

abstract: Despite all the progress in neural networks field the technology is brittle and sometimes difficult to apply. Good initialization of adaptive parameters in neural networks and optimization of architecture are the key factor to create robust neural networks. Methods of initialization of MLPs are reviewed and new methods based on clusterization techniques are suggested. Penalty term added to the error function leads to optimized, small and accurate networks. INDING global minimum of a nonlinear function with many parameters is an NP-hard problem [1]. Learning in neural networks is most frequently based on minimization of a cost function. Good initialization of adaptive parameters may enable finding solutions in complex, real-world problems and may significantly decrease learning time. Subsequent optimization should lead to compact networks capable of good generalization. In this paper methods of initialization and optimization of the multi-layer perceptrons (MLPs) used for classification problems are investigated. In MLP networks sigmoidal transfer functions provide hyperplanes, dividing the input space into classification regions. Coefficients of these hyperplanes – called activation weights – are usually the only adaptive parameters of such networks. Initialization procedures should propose architecture (the number of nodes and connections), weights (biases are also counted as weights), and in some cases also the slopes of sigmoidal functions. Initial structure of MLP networks is frequently optimized during learning, either using genetic or other global minimization methods, or enforcing skeletonization of networks using penalty terms in the cost functions. In the next section methods of initialization of MLPs are discussed and new methods, allowing to solve some classification problems without tedious optimization, proposed. In the third section optimization of MLP architecture based on new form of error function is described and results compared with other methods. Short discussion closes this paper. II. Initialization of MLP weights


The Business Model Eco � System


title: Forecasting Big Time Series: Old and New

abstract: Time series forecasting is a key ingredient in the automation and optimization of business processes: in retail, deciding which products to order and where to store them depends on the forecasts of future demand in different regions; in cloud computing, the estimated future usage of services and infrastructure components guides capacity planning; and workforce scheduling in warehouses, call centers, factories requires forecasts of the future workload. Recent years have witnessed a paradigm shift in forecasting techniques and applications, from computer-assisted model- and assumption-based to data-driven and fully-automated. This shift can be attributed to the availability of large, rich, and diverse time series data sources, posing unprecedented challenges to traditional time series forecasting methods. As such, how can we build statistical models to efficiently and effectively learn to forecast from large and diverse data sources? How can we leverage the statistical power of "similar" time series to improve forecasts in the case of limited observations? What are the implications for building forecasting systems that can handle large data volumes? ::: ::: The objective of this tutorial is to provide a concise and intuitive overview of the most important methods and tools available for solving large-scale forecasting problems. We review the state of the art in three related fields: (1) classical modeling of time series, (2) scalable tensor methods, and (3) deep learning for forecasting. Further, we share lessons learned from building scalable forecasting systems. While our focus is on providing an intuitive overview of the methods and practical issues, we also present technical details underlying these powerful tools.


title: Dual-Polarized Slot-Coupled Microstrip Antenna Array With Stable Active Element Pattern

abstract: A dual-polarized slot-coupled microstrip antenna array with stable active element pattern is investigated. The antenna element of the array is a slot-coupled microstrip antenna with a reflector. The effect of the ground size on the radiation pattern of the antenna element is analyzed based on the electric field and current distributions. The method of adding copper pillars around the antenna element is proposed to mitigate the influence of the ground plane. A prototype of ${1} \times {3}$ microstrip antenna array is fabricated and measured. Its measured impedance bandwidth for $\mathrm{VSWR} is 520 MHz (1.69–2.21 GHz). Within the operating band, the performances of low mutual coupling, low cross polarization, and stable active element pattern are achieved.


title: Taming Non-stationary Bandits: A Bayesian Approach

abstract: We consider the multi armed bandit problem in non-stationary environments. Based on the Bayesian method, we propose a variant of Thompson Sampling which can be used in both rested and restless bandit scenarios. Applying discounting to the parameters of prior distribution, we describe a way to systematically reduce the effect of past observations. Further, we derive the exact expression for the probability of picking sub-optimal arms. By increasing the exploitative value of Bayes' samples, we also provide an optimistic version of the algorithm. Extensive empirical analysis is conducted under various scenarios to validate the utility of proposed algorithms. A comparison study with various state-of-the-arm algorithms is also included.


title: An optimization framework for the development of efficient one-way car-sharing systems

abstract: Electric vehicle-sharing systems have been introduced to a number of cities as a means of increasing mobility, reducing congestion, and pollution. Electric vehicle-sharing systems can offer one or two-way services. One-way systems provide more flexibility to users since they can be dropped-off at any station. However, their modeling involves a number of complexities arising from the need to relocate vehicles accumulated at certain stations. The planning of one-way electric vehicle-sharing systems involves a host of strongly interacting decisions regarding the number, size and location of stations, as well as the fleet size. In this paper we develop and solve a multi-objective MILP model for planning one-way vehicle-sharing systems taking into account vehicle relocation and electric vehicle charging requirements. For real world problems the size of the problem becomes intractable due to the extremely large number of relocation variables. In order to cope with this problem we introduce an aggregate model using the concept of the virtual hub. This transformation allows the solution of the problem with a branch-and-bound approach. The proposed approach generates the efficient frontier and allows decision makers to examine the trade-off between operator’s and users’ benefits. The capabilities of the proposed approach are demonstrated on a large scale real world problem with available data from Nice, France. Extensive sensitivity analysis was performed by varying demand, station accessibility distance and subsidy levels. The results provide useful insights regarding the efficient planning of one-way electric vehicle-sharing systems and allow decision makers to quantify the trade-off between operator’s and users’ benefits.


title: A ground level causal learning algorithm

abstract: Open domain causal learning involves learning and establishing causal connections between events directly from sensory experiences. It has been established in psychology that this often requires background knowledge. However, background knowledge has to be built from first experiences, which we term ground level causal learning, which basically involves observing temporal correlations. Subsequent knowledge level causal learning can then be based on this ground level causal knowledge. The causal connections between events, such as between lightning and thunder, are often hard to discern based on simple temporal correlations because there might be noise - e.g., wind, headlights, sounds of vehicles, etc. - that intervene between lightning and thunder. In this paper, we adopt the position that causal learning is inductive and pragmatic, and causal connections exist on a scale of graded strength. We describe a method that is able to filter away noise in the environment to obtain likely causal connections between events.


title: Text Categorization with Class-Based and Corpus-Based Keyword Selection

abstract: In this paper, we examine the use of keywords in text categorization with SVM. In contrast to the usual belief, we reveal that using keywords instead of all words yields better performance both in terms of accuracy and time. Unlike the previous studies that focus on keyword selection metrics, we compare the two approaches for keyword selection. In corpus-based approach, a single set of keywords is selected for all classes. In class-based approach, a distinct set of keywords is selected for each class. We perform the experiments with the standard Reuters-21578 dataset, with both boolean and tf-idf weighting. Our results show that although tf-idf weighting performs better, boolean weighting can be used where time and space resources are limited. Corpus-based approach with 2000 keywords performs the best. However, for small number of keywords, class-based approach outperforms the corpus-based approach with the same number of keywords.


title: Traffic signal timing via deep reinforcement learning

abstract: In this paper, we propose a set of algorithms to design signal timing plans via deep reinforcement learning. The core idea of this approach is to set up a deep neural network (DNN) to learn the Q-function of reinforcement learning from the sampled traffic state/control inputs and the corresponding traffic system performance output. Based on the obtained DNN, we can find the appropriate signal timing policies by implicitly modeling the control actions and the change of system states. We explain the possible benefits and implementation tricks of this new approach. The relationships between this new approach and some existing approaches are also carefully discussed.


title: Commercial Visual Analytics Systems–Advances in the Big Data Analytics Field

abstract: Five years after the first state-of-the-art report on Commercial Visual Analytics Systems we present a reevaluation of the Big Data Analytics field. We build on the success of the 2012 survey, which was influential even beyond the boundaries of the InfoVis and Visual Analytics (VA) community. While the field has matured significantly since the original survey, we find that innovation and research-driven development are increasingly sacrificed to satisfy a wide range of user groups. We evaluate new product versions on established evaluation criteria, such as available features, performance, and usability, to extend on and assure comparability with the previous survey. We also investigate previously unavailable products to paint a more complete picture of the commercial VA landscape. Furthermore, we introduce novel measures, like suitability for specific user groups and the ability to handle complex data types, and undertake a new case study to highlight innovative features. We explore the achievements in the commercial sector in addressing VA challenges and propose novel developments that should be on systems’ roadmaps in the coming years.


title: Revised and abbreviated forms of the Genderism and Transphobia Scale: tools for assessing anti-trans prejudice.

abstract: Many studies of anti-trans prejudice have measured such attitudes using the Genderism and Transphobia Scale (GTS; Hill & Willoughby, 2005). The GTS is unique in assessing negative attitudes and propensity for violence toward trans people. The present research addressed previously observed limitations in the psychometric properties of data produced by the GTS, including inconsistencies in factor structure and subscale scoring across studies. Results across the present 2 studies (Ns = 314 and 250) yielded 2 refined versions of the GTS: the 22-item GTS-Revised (GTS-R) and a more abbreviated 13-item GTS-R-Short Form (GTS-R-SF), each of which produced stable 2-factor structures corresponding with the intended negative attitudes and propensity for violence dimensions of the GTS. The 2 versions differ in that the GTS-R-SF Genderism/Transphobia subscale focuses on more severe expressions of prejudicial attitudes, whereas the longer GTS-R Genderism/Transphobia subscale assesses subtle expressions of prejudice as well. The Gender-Bashing subscale is identical across the 2 versions. Thus, researchers and practitioners may choose the GTS-R or GTS-R-SF depending on the breadth of prejudicial attitudes they wish to assess. Reliability estimates for GTS-R and GTS-R-SF scale and subscale item responses were acceptable and stable across the 2 studies, and validity evidence was garnered in Study 2. These findings can inform use of the GTS-R and GTS-R-SF in research and practice settings, where psychometric precision and efficiency are both critical.


title: Selective Encoding for Abstractive Sentence Summarization

abstract: We propose a selective encoding model to extend the sequence-to-sequence framework for abstractive sentence summarization. It consists of a sentence encoder, a selective gate network, and an attention equipped decoder. The sentence encoder and decoder are built with recurrent neural networks. The selective gate network constructs a second level sentence representation by controlling the information flow from encoder to decoder. The second level representation is tailored for sentence summarization task, which leads to better performance. We evaluate our model on the English Gigaword, DUC 2004 and MSR abstractive sentence summarization datasets. The experimental results show that the proposed selective encoding model outperforms the state-of-the-art baseline models.


A gcd-sum function over regular integers modulo n


title: San Francisco Crime Classification

abstract: San Francisco Crime Classification is an online competition administered by Kaggle Inc. The competition aims at predicting the future crimes based on a given set of geographical and time-based features. In this paper, I achieved a an accuracy that ranks at top %18, as of May 19th, 2016. I will explore the data, and explain in details the tools I used to achieve that result.


title: A taxonomy for mechanical ventilation: 10 fundamental maxims.

abstract: The American Association for Respiratory Care has declared a benchmark for competency in mechanical ventilation that includes the ability to "apply to practice all ventilation modes currently available on all invasive and noninvasive mechanical ventilators." This level of competency presupposes the ability to identify, classify, compare, and contrast all modes of ventilation. Unfortunately, current educational paradigms do not supply the tools to achieve such goals. To fill this gap, we expand and refine a previously described taxonomy for classifying modes of ventilation and explain how it can be understood in terms of 10 fundamental constructs of ventilator technology: (1) defining a breath, (2) defining an assisted breath, (3) specifying the means of assisting breaths based on control variables specified by the equation of motion, (4) classifying breaths in terms of how inspiration is started and stopped, (5) identifying ventilator-initiated versus patient-initiated start and stop events, (6) defining spontaneous and mandatory breaths, (7) defining breath sequences (8), combining control variables and breath sequences into ventilatory patterns, (9) describing targeting schemes, and (10) constructing a formal taxonomy for modes of ventilation composed of control variable, breath sequence, and targeting schemes. Having established the theoretical basis of the taxonomy, we demonstrate a step-by-step procedure to classify any mode on any mechanical ventilator.


title: Research advances in nano-composite solders

abstract: Recently, nano-composite solders have been developed in the electronic packaging materials industry to improve the creep and thermo-mechanical fatigue resistance of solder joints to be used in service at high temperatures and under thermo-mechanical fatigue conditions. This paper reviews the driving force for the development of nano-composite solders in the electronic packaging industry and the research advances of the composite solders developed. The rationale for the preparation of nano-composite solders are presented at first. Examples of two nano-composite solder fabrication methods, a mechanical mixing method and an in-situ method, are explained in detail. The achievements and enhancements in the nano-composite prepared solders are summarized. The difficulties and problems existing in the fabrication of nano-composite solders are discussed. Finally, a novel nano-structure composite solder, which attempts to solve the problems encountered in the fabrication of nano-composite solders, is introduced in detail. Guidelines for the development of nano-composite solders are then provided.


title: Mid-Term Electricity Market Clearing Price Forecasting with Sparse Data: A Case in Newly-Reformed Yunnan Electricity Market

abstract: For the power systems, for which few data are available for mid-term electricity market clearing price (MCP) forecasting at the early stage of market reform, a novel grey prediction model (defined as interval GM(0, N) model) is proposed in this paper. Over the traditional GM(0, N) model, three major improvements of the proposed model are: (i) the lower and upper bounds are firstly identified to give an interval estimation of the forecasting value; (ii) a novel whitenization method is then established to determine the definite forecasting value from the forecasting interval; and (iii) the model parameters are identified by an improved particle swarm optimization (PSO) instead of the least square method (LSM) for the limitation of LSM. Finally, a newly-reformed electricity market in Yunnan province of China is studied, and input variables are contrapuntally selected. The accuracy of the proposed model is validated by observed data. Compared with the multiple linear regression (MLR) model, the traditional GM(0, N) model and the artificial neural network (ANN) model, the proposed model gives a better performance and its superiority is further ensured by the use of the modified Diebold–Mariano (MDM) test, suggesting that it is suitable for mid-term electricity MCP forecasting in a data-sparse electricity market.


title: A Novel Cluster Head Selection Algorithm for Energy-Efficient Routing in Wireless Sensor Network

abstract: Wireless sensor networks consist of low power lightweight sensor nodes that are often placed remotely for applications like wildlife monitoring, rainforest monitoring, military surveillance, forest fire detection etc. Here energy is a critical issue as nodes cannot be recharged and/or replaced frequently. In this paper we propose an energy efficient hierarchical routing protocol for Wireless Sensor Networks to increase network lifetime. Here the cluster head is selected based on the residual energy, distance of the node from the base station, how recently the node was selected as cluster-head etc. Along with this, the proposed solution can also detect malicious nodes in the network and can prevent them from becoming cluster heads. The simulation results show that proposed solution provides good performance in terms of extending network lifetime, uniform selection of nodes as cluster head that eliminates the probability of a single node from draining away its power by its repetitive selection as a cluster head.


title: Automatic human spermatozoa detection in microscopic video streams based on OpenCV

abstract: The analysis of semen plays an important role in male fertility evaluations. Computer-aided Sperm Analysis systems have been working on providing more accurate information about the sperm motility and quantity. However, the existing sperm detection algorithms which segment sperms according to grey levels are not able to preclude bright non-sperm objects, like the round cells. The contribution of this paper is a solution to this problem. The use of Gaussian-modeling method makes our algorithm able to filter the bright non-target objects. We also apply the morphological image processing method to our algorithm to improve the targets dispersion quality. This algorithm has good prospects and accomplishes both an accuracy of 95% in average and a real-time processing according to our tests. It is helpful for semen quality analysis, eugenics and test-tube babies.


title: The label complexity of mixed-initiative classifier training

abstract: Mixed-initiative classifier training, where the human teacher can choose which items to label or to label items chosen by the computer, has enjoyed empirical success but without a rigorous statistical learning theoretical justification. We analyze the label complexity of a simple mixed-initiative training mechanism using teaching dimension and active learning. We show that mixed-initiative training is advantageous compared to either computer-initiated (represented by active learning) or human-initiated classifier training. The advantage exists across all human teaching abilities, from optimal to completely unhelpful teachers. We further improve classifier training by educating the human teachers. This is done by showing, or explaining, optimal teaching sets to the human teachers. We conduct Mechanical Turk human experiments on two stylistic classifier training tasks to illustrate our approach.


title: Separation of Water and Fat Magnetic Resonance Imaging Signals Using Deep Learning with Convolutional Neural Networks

abstract: Purpose: A new method for magnetic resonance (MR) imaging water-fat separation using a convolutional neural network (ConvNet) and deep learning (DL) is presented. Feasibility of the method with complex and magnitude images is demonstrated with a series of patient studies and accuracy of predicted quantitative values is analyzed. ::: Methods: Water-fat separation of 1200 gradient-echo acquisitions from 90 imaging sessions (normal, acute and chronic myocardial infarction) was performed using a conventional model based method with modeling of R2* and off-resonance and a multi-peak fat spectrum. A U-Net convolutional neural network for calculation of water-only, fat-only, R2* and off-resonance images was trained with 900 gradient-echo Multiple and single-echo complex and magnitude input data algorithms were studied and compared to conventional extended echo modeling. ::: Results: The U-Net ConvNet was easily trained and provided water-fat separation results visually comparable to conventional methods. Myocardial fat deposition in chronic myocardial infarction and intramyocardial hemorrhage in acute myocardial infarction were well visualized in the DL results. Predicted values for R2*, off-resonance, water and fat signal intensities were well correlated with conventional model based water fat separation (R2>=0.97, p<0.001). DL images had a 14% higher signal-to-noise ratio (p<0.001) when compared to the conventional method. ::: Conclusion: Deep learning utilizing ConvNets is a feasible method for MR water-fat separationimaging with complex, magnitude and single echo image data. A trained U-Net can be efficiently used for MR water-fat separation, providing results comparable to conventional model based methods.


title: A Synthesis Methodology for Ternary Logic Circuits in Emerging Device Technologies

abstract: Automatic synthesis of digital circuits has played a key role in obtaining high-performance designs. While considerable work has been done in the past, emerging device technologies call for a need to re-examine the synthesis approaches, so that better circuits that harness the true power of these technologies can be developed. This paper presents a methodology for synthesis applicable to devices that support ternary logic. We present an algorithm for synthesis that combines a geometrical representation with unary operators of multivalued logic . The geometric representation facilitates scanning appropriately to obtain simple sum-of-products expressions in terms of unary operators. An implementation based on Python is described. The power of the approach lies in its applicability to a wide variety of circuits. The proposed approach leads to the savings of 26% and 22% in transistor-count, respectively, for a ternary full-adder and a ternary content-addressable memory (TCAM) over the best existing designs. Furthermore, the proposed approach requires, on an average, less than 10% of the number of the transistors in comparison with a recent decoder-based design for various ternary benchmark circuits. Extensive HSPICE simulation results show roughly 92% reduction in power-delay product (PDP) for a $12\times 12$ TCAM and 60% reduction in PDP for a 24-ternary digit barrel shifter over recent designs.


title: Design patterns applied for game design patterns

abstract: We analyzed common game design patterns such as enemy, weapon, communication, and creative control as well as social interaction and explored design patterns that are applicable to these game design patterns. The reusability, maintainability, and quality of game development are supposed to be improved and game development is able to adapt to constant changing and evolutionary game building process. Although some design patterns are discussed, other design patterns might suitable as well because game programming is too complicated to separate and apply each pattern independently.


title: A technology review paper for drives used in electrical vehicle (EV) & hybrid electrical vehicles (HEV)

abstract: The pollution of environment is increasing due a very large numbers of conventional vehicles present today. To reduce pollution the electric and hybrid electric vehicle are very beneficial. As the decade of low cost fuel is coming to an end the electric vehicle or hybrid electric vehicle is a good alternative to the conventional vehicles. These electric vehicles are driven by an electric motor may be AC or DC fed by battery through electric converter. The various drives used for the EVs & HEVs are discussed in this paper. The main two types of drive based on the supply are used DC or AC drive. The AC drives consisting of three phase permanent magnet synchronous motor (PMSM) is most popular amongst them. The other widely used drives are three phase Induction motor (IM), Switched reluctance motor (SRM), Brushless DC motor (BLDC). These motor are fed by different power electronic converters such as DC-DC converter, DC- AC converters per the supply requirements.


title: Cloning Your Gadgets: Complete ROP Attack Immunity with Multi-Variant Execution

abstract: In this paper, we present disjoint code layouts (DCL), a technique that complements multi-variant execution [1] and W $\oplus$ X protection to effectively immunize programs against control flow hijacking exploits such as return oriented programming (ROP) [2] and return-to-libc attacks [3] . DCL improves upon address space partitioning (ASP), an earlier technique presented to defeat memory exploits. Unlike ASP, our solution keeps the full virtual address space available to the protected program. Additionally, our combination of DCL with Multi-Variant Execution is transparent to both the user and the programmer and incurs much less overhead than other ROP defense tools, both in terms of run time and memory footprint.


title: Learning from hints in neural networks

abstract: Abstract Learning from examples is the process of taking input-output examples of an unknown function ƒ and infering an implementation of ƒ. Learning from hints allows for general information about ƒ to be used instead of just input-output examples. We introduce a method for incorporating any invariance hint about ƒ in any descent method for learning from examples. We also show that learning in a neural network remains NP-complete with a certain, biologically plausible, hint about the network. We discuss the information value and the complexity value of hibts.


title: A clustered manycore processor architecture for embedded and accelerated applications

abstract: The Kalray MPPA-256 processor integrates 256 user cores and 32 system cores on a chip with 28nm CMOS technology. Each core implements a 32-bit 5-issue VLIW architecture. These cores are distributed across 16 compute clusters of 16+1 cores, and 4 quad-core I/O subsystems. Each compute cluster and I/O subsystem owns a private address space, while communication and synchronization between them is ensured by data and control Networks-On-Chip (NoC). The MPPA-256 processor is also fitted with a variety of I/O controllers, in particular DDR, PCI, Ethernet, Interlaken and GPIO. We demonstrate that the MPPA-256 processor clustered manycore architecture is effective on two different classes of applications: embedded computing, with the implementation of a professional H.264 video encoder that runs in real-time at low power; and high-performance computing, with the acceleration of a financial option pricing application. In the first case, a cyclostatic dataflow programming environment is utilized, that automates application distribution over the execution resources. In the second case, an explicit parallel programming model based on POSIX processes, threads, and NoC-specific IPC is used.


title: Identifying Word Relations in Software: A Comparative Study of Semantic Similarity Tools

abstract: Modern software systems are typically large and complex, making comprehension of these systems extremely difficult. Experienced programmers comprehend code by seamlessly processing synonyms and other word relations. Thus, we believe that automated comprehension and software tools can be significantly improved by leveraging word relations in software. In this paper, we perform a comparative study of six state of the art, English-based semantic similarity techniques and evaluate their effectiveness on words from the comments and identifiers in software. Our results suggest that applying English-based semantic similarity techniques to software without any customization could be detrimental to the performance of the client software tools. We propose strategies to customize the existing semantic similarity techniques to software, and describe how various program comprehension tools can benefit from word relation information.


title: Suspended accounts in retrospect: an analysis of twitter spam

abstract: In this study, we examine the abuse of online social networks at the hands of spammers through the lens of the tools, techniques, and support infrastructure they rely upon. To perform our analysis, we identify over 1.1 million accounts suspended by Twitter for disruptive activities over the course of seven months. In the process, we collect a dataset of 1.8 billion tweets, 80 million of which belong to spam accounts. We use our dataset to characterize the behavior and lifetime of spam accounts, the campaigns they execute, and the wide-spread abuse of legitimate web services such as URL shorteners and free web hosting. We also identify an emerging marketplace of illegitimate programs operated by spammers that include Twitter account sellers, ad-based URL shorteners, and spam affiliate programs that help enable underground market diversification. Our results show that 77% of spam accounts identified by Twitter are suspended within on day of their first tweet. Because of these pressures, less than 9% of accounts form social relationships with regular Twitter users. Instead, 17% of accounts rely on hijacking trends, while 52% of accounts use unsolicited mentions to reach an audience. In spite of daily account attrition, we show how five spam campaigns controlling 145 thousand accounts combined are able to persist for months at a time, with each campaign enacting a unique spamming strategy. Surprisingly, three of these campaigns send spam directing visitors to reputable store fronts, blurring the line regarding what constitutes spam on social networks.


title: Object classification in radar using ensemble methods

abstract: To enable autonomous driving, a semantic knowledge of the environment is unavoidable. We therefore introduce a multiclass classifier to determine the classes of an object relying solely on radar data. This is a challenging problem as objects of the same category have often a diverse appearance in radar data. As classification methods a random forest classifier and a deep convolutional neural network are evaluated. To get good results despite the limited training data available, we introduce a hybrid approach using an ensemble consisting of the two classifiers. Further we show that the accuracy can be improved significantly by allowing a lower detection rate.


title: Finding the Answers with Definition Models

abstract: Inspired by a previous attempt to answer crossword questions using neural networks (Hill, Cho, Korhonen, & Bengio, 2015), this dissertation implements extensions to improve the performance of this existing definition model on the task of answering crossword questions. A discussion and evaluation of the original implementation finds that there are some ways in which the recurrent neural model could be extended. Insights from related fields neural language modeling and neural machine translation provide the justification and means required for these extensions. Two extensions are applied to the LSTM encoder, first taking the average of LSTM states across the sequence and secondly using a bidirectional LSTM, both implementations serve to improve model performance on a definitions and crossword test set. In order to improve performance on crossword questions, the training data is increased to include crossword questions and answers, and this serves to improve results on definitions as well as crossword questions. The final experiments are conducted using sub-word unit segmentation, first on the source side and then later preliminary experimentation is conducted to facilitate character-level output. Initially, an exact reproduction of the baseline results proves unsuccessful. Despite this, the extensions improve performance, allowing the definition model to surpass the performance of the recurrent neural network variants of the previous work (Hill, et al., 2015).


An optimal structural design algorithm using optimality criteria


title: 5G next generation VANETs using SDN and fog computing framework

abstract: The growth of technical revolution towards 5G Next generation networks is expected to meet various communication requirements of future Intelligent Transportation Systems (ITS). Motivated by the consumer needs for variety of ITS applications, bandwidth, high speed and ubiquity, researches are currently exploring different network architectures and techniques, which could be employed in Next generation ITS. To provide flexible network management, control and high resource utilization in Vehicular Ad-hoc Networks (VANETs) on large scale, a new hierarchical 5G Next generation VANET architecture is proposed. The key idea of this holistic architecture is to integrate the centralization and flexibility of Software Defined Networking (SDN) and Cloud-RAN (CRAN), with 5G communication technologies, to effectively allocate resources with a global view. Moreover, a fog computing framework (comprising of zones and clusters) has been proposed at the edge, to avoid frequent handovers between vehicles and RSUs. The transmission delay, throughput and control overhead on controller are analyzed and compared with other architectures. Simulation results indicate reduced transmission delay and minimized control overhead on controllers. Moreover, the throughput of proposed system is also improved.


title: Extending Adversarial Attacks and Defenses to Deep 3D Point Cloud Classifiers

abstract: 3D object classification using deep neural networks has been extremely successful. As the problem of identifying 3D objects has many safety-critical applications, the neural networks have to be robust against adversarial changes to the input data set. We present a preliminary evaluation of adversarial attacks on 3D point cloud classifiers by evaluating adversarial attacks that were proposed for 2D images, and extending those attacks to reduce the perceptibility of the perturbations in 3D space. We also show the effectiveness of simple defenses against those attacks. Finally, we attempt to explain the effectiveness of the defenses through the intrinsic structures of both the point clouds and the neural networks. Overall, we find that 3D point cloud classifiers are weak to adversarial attacks, but they are also more easily defensible compared to 2D image classifiers. Our investigation will provide the groundwork for future studies on improving the robustness of deep neural networks that handle 3D data.


title: A Method for Finding the Routes of Mazes

abstract: In this paper, we propose a potential modified route-searching algorithm to find all feasible paths in a new place which is like an unknown maze. The maze-like place is assumed to be a region constructed by grids and each grid is supposed to have at most four directions for moving. A searching algorithm is developed to accomplish the required tasks for patrolling every path in the new place. Based on the traditional depth-first graph traversal method, we build up a local-maze coordinate system to record the visited pathway. The main results of this paper will give an experiment simulation to demonstrate the success of proposed design.


Natural Light and Productivity: Analyzing the Impacts of Daylighting on Students’ and Workers’ Health and Alertness


title: Security Considerations for Peer-to-Peer Distributed Hash Tables

abstract: Recent peer-to-peer research has focused on providing efficient hash lookup systems that can be used to build more complex systems. These systems have good properties when their algorithms are executed correctly but have not generally considered how to handle misbehaving nodes. This paper looks at what sorts of security problems are inherent in large peer-to-peer systems based on distributed hash lookup systems. We examine the types of problems that such systems might face, drawing examples from existing systems, and propose some design principles for detecting and preventing these problems.


title: Leveraging Contextual Sentence Relations for Extractive Summarization Using a Neural Attention Model

abstract: As a framework for extractive summarization, sentence regression has achieved state-of-the-art performance in several widely-used practical systems. The most challenging task within the sentence regression framework is to identify discriminative features to encode a sentence into a feature vector. So far, sentence regression approaches have neglected to use features that capture contextual relations among sentences. We propose a neural network model, Contextual Relation-based Summarization (CRSum), to take advantage of contextual relations among sentences so as to improve the performance of sentence regression. Specifically, we first use sentence relations with a word-level attentive pooling convolutional neural network to construct sentence representations. Then, we use contextual relations with a sentence-level attentive pooling recurrent neural network to construct context representations. Finally, CRSum automatically learns useful contextual features by jointly learning representations of sentences and similarity scores between a sentence and sentences in its context. Using a two-level attention mechanism, CRSum is able to pay attention to important content, i.e., words and sentences, in the surrounding context of a given sentence. We carry out extensive experiments on six benchmark datasets. CRSum alone can achieve comparable performance with state-of-the-art approaches; when combined with a few basic surface features, it significantly outperforms the state-of-the-art in terms of multiple ROUGE metrics.


title: A 1.8-V 6.9-mW 120-fps 50-Channel Capacitive Touch Readout With Current Conveyor AFE and Current-Driven $\Delta \Sigma $ ADC

abstract: This paper presents an area- and energy-efficient readout for capacitive touch sensors. An analog front end (AFE) with current conveyor mitigates the requirements of front-end opamp and provides a differential current from adjacent channels, removing sensor’s baseline and interferences. The sensing current from the AFE is directly digitized by a current-driven 2nd-order $\Delta \Sigma $ analog-to-digital converter and its direct interfacing with the current signal achieves high signal-to-noise ratio (SNR) without suffering voltage saturation in the AFE, and provides reconfigurable SNRs and frame rates with respect to oversampling ratio. An area-efficient sinc2 filter, whose filter coefficients are commonly provided, enables full parallel implementation of $\Delta \Sigma $ ADCs. A 50-channel prototype IC is fabricated in a 0.18- $\mu \text{m}$ CMOS process, occupying only 1.96 mm2. Using a 10.1-in $28 \times 50$ touch screen panel and a 3.3-V transmitter, this work achieves SNRs of 53.3 and 41.7 dB with finger and 1-mm- $\phi $ stylus, respectively, while drawing only 6.9 mW from 1.8-V supply at 120 fps. This results in the state-of-the-art figure-of-merit of 0.11 and 0.41 nJ/step for finger and 1-mm- $\phi $ stylus, respectively. Moreover, 500-dpi on-glass fingerprint sensor is verified with the same prototype IC and 15-V transmitter, and $70 \times 50$ fingerprint image is successfully captured with 150- $\mu \text{m}$ cover glass.


title: A Brief Introduction to Temporality and Causality

abstract: Causality is a non-obvious concept that is often considered to be related to temporality. In this paper we present a number of past and present approaches to the definition of temporality and causality from philosophical, physical, and computational points of view. We note that time is an important ingredient in many relationships and phenomena. The topic is then divided into the two main areas of temporal discovery, which is concerned with finding relations that are stretched over time, and causal discovery, where a claim is made as to the causal influence of certain events on others. We present a number of computational tools used for attempting to automatically discover temporal and causal relations in data.


title: Genetic algorithm based NoC design with voltage/frequency islands

abstract: Due to increasing complexity of the embedded applications, designing a Network-on-Chip (NoC) architecture under energy, cost, and performance constraints has become challenging issue. Especially, energy management and clock distribution stand in the center of NoC synthesis flow. NoC communication infrastructure allows us to design globally asynchronous locally synchronous (GALS) systems, which suits well for energy minimization and clock speed control. In this paper, we present a genetic algorithm based method that maps the tasks of the given application onto two different voltage/frequency islands (VFIs) to minimize the total energy consumption of the system. We compared the proposed method with the one that does not consider VFI. Our experiments on several benchmarks show that VFI based method brings up to 37% energy reduction under given timing constraint.


title: Towards Contactless, Low-Cost and Accurate 3D Fingerprint Identification

abstract: Human identification using fingerprint impressions has been widely studied and employed for more than 2000 years. Despite new advancements in the 3D imaging technologies, widely accepted representation of 3D fingerprint features and matching methodology is yet to emerge. This paper investigates 3D representation of widely employed 2D minutiae features by recovering and incorporating (i) minutiae height z and (ii) its 3D orientation φ information and illustrates an effective matching strategy for matching popular minutiae features extended in 3D space. One of the obstacles of the emerging 3D fingerprint identification systems to replace the conventional 2D fingerprint system lies in their bulk and high cost, which is mainly contributed from the usage of structured lighting system or multiple cameras. This paper attempts to addresses such key limitations of the current 3D fingerprint technologies bydeveloping the single camera-based 3D fingerprint identification system. We develop a generalized 3D minutiae matching model and recover extended 3D fingerprint features from the reconstructed 3D fingerprints. 2D fingerprint images acquired for the 3D fingerprint reconstruction can themselves be employed for the performance improvement and have been illustrated in the work detailed in this paper. This paper also attempts to answer one of the most fundamental questions on the availability of inherent discriminableinformation from 3D fingerprints. The experimental results are presented on a database of 240 clients 3D fingerprints, which is made publicly available to further research efforts in this area, and illustrate the discriminant power of 3D minutiae representation andmatching to achieve performance improvement.


title: Automatic Number Plate Recognition System Using Improved Segmentation Method

abstract: Automatic license plate recognition (ANPR) system has been intensively studied in many countries. Due to the different types of number plates being used, the requirements of an automatic license plate recognition system are different for each country. In this paper, a number plate localization and recognition system for Indian vehicles. This system is developed based on digital images and can be easily applied to car park systems for the use of documenting access of parking services, secure usage of parking houses and also to prevent car theft issues. Automatic license plate recognition system is to extract vehicle license plate from a digital image. The paper based on a combination of thresolding, labeling, filling up the holes approach method and region props method with area criteria test for the number plate localization. Segmentation of the plate characters was achieved by horizontal and vertical scanning method. The character recognition was accomplished with the aid of optical characters by the process of Template matching. We mainly concrete on three steps: one is to locate the number plate, second is to segment all the number and to identify each number separately, third is recognize each character.


title: ArcCN-Runoff: an ArcGIS tool for generating curve number and runoff maps

abstract: Abstract The development and the application of ArcCN-Runoff tool, an extension of ESRI@ ArcGIS software, are reported. This tool can be applied to determine curve numbers and to calculate runoff or infiltration for a rainfall event in a watershed. Implementation of GIS techniques such as dissolving, intersecting, and a curve-number reference table improve efficiency. Technical processing time may be reduced from days, if not weeks, to hours for producing spatially varied curve number and runoff maps. An application example for a watershed in Lyon County and Osage County, Kansas, USA, is presented.


title: Predicting movie box-office revenues using deep neural networks

abstract: In the film industry, the ability to predict a movie’s box-office revenues before its theatrical release can decrease its financial risk. However, accurate predictions are not easily obtained. The complex relationship between movie-related data and movie box-office revenues, plus the increasing volume of data in online movie databases, pose challenges for their effective analysis. In this paper, a multimodal deep neural network, incorporating input about movie poster features learned in a data-driven fashion, is proposed for movie box-office revenues prediction. A convolutional neural network (CNN) is built to extract features from movie posters. By pre-training the CNN, features that are relevant to movie box-office revenues can be learned. To evaluate the performance of the proposed multimodal deep neural network, comparative studies with other prediction techniques were carried out on an Internet Movie Database dataset, and visualization of movie poster features was also performed. Experimental results demonstrate the superiority of the proposed multimodal deep neural network for movie box-office revenues prediction.


title: High-Resolution Stereo Datasets with Subpixel-Accurate Ground Truth

abstract: We present a structured lighting system for creating high-resolution stereo datasets of static indoor scenes with highly accurate ground-truth disparities. The system includes novel techniques for efficient 2D subpixel correspondence search and self-calibration of cameras and projectors with modeling of lens distortion. Combining disparity estimates from multiple projector positions we are able to achieve a disparity accuracy of 0.2 pixels on most observed surfaces, including in half-occluded regions. We contribute 33 new 6-megapixel datasets obtained with our system and demonstrate that they present new challenges for the next generation of stereo algorithms.


title: COA: finding novel patents through text analysis

abstract: In recent years, the number of patents filed by the business enterprises in the technology industry are growing rapidly, thus providing unprecedented opportunities for knowledge discovery in patent data. One important task in this regard is to employ data mining techniques to rank patents in terms of their potential to earn money through licensing. Availability of such ranking can substantially reduce enterprise IP (Intellectual Property) management costs. Unfortunately, the existing software systems in the IP domain do not address this task directly. Through our research, we build a patent ranking software, named COA (Claim Originality Analysis) that rates a patent based on its value by measuring the recency and the impact of the important phrases that appear in the "claims" section of a patent. Experiments show that COA produces meaningful ranking when comparing it with other indirect patent evaluation metrics--citation count, patent status, and attorney's rating. In reallife settings, this tool was used by beta-testers in the IBM IP department. Lawyers found it very useful in patent rating, specifically, in highlighting potentially valuable patents in a patent cluster. In this article, we describe the ranking techniques and system architecture of COA. We also present the results that validate its effectiveness.