title: Detection of lung cancer using marker-controlled watershed transform

abstract: The major cause of cancer death is lung cancer. Detection of cancer in the early phase can provide more treatment options, less invasive surgery and increases the survival rate. For lung cancer, if the disease is detected in time, the survival rate of patient increases from 14 to 49% in recent 5 years. It is the most dangerous and widespread disease in the world. The cancer cells present in lung causes lung cancer disease. This cells detection is very important issue for medical researchers. The chances of an effective treatment will significantly increases with early detection. The Computed Tomography (CT) images are used which are more efficient than X-ray. MATLAB is widely used software for the study of lung cancer detection from CT scan images. The process includes image pre-processing, image segmentation, feature extraction and classification technique. This present work proposes a method to detect the cancerous cells effectively from the lung CT scan images. It will minimize the detection error made by the physicians' naked eye.


title: Teachers , Schools , and Academic Achievement

abstract: Considerable controversy surrounds the impact of schools and teachers on the achievement of students. This paper disentangles the separate factors influencing achievement with special attention given to the role of teacher differences and other aspects of schools. Unique matched panel data from the Harvard/UTD Texas Schools Project permit distinguishing between total effects and the impact of specific, measured components of teachers and schools. While schools are seen to have powerful effects on achievement differences, these effects appear to derive most importantly from variations in teacher quality. A lower bound suggests that variations in teacher quality account for at least 7« percent of the total variation in student achievement, and there are reasons to believe that the true percentage is considerably larger. The subsequent analysis estimates educational production functions based on models of achievement growth with individual fixed effects. It identifies a few systematic factors a negative impact of initial years of teaching and a positive effect of smaller class sizes for low income children in earlier grades but these effects are very small relative to the effects of overall teacher quality differences.


title: A Novel Microstrip Meander-Line Antenna With A Very High Relative Permittivity Substrate For 315-MHz Band Applications

abstract: This paper presents a design, simulation, implementation and measurement of a novel microstrip meander patch antenna for the application of sensor networks. The dimension of the microstrip chip antenna is 15 mm times 15 mm times 2 mm. The meander-type radiating patch is constructed on the upper layer of the 2 mm height substrate with 0.0 5 mm height metallic conduct lines. Because of using the very high relative permittivity substrate ( epsivr=90), the proposed antenna achieves 315 MHz band operations.


title: Development of a low-cost ultra-tiny line laser range sensor

abstract: To enable robotic sensing for tasks with requirements on weight, size, and cost, we develop an ultra-tiny line laser range sensor based on the Time-of-Flight (TOF) principle. With delicate circuit design and optical attachments, we create a sensor as small as 35[mm] × 27[mm] × 30[mm] and as light as 20[g]. The line sensor samples 272 pixels (256 effective pixels) uniformly distributed within the measurement field of view customizable using different laser lenses. The optimal measurement range of the sensor is 0.05[m] ∼ 2[m]. Higher sampling rates can be achieved with a shorter range. The sensor can also extend its range to 3[m] with reduced accuracy. We model the overall errors of the sensor and formulate calibration methods, achieving repeatable accuracy and measurement bias both within 2[cm] with our tested ambient lighting conditions and measurement ranges. The sensor is applicable to range sensing tasks including humanoid hand-eye measurement, UAV safe landing, tiny robot range sensing, and object detection.


title: Performance‐improving techniques in web‐based GIS

abstract: WebGIS (also known as web‐based GIS and Internet GIS) denotes a type of Geographic Information System (GIS), whose client is implemented in a Web browser. WebGISs have been developed and used extensively in real‐world applications. However, when such a complex web‐based system involves the dissemination of large volumes of data and/or massive user interactions, its performance can become an issue. In this paper, we first identify several major potential performance problems with WebGIS. Then, we discuss several possible techniques to improve the performance. These techniques include the use of pyramids and hash indices on the server side to handle large images. To resolve server‐side conflicts originating from concurrent massive access and user interactions, we suggest clustering and multithreading techniques. Multithreading is also used to break down the long sequential, layer‐based data access to concurrent data access on the client side. Caching is suggested as a means to enhance concurrent data access...


title: A Parallel Hill-Climbing Refinement Algorithm for Graph Partitioning

abstract: Graph partitioning is important in distributing workloads on parallel compute systems, computing sparse matrix re-orderings, and designing VLSI circuits. Refinement algorithms are used to improve existing partitionings, and are essential for obtaining high-quality partitionings. Existing parallel refinement algorithms either extract concurrency by sacrificing in terms of quality, or preserve quality by restricting concurrency. In this work we present a new shared-memory parallel algorithm for refining an existing k-way partitioning that can break out of local minima and produce high-quality partitionings. This allows our algorithm to scale well in terms of the number of processing cores and produce clusterings of quality equal to serial algorithms. Our algorithm achieves speedups of 5.7 -- 16.7× using 24 cores, while exhibiting only 0.52% higher edgecuts than when run serially. This is 6.3x faster and 1.9% better quality than other parallel refinement algorithms which can break out of local minima.


title: The impact of forecasting model selection on the value of information sharing in a supply chain

abstract: This paper presents a study on the impact of forecasting model selection on the value of information sharing in a supply chain with one capacitated supplier and multiple retailers. Using a computer simulation model, this study examines demand forecasting and inventory replenishment decisions by the retailers, and production decisions by the supplier under different demand patterns and capacity tightness. Analyses of the simulation output indicate that the selection of the forecasting model significantly influences the performance of the supply chain and the value of information sharing. Furthermore, demand patterns faced by retailers and capacity tightness faced by the supplier also significantly influence the value of information sharing. The result also shows that substantial cost savings can be realized through information sharing and thus help to motivate trading partners to share information in the supply chain. The findings can also help supply chain managers select suitable forecasting models to improve supply chain performance.


Benign congenital parotid hemangiomas in infants


title: Tissue culture and Agrobacterium-mediated transformation of hemp (Cannabis sativa L.)

abstract: Hemp (Cannabis sativa L.) is cultivated in many parts of the world for ils fiber, oil, and seed. The development of new hemp cultivars with improved traits could be facilitated through the application of biotechnological strategies. The purpose of this study was to investigate the propagation of hemp in tissue culture and to establish a protocol for Agrobacterium-mediated transformation for foreign gene introduction. Stem and leaf segments from seedlings of four hemp varieties were placed on Murashige and Skoog medium with Gamborg B5 vitamins (MB) supplemented with 5 μM 2,4-dichlorophenoxyacetic acid (2,4-D) and 1 μM kinetin, 3% sucrose, and 8 gl−1 agar. Large masses of callus were produced within 4 wk for all cultivars. Suspension cultures were established in MB medium containing 2.5 μM 2,4-D. To promote embryogenesis or organogenesis, explants, callus, and suspension cultures derived from a range of explant sources and seedling ages were exposed to variations in the culture medium and changes to the culture environment. None of the treatments tested were successful in promoting plantlet regeneration. Suspension cells were transformed with Agrobacterium tumefaciens strain EHA101 carrying the binary vector pNOV3635 with a gene encoding phosphomannose isomerase (PMI). Transformed callus was selected on medium containing 1–2% mannose. A chlorophenol red assay was used to confirm that the PMI gene was expressed. Polymerase chain reaction and Southern hybridization detected the presence of the PMI gene. Copy number in different lines ranged from one to four.


The Collateral Damage of Internet Censorship by DNS Injection


title: Chip design of a 10-MHz switched capacitor low-pass filter for wireless application

abstract: By using bilinear transformation to a 5th-order elliptic low pass ladder filter, a 10 MHz switched capacitor filter (SFC) is presented. The proposed SC filter is fabricated with tsmc 0.18 μm CMOS process over WiFi, WiMAX and WCDMA wireless applications. With a sampling rate of 80 MHz, measured results achieve filter cutoff frequency of 10.2 MHz, spurious free dynamic range (SFDR) of 47 dB, stop band attenuation greater than 35 dB and power consumption of 18 mW from a 1.8 V voltage supply. Including pads, the chip area is 0.412 (0.782×0.527) mm2.


title: The NLRP3 inflammasome instigates obesity-induced inflammation and insulin resistance

abstract: Obesity is generally considered an inflammatory state. Vishwa Dixit and his colleagues have now shown that excess dietary lipids leads to the activation of the Nlrp3 inflammasome, a sensor of the innate immune system, and that its genetic deficiency results in decreased inflammation and improved insulin sensitivity. These results suggest a possible new therapeutic avenue to treat the effects of obesity.


title: Insider Threat Detection using Stream Mining and Graph Mining

abstract: Evidence of malicious insider activity is often buried within large data streams, such as system logs accumulated over months or years. Ensemble-based stream mining leverages multiple classification models to achieve highly accurate anomaly detection in such streams even when the stream is unbounded, evolving, and unlabeled. This makes the approach effective for identifying insider threats who attempt to conceal their activities by varying their behaviors over time. This paper applies ensemble-based stream mining, unsupervised learning, and graph-based anomaly detection to the problem of insider threat detection, demonstrating that the ensemble-based approach is significantly more effective than traditional single-model methods.


title: Factors affecting sustainable adoption of e-health technology in developing countries: an exploratory survey of Nigerian hospitals from the perspective of healthcare professionals

abstract: Background ::: E-health technology applications are essential tools of modern information technology that improve quality of healthcare delivery in hospitals of both developed and developing countries. However, despite its positive benefits, studies indicate that the rate of the e-health adoption in some developing countries is either low or underutilized. This is due in part, to barriers such as resistance from healthcare professionals, poor infrastructure, and low technical expertise among others. ::: ::: ::: Objective ::: The aim of this study is to investigate, identify and analyze the underlying factors that affect healthcare professionals decision to adopt and use e-health technology applications in developing countries, with particular reference to hospitals in Nigeria. ::: ::: ::: Methods ::: The study used a cross sectional approach in the form of a close-ended questionnaire to collect quantitative data from a sample of 465 healthcare professionals randomly selected from 15 hospitals in Nigeria. We used the modified Technology Acceptance Model (TAM) as the dependent variable and external factors as independent variables. The collected data was then analyzed using SPSS statistical analysis such as frequency test, reliability analysis, and correlation coefficient analysis. ::: ::: ::: Results ::: The results obtained, which correspond with findings from other researches published, indicate that perceived usefulness, belief, willingness, as well as attitude of healthcare professionals have significant influence on their intention to adopt and use the e-health technology applications. Other strategic factors identified include low literacy level and experience in using the e-health technology applications, lack of motivation, poor organizational and management policies. ::: ::: ::: Conclusion ::: The study contributes to the literature by pinpointing significant areas where findings can positively affect, or be found useful by, healthcare policy decision makers in Nigeria and other developing countries. This can help them understand their areas of priorities and weaknesses when planning for e-health technology adoption and implementation.


Mininet as Software Defined Networking Testing Platform


title: Query Expansion by Mining User Logs

abstract: Queries to search engines on the Web are usually short. They do not provide sufficient information for an effective selection of relevant documents. Previous research has proposed the utilization of query expansion to deal with this problem. However, expansion terms are usually determined on term co-occurrences within documents. In this study, we propose a new method for query expansion based on user interactions recorded in user logs. The central idea is to extract correlations between query terms and document terms by analyzing user logs. These correlations are then used to select high-quality expansion terms for new queries. Compared to previous query expansion methods, ours takes advantage of the user judgments implied in user logs. The experimental results show that the log-based query expansion method can produce much better results than both the classical search method and the other query expansion methods.


title: Coexistence of WiFi and LTE in unlicensed bands: A proportional fair allocation scheme

abstract: The use of the unlicensed spectrum by LTE networks (LTE-U or LAA-LTE) is being considered by mobile operators in order to satisfy increasing traffic demands and to make better use of the licensed spectrum. However, coexistence issues arise when LTE-U coverage overlaps with other technologies currently operating in unlicensed bands, in particular WiFi. Since LTE uses a TDMA/OFDMA scheduled approach, coexisting WiFi networks may face starvation if the channel is fully occupied by LTE-U transmissions. In this paper we derive a novel proportional fair allocation scheme that ensures fair coexistence between LTE-U and WiFi. Importantly, we find that the proportional fair allocation is qualitatively different from previously consideredWiFi-only settings and that since the resulting allocation requires only quite limited knowledge of network parameters it is potentially easy to implement in practice, without the need for message-passing between heterogeneous networks.


title: Ontology Patterns: Clarifying Concepts and Terminology

abstract: Ontology patterns have been pointed out as a promising approach for ontology engineering. The goal of this paper is to clarify concepts and the terminology used in Ontology Engineering to talk about the notion of ontology patterns taking into account already well-established notions of patterns in Software Engineering.


title: Introduction to Bilingualism and Cognitive Control

abstract: In the past decade, there has been an upsurge of research on bilingualism. A theme in this work is that the bilingual's two languages are always active, at times converging with one another to produce benefits to comprehension and production, but at other times conflicting, with the requirement to negotiate cross-language competition. A goal in the recent work has been to characterize the cognitive processes that enable bilinguals to negotiate the cross-talk between their two languages. The ease with which highly proficient bilinguals are able to speak each of their languages without frequent errors or intrusions and, at the same time, switch between the two languages in contexts in which code switching is appropriate or encouraged, suggests the presence of a high level of cognitive control. At the same time, behavioral and neurocognitive studies have shown that bilinguals differ from monolinguals in their performance on tasks that are purely cognitive, often showing advantages relative to monolinguals, and clear differences in neural function and structure. A key question is how we might begin to relate the findings on language control to the documented cognitive consequences of bilingualism. The papers in this special issue on Bilingualism and Cognitive Control represent the best of the new research on each of these issues to understand how control in language processing is achieved and how domain-general cognitive processes are themselves affected by language experience.


title: Sinfonia: a new paradigm for building scalable distributed systems

abstract: We propose a new paradigm for building scalable distributed systems. Our approach does not require dealing with message-passing protocols -- a major complication in existing distributed systems. Instead, developers just design and manipulate data structures within our service called Sinfonia. Sinfonia keeps data for applications on a set of memory nodes, each exporting a linear address space. At the core of Sinfonia is a novel minitransaction primitive that enables efficient and consistent access to data, while hiding the complexities that arise from concurrency and failures. Using Sinfonia, we implemented two very different and complex applications in a few months: a cluster file system and a group communication service. Our implementations perform well and scale to hundreds of machines.


title: Computer poker: a review

abstract: The game of poker has been identified as a beneficial domain for current AI research because of the properties it possesses such as the need to deal with hidden information and stochasticity. The identification of poker as a useful research domain has inevitably resulted in increased attention from academic researchers who have pursued many separate avenues of research in the area of computer poker. The poker domain has often featured in previous review papers that focus on games in general, however a comprehensive review paper with a specific focus on computer poker has so far been lacking in the literature. In this paper, we present a review of recent algorithms and approaches in the area of computer poker, along with a survey of the autonomous poker agents that have resulted from this research. We begin with the first serious attempts to create strong computerised poker players by constructing knowledge-based and simulation-based systems. This is followed by the use of computational game theory to construct robust poker agents and the advances that have been made in this area. Approaches to constructing exploitive agents are reviewed and the challenging problems of creating accurate and dynamic opponent models are addressed. Finally, we conclude with a selection of alternative approaches that have received attention in previously published material and the interesting problems that they pose.


title: Tracking attack sources based on traceback honeypot for ICS network

abstract: In Industrial Control System (ICS) networks, it is generally difficult to discover security threats from logs recorded in ICS devices, such as packets originated from malware-infected devices and evidence of the devices remotely controlled by attackers. This is due to the fact that ICS devices output logs not for detecting security threats but simply for recording operation history. Some researchers have suggested placing honeypots within ICS networks to observe packets from attackers in order to detect threats. In the research for this paper, the suggested method was further improved so that it responds to packets reaching the honeypots and collects information of the attack sources. Previous analysis has revealed that machines infected with some known malware (e.g. Havex RAT — a Remote Access Tool) in ICS networks conduct scan activities against certain devices, and therefore the traceback honeypots are expected to identify infected devices out of such scans in an effective manner. Information about attack sources collected from the analysis can be utilised for proactive purposes, which could be useful in detecting or blocking certain communication to prevent further infection. This paper discusses methods of tracking attack sources using traceback honeypots.


title: MoDisco: a Model Driven Reverse Engineering Framework

abstract: Context: Most companies, independently of their size and activity type, are facing the problem of managing, maintaining and/or replacing (part of) their existing software systems. These legacy systems are often large applications playing a critical role in the company’s information system and with a non-negligible impact on its daily operations. Improving their comprehension (e.g., architecture, features, enforced rules, handled data) is a key point when dealing with their evolution/modernization. Objective: The process of obtaining useful higher-level representations of (legacy) systems is called reverse engineering (RE), and remains a complex goal to achieve. Socalled Model Driven Reverse Engineering (MDRE) has been proposed to enhance more traditional RE processes. However, generic and extensible MDRE solutions potentially addressing several kinds of scenarios relying on different legacy technologies are still missing or incomplete. This paper proposes to make a step in this direction. Method: MDRE is the application of Model Driven Engineering (MDE) principles and techniques to RE in order to generate relevant model-based views on legacy systems, thus facilitating their understanding and manipulation. In this context, MDRE is practically used in order to 1) discover initial models from the legacy artifacts composing a given system and 2) understand (process) these models to generate relevant views (i.e., derived models) on this system. Results: Capitalizing on the different MDRE practices and our previous experience (e.g., in real modernization projects), this paper introduces and details the MoDisco open source MDRE framework. It also presents the underlying MDRE global methodology and architecture accompanying this proposed tooling. Conclusion: MoDisco is intended to make easier the design and building of modelbased solutions dedicated to legacy systems RE. As an empirical evidence of its relevance and usability, we report on its successful application in real industrial projects and on the concrete experience we gained from that.


title: Parking Sensors: Analyzing and Detecting Parked Domains

abstract: A parked domain is an undeveloped domain which has no content other than automatically computed advertising banners and links, used to generate profit. Despite the apparent popularity of this practice, little is known about parked domains and domain parking services that assist domain owners in parking and monetizing unused domains. This paper presents and in-depth exploration of the ecosystem of domain parking services from a security point of view, focusing mostly on the consequences for everyday users who land on parked pages. By collecting data from over 8 million parked domains, we are able to map out the entities that constitute the ecosystem, thus allowing us to analyze the domain owners, parking services, and advertisement syndicators involved. We show that users who land on parked websites are exposed to malware, inappropriate content, and elaborate scams, such as fake antivirus warnings and costly remote “technicians”. At the same time, we find a significant number of parked domains to be abusing popular names and trademarks through typosquatting and through domain names confusingly similar to authoritative ones. Given the extent of observed abuse, we propose a set of features that are representative of parked pages and build a robust client-side classifier which achieves high accuracy with a negligible percentage of false positives.


title: K-d trees for semidynamic point sets

abstract: A K -d tree represents a set of N points in K -dimensional space. Operations on a semidynamic tree may delete and undelete points, but may not insert new points. This paper shows that several operations that require O (log N ) expected time in general K -d trees may be performed in constant expected time in semidynamic trees. These operations include deletion, undeletion, nearest neighbor searching, and fixed-radius near neighbor searching (the running times of the first two are proved, while the last two are supported by experiments and heuristic arguments). Other new techniques can also be applied to general K -d trees: simple sampling reduces the time to build a tree from O ( KN log N ) to O ( KN + N log N ), and more advanced sampling builds a robust tree in the same time. The methods are straightforward to implement, and lead to a data structure that is significantly faster and less vulnerable to pathological inputs than ordinary K -d trees.


title: Supply chain performance measurement: a literature review

abstract: This paper is intended to provide a critical literature review on supply chain performance measurement. The study aims at revealing the basic research methodologies/approaches followed, problem areas and requirements for the performance management of the new supply chain era. The review study covers articles coming from major journals related with the topic, including a taxonomy study and detailed investigation as to the methodologies, approaches and findings of these works. The methodology followed during the conduct of this research includes starting with a broad base of articles lying at the intersection of supply chain, information technology (IT), performance measurement and business process management topics and then screening the list to have a focus on supply chain performance measurement. Findings reveal that performance measurement in the new supply era is still an open area of research. Further need of research is identified regarding framework development, empirical cross-industry research and adoption of performance measurement systems for the requirements of the new era, to include the development of partnership, collaboration, agility, flexibility, information productivity and business excellence metrics. The contribution of this study lies in the taxonomy study, detailed description and treatment of methodologies followed and in shedding light on future research.


title: Graft: An Efficient Graphlet Counting Method for Large Graph Analysis

abstract: Majority of the existing works on network analysis study properties that are related to the global topology of a network. Examples of such properties include diameter, power-law exponent, and spectra of graph Laplacian. Such works enhance our understanding of real-life networks, or enable us to generate synthetic graphs with real-life graph properties. However, many of the existing problems on networks require the study of local topological structures of a network, which did not get the deserved attention in the existing works. In this work, we use graphlet frequency distribution (GFD) as an analysis tool for understanding the variance of local topological structure in a network; we also show that it can help in comparing, and characterizing real-life networks. The main bottleneck to obtain GFD is the excessive computation cost for obtaining the frequency of each of the graphlets in a large network. To overcome this, we propose a simple, yet powerful algorithm, called Graft , that obtains the approximate graphlet frequency for all graphlets that have up-to five vertices. Comparing to an exact counting algorithm, our algorithm achieves a speedup factor between 10 and 100 for a negligible counting error, which is, on average, less than 5 percent.


title: Full-Spectrum Fluorescent Lighting Effects on People: A Critical Review

abstract: Full-spectrum fluorescent lighting has been credited with causing dramatic improvements in a wide variety of behaviours, mental health outcomes, and physical health effects. These effects include reduction in dental caries, improved classroom behaviour in schoolchildren, enhanced academic achievement, more efficient visual performance, more attractive appearance of both people and spaces, and improved mood in cases of seasonal depression. Popular media reports have tended to emphasize studies that have demonstrated differences in outcomes as a function of light source spectral power distribution. However, the scientific literature also includes reports with null and contradictory effects. This comprehensive review covers the period 1945-1993 and includes a critical appraisal of the methodology in each study. The studies are grouped according to their dependent variables under the broad headings "Behaviour and Performance", "Mental Well-Being and Affect", and "Physiology and Health". In general, the quality of the research is poor, making it difficult to determine whether or not treatment effects may legitimately be attributed to light source spectral power distribution. A few rigorous investigations of full-spectrum fluorescent lighting have demonstrated small effects; however, few researchers have taken up the challenge to replicate their work. These small effects do not support the claims that full-spectrum fluorescent lighting will produce better performance, mood, or health in the general population. More fruitful avenues for future research include investigations of other lighting parameters including intensity, variability, and flicker and an emphasis on the possibility that individual differences exist in sensitivity to the spectral composition of light sources. The role of mental processes -for example, beliefs, memories, and expectations -in mediating the effects of lighting on behaviour stands out as most promising for future research.


title: New Flexible Silicone-Based EEG Dry Sensor Material Compositions Exhibiting Improvements in Lifespan, Conductivity, and Reliability

abstract: This study investigates alternative material compositions for flexible silicone-based dry electroencephalography (EEG) electrodes to improve the performance lifespan while maintaining high-fidelity transmission of EEG signals. Electrode materials were fabricated with varying concentrations of silver-coated silica and silver flakes to evaluate their electrical, mechanical, and EEG transmission performance. Scanning electron microscope (SEM) analysis of the initial electrode development identified some weak points in the sensors’ construction, including particle pull-out and ablation of the silver coating on the silica filler. The newly-developed sensor materials achieved significant improvement in EEG measurements while maintaining the advantages of previous silicone-based electrodes, including flexibility and non-toxicity. The experimental results indicated that the proposed electrodes maintained suitable performance even after exposure to temperature fluctuations, 85% relative humidity, and enhanced corrosion conditions demonstrating improvements in the environmental stability. Fabricated flat (forehead) and acicular (hairy sites) electrodes composed of the optimum identified formulation exhibited low impedance and reliable EEG measurement; some initial human experiments demonstrate the feasibility of using these silicone-based electrodes for typical lab data collection applications.


title: A distributed representation of temporal context

abstract: The principles of recency and contiguity are two cornerstones of the theoretical and empirical analysis of human memory. Recency has been alternatively explained by mechanisms of decay, displacement, and retroactive interference. Another account of recency is based on the idea of variable context (Estes, 1955; Mensink & Raaijmakers, 1989). Such notions are typically cast in terms of a randomly fluctuating population of elements reflective of subtle changes in the environment or in the subjects' mental state. This random context view has recently been incorporated into distributed and neural network memory models (Murdock, 1997; Murdock, Smith, & Bai, 2001). Here we propose an alternative model. Rather than being driven by random fluctuations, this formulation, the temporal context model (TCM), uses retrieval of prior contextual states to drive contextual drift. In TCM, retrieved context is an inherently asymmetric retrieval cue. This allows the model to provide a principled explanation of the widespread advantage for forward recalls in free and serial recall. Modeling data from single-trial free recall, we demonstrate that TCM can simultaneously explain recency and contiguity effects across time scales.


title: Aggressive Text Detection for Cyberbullying

abstract: Aggressive text detection in social networks allows to identify offenses and misbehavior, and leverages tasks such as cyberbullying detection. We propose to automatically map a document with an aggressiveness score (thus treating aggressive text detection as a regression problem) and explore different approaches for this purpose. These include lexicon-based, supervised, fuzzy, and statistical approaches. We test the different methods over a dataset extracted from Twitter and compare them against human evaluation. Our results favor approaches that consider several features (particularly the presence of swear or profane words).


title: Motion dense sampling and component clustering for action recognition

abstract: In this paper, we propose Motion Dense Sampling (MDS) for action recognition, which detects very informative interest points from video frames. MDS has three advantages compared to other existing methods. The first advantage is that MDS detects only interest points which belong to action regions of all regions of a video frame. The second one is that it can detect the constant number of points even when the size of action region in an image drastically changes. The Third one is that MDS enables to describe scale invariant features by computing sampling scale for each frame based on the size of action regions. Thus, our method detects much more informative interest points from videos unlike other methods. We also propose Category Clustering and Component Clustering, which generate the very effective codebook for action recognition. Experimental results show a significant improvement over existing methods on YouTube dataset. Our method achieves 87.5 % accuracy for video classification by using only one descriptor.


title: Microbacterium oxydans and Microbacterium liquefaciens: a biological alternative for the treatment of Ni-V-containing wastes.

abstract: The present study evaluated 15 isolates obtained of environmental samples capable of tolerating high Ni and V concentrations. Those coded as MNSH2-PHGII-1, MNSH2-PHGII-2 and MV-PHGII-2 showed a minimum inhibitory concentration higher than 200 ppm for Ni and V and showed removal percentages corresponding to 84, 75 and 26% for Ni and 60, 55 and 20.3% for V, respectively, in liquid medium. When spent catalyst was added at 16% (w/v) pulp density, the highest Ni and V removal corresponded to MNSH2-PHGII-1 and MNSH2-PHGII-2, which were identified as Microbacterium oxydans and Microbacterium liquefaciens respectively, Microbacterium oxydans was able to remove Ni at the extent of 45.4% and V at 30.4% while Microbacterium liquefaciens removed Ni at 51% and V at 41.4% from the spent catalyst. The isolate MV-PHGII-2 identified also as Microbacterium oxydans showed the lowest removal for Ni and V corresponding to 16% and 9.5%, respectively. This is the first report where strains of Microbacterium were tested for their abilities to remove Ni and V from spent catalyst, suggesting its potential use in the treatment of this solid industrial waste.


title: Delinquency and peer acceptance in adolescence: A within-person test of Moffitt's hypotheses

abstract: We tested 2 hypotheses derived from Moffitt’s (1993) taxonomic theory of antisocial behavior, both of which are central to her explanation for the rise in delinquency during adolescence. We tested whether persistently delinquent individuals become more accepted by their peers during adolescence and whether individuals who abstain from delinquent behavior become less accepted. Participants were 4,359 adolescents from 14 communities in the PROSPER study, which assessed friendship networks and delinquency from 6th (M = 11.8 years) to 9th (M = 15.3 years) grade. We operationalized peer acceptance as number of nominations received (indegree centrality), attractiveness as a friend (adjusted indegree centrality), and network bridging potential (betweenness centrality) and tested the hypotheses with multilevel modeling. Contrary to Moffitt’s hypothesis, persistently delinquent youths did not become more accepted between early and middle adolescence, and although abstainers were less accepted in early adolescence, they became more accepted over time. Results were similar for boys and girls; when differences occurred, they provided no support for Moffitt’s hypotheses for boys and were opposite of her hypotheses for girls. Sensitivity analyses in which alternative strategies and additional data were used to identify persistently delinquent adolescents produced similar results. We explore the implications of these results for Moffitt’s assertions that social mimicry of persistently antisocial adolescents leads to increases in delinquency and that social isolation leads to abstention.


title: Domain Randomization and Generative Models for Robotic Grasping

abstract: Deep learning-based robotic grasping has made significant progress the past several years thanks to algorithmic improvements and increased data availability. However, state-of-the-art models are often trained on as few as hundreds or thousands of unique object instances, and as a result generalization can be a challenge. ::: In this work, we explore a novel data generation pipeline for training a deep neural network to perform grasp planning that applies the idea of \emph{domain randomization} to object synthesis. We generate millions of unique, unrealistic procedurally generated objects, and train a deep neural network to perform grasp planning on these objects. ::: Since the distribution of successful grasps for a given object can be highly multimodal, we propose an autoregressive grasp planning model that maps sensor inputs of a scene to a probability distribution $p_{\theta}$, where $p_{\theta} (g \mid I)$ corresponds to the model's estimate of the normalized probability of success of the grasp $g$ conditioned on the observations $I$. Our model allows us to sample grasps efficiently at test time (or avoid sampling entirely). ::: We evaluate our model architecture and data generation pipeline in simulation and find we can achieve $>$90\% success rate on previously unseen realistic objects at test time despite having only been trained on random objects.


title: Optical Music Recognition with Convolutional Sequence-to-Sequence Models

abstract: Optical Music Recognition (OMR) is an important technology within Music Information Retrieval. Deep learning models show promising results on OMR tasks, but symbol-level annotated data sets of sufficient size to train such models are not available and difficult to develop. We present a deep learning architecture called a Convolutional Sequence-to-Sequence model to both move towards an end-to-end trainable OMR pipeline, and apply a learning process that trains on full sentences of sheet music instead of individually labeled symbols. The model is trained and evaluated on a human generated data set, with various image augmentations based on real-world scenarios. This data set is the first publicly available set in OMR research with sufficient size to train and evaluate deep learning models. With the introduced augmentations a pitch recognition accuracy of 81% and a duration accuracy of 94% is achieved, resulting in a note level accuracy of 80%. Finally, the model is compared to commercially available methods, showing a large improvements over these applications.


title: RRT-based 3D path planning for formation landing of quadrotor UAVs

abstract: This paper discusses the formation landing problem of quadrotor UAVs, which is considered as a UAV leader-follower problem, avoiding static obstacles. Rapidly-exploring random tree algorithm is used to generate the path for the leader UAV firstly. In particular, specifics of tree-grow including nodes selection, parent node connection, feasible and optimal path generation are explained. Given the leader UAV position, path finding for the follower UAV is conducted to avoid both static obstacles and the leader quadrotor. Based on the intensive simulations, which are conducted in ROS-Gazebo environment, the proposed framework is considered to be applicable in real-time formation landing of quadrotor UAVs.


title: YAFIM: A Parallel Frequent Itemset Mining Algorithm with Spark

abstract: The frequent itemset mining (FIM) is one of the most important techniques to extract knowledge from data in many real-world applications. The Apriori algorithm is the widely-used algorithm for mining frequent itemsets from a transactional dataset. However, the FIM process is both data-intensive and computing-intensive. On one side, large scale data sets are usually adopted in data mining nowadays, on the other side, in order to generate valid information, the algorithm needs to scan the datasets iteratively for many times. These make the FIM algorithm very time-consuming over big data. The parallel and distributed computing is effective and mostly-used strategy for speeding up large scale dataset algorithms. However, the existing parallel Apriori algorithms implemented with the MapReduce model are not efficient enough for iterative computation. In this paper, we proposed YAFIM (Yet Another Frequent Itemset Mining), a parallel Apriori algorithm based on the Spark RDD framework -- a specially-designed in-memory parallel computing model to support iterative algorithms and interactive data mining. Experimental results show that, compared with the algorithms implemented with MapReduce, YAFIM achieved 18× speedup in average for various benchmarks. Especially, we apply YAFIM in a real-world medical application to explore the relationships in medicine. It outperforms the MapReduce method around 25 times.


title: Design of dualband antenna with improved gain and bandwidth using defected ground structure

abstract: A dual-frequency microstrip line-fed planar microstrip antenna for multiband operation is proposed using defected ground structure (DGS). This antenna has a rectangular patch with rectangular-shaped strips cut in ground and is fed by a microstrip line, for achieving additional resonances and bandwidth enhancements. The designed antenna has overall size of 32×32 mm2, and resonates at 6.1 GHz and 8.9 GHz frequencies. Gain enhancement of 3db and 10 db is achieved at 6.1 GHz and 8.9 GHz respectively and 95% bandwidth enhancement is attained at 8.9 GHz. Therefore proposed dualband antenna can be effectively used for C-band and X-band wireless applications.


title: The MRI geyser sign: acromioclavicular joint cysts in the setting of a chronic rotator cuff tear.

abstract: We present the case of a 71-year-old man with a large acromioclavicular (AC) joint cyst successfully man- aged with surgical excision. AC joint cysts are soft tis- sue masses generally signifying underlying rotator cuff pathology. Traditionally, these cysts were identified with shoulder arthrography as a "geyser" of fluid escaping through the AC joint. Magnetic resonance imaging (MRI) is today's preferred imaging modality; we describe the MRI equivalent of the "geyser sign," signifying synovial fluid escaping through the cuff defect, across the sub- acromial bursa, and decompressing superiorly through a degenerated AC joint. Surgical management is preferred for symptomatic cysts. Based on a review of limited retrospective case series, recommendations for man- agement of these lesions are as follows. Repair of the rotator cuff is preferable whenever possible. In the case of an irreparable defect, good results can be achieved through excisional AC joint arthroplasty and resection of the cyst base. Aspiration of these cysts should not be attempted, due to the high recurrence rate and poten- tial for a draining sinus. Hemiarthroplasty also may be effective in indirectly decompressing these cysts; but given the invasive nature of this procedure, it should be reserved for patients who are also symptomatic from cuff arthropathy.


Deep Learning for Real-time Gravitational Wave Detection and Parameter Estimation with Advanced LIGO Data


title: FrauDroid: An Accurate and Scalable Approach to Automated Mobile Ad Fraud Detection

abstract: Previous studies on mobile ad fraud detection are limited to detecting certain types of ad fraud (e.g., placement fraud and click fraud). Dynamic interactive ad fraud has never been explored by previous work. In this paper, we propose an explorative study of ad fraud in Android apps. We first created a taxonomy of existing mobile ad fraud. Besides static placement fraud, we also created a new category called dynamic interactive fraud and found three new types of them. Then we propose FrauDroid, a new approach to detect ad fraud based on user interface (UI) state transition graph and networking traffic. To achieve accuracy and scalability, we use an optimized exploration strategy to traverse the UI states that contain ad views, and design a set of heuristic rules to identify kinds of ad fraud. Experiment results show that FrauDroid could achieve a precision of 80.9\% and it is capable of detecting all the 7 types of mobile ad fraud. We then apply FrauDroid to more than 80,000 apps from 21 app markets to study the prevalene of ad fraud. We find that 0.29\% of apps that use ad libraries are identified containing frauds, with 23 ad networks suffering from ad fraud. Our finding also suggests that Dynamic interactive fraud is more prevalent than static placement fraud in real world apps.


Idealised Bayesian Neural Networks Cannot Have Adversarial Examples: Theoretical and Empirical Study


title: Parallel neural networks for learning sequential procedures

abstract: Recent studies have shown that multiple brain areas contribute to different stages and aspects of procedural learning. On the basis of a series of studies using a sequence-learning task with trial-and-error, we propose a hypothetical scheme in which a sequential procedure is acquired independently by two cortical systems, one using spatial coordinates and the other using motor coordinates. They are active preferentially in the early and late stages of learning, respectively. Both of the two systems are supported by loop circuits formed with the basal ganglia and the cerebellum, the former for reward-based evaluation and the latter for processing of timing. The proposed neural architecture would operate in a flexible manner to acquire and execute multiple sequential procedures.


title: CoDisasm: Medium Scale Concatic Disassembly of Self-Modifying Binaries with Overlapping Instructions

abstract: Fighting malware involves analyzing large numbers of suspicious binary files. In this context, disassembly is a crucial task in malware analysis and reverse engineering. It involves the recovery of assembly instructions from binary machine code. Correct disassembly of binaries is necessary to produce a higher level representation of the code and thus allow the analysis to develop high-level understanding of its behavior and purpose. Nonetheless, it can be problematic in the case of malicious code, as malware writers often employ techniques to thwart correct disassembly by standard tools. In this paper, we focus on the disassembly of x86 self-modifying binaries with overlapping instructions. Current state-of-the-art disassemblers fail to interpret these two common forms of obfuscation, causing an incorrect disassembly of large parts of the input. We introduce a novel disassembly method, called concatic disassembly, that combines CONCrete path execution with stATIC disassembly. We have developed a standalone disassembler called CoDisasm that implements this approach. Our approach substantially improves the success of disassembly when confronted with both self-modification and code overlap in analyzed binaries. To our knowledge, no other disassembler thwarts both of these obfuscations methods together.


title: Design and Evaluation of the Driving Coil on Induction Coilgun

abstract: Driving coils in a multistage induction coilgun are stacked end to end forming a barrel. During launching, the pulsed current flows through the driving coil, and the electromagnetic force propels the projectile ahead. The driving coil endures not only the radial expansionary force because of the magnetic flux compression, but also the axial force due to the compressive force and counterforce of the projectile. In the process of coil design, both the mechanical strength and the electrical insulation must be considered. Due to the nonlinearity of insulation materials, the theoretical calculation cannot reflect the true state for the driving coil. The conventional static-state test method is analyzed and verified through experiments. The experimental results show that this method is ineffective for the multistage launching, because it neglects the axial counterforce and does not reflect the true state of launching. An improved method for evaluating the coil strength is proposed based on the analysis of the basic principle. The projectile is accelerated by the former stages, and the testing coil accelerates the projectile with an initial speed. Equipment for testing single coil is designed and installed in the muzzle of the existing multistage coilgun. The experimental results show that this method can test the coil's mechanical strength and electrical insulation more effectively than the conventional method. The dynamic-state method can be used to approximately evaluate the performance of different types of the coil. The structure of the driving coil can be improved through testing and evaluating, which provides experimental reference for the multistage induction coilgun design.


title: Elimination of Vmin failures in 0.15um logic process

abstract: A significant increase in Vmin failures was observed with device shrinkage. These Vmin failures were observed to be random on wafer and were highly correlated to I DDQ and Delta I DDQ failures. A strong correlation of Vmin failures to STI depth was found. Fault isolation showed that the root cause of our Vmin failures was due to stress related dislocations. Structural and electrical characterizations were used to confirm the proposed root cause mechanism. This paper presents a simple and cost effective method in resolving high Vmin failure rate in production devices. With the new process, a significant yield gain was achieved.


title: Double-Quadrant State-of-Charge-Based Droop Control Method for Distributed Energy Storage Systems in Autonomous DC Microgrids

abstract: In this paper, a double-quadrant state-of-charge (SoC)-based droop control method for distributed energy storage system is proposed to reach the proper power distribution in autonomous dc microgrids. In order to prolong the lifetime of the energy storage units (ESUs) and avoid the overuse of a certain unit, the SoC of each unit should be balanced and the injected/output power should be gradually equalized. Droop control as a decentralized approach is used as the basis of the power sharing method for distributed energy storage units. In the charging process, the droop coefficient is set to be proportional to the nth order of SoC, while in the discharging process, the droop coefficient is set to be inversely proportional to the nth order of SoC. Since the injected/output power is inversely proportional to the droop coefficient, it is obtained that in the charging process the ESU with higher SoC absorbs less power, while the one with lower SoC absorbs more power. Meanwhile, in the discharging process, the ESU with higher SoC delivers more power and the one with lower SoC delivers less power. Hence, SoC balancing and injected/output power equalization can be gradually realized. The exponent n of SoC is employed in the control diagram to regulate the speed of SoC balancing. It is found that with larger exponent n, the balancing speed is higher. MATLAB/simulink model comprised of three ESUs is implemented and the simulation results are shown to verify the proposed approach.


title: Analysis of Circulating Tumor DNA to Monitor Metastatic Breast Cancer

abstract: Background The management of metastatic breast cancer requires monitoring of the tumor burden to determine the response to treatment, and improved biomarkers are needed. Biomarkers such as cancer antigen 15-3 (CA 15-3) and circulating tumor cells have been widely studied. However, circulating cell-free DNA carrying tumor-specific alterations (circulating tumor DNA) has not been extensively investigated or compared with other circulating biomarkers in breast cancer. Methods We compared the radiographic imaging of tumors with the assay of circulating tumor DNA, CA 15-3, and circulating tumor cells in 30 women with metastatic breast cancer who were receiving systemic therapy. We used targeted or whole-genome sequencing to identify somatic genomic alterations and designed personalized assays to quantify circulating tumor DNA in serially collected plasma specimens. CA 15-3 levels and numbers of circulating tumor cells were measured at identical time points. Results Circulating tumor DNA was successfully detected in 29 of the 30 women (97%) in whom somatic genomic alterations were identified; CA 15-3 and circulating tumor cells were detected in 21 of 27 women (78%) and 26 of 30 women (87%), respectively. Circulating tumor DNA levels showed a greater dynamic range, and greater correlation with changes in tumor burden, than did CA 15-3 or circulating tumor cells. Among the measures tested, circulating tumor DNA provided the earliest measure of treatment response in 10 of 19 women (53%). Conclusions This proof-of-concept analysis showed that circulating tumor DNA is an informative, inherently specific, and highly sensitive biomarker of metastatic breast cancer. (Funded by Cancer Research UK and others.)


Positive Unlabeled Learning for Time Series Classification


title: Language constructs for context-oriented programming: an overview of ContextL

abstract: ContextL is an extension to the Common Lisp Object System that allows for Context-oriented Programming. It provides means to associate partial class and method definitions with layers and to activate and deactivate such layers in the control flow of a running program. When a layer is activated, the partial definitions become part of the program until this layer is deactivated. This has the effect that the behavior of a program can be modified according to the context of its use without the need to mention such context dependencies in the affected base program. We illustrate these ideas by providing different UI views on the same object while, at the same time, keeping the conceptual simplicity of object-oriented programming that objects know by themselves how to behave, in our case how to display themselves. These seemingly contradictory goals can be achieved by separating class definitions into distinct layers instead of factoring out the display code into different classes.


title: Achieving k-anonymity by clustering in attribute hierarchical structures

abstract: Individual privacy will be at risk if a published data set is not properly de-identified. k-anonymity is a major technique to de-identify a data set. A more general view of k-anonymity is clustering with a constraint of the minimum number of objects in every cluster. Most existing approaches to achieving k-anonymity by clustering are for numerical (or ordinal) attributes. In this paper, we study achieving k-anonymity by clustering in attribute hierarchical structures. We define generalisation distances between tuples to characterise distortions by generalisations and discuss the properties of the distances. We conclude that the generalisation distance is a metric distance. We propose an efficient clustering-based algorithm for k-anonymisation. We experimentally show that the proposed method is more scalable and causes significantly less distortions than an optimal global recoding k-anonymity method.


title: Joint-sequence models for grapheme-to-phoneme conversion

abstract: Grapheme-to-phoneme conversion is the task of finding the pronunciation of a word given its written form. It has important applications in text-to-speech and speech recognition. Joint-sequence models are a simple and theoretically stringent probabilistic framework that is applicable to this problem. This article provides a self-contained and detailed description of this method. We present a novel estimation algorithm and demonstrate high accuracy on a variety of databases. Moreover, we study the impact of the maximum approximation in training and transcription, the interaction of model size parameters, n-best list generation, confidence measures, and phoneme-to-grapheme conversion. Our software implementation of the method proposed in this work is available under an Open Source license.


title: Social Media Usage and Organizational Performance: Reflections of Malaysian Social Media Managers

abstract: Abstract Social media usage among organizations is growing tremendously. Organizations are now building and maintaining social media public pages to improve their social network salience, enhance interest in their organizations, and build relationships with the online public. The majority of the studies on social media usage are based on the individual perspective while some are from the organizational perspective. However, not many studies have investigated the actual impact of social media usage on organizational performance. Therefore, using the qualitative approach, this study investigates the various purposes of social media usage and its impact on organizational performance. This study however, focuses only on the social media managers’ views. The senior managers of six organizations that are using social media are interviewed from which we find that social media is used for various purposes in organizations, such as advertising and promotion, branding, information search, building customer relations and many more. The results also show that social media has a greater impact on the performance of organizations in terms of enhancement in customer relations and customer service activities, improvement in information accessibility and cost reduction in terms of marketing and customer service.


title: Cloud Computing Features, Issues, and Challenges: A Big Picture

abstract: Since the phenomenon of cloud computing was proposed, there is an unceasing interest for research across the globe. Cloud computing has been seen as unitary of the technology that poses the next-generation computing revolution and rapidly becomes the hottest topic in the field of IT. This fast move towards Cloud computing has fuelled concerns on a fundamental point for the success of information systems, communication, virtualization, data availability and integrity, public auditing, scientific application, and information security. Therefore, cloud computing research has attracted tremendous interest in recent years. In this paper, we aim to precise the current open challenges and issues of Cloud computing. We have discussed the paper in three-fold: first we discuss the cloud computing architecture and the numerous services it offered. Secondly we highlight several security issues in cloud computing based on its service layer. Then we identify several open challenges from the Cloud computing adoption perspective and its future implications. Finally, we highlight the available platforms in the current era for cloud research and development.


title: SPADE: An Efficient Algorithm for Mining Frequent Sequences

abstract: In this paper we present SPADE, a new algorithm for fast discovery of Sequential Patterns. The existing solutions to this problem make repeated database scans, and use complex hash structures which have poor locality. SPADE utilizes combinatorial properties to decompose the original problem into smaller sub-problems, that can be independently solved in main-memory using efficient lattice search techniques, and using simple join operations. All sequences are discovered in only three database scans. Experiments show that SPADE outperforms the best previous algorithm by a factor of two, and by an order of magnitude with some pre-processed data. It also has linear scalability with respect to the number of input-sequences, and a number of other database parameters. Finally, we discuss how the results of sequence mining can be applied in a real application domain.


title: New insights into the noise reduction Wiener filter

abstract: The problem of noise reduction has attracted a considerable amount of research attention over the past several decades. Among the numerous techniques that were developed, the optimal Wiener filter can be considered as one of the most fundamental noise reduction approaches, which has been delineated in different forms and adopted in various applications. Although it is not a secret that the Wiener filter may cause some detrimental effects to the speech signal (appreciable or even significant degradation in quality or intelligibility), few efforts have been reported to show the inherent relationship between noise reduction and speech distortion. By defining a speech-distortion index to measure the degree to which the speech signal is deformed and two noise-reduction factors to quantify the amount of noise being attenuated, this paper studies the quantitative performance behavior of the Wiener filter in the context of noise reduction. We show that in the single-channel case the a posteriori signal-to-noise ratio (SNR) (defined after the Wiener filter) is greater than or equal to the a priori SNR (defined before the Wiener filter), indicating that the Wiener filter is always able to achieve noise reduction. However, the amount of noise reduction is in general proportional to the amount of speech degradation. This may seem discouraging as we always expect an algorithm to have maximal noise reduction without much speech distortion. Fortunately, we show that speech distortion can be better managed in three different ways. If we have some a priori knowledge (such as the linear prediction coefficients) of the clean speech signal, this a priori knowledge can be exploited to achieve noise reduction while maintaining a low level of speech distortion. When no a priori knowledge is available, we can still achieve a better control of noise reduction and speech distortion by properly manipulating the Wiener filter, resulting in a suboptimal Wiener filter. In case that we have multiple microphone sensors, the multiple observations of the speech signal can be used to reduce noise with less or even no speech distortion


title: Correlation Pattern Recognition for Face Recognition

abstract: Two-dimensional (2-D) face recognition (FR) is of interest in many verification (1:1 matching) and identification (1:N matching) applications because of its nonintrusive nature and because digital cameras are becoming ubiquitous. However, the performance of 2-D FR systems can be degraded by natural factors such as expressions, illuminations, pose, and aging. Several FR algorithms have been proposed to deal with the resulting appearance variability. However, most of these methods employ features derived in the image or the space domain whereas there are benefits to working in the spatial frequency domain (i.e., the 2-D Fourier transforms of the images). These benefits include shift-invariance, graceful degradation, and closed-form solutions. We discuss the use of spatial frequency domain methods (also known as correlation filters or correlation pattern recognition) for FR and illustrate the advantages. However, correlation filters can be computationally demanding due to the need for computing 2-D Fourier transforms and may not match well for large-scale FR problems such as in the Face Recognition Grand Challenge (FRGC) phase-II experiments that require the computation of millions of similarity metrics. We will discuss a new method [called the class-dependence feature analysis (CFA)] that reduces the computational complexity of correlation pattern recognition and show the results of applying CFA to the FRGC phase-II data


Characterizing the Google Books Corpus: Strong Limits to Inferences of Socio-Cultural and Linguistic Evolution


title: Scaling agile across the global organization: an early stage industrial SAFe self-assessment

abstract: The adoption, scaling and tailoring of agile methods depends on several factors, such as the size of the software development organization, business goals, and operative model. The Scaled Agile Framework (SAFe) was developed to support organizations in scaling agile practices across the enterprise. Large multi-national enterprises report that adoption of SAFe led to significant productivity and quality gains. However, whether these benefits translate to small to medium sized enterprises (SMEs) is unknown. As such, in this study we ask: To what extent can SAFe practices be implemented in a global SME? We administrated three surveys to members of the development organization of an Irish SME, to identify and evaluate the adoption rate of SAFe practices in the Global environment. We found teams and program level personnel are transitioning well towards SAFe. But the Portfolio level personnel appear not to have, as yet, adopted many SAFe practices. In an SME context it might not be necessary for Portfolio level members to fully adopt SAFe providing they are supportive of their teams. The SAFe self-assessment highlighted where training is required, to improve the vertical communication between teams, program level and upper management.


title: Coupled Context Modeling for Deep Chit-Chat: Towards Conversations between Human and Computer

abstract: To have automatic conversations between human and computer is regarded as one of the most hardcore problems in computer science. Conversational systems are of growing importance due to their promising potentials and commercial values as virtual assistants and chatbots. To build such systems with adequate intelligence is challenging, and requires abundant resources including an acquisition of big conversational data and interdisciplinary techniques, such as content analysis, text mining, and retrieval. The arrival of big data era reveals the feasibility to create a conversational system empowered by data-driven approaches. Now we are able to collect an extremely large number of human-human conversations on Web, and organize them to launch human-computer conversational systems. Given a human issued utterance, i.e., a query, a conversational system will search for appropriate responses, conduct relevance ranking using contexts information, and then output the highly relevant result. In this paper, we propose a novel context modeling framework with end-to-end neural networks for human-computer conversational systems. The proposed model is general and unified. In the experiments, we demonstrate the effectiveness of the proposed model for human-computer conversations using p@1, MAP, nDCG, and MRR metrics.


RUSLANA: a database of Russian emotional utterances.


title: State of the art in infrared face recognition

abstract: Face recognition is an area that has attracted a lot of interest. Much of the research in this field was conducted using visible images. With visible cameras the recognition is prone to errors due to illumination changes. To avoid the problems encountered in the visible spectrum many authors have proposed the use of infrared. In this paper we give an overview of the state of the art in face recognition using infrared images. Emphasis is given to more recent works. A growing field in this area is multimodal fusion; work conducted in this field is also presented in this paper and publicly available Infrared face image databases are introduced.


title: A New Digital Signature Scheme from Layered Cellular Automata

abstract: Cellular Automata (CA) is one of the important tools to design cryptographic algorithms, and in the past years many researchers have explored several cryptographic algorithms based on CA. However, most of reported CAbased cryptographic algorithms focus on the symmetric key encryption schemes and few CA-based asymmetric encryption scheme has been proposed, let alone the CAbased digital signature scheme. In this paper, to fill this gap, we present a new digital signature scheme based on the layered CA technique. Specifically, in the proposed layered CA-based digital signature scheme, we combine the transition rules of some one-dimensional (1D) reversible CAs to generate the rules of a two-dimensional (2D) CA, where the reverse of the 1D transition rules are kept as the private key and the 2D transition rules are set as the corresponding public key. Based on the hardness assumption of the layered CA reversibility (LCAR) problem, we formally prove the proposed scheme is semantically secure against chosen-message attack in the random oracle model.


LEXICAL ENTAILMENT AND ITS EXTRACTION FROM WIKIPEDIA


title: Achieving MAC layer fairness in wireless packet networks

abstract: Link-layer fairness models that have been proposed for wireline and packet cellular networks cannot be generalized for shared channel wireless networks because of the unique characteristics of the wireless channel, such as location-dependent contention, inherent conflict between optimizing channel utilization and achieving fairness, and the absence of any centralized control. In this paper, we propose a general analytical framework that captures the unique characteristics of shared wireless channels and allows the modeling of a large class of system-wide fairness models via the specification of per-flow utility functions. We show that system-wide fairness can be achieved without explicit global coordination so long as each node executes a contention resolution algorithm that is designed to optimize its local utility function. We present a general mechanism for translating a given fairness model in our framework into a corresponding contention resolution algorithm . Using this translation, we derive the backoff algorithm for achieving proportional fairness in wireless shared channels, and compare the fairness properties of this algorithm with both the ideal proportional fairness objective, and state-of-the-art backoff-based contention resolution algorithms. We believe that the two aspects of the proposed framework, i.e. the ability to specify arbitrary fairness models via local utility functions, and the ability to automatically generate local contention resolution mechanisms in response to a given utility function, together provide the path for achieving flexible service differentiation in future shared channel wireless networks.


title: A systematic review of provenance systems

abstract: Provenance refers to the entire amount of information, comprising all the elements and their relationships, that contribute to the existence of a piece of data. The knowledge of provenance data allows a great number of benefits such as verifying a product, result reproductivity, sharing and reuse of knowledge, or assessing data quality and validity. With such tangible benefits, it is no wonder that in recent years, research on provenance has grown exponentially, and has been applied to a wide range of different scientific disciplines. Some years ago, managing and recording provenance information were performed manually. Given the huge volume of information available nowadays, the manual performance of such tasks is no longer an option. The problem of systematically performing tasks such as the understanding, capture and management of provenance has gained significant attention by the research community and industry over the past decades. As a consequence, there has been a huge amount of contributions and proposed provenance systems as solutions for performing such kinds of tasks. The overall objective of this paper is to plot the landscape of published systems in the field of provenance, with two main purposes. First, we seek to evaluate the desired characteristics that provenance systems are expected to have. Second, we aim at identifying a set of representative systems (both early and recent use) to be exhaustively analyzed according to such characteristics. In particular, we have performed a systematic literature review of studies, identifying a comprehensive set of 105 relevant resources in all. The results show that there are common aspects or characteristics of provenance systems thoroughly renowned throughout the literature on the topic. Based on these results, we have defined a six-dimensional taxonomy of provenance characteristics attending to: general aspects, data capture, data access, subject, storage, and non-functional aspects. Additionally, the study has found that there are 25 most referenced provenance systems within the provenance context. This study exhaustively analyzes and compares such systems attending to our taxonomy and pinpoints future directions.


title: GUITAR: an innovative tool for automated testing of GUI-driven software

abstract: Most of today's software applications feature a graphical user interface (GUI) front-end. System testing of these applications requires that test cases, modeled as sequences of GUI events, be generated and executed on the software. We term GUI testing as the process of testing a software application through its GUI. Researchers and practitioners agree that one must employ a variety of techniques (e.g., model-based, capture/replay, manually scripted) for effective GUI testing. Yet, the tools available today for GUI testing are limited in the techniques they support. In this paper, we describe an innovative tool called GUITAR that supports a wide variety of GUI testing techniques. The innovation lies in the architecture of GUITAR, which uses plug-ins to support flexibility and extensibility. Software developers and quality assurance engineers may use this architecture to create new toolchains, new workflows based on the toolchains, and plug in a variety of measurement tools to conduct GUI testing. We demonstrate these features of GUITAR via several carefully crafted case studies.


title: A hierarchical Dirichlet model for taxonomy expansion for search engines

abstract: Emerging trends and products pose a challenge to modern search engines since they must adapt to the constantly changing needs and interests of users. For example, vertical search engines, such as Amazon, eBay, Walmart, Yelp and Yahoo! Local, provide business category hierarchies for people to navigate through millions of business listings. The category information also provides important ranking features that can be used to improve search experience. However, category hierarchies are often manually crafted by some human experts and they are far from complete. Manually constructed category hierarchies cannot handle the ever-changing and sometimes long-tail user information needs. In this paper, we study the problem of how to expand an existing category hierarchy for a search/navigation system to accommodate the information needs of users more comprehensively. We propose a general framework for this task, which has three steps: 1) detecting meaningful missing categories; 2) modeling the category hierarchy using a hierarchical Dirichlet model and predicting the optimal tree structure according to the model; 3) reorganizing the corpus using the complete category structure, i.e., associating each webpage with the relevant categories from the complete category hierarchy. Experimental results demonstrate that our proposed framework generates a high-quality category hierarchy and significantly boosts the retrieval performance.


Time Series Prediction with Multilayer Perceptron, FIR and Elman Neural Networks


Ontology Based Semantic Search in Holy Quran


title: Trust in Automation

abstract: This essay focuses on trust in the automation within macrocognitive work systems. The authors emphasize the dynamics of trust. They consider numerous different meanings or kinds of trust, and different modes of operation in which trust dynamics play a role. Their goal is to contribute to the development of a methodology for designing and analyzing collaborative human-centered work systems, a methodology that might promote both trust "calibration" and appropriate reliance. The analysis suggests an ontology for what the authors call "active exploration for trusting" (AET).


title: Q-clouds: managing performance interference effects for QoS-aware clouds

abstract: Cloud computing offers users the ability to access large pools of computational and storage resources on demand. Multiple commercial clouds already allow businesses to replace, or supplement, privately owned IT assets, alleviating them from the burden of managing and maintaining these facilities. However, there are issues that must be addressed before this vision of utility computing can be fully realized. In existing systems, customers are charged based upon the amount of resources used or reserved, but no guarantees are made regarding the application level performance or quality-of-service (QoS) that the given resources will provide. As cloud providers continue to utilize virtualization technologies in their systems, this can become problematic. In particular, the consolidation of multiple customer applications onto multicore servers introduces performance interference between collocated workloads, significantly impacting application QoS. To address this challenge, we advocate that the cloud should transparently provision additional resources as necessary to achieve the performance that customers would have realized if they were running in isolation. Accordingly, we have developed Q-Clouds, a QoS-aware control framework that tunes resource allocations to mitigate performance interference effects. Q-Clouds uses online feedback to build a multi-input multi-output (MIMO) model that captures performance interference interactions, and uses it to perform closed loop resource management. In addition, we utilize this functionality to allow applications to specify multiple levels of QoS as application Q-states. For such applications, Q-Clouds dynamically provisions underutilized resources to enable elevated QoS levels, thereby improving system efficiency. Experimental evaluations of our solution using benchmark applications illustrate the benefits: performance interference is mitigated completely when feasible, and system utilization is improved by up to 35% using Q-states.


title: Malformations in a cohort of 284 women with Mayer-Rokitansky-Küster-Hauser syndrome (MRKH)

abstract: The aim of this retrospective study was to describe the spectrum of genital and associated malformations in women with Mayer-Rokitansky-Küster-Hauser syndrome using evaluated diagnostic procedures and the Vagina Cervix Uterus Adnex – associated Malformation classification system (VCUAM). 290 women with MRKH syndrome were clinically evaluated with using clinical examinations, abdominal and perineal/rectal ultrasound, MRI, and laparoscopy. Classification of female genital malformation according to the Vagina Cervix Uterus Adnex – associated Malformation classification system was possible in 284 women (97.9%). Complete atresia of Vagina (V5b) and bilateral atresia of Cervix (C2b) were found in 284 patients (100%). Uterus: bilateral rudimentary or a plastic uterine horns were found in 239 women (84.2%). Adnexa: normal Adnexa were found in 248 women (87.3%). Malformations: associated malformations were found in 126 of 282 evaluable women (44.7%), 84 women (29.6%) had malformations of the renal system. Of 284 women with Mayer-Rokitansky-Küster-Hauser syndrome 212 women (74.7%) could be classified as V5bC2bU4bA0. The most frequent classification was V5bC2bU4bA0M0 (46.8%) diagnosed in 133 of 284 women. Complete atresia of vagina and cervix were found in all patients, variable malformations were found with uterus and adnexa. A variety of associated malformations were present, predominantly of the renal system. It is therefore recommended that all patients with genital malformations should be evaluated for renal abnormalities.


title: Structural basis of PAM-dependent target DNA recognition by the Cas9 endonuclease

abstract: The CRISPR-associated protein Cas9 is an RNA-guided endonuclease that cleaves double-stranded DNA bearing sequences complementary to a 20-nucleotide segment in the guide RNA. Cas9 has emerged as a versatile molecular tool for genome editing and gene expression control. RNA-guided DNA recognition and cleavage strictly require the presence of a protospacer adjacent motif (PAM) in the target DNA. Here we report a crystal structure of Streptococcus pyogenes Cas9 in complex with a single-molecule guide RNA and a target DNA containing a canonical 5'-NGG-3' PAM. The structure reveals that the PAM motif resides in a base-paired DNA duplex. The non-complementary strand GG dinucleotide is read out via major-groove interactions with conserved arginine residues from the carboxy-terminal domain of Cas9. Interactions with the minor groove of the PAM duplex and the phosphodiester group at the +1 position in the target DNA strand contribute to local strand separation immediately upstream of the PAM. These observations suggest a mechanism for PAM-dependent target DNA melting and RNA-DNA hybrid formation. Furthermore, this study establishes a framework for the rational engineering of Cas9 enzymes with novel PAM specificities.


title: MILLIMETER-WAVE SLOTTED WAVEGUIDE ARRAY ANTENNA MANUFACTURED BY METAL INJECTION MOLDING FOR AUTOMOTIVE RADAR SYSTEMS

abstract: This paper describes a novel millimeter wave slotted waveguide array antenna that was developed for automotive radar systems. The antenna which is fabricated by metal injection molding, can be manufactured at low cost and is suitable for mass production. The paper describes the design of the antenna and how it has achieved high efficiency results.


title: Mayflies: A Moving Target Defense Framework for Distributed Systems

abstract: prevent attackers from gaining control of the system using well established techniques such as; perimeter-based fire walls, redundancy and replications, and encryption. However, given sufficient time and resources, all these methods can be defeated. Moving Target Defense (MTD), is a defensive strategy that aims to reduce the need to continuously fight against attacks by disrupting attackers gain-loss balance. We present Mayflies, a bio-inspired generic MTD framework for distributed systems on virtualized cloud platforms. The framework enables systems designed to defend against attacks for their entire runtime to systems that avoid attacks in time intervals. We discuss the design, algorithms and the implementation of the framework prototype. We illustrate the prototype with a quorum-based Byzantime Fault Tolerant system and report the preliminary results.


title: Geometric Methods for Feature Extraction and Dimensional Reduction - A Guided Tour

abstract: We give a tutorial overview of several geometric methods for feature extractionand dimensional reduction. We divide the methods into projective methods and methods thatmodel the manifold on which the data lies. For projective methods, we review projectionpursuit, principal component analysis (PCA), kernel PCA, probabilistic PCA, and orientedPCA; and for the manifold methods, we review multidimensional scaling (MDS), landmarkMDS, Isomap, locally linear embedding, Laplacian eigenmaps and spectral clustering. TheNystrom method, which links several of the algorithms, is also reviewed. The goal is to providea self-contained review of the concepts and mathematics underlying these algorithms.


title: A CMOS linear-in-dB VGA based on exponential current generator

abstract: This paper proposes a simple CMOS exponential current circuit that is capable to control a Variable Gain Amplifier with a linear-in-dB manner. The proposed implementation is based on a Taylor's series approximation of the exponential function. A simple VGA architecture has been designed in a CMOS 90nm technology, in order to validate the theoretical analysis. The approximation achieves a 17dB linear range with less than 0.5dB approximation error, while the overall power consumption is less than 300μW.


title: A Compact 2.45-GHz Broadband Rectenna Using Grounded Coplanar Waveguide

abstract: This letter presents a compact 2.45 GHz broadband rectenna using the grounded coplanar waveguide (GCPW). A new broadband slot antenna fed by GCPW with a high gain of 10 dBi and a wide half-power beamwidth of 60 $^{\circ}$ is designed as the receiving antenna. By designing an input and an output impedance match network, a compact GCPW rectifying circuit based on the voltage doubler principle is proposed, which has broadband performance and is easy to be integrated with the novel GCPW antenna. The receiving antenna and the rectifying circuit are simulated and measured. The measured results of the rectenna agree well with those of the rectifying circuit, which validate the effectiveness of the design. The microwave-direct current (mw-dc) conversion efficiencies of the rectenna keep higher than 50% within the band from 2.2 to 2.6 GHz at 13 dBm received power on a $900\Omega $ load, and the highest efficiency is 72.5%. This rectenna has good performances of broadband, high mw-dc efficiency and compact structure.


title: An IoT based weather information prototype using WeMos

abstract: The Internet of Things (IOT) describes the interconnection of devices and people through the traditional internet and social networks for various day-to-day applications like weather monitoring, healthcare systems, smart cities, irrigation field, and smart lifestyle. IOT is the new revolution of today's internet world which monitors live streaming of the entire world's status like temperature, humidity, thunderstorm, earthquake, floods etc. that can stagger an alarm to human life. This paper proposes a low-cost weather monitoring system which retrieves the weather condition of any location from the cloud database management system and shows the output on an OLED display. The proposed system uses an ESP8266-EX microcontroller based Wemos D1 board and it is implemented on Arduino platform which is used to retrieve the data from the cloud. The main objective of this paper is to view weather conditions of any location and allows to access the current data of any station.


title: Autonomous multi-floor indoor navigation with a computationally constrained MAV

abstract: In this paper, we consider the problem of autonomous navigation with a micro aerial vehicle (MAV) in indoor environments. In particular, we are interested in autonomous navigation in buildings with multiple floors. To ensure that the robot is fully autonomous, we require all computation to occur on the robot without need for external infrastructure, communication, or human interaction beyond high-level commands. Therefore, we pursue a system design and methodology that enables autonomous navigation with real-time performance on a mobile processor using only onboard sensors. Specifically, we address multi-floor mapping with loop closure, localization, planning, and autonomous control, including adaptation to aerodynamic effects during traversal through spaces with low vertical clearance or strong external disturbances. We present experimental results with ground truth comparisons and performance analysis.


title: A Soft Three-Axis Load Cell Using Liquid-Filled Three-Dimensional Microchannels in a Highly Deformable Elastomer

abstract: The advances in soft robotics have increased the need of soft sensors in various applications involved with physical interactions between humans and robots. In this letter, we propose a soft multiaxis force sensor made of multimaterial elastomer layers and embedded microfluidic channels that are sensitive to compression perpendicular to the channel length. The microchannels are geometrically divided into multiple segments for detecting forces in three axes. When a force is applied to the top surface of the sensor, the microchannels are compressed by multisegmented force plates made of rigid plastic. While the microchannels located on the sides in the structure detect shear forces, the microchannel at the bottom detects normal force. The three-dimensional configuration of the microchannel physically separates the side channels from the bottom channel and consequently enables mechanical decoupling of shear forces from normal force. The letter describes the design and fabrication of the proposed sensor and discusses the experimental results for sensor characterization.


The Maximum Clique Problem


title: Are genetic algorithms function optimizers

abstract: Genetic Algorithms (GAs) have received a great deal of attention regarding their poten tial as optimization techniques for complex functions. The level of interest and success in this area has led to a number of improvements to GA-based function optimizers and a good deal of progress in characterizing the kinds of functions that are easy/hard for GAs to optim ize. With all this activity, there has been a natural tendency to equate GAs with function optimization. However, the motivating context of Holland's initial GA work was the design and implementation of robust adaptive systems. In this paper we argue that a proper under standing of GAs in this broader adaptive systems context is a necessary prerequisite for understanding their potential application to any problem domain. We then use these insights to better understand the strengths and limitations of GAs as function optimizers.


title: Half-Against-Half Structure with SVM and k-NN Classifiers in Benthic Macroinvertebrate Image Classification

abstract: We investigated how Half-Against-Half Support Vector Machine (HAH-SVM) and Half-Against-Half k-Nearest Neighbour (HAH-KNN) methods succeed in the classification of the benthic macroinvertebrate images. Automated taxa identification of benthic macroinvertebrates is a slightly researched area and in this paper HAH-KNN was for the first time applied to this application area. The main problem, when Half-Against-Half structure is used, is to find the right way to divide the classes in nodes. This problem was solved by using two different approaches. Firstly, we applied the Scatter method for the class division problem. Secondly, we formed the class divisions in a Half-Against-Half structure by a random choice. We performed extensive experimental tests with four different feature sets and tested every feature set with seven different kernel functions in the case of HAH-SVM. Furthermore, HAH-KNN was tested with four measures. The tests showed that by the Scatter method and random choice formed HAH-SVMs performed the classification problem very well obtaining over 95% accuracy while with HAH-KNN above 92% accuracy was achieved. Moreover, the 7D and 15D feature sets together with the RBF kernel function are good choices for this classification task when HAH-SVM was used and 15D feature set, when HAH-KNN was used. Generally speaking, Half-Against-Half structure is a promising multi-class extension for SVM and an interesting variant for k-NN classifier.


title: Experimental Comparison of Low-Cost Sub-Nanosecond Pulse Generators

abstract: In this paper, six low-cost pulse generator devices having sub-nanosecond transition time are analyzed and compared. Some of the possible applications for these devices are also described, along with an overview of the different approaches for sub-nanosecond pulse generation. Furthermore, the architecture and principle of operation of the realized prototypes are explained. The considered pulse generation techniques are based on logic gates, Step-Recovery Diodes, and transistors driven in the avalanche region. Some experimental results obtained by using an equivalent-time sampling oscilloscope and a spectrum analyzer are shown and discussed, providing both a time-domain and a frequency-domain characterization of the prototypes. Finally, an approach for the generation of modulated pulses in the 6-GHz band (for ultra-wideband applications) is investigated, and experimental results are provided.


title: Calibration of Spatially Distributed Hydrological Processes and Model Parameters in SWAT Using Remote Sensing Data and an Auto-Calibration Procedure: A Case Study in a Vietnamese River Basin

abstract: In this paper, evapotranspiration (ET) and leaf area index (LAI) were used to calibrate the SWAT model, whereas remotely sensed precipitation and other climatic parameters were used as forcing data for the 6300 km2 Day Basin, a tributary of the Red River in Vietnam. The efficacy of the Sequential Uncertainty Fitting (SUFI-2) parameter sensitivity and optimization model was tested with area specific remote sensing input parameters for every Hydrological Response Units (HRU), rather than with measurements of river flow representing a large set of HRUs, i.e., a bulk calibration. Simulated monthly ET correlations with remote sensing estimates showed an R2 = 0.71, Nash–Sutcliffe Efficiency NSE = 0.65, and Kling Gupta Efficiency KGE = 0.80 while monthly LAI showed correlations of R2 = 0.59, NSE = 0.57 and KGE = 0.83 over a five-year validation period. Accumulated modelled ET over the 5-year calibration period amounted to 5713 mm compared to 6015 mm of remotely sensed ET, yielding a difference of 302 mm (5.3%). The monthly flow at two flow measurement stations were adequately estimated (R2 = 0.78 and 0.55, NSE = 0.71 and 0.63, KGE = 0.59 and 0.75 for Phu Ly and Ninh Binh, respectively). This outcome demonstrates the capability of SWAT model to obtain spatial and accurate simulation of eco-hydrological processes, also when rivers are ungauged and the water withdrawal system is complex.


title: Summarization based on task-oriented discourse parsing

abstract: Previous research demonstrates that discourse relations can help generate high-quality summaries. Existing studies usually adopt existing discourse parsers directly with no modifications, hence cannot take full advantage of discourse parsing. This paper describes a new single document summarization system. In contrast to previous work, we train a discourse parser specially for summarization by using summaries. The training data are dynamically changed during the training phase to enable the parser to grab the text units that are important for summaries. A special tree-based summary extraction algorithm is designed to work with the new parser. The proposed system enables us to combine discourse parsing and summarization in a unified scheme. Experiments on both the RST-DT and DUC2001 datasets show the effectiveness of the proposed method.


title: Applying Deep Learning to Basketball Trajectories

abstract: One of the emerging trends for sports analytics is the growing use of player and ball tracking data. A parallel development is deep learning predictive approaches that use vast quantities of data with less reliance on feature engineering. This paper applies recurrent neural networks in the form of sequence modeling to predict whether a three-point shot is successful. The models are capable of learning the trajectory of a basketball without any knowledge of physics. For comparison, a baseline static machine learning model with a full set of features, such as angle and velocity, in addition to the positional data is also tested. Using a dataset of over 20,000 three pointers from NBA SportVu data, the models based simply on sequential positional data outperform a static feature rich machine learning model in predicting whether a three-point shot is successful. This suggests deep learning models may offer an improvement to traditional feature based machine learning methods for tracking data.


title: Performance of digital matched filters in a direct sequence spread spectrum receiver

abstract: Preamble processing combined with an adaptive sampling window technique and selection of the largest value of a certain number of matched-filter output samples is proposed as a data decision strategy for direct-sequence spread-spectrum receivers using digital correlators for baseband matched filtering. The preamble sequence provides exact knowledge of the occurrence of the correlation maximum. System performance is analyzed on the basis of a combinatorial model for the deviation of the signal statistics at the matched filter outputs and a Markov model for the dynamic behavior of the sampling window during reception of a burst. Theoretical system analysis is compared with measurements of the bit-error probability as a function of the receiver input signal/noise ratio. >