title: Event identification for local areas using social media streaming data

abstract: Unprecedented success and active usage of social media services result in massive amounts of user-generated data. An increasing interest in the contained information from social media data leads to more and more sophisticated analysis and visualization applications. Because of the fast pace and distribution of news in social media data it is an appropriate source to identify events in the data and directly display their occurrence to analysts or other users. This paper presents a method for event identification in local areas using the Twitter data stream. We implement and use a combined log-likelihood ratio approach for the geographic and time dimension of real-life Twitter data in predefined areas of the world to detect events occurring in the message contents. We present a case study with two interesting scenarios to show the usefulness of our approach.


title: Experimental Synthetic Aperture Radar with Dynamic Metasurfaces

abstract: We investigate the use of a dynamic metasurface as the transmitting antenna for a synthetic aperture radar (SAR) imaging system. The dynamic metasurface consists of a one-dimensional microstrip waveguide with complementary electric resonator (cELC) elements patterned into the upper conductor. Integrated into each of the cELCs are two diodes that can be used to shift each cELC resonance out of band with an applied voltage. The aperture is designed to operate at K band frequencies (17.5 to 20.3 GHz), with a bandwidth of 2.8 GHz. We experimentally demonstrate imaging with a fabricated metasurface aperture using existing SAR modalities, showing image quality comparable to traditional antennas. The agility of this aperture allows it to operate in spotlight and stripmap SAR modes, as well as in a third modality inspired by computational imaging strategies. We describe its operation in detail, demonstrate high-quality imaging in both 2D and 3D, and examine various trade-offs governing the integration of dynamic metasurfaces in future SAR imaging platforms.


title: Wearable object detection system for the blind

abstract: The blind's capacities to navigate in a particular place and to organize their daily activities are of vital importance for their health and well-being. Organizing any kind of simple daily activity can be especially difficult; it is not easy for the blind to distinguish the different items, such as packaged foods and drug containers just by touching with their hands. RFID, or radio frequency identification, is a technology that can provide a support for improving the organization and orientation during the daylight activities. RFID uses radio waves to deliver data from a tag, which stores information, to a reader, which can elaborate the information making decisions. This technology is very useful in many different contexts such as scanning passports, shipments and automatic highway toll collecting. As the RFID technology stands out for its inherent technical nature of any basic RFID system, it may involve additional improvements for numerous applications in the field of health care. In this paper, a RFID device designed as a support for the blind in searching some objects is presented; in particular, it has been develop for searching the medicines in a cabinet at home. The device is able to provide to the blind some pieces of information about the distance and simplify the search; besides identifying the medicines, the device is able to provide the user an acoustic signal in order to find easily the desired product as soon as possible. It is noteworthy the fact that it gives the blind some items of information about the distance of a defined object, that is how near or far it is. This application is obtained using the RSSI (Received Strength Signal Indicator) value, measuring the power of the received signal of the tag.


title: An Analysis of Exercising Behavior in Online Populations.

abstract: Exercise plays a central role in many peoples’ fitness goals. While prior work has examined how individuals pursue these health and fitness goals on general purpose platforms such as Twitter, the lack of precise activity recording has limited detailed analyses of individual and group behaviors. In this study, we explore a recent social media platform dedicated to exercise and use nearly four years of longitudinal exercising history of over 188,000 users to discover large-scale exercising patterns corresponding to different motivations such as sports or general fitness.


title: A legal case OWL ontology with an instantiation of Popov v. Hayashi

abstract: The paper provides an OWL ontology for legal cases with an instantiation of the legal case Popov v. Hayashi. The ontology makes explicit the conceptual knowledge of the legal case domain, supports reasoning about the domain, and can be used to annotate the text of cases, which in turn can be used to populate the ontology. A populated ontology is a case base which can be used for information retrieval, information extraction, and case based reasoning. The ontology contains not only elements for indexing the case (e.g. the parties, jurisdiction, and date), but as well elements used to reason to a decision such as argument schemes and the components input to the schemes. We use the Protege ontology editor and knowledge acquisition system, current guidelines for ontology development, and tools for visual and linguistic presentation of the ontology.


title: HDR CFA image rendering

abstract: We propose a method for high dynamic range (HDR) mapping that is directly applied on the color filter array (CFA) image instead of the already demosaiced image. This rendering is closer to retinal processing where an image is acquired by a mosaic of cones and where adaptive non-linear functions apply before interpolation. Thus, in our framework, demosaicing is the final step of the rendering. Our method, inspired by retinal sampling and adaptive processing is very simple, fast because only one third of operations are needed, and gives good result as shown by experiments.


title: Multiple Instance Learning with Bag Dissimilarities

abstract: Multiple instance learning (MIL) is concerned with learning from sets (bags) of objects (instances), where the individual instance labels are ambiguous. In this setting, supervised learning cannot be applied directly. Often, specialized MIL methods learn by making additional assumptions about the relationship of the bag labels and instance labels. Such assumptions may fit a particular dataset, but do not generalize to the whole range of MIL problems. Other MIL methods shift the focus of assumptions from the labels to the overall (dis)similarity of bags, and therefore learn from bags directly. We propose to represent each bag by a vector of its dissimilarities to other bags in the training set, and treat these dissimilarities as a feature representation. We show several alternatives to define a dissimilarity between bags and discuss which definitions are more suitable for particular MIL problems. The experimental results show that the proposed approach is computationally inexpensive, yet very competitive with state-of-the-art algorithms on a wide range of MIL datasets.


title: A Load-Power Adaptive Dual Pulse Modulated Current Phasor-Controlled ZVS High-Frequency Resonant Inverter for Induction Heating Applications

abstract: A new prototype of an efficiency-improved zero voltage soft-switching (ZVS) high-frequency resonant (HF-R) inverter for induction heating (IH) applications is presented in this paper. By adopting the dual pulse modulation mode (DPMM) that incorporates a submode power regulation scheme such as pulse density modulation, pulse frequency modulation, and asymmetrical pulse width modulation into main one of the resonant current phase angle difference ( θ) control, the IH load power can be widely regulated under the condition of ZVS, while significantly improving the efficiency in the low output power setting. The essential performances on the output power regulations and ZVS operations with the DPMM schemes are demonstrated in an experiment based on a 1 kW-60 kHz laboratory prototype of the ZVS HF-R inverter. The validity of each DPMM scheme is originally compared and evaluated from a practical point of view.


title: Generating What-If Scenarios for Time Series Data

abstract: Time series data has become a ubiquitous and important data source in many application domains. Most companies and organizations strongly rely on this data for critical tasks like decision-making, planning, predictions, and analytics in general. While all these tasks generally focus on actual data representing organization and business processes, it is also desirable to apply them to alternative scenarios in order to prepare for developments that diverge from expectations or assess the robustness of current strategies. When it comes to the construction of such what-if scenarios, existing tools either focus on scalar data or they address highly specific scenarios. In this work, we propose a generally applicable and easy-to-use method for the generation of what-if scenarios on time series data. Our approach extracts descriptive features of a data set and allows the construction of an alternate version by means of filtering and modification of these features.


title: Pattern-aware shape deformation using sliding dockers

abstract: This paper introduces a new structure-aware shape deformation technique. The key idea is to detect continuous and discrete regular patterns and ensure that these patterns are preserved during free-form deformation. We propose a variational deformation model that preserves these structures, and a discrete algorithm that adaptively inserts or removes repeated elements in regular patterns to minimize distortion. As a tool for such structural adaptation, we introduce sliding dockers, which represent repeatable elements that fit together seamlessly for arbitrary repetition counts. We demonstrate the presented approach on a number of complex 3D models from commercial shape libraries.


title: Organ printing: computer-aided jet-based 3D tissue engineering, Trends in Biotechnology 21

abstract: Tissue engineering technology promises to solve the organ transplantation crisis. However, assembly of vascularized 3D soft organs remains a big challenge. Organ printing, which we define as computer-aided, jet-based 3D tissue-engineering of living human organs, offers a possible solution. Organ printing involves three sequential steps: pre-processing or development of "blueprints" for organs; processing or actual organ printing; and postprocessing or organ conditioning and accelerated organ maturation. A cell printer that can print gels, single cells and cell aggregates has been developed. Layer-by-layer sequentially placed and solidified thin layers of a thermo-reversible gel could serve as "printing paper". Combination of an engineering approach with the developmental biology concept of embryonic tissue fluidity enables the creation of a new rapid prototyping 3D organ printing technology, which will dramatically accelerate and optimize tissue and organ assembly.


title: Parallel and distributed graph cuts by dual decomposition

abstract: Graph cuts methods are at the core of many state-of-the-art algorithms in computer vision due to their efficiency in computing globally optimal solutions. In this paper, we solve the maximum flow/minimum cut problem in parallel by splitting the graph into multiple parts and hence, further increase the computational efficacy of graph cuts. Optimality of the solution is guaranteed by dual decomposition, or more specifically, the solutions to the subproblems are constrained to be equal on the overlap with dual variables. We demonstrate that our approach both allows (i) faster processing on multi-core computers and (ii) the capability to handle larger problems by splitting the graph across multiple computers on a distributed network. Even though our approach does not give a theoretical guarantee of speedup, an extensive empirical evaluation on several applications with many different data sets consistently shows good performance. An open source implementation of the dual decomposition method is also made publicly available.


title: Phase-change random access memory: A scalable technology

abstract: Nonvolatile RAM using resistance contrast in phase-change materials [or phase-change RAM (PCRAM)] is a promising technology for future storage-class memory. However, such a technology can succeed only if it can scale smaller in size, given the increasingly tiny memory cells that are projected for future technology nodes (i.e., generations). We first discuss the critical aspects that may affect the scaling of PCRAM, including materials properties, power consumption during programming and read operations, thermal cross-talk between memory cells, and failure mechanisms. We then discuss experiments that directly address the scaling properties of the phase-change materials themselves, including studies of phase transitions in both nanoparticles and ultrathin films as a function of particle size and film thickness. This work in materials directly motivated the successful creation of a series of prototype PCRAM devices, which have been fabricated and tested at phase-change material cross-sections with extremely small dimensions as low as 3 nm × 20 nm. These device measurements provide a clear demonstration of the excellent scaling potential offered by this technology, and they are also consistent with the scaling behavior predicted by extensive device simulations. Finally, we discuss issues of device integration and cell design, manufacturability, and reliability.


title: Virtualized FPGA Accelerators for Efficient Cloud Computing

abstract: Hardware accelerators implement custom architectures to significantly speed up computations in a wide range of domains. As performance scaling in server-class CPUs slows, we propose the integration of hardware accelerators in the cloud as a way to maintain a positive performance trend. Field programmable gate arrays (FPGAs) represent the ideal way to integrate accelerators in the cloud, since they can be reprogrammed as needs change and allow multiple accelerators to share optimised communication infrastructure. We discuss a framework that integrates reconfigurable accelerators in a standard server with virtualised resource management and communication. We then present a case study that quantifies the efficiency benefits and break-even point for integrating FPGAs in the cloud.


title: Emission Enhancement and Intermittency in Polycrystalline Organolead Halide Perovskite Films

abstract: Inorganic-organic halide organometal perovskites have demonstrated very promising performance for opto-electronic applications, such as solar cells, light-emitting diodes, lasers, single-photon sources, etc. However, the little knowledge on the underlying photophysics, especially on a microscopic scale, hampers the further improvement of devices based on this material. In this communication, correlated conventional photoluminescence (PL) characterization and wide-field PL imaging as a function of time are employed to investigate the spatially- and temporally-resolved PL in CH3NH3PbI3−xClx perovskite films. Along with a continuous increase of the PL intensity during light soaking, we also observe PL blinking or PL intermittency behavior in individual grains of these films. Combined with significant suppression of PL blinking in perovskite films coated with a phenyl-C61-butyric acid methyl ester (PCBM) layer, it suggests that this PL intermittency is attributed to Auger recombination induced by photoionized defects/traps or mobile ions within grains. These defects/traps are detrimental for light conversion and can be effectively passivated by the PCBM layer. This finding paves the way to provide a guideline on the further improvement of perovskite opto-electronic devices.


title: Anti-forensics: The Next Step in Digital Forensics Tool Testing

abstract: We classify and present established and new attacks on digital forensics tools. In particular, we present the first and surprisingly simple code injection attack on a commercial analysis tool that potentially allows to infiltrate the analysis system. We argue that digital forensics tool testing must mature to cater for malicious adversaries. We also discuss possible countermeasures.


title: Classification of lung sounds using convolutional neural networks

abstract: In the field of medicine, with the introduction of computer systems that can collect and analyze massive amounts of data, many non-invasive diagnostic methods are being developed for a variety of conditions. In this study, our aim is to develop a non-invasive method of classifying respiratory sounds that are recorded by an electronic stethoscope and the audio recording software that uses various machine learning algorithms. In order to store respiratory sounds on a computer, we developed a cost-effective and easy-to-use electronic stethoscope that can be used with any device. Using this device, we recorded 17,930 lung sounds from 1630 subjects. We employed two types of machine learning algorithms; mel frequency cepstral coefficient (MFCC) features in a support vector machine (SVM) and spectrogram images in the convolutional neural network (CNN). Since using MFCC features with a SVM algorithm is a generally accepted classification method for audio, we utilized its results to benchmark the CNN algorithm. We prepared four data sets for each CNN and SVM algorithm to classify respiratory audio: (1) healthy versus pathological classification; (2) rale, rhonchus, and normal sound classification; (3) singular respiratory sound type classification; and (4) audio type classification with all sound types. Accuracy results of the experiments were; (1) CNN 86%, SVM 86%, (2) CNN 76%, SVM 75%, (3) CNN 80%, SVM 80%, and (4) CNN 62%, SVM 62%, respectively. As a result, we found out that spectrogram image classification with CNN algorithm works as well as the SVM algorithm, and given the large amount of data, CNN and SVM machine learning algorithms can accurately classify and pre-diagnose respiratory audio.


title: Abstract Evaluation and comparison of texture descriptors proposed in MPEG-7

abstract: Abstract Texture description contributes as one of the most important low-level features in content-based image retrieval. In MPEG-7, homogeneous texture descriptor (HTD), texture browsing descriptor (TBD), and edge histogram descriptor (EHD) have been proposed as texture descriptors. However, no comprehensive evaluation and comparison of these three descriptors have been made. In this paper, we propose a comprehensive evaluation and comparison benchmark for feature descriptors, especially for visual descriptors in MPEG-7. In the proposed benchmark, three texture descriptors in MPEG-7 are evaluated and compared. First, the descriptors are analyzed according to the standard criteria. Second, experiments are implemented on the Brodatz texture image database. Analysis of the experimental results shows that each descriptor has some specific characteristics and performs better than the other two in certain applications. The applicability is also summarized for each descriptor. The survey as well as performance evaluation and comparison in this paper provide several guidelines for using these descriptors in image retrieval and other applications.


title: Physical Cryptanalysis of KeeLoq Code Hopping Applications

abstract: KeeLoq remote keyless entry systems are widely used for access control purposes such as garage door openers or car anti-theft systems. We present the first successful differential power analysis attacks on numerous commercially available products employing KeeLoq code hopping. Our new techniques combine side-channel cryptanalysis with specific properties of the KeeLoq algorithm. They allow for efficiently revealing both the secret key of a remote transmitter and the manufacturer key stored in a receiver. As a result, a remote control can be cloned from only ten power traces, allowing for a practical key recovery in few minutes. Once knowing the manufacturer key, we demonstrate how to disclose the secret key of a remote control and replicate it from a distance, just by eavesdropping at most two messages. This key-cloning without physical access to the device has serious real-world security implications. Finally, we mount a denial-of-service attack on a KeeLoq access control system. All the proposed attacks have been verified on several commercial KeeLoq products.


title: Security vulnerabilities in DNS and DNSSEC

abstract: We present an analysis of security vulnerabilities in the domain name system (DNS) and the DNS security extensions (DNSSEC). DNS data that is provided by name servers lacks support for data origin authentication and data integrity. This makes DNS vulnerable to man in the middle (MITM) attacks, as well as a range of other attacks. To make DNS more robust, DNSSEC was proposed by the Internet Engineering Task Force (IETF). DNSSEC provides data origin authentication and integrity by using digital signatures. Although DNSSEC provides security for DNS data, it suffers from serious security and operational flaws. We discuss the DNS and DNSSEC architectures, and consider the associated security vulnerabilities


title: A method for assessing the reusability of object-oriented code using a validated set of automated measurements

abstract: A method for judging the reusability of C++ code components and for assessing indirect quality attributes from the direct attributes measured by an automated tool was demonstrated. The method consisted of two phases. The first phase identified and analytically validated a set of measurements for assessing direct quality attributes based on measurement theory. An automated tool was used to compute actual measures for a repository of C++ classes. A taxonomy relating reuse, indirect quality attributes, and measurements identified and validated during the first part of this research was defined. The second phase consisted of identifying and validating a set of measurements for assessing indirect quality attributes. A case study of the feasibility of applying direct measurements to assess the indirect quality attributes was conducted. The comparison and analysis of indirect quality attributes measured by human analysis with direct quality attributes measured by the automated tool provided empirical evidence that the two sets of quality attributes, direct and indirect, do correlate.


Optimizing Shuffle Performance in Spark


title: Single-phase Z-source PWM AC-AC converters

abstract: The letter proposes a new family of simple topologies of single-phase PWM ac-ac converters with a minimal number of switches: voltage-fed Z-source converter and current-fed Z-source converter. By PWM duty-ratio control, they become "solid-state transformers" with a continuously variable turns ratio. All the proposed ac-ac converters in this paper employ only two switches. Compared to the existing PWM ac-ac converter circuits, they have unique features: providing a larger range of output ac voltage with buck-boost, reversing or maintaining phase angle, reducing in-rush and harmonic current, and improving reliability. The operating principle and control method of the proposed topologies are presented. Analysis, simulation, and experimental results are given using the voltage-fed Z-source ac-ac converter as an example. The analysis can be easily extended to other converters of the proposed family. The proposed converters could be used in voltage regulation, power regulation, and so on.


title: Polydopamine-Coated Main-Chain Liquid Crystal Elastomer as Optically Driven Artificial Muscle.

abstract: Optically driven active materials have received much attention because their deformation and motion can be controlled remotely, instantly, and precisely in a contactless way. In this study, we investigated an optically actuated elastomer with rapid response: polydopamine (PDA)-coated liquid crystal elastomer (LCE). Because of the photothermal effect of PDA coating and thermal responsiveness of LCE, the elastomer film contracted significantly with near-infrared (NIR) irradiation. With a fixed strain, light-induced actuating stress in the film could be as large as 1.5 MPa, significantly higher than the maximum stress generated by most mammalian skeletal muscle (0.35 MPa). The PDA-coated LCE films could also bend or roll up by surface scanning of an NIR laser. The response time of the film to light exposure could be as short as 1/10 of a second, comparable to or even faster than that of mammalian skeletal muscle. Using the PDA-coated LCE film, we designed and fabricated a prototype of robotic swimmer that was able to swim near the water-air interface by performing "swimming strokes" through reversible bending and unbending motions induced and controlled by an NIR laser. The results presented in this study clearly demonstrated that PDA-coated LCE is a promising optically driven artificial muscle, which may have great potential for applications of soft robotics and optomechanical coupling devices.


title: Developing a successful SemEval task in sentiment analysis of Twitter and other social media texts

abstract: We present the development and evaluation of a semantic analysis task that lies at the intersection of two very trendy lines of research in contemporary computational linguistics: (1) sentiment analysis, and (2) natural language processing of social media text. The task was part of SemEval, the International Workshop on Semantic Evaluation, a semantic evaluation forum previously known as SensEval. The task ran in 2013 and 2014, attracting the highest number of participating teams at SemEval in both years, and there is an ongoing edition in 2015. The task included the creation of a large contextual and message-level polarity corpus consisting of tweets, SMS messages, LiveJournal messages, and a special test set of sarcastic tweets. The evaluation attracted 44 teams in 2013 and 46 in 2014, who used a variety of approaches. The best teams were able to outperform several baselines by sizable margins with improvement across the 2 years the task has been run. We hope that the long-lasting role of this task and the accompanying datasets will be to serve as a test bed for comparing different approaches, thus facilitating research.


title: Strengthening the rice production in Thailand through Community Rice Centers (CRCs) using ICT policies

abstract: The focus of this is on the strengthening of rice farmers for more efficient produce cropping, using Information and Communication Technology (ICT) policies as a tool to improve knowledge and skill, which leads to the better cropping yield. Thai government is currently aware of the related issues and attempts to support ICT policies to rice production especially the field data management and the transferring of knowledge and know-how to the rice farmers. The ultimate goal is to develop of the smarter farmers to elevate the Thai farmers' potential as do developed countries. ICT policies are one of solutions to assist farmers to grain better cropping yield via the accessibility to the publicized information. The Community Rice Centers (CRCs), under the supervision of the Rice Department of Thailand, was established as direct information channels from the government to rice farmers. Next, the development of current ICT policies is proposed, in order to address these issues. The final goal is to guide the development of ICT policies in the related government entities.


title: Urban Planning and Smart City Decision Management Empowered by Real-Time Data Processing Using Big Data Analytics

abstract: The Internet of Things (IoT), inspired by the tremendous growth of connected heterogeneous devices, has pioneered the notion of smart city. Various components, i.e., smart transportation, smart community, smart healthcare, smart grid, etc. which are integrated within smart city architecture aims to enrich the quality of life (QoL) of urban citizens. However, real-time processing requirements and exponential data growth withhold smart city realization. Therefore, herein we propose a Big Data analytics (BDA)-embedded experimental architecture for smart cities. Two major aspects are served by the BDA-embedded smart city. Firstly, it facilitates exploitation of urban Big Data (UBD) in planning, designing, and maintaining smart cities. Secondly, it occupies BDA to manage and process voluminous UBD to enhance the quality of urban services. Three tiers of the proposed architecture are liable for data aggregation, real-time data management, and service provisioning. Moreover, offline and online data processing tasks are further expedited by integrating data normalizing and data filtering techniques to the proposed work. By analyzing authenticated datasets, we obtained the threshold values required for urban planning and city operation management. Performance metrics in terms of online and offline data processing for the proposed dual-node Hadoop cluster is obtained using aforementioned authentic datasets. Throughput and processing time analysis performed with regard to existing works guarantee the performance superiority of the proposed work. Hence, we can claim the applicability and reliability of implementing proposed BDA-embedded smart city architecture in the real world.


title: Smashing the Stack Protector for Fun and Profit

abstract: Software exploitation has been proven to be a lucrative business for cybercriminals. Unfortunately, protecting software against attacks is a long-lasting endeavor that is still under active research. However, certain software-hardening schemes are already incorporated into current compilers and are actively used to make software exploitation a complicated procedure for the adversaries. Stack canaries are such a protection mechanism. Stack canaries aim to prevent control flow hijack by detecting corruption of a specific value on the program’s stack. Careful design and implementation of this conceptually straightforward mechanism is crucial to defeat stack-based control flow detours. In this paper, we examine 17 different stack canary implementations across multiple versions of the most popular Operating Systems running on various architectures. We systematically compare critical implementation details and introduce one new generic attack vector which allows bypassing stack canaries on current Linux systems running up-to-date multi-threaded software altogether. We release an open-source framework (CookieCrumbler) that identifies the characteristics of stack canaries on any platform it is compiled on and we propose mitigation techniques against stack-based attacks. Although stack canaries may appear obsolete, we show that when they are used correctly, they can prevent intrusions which even the more sophisticated solutions may potentially fail to block.


title: Name disambiguation from link data in a collaboration graph using temporal and topological features

abstract: In a social community, multiple persons may share the same name, phone number or some other identifying attributes. This, along with other phenomena, such as name abbreviation, name misspelling, and human error lead to erroneous aggregation of records of multiple persons under a single reference. Such mistakes affect the performance of document retrieval, web search, database integration, and more importantly, improper attribution of credit (or blame). The task of entity disambiguation partitions the records belonging to multiple persons with the objective that each partition is composed of records of a unique person. Existing solutions to this task use either biographical attributes, or auxiliary features that are collected from external sources, such as Wikipedia. However, for many scenarios, such auxiliary features are not available, or they are costly to obtain. Besides, the attempt of collecting biographical or external data sustains the risk of privacy violation. In this work, we propose a method for solving entity disambiguation task from timestamped link information obtained from a collaboration network. Our method is non-intrusive of privacy as it uses only the graph topology of an anonymized network. Experimental results on two real-life academic collaboration networks show that the proposed method has satisfactory performance.


title: PTE: Predictive Text Embedding through Large-scale Heterogeneous Text Networks

abstract: Unsupervised text embedding methods, such as Skip-gram and Paragraph Vector, have been attracting increasing attention due to their simplicity, scalability, and effectiveness. However, comparing to sophisticated deep learning architectures such as convolutional neural networks, these methods usually yield inferior results when applied to particular machine learning tasks. One possible reason is that these text embedding methods learn the representation of text in a fully unsupervised way, without leveraging the labeled information available for the task. Although the low dimensional representations learned are applicable to many different tasks, they are not particularly tuned for any task. In this paper, we fill this gap by proposing a semi-supervised representation learning method for text data, which we call the predictive text embedding (PTE). Predictive text embedding utilizes both labeled and unlabeled data to learn the embedding of text. The labeled information and different levels of word co-occurrence information are first represented as a large-scale heterogeneous text network, which is then embedded into a low dimensional space through a principled and efficient algorithm. This low dimensional embedding not only preserves the semantic closeness of words and documents, but also has a strong predictive power for the particular task. Compared to recent supervised approaches based on convolutional neural networks, predictive text embedding is comparable or more effective, much more efficient, and has fewer parameters to tune.


title: A Survey of Symbolic Execution Techniques

abstract: Many security and software testing applications require checking whether certain properties of a program hold for any possible usage scenario. For instance, a tool for identifying software vulnerabilities may need to rule out the existence of any backdoor to bypass a program’s authentication. One approach would be to test the program using different, possibly random inputs. As the backdoor may only be hit for very specific program workloads, automated exploration of the space of possible inputs is of the essence. Symbolic execution provides an elegant solution to the problem, by systematically exploring many possible execution paths at the same time without necessarily requiring concrete inputs. Rather than taking on fully specified input values, the technique abstractly represents them as symbols, resorting to constraint solvers to construct actual instances that would cause property violations. Symbolic execution has been incubated in dozens of tools developed over the past four decades, leading to major practical breakthroughs in a number of prominent software reliability applications. The goal of this survey is to provide an overview of the main ideas, challenges, and solutions developed in the area, distilling them for a broad audience.


title: Google this!: using Google apps for collaboration and productivity

abstract: In 2009, Colorado State University migrated to Google Apps for Education as an e-mail hosting solution for its students from an internal on-premise e-mail system. The additional capabilities of Google Apps, originally seen as a nonessential add-on to the e-mail solution, have boosted the collaboration and communication among CSU's students beyond our expectations. Once the faculty and staff saw the potential for collaboration the requests to opt-in increased. This allowed collaboration between faculty and students on a scale not previously witnessed at CSU. Faculty who have made the switch to Google Apps are satisfied and enthusiastic with the service. The Google Apps for Education suite comprises Google Mail, Calendar, Talk, Docs, Sites and Video. In this paper I will provide an overview of each App as well as specific techniques on using each App. We will discuss the potential of teamwork and idea exchanges made possible by Google Apps and how they can be implemented in the academic environment. We will discuss and demonstrate interoperability between Google Apps and external applications, IT team collaboration, student employee management integration, migration techniques and more.


title: A two-and-a-half-D motion-blur algorithm

abstract: Algorithms are presented for raster and vector motion blur, which produce images and masks suitable for combination by the 21/2-D compositing process. The raster algorithm is based on a "skew, blur, unskew" scheme, using a very efficient one-dimensional blurring algorithm. The vector algorithm extends the ideas of anti-aliased scan conversion to motion blur.


title: A Review of Performance Variations in SMR-Based Brain–Computer Interfaces (BCIs)

abstract: The ability to operate a brain-computer interface (BCI) varies not only across subjects but also across time within each individual subject. In this article, we review recent progress in understanding the origins of such variations for BCIs based on the sensorimotor-rhythm (SMR). We propose a classification of studies according to four categories, and argue that an investigation of the neuro-physiological correlates of within-subject variations is likely to have a large impact on the design of future BCIs. We place a special emphasis on our own work on the neuro-physiological causes of performance variations, and argue that attentional networks in the gamma-range (\({>}40\) Hz) are likely to play a critical role in this context. We conclude the review with a discussion of outstanding problems.


title: Agricultural waste utilisation strategies and demand for urban waste compost: evidence from smallholder farmers in Ethiopia

abstract: The use of agricultural waste for soil amendment is limited in developing countries. Competition between fuel and feed is the major cause for the insufficient application of agricultural waste on cropland. The aims of this study were therefore (i) to investigate variation in agricultural waste allocation between groups of farmers with different livelihood strategies and link this allocation with the nutrient balances of their production systems, (ii) to identify farm characteristics that influence utilisation of agricultural waste for soil amendment, and (iii) to assess demand for urban waste compost. A total of 220 farmers were selected randomly and interviewed using standardised semi-structured questionnaires. Four groups of farmers, namely (i) field crop farmers, (ii) vegetable producers, (iii) ornamental-plant growers, and (iv) farmers practising mixed farming, were identified using categorical principal component and two-step cluster analyses. Field crop farmers produced the largest quantity of agricultural waste, but they allocated 80% of manure to fuel and 85% of crop residues to feed. Only <10% of manure and crop residues were applied on soils. Farmers also sold manure and crop residues, and this generated 5–10% of their annual income. Vegetable and ornamental-plant growers allocated over 40% of manure and crop residues to soil amendment. Hence, nutrient balances were less negative in vegetable production systems. Education, farm size, land tenure and access to extension services were the variables that impeded allocation of agricultural waste to soil amendment. Replacement of fuel and feed through sustainable means is a viable option for soil fertility management. Urban waste compost should also be used as alternative option for soil amendment. Our results showed variation in compost demand between farmers. Education, landownership, experience with compost and access to extension services explained variation in compost demand. We also demonstrated that labour availability should be used to estimate compost demand beside cash.


title: A Point Set Generation Network for 3D Object Reconstruction from a Single Image

abstract: Generation of 3D data by deep neural network has been attracting increasing attention in the research community. The majority of extant works resort to regular representations such as volumetric grids or collection of images, however, these representations obscure the natural invariance of 3D shapes under geometric transformations, and also suffer from a number of other issues. In this paper we address the problem of 3D reconstruction from a single image, generating a straight-forward form of output – point cloud coordinates. Along with this problem arises a unique and interesting issue, that the groundtruth shape for an input image may be ambiguous. Driven by this unorthordox output form and the inherent ambiguity in groundtruth, we design architecture, loss function and learning paradigm that are novel and effective. Our final solution is a conditional shape sampler, capable of predicting multiple plausible 3D point clouds from an input image. In experiments not only can our system outperform state-of-the-art methods on single image based 3D reconstruction benchmarks, but it also shows strong performance for 3D shape completion and promising ability in making multiple plausible predictions.


title: Acoustic Denial of Service Attacks on HDDs

abstract: Among storage components, hard disk drives (HDDs) have become the most commonly-used type of non-volatile storage due to their recent technological advances, including, enhanced energy efficacy and significantly-improved areal density. Such advances in HDDs have made them an inevitable part of numerous computing systems, including, personal computers, closed-circuit television (CCTV) systems, medical bedside monitors, and automated teller machines (ATMs). Despite the widespread use of HDDs and their critical role in real-world systems, there exist only a few research studies on the security of HDDs. In particular, prior research studies have discussed how HDDs can potentially leak critical private information through acoustic or electromagnetic emanations. Borrowing theoretical principles from acoustics and mechanics, we propose a novel denial-of-service (DoS) attack against HDDs that exploits a physical phenomenon, known as acoustic resonance. We perform a comprehensive examination of physical characteristics of several HDDs and create acoustic signals that cause significant vibrations in HDD's internal components. We demonstrate that such vibrations can negatively influence the performance of HDDs embedded in real-world systems. We show the feasibility of the proposed attack in two real-world case studies, namely, personal computers and CCTVs.


title: A metagenome-wide association study of gut microbiota in type 2 diabetes

abstract: Assessment and characterization of gut microbiota has become a major research area in human disease, including type 2 diabetes, the most prevalent endocrine disease worldwide. To carry out analysis on gut microbial content in patients with type 2 diabetes, we developed a protocol for a metagenome-wide association study (MGWAS) and undertook a two-stage MGWAS based on deep shotgun sequencing of the gut microbial DNA from 345 Chinese individuals. We identified and validated approximately 60,000 type-2-diabetes-associated markers and established the concept of a metagenomic linkage group, enabling taxonomic species-level analyses. MGWAS analysis showed that patients with type 2 diabetes were characterized by a moderate degree of gut microbial dysbiosis, a decrease in the abundance of some universal butyrate-producing bacteria and an increase in various opportunistic pathogens, as well as an enrichment of other microbial functions conferring sulphate reduction and oxidative stress resistance. An analysis of 23 additional individuals demonstrated that these gut microbial markers might be useful for classifying type 2 diabetes.


title: Utilization of Data mining Approaches for Prediction of Life Threatening Diseases Survivability

abstract: techniques. The Classification algorithms adopts and makes use of decision trees, Bayesian classifier, back propagation neural network, multivariate adaptive regression splines,Adaptive-network-based fuzzy inference system ,genetic algorithm, Fuzzy rulebase,Association rule and k means clustering for prediction of the diseases mentioned above. It consists of attributes containing patient‘s medical history and symptoms. The records used for this study of prediction of diseases are cleaned and filtered with the data‘s which are irrelevant and aims to analyze the predictive/descriptive data mining techniques developed for the diagnosis of life threatening diseases.


title: Stereo-vision for autonomous industrial inspection robots

abstract: Camera-based stereo-vision provides cost-efficient vision capabilities for robotic systems. The objective of this paper is to examine the performance of stereo-vision as means to enable a robotic inspection cell for haptic quality testing with the ability to detect relevant information related to the inspection task. This information comprises the location and 3D representation of a complex object under inspection as well as the location and type of quality features which are subject to the inspection task. Among the challenges is the low-distinctiveness of features in neighboring area, inconsistent lighting, similar colors as well as low intra-class variances impeding the retrieval of quality characteristics. The paper presents the general outline of the vision chain as well as performance analysis of various algorithms for relevant steps in the machine vision chain thus indicating the capabilities and drawbacks of a camera-based stereo-vision for flexible use in complex machine vision tasks.


title: A competitive dynamic pricing model when demand is interdependent over time

abstract: In this study, we contribute to the dynamic pricing literature by developing a finite horizon model for two firms offering substitutable and nonperishable products with different quality levels. Customers can purchase and store the products, even if they do not need them at the time, in order to use them in future. The stockpile of the products generated by customers affects the demand in future periods. Therefore, the demand for each product not only is a function of prices and quality levels, but also of the products' stockpile levels. In addition, the stockpile levels change the customers' consumption behavior; more product in a stockpile leads to more consumption. Therefore, we address not only the price and demand relationship but also the stockpiling and consumption relationship in a competitive environment. The decision variable of each firm at the beginning of each period is its unit sale price. We use a deterministic dynamic program to calculate the equilibrium prices at the beginning of each period. By assuming that the market stockpile is public information, we show the existence of a unique Nash equilibrium. We next consider the case when the firms do not know the market stockpile. We then develop appropriate heuristics to calculate the optimal prices in each case. A numerical study is also provided to calculate the price levels in different scenarios and compare their performances.


Modeling Small Business Credit Scoring by Using Logistic Regression , Neural Networks , and Decision Trees


Disruptive Innovation and Health Literacy


title: Coupled Node Similarity Learning for Community Detection in Attributed Networks

abstract: Attributed networks consist of not only a network structure but also node attributes. Most existing community detection algorithms only focus on network structures and ignore node attributes, which are also important. Although some algorithms using both node attributes and network structure information have been proposed in recent years, the complex hierarchical coupling relationships within and between attributes, nodes and network structure have not been considered. Such hierarchical couplings are driving factors in community formation. This paper introduces a novel coupled node similarity (CNS) to involve and learn attribute and structure couplings and compute the similarity within and between nodes with categorical attributes in a network. CNS learns and integrates the frequency-based intra-attribute coupled similarity within an attribute, the co-occurrence-based inter-attribute coupled similarity between attributes, and coupled attribute-to-structure similarity based on the homophily property. CNS is then used to generate the weights of edges and transfer a plain graph to a weighted graph. Clustering algorithms detect community structures that are topologically well-connected and semantically coherent on the weighted graphs. Extensive experiments verify the effectiveness of CNS-based community detection algorithms on several data sets by comparing with the state-of-the-art node similarity measures, whether they involve node attribute information and hierarchical interactions, and on various levels of network structure complexity.


title: Pros and Cons of GAN Evaluation Measures

abstract: Abstract Generative models, in particular generative adversarial networks (GANs), have gained significant attention in recent years. A number of GAN variants have been proposed and have been utilized in many applications. Despite large strides in terms of theoretical progress, evaluating and comparing GANs remains a daunting task. While several measures have been introduced, as of yet, there is no consensus as to which measure best captures strengths and limitations of models and should be used for fair model comparison. As in other areas of computer vision and machine learning, it is critical to settle on one or few good measures to steer the progress in this field. In this paper, I review and critically discuss more than 24 quantitative and 5 qualitative measures for evaluating generative models with a particular emphasis on GAN-derived models. I also provide a set of 7 desiderata followed by an evaluation of whether a given measure or a family of measures is compatible with them.


title: Threshold Implementations of $\mathtt{GIFT}$ : A Trade-Off Analysis

abstract: Threshold Implementation ( TI ) is one of the most widely used countermeasure for side channel attacks. Over the years several TI techniques have been proposed for randomizing cipher execution using different variations of secret-sharing and implementation techniques. For instance, sharing without decomposition ( 4-shares ) is the most straightforward implementation of the threshold countermeasure. However, its usage is limited due to its high area requirements. On the other hand, sharing using decomposition ( 3-shares ) countermeasure for cubic non-linear functions significantly reduces area and complexity in comparison to 4-shares . Nowadays, security of ciphers using a side channel countermeasure is of utmost importance. This is due to the wide range of security critical applications from smart cards, battery operated IoT devices, to accelerated crypto-processors. Such applications have different requirements (higher speed, energy efficiency, low latency, small area etc.) and hence need different implementation techniques. Although, many TI strategies and implementation techniques are known for different ciphers, there is no single study comparing these on a single cipher. Such a study would allow a fair comparison of the various methodologies. In this work, we present an in-depth analysis of the various ways in which TI can be implemented for a lightweight cipher. We chose GIFT for our analysis as it is currently one of the most energy-efficient lightweight ciphers. The experimental results show that different implementation techniques have distinct applications. For example, the 4-shares technique is good for applications demanding high throughput whereas 3-shares is suitable for constrained environments with less area and moderate throughput requirements. The techniques presented in the paper are also applicable to other blockciphers. For security evaluation, we performed TVLA (test vector leakage assessment) on all the design strategies. Experiments using up to 50 million traces show that the designs are protected against first-order attacks.


title: Thyristorized rectifier bridge controlled through a PSoC

abstract: This work presents the design and implementation of a control system for a thyristorized rectifier bridge held by PSoC (Programmable System-on-Chip), which is able to maintain the output current of the circuit in a predetermined reference supporting changes in input voltage, as well as changes in load. Using the PSoC — an integrated circuit that has programmable analog and digital resources — it is possible to reduce the external circuitry. The project built with the PSoC, compared with a conventional implementation, is smaller and has lower costs. Another important aspect is related to the decrease in energy consumption due to the use of fewer components. The rectifier and its control system were designed and simulated. Additionally, a prototype was built and the tests proved the feasibility of using PSoC.


title: A Study of Neural Word Embeddings for Named Entity Recognition in Clinical Text.

abstract: Clinical Named Entity Recognition (NER) is a critical task for extracting important patient information from clinical text to support clinical and translational research. This study explored the neural word embeddings derived from a large unlabeled clinical corpus for clinical NER. We systematically compared two neural word embedding algorithms and three different strategies for deriving distributed word representations. Two neural word embeddings were derived from the unlabeled Multiparameter Intelligent Monitoring in Intensive Care (MIMIC) II corpus (403,871 notes). The results from both 2010 i2b2 and 2014 Semantic Evaluation (SemEval) data showed that the binarized word embedding features outperformed other strategies for deriving distributed word representations. The binarized embedding features improved the F1-score of the Conditional Random Fields based clinical NER system by 2.3% on i2b2 data and 2.4% on SemEval data. The combined feature from the binarized embeddings and the Brown clusters improved the F1-score of the clinical NER system by 2.9% on i2b2 data and 2.7% on SemEval data. Our study also showed that the distributed word embedding features derived from a large unlabeled corpus can be better than the widely used Brown clusters. Further analysis found that the neural word embeddings captured a wide range of semantic relations, which could be discretized into distributed word representations to benefit the clinical NER system. The low-cost distributed feature representation can be adapted to any other clinical natural language processing research.


title: Towards Improving Top-N Recommendation by Generalization of SLIM

abstract: Sparse Linear Methods (SLIM) are state-of-the-art recommendation approaches based on matrix factorization, which rely on a regularized !1-norm and !2-norm optimization –an alternative optimization problem to the traditional Frobenious norm. Although they have shown outstanding performance in Top-N recommendation, existent works have not yet analyzed some inherent assumptions that can have an important effect on the performance of these algorithms. In this paper, we attempt to improve the performance of SLIM by proposing a generalized formulation of the aforementioned assumptions. Instead of directly learning a sparse representation of the user-item matrix, we (i) learn the latent factors’ matrix of the users and the items via a traditional matrix factorization approach, and then (ii) reconstruct the latent user or item matrix via prototypes which are learned using sparse coding, an alternative SLIM commonly used in the image processing domain. The results show that by tuning the parameters of our generalized model we are able to outperform SLIM in several Top-N recommendation experiments conducted on two different datasets, using both nDCG and nDCG@10 as evaluation metrics. These preliminary results, although not conclusive, indicate a promising line of research to improve the performance of SLIM recommendation.


title: Smoke Detection in Video: An Image Separation Approach

abstract: Existing video-based smoke detection methods often rely on the visual features extracted directly from the original frames. In the case of light smoke, the background is still visible and it deteriorates the quality of the features. This paper presents an approach to separating the smoke component from the background such that visual features can be extracted from the smoke component for reliable smoke detection. Specifically, an image is assumed to be a linear blending of a smoke component and a background image. Given a video frame and its background, the estimation of the blending parameter and the actual smoke component can be formulated as an optimization problem. Three methods based on different models for the smoke component are proposed to solve the optimization problem. Experimental results on synthesized and real video data have shown that the proposed approach can effectively separate the smoke component and the smoke detection performance is significantly improved by using the visual features extracted from the smoke component.


title: FPGA Implementation for Real-Time Background Subtraction Based on Horprasert Model

abstract: Background subtraction is considered the first processing stage in video surveillance systems, and consists of determining objects in movement in a scene captured by a static camera. It is an intensive task with a high computational cost. This work proposes an embedded novel architecture on FPGA which is able to extract the background on resource-limited environments and offers low degradation (produced because of the hardware-friendly model modification). In addition, the original model is extended in order to detect shadows and improve the quality of the segmentation of the moving objects. We have analyzed the resource consumption and performance in Spartan3 Xilinx FPGAs and compared to others works available on the literature, showing that the current architecture is a good trade-off in terms of accuracy, performance and resources utilization. With less than a 65% of the resources utilization of a XC3SD3400 Spartan-3A low-cost family FPGA, the system achieves a frequency of 66.5 MHz reaching 32.8 fps with resolution 1,024 × 1,024 pixels, and an estimated power consumption of 5.76 W.


title: On feature combination for multiclass object classification

abstract: A key ingredient in the design of visual object classification systems is the identification of relevant class specific aspects while being robust to intra-class variations. While this is a necessity in order to generalize beyond a given set of training images, it is also a very difficult problem due to the high variability of visual appearance within each class. In the last years substantial performance gains on challenging benchmark datasets have been reported in the literature. This progress can be attributed to two developments: the design of highly discriminative and robust image features and the combination of multiple complementary features based on different aspects such as shape, color or texture. In this paper we study several models that aim at learning the correct weighting of different features from training data. These include multiple kernel learning as well as simple baseline methods. Furthermore we derive ensemble methods inspired by Boosting which are easily extendable to several multiclass setting. All methods are thoroughly evaluated on object classification datasets using a multitude of feature descriptors. The key results are that even very simple baseline methods, that are orders of magnitude faster than learning techniques are highly competitive with multiple kernel learning. Furthermore the Boosting type methods are found to produce consistently better results in all experiments. We provide insight of when combination methods can be expected to work and how the benefit of complementary features can be exploited most efficiently.


title: EM analysis of Ka band multi-throw PIN diode MMIC switches

abstract: Advantages and results of using a 3D EM simulator such as HFSS to design a compact integrated circuit (IC) are presented. Any electromagnetic (EM) fields' disturbances and interactions caused by the size and shapes of high frequency circuit elements have a significant effect on IC performance, die size and cost. Two SP4T four-throw PIN diode switches with different topologies are analyzed. The first switch uses a topology of two shunt diode with % wavelength elements. The second switch uses series-series-shunt diode topology. Both switches have integrated bias circuits and DC blocking capacitors. These SP4T switches use the patented MACOM AlGaAs heterojunction PIN Diode process. The optimal performance was achieved due to intense use of the EM simulator.


title: Scaling up: a research agenda for software engineering

abstract: The following excerpts have been gleaned from a report by the Computer Science and Technology Board that summarizes the deliberations of a group of software engineers participating in a CSTB workshop that focused on setting research priorities.


title: Robust Lane Detection Using Multiple Features

abstract: Lane marker detection is a crucial challenge in developing self-driving cars. Despite significant research, large gaps remain between research and needs for fully autonomous driving. We highlight the limitations of present work and present a unified approach for robust and real-time lane marker detection. We present a multi-feature lane detection algorithm and give evidence why relying on one type of features can be harmful. We design a lane model using geometric constraints on lane shape and fit the lane model to the visual cues extracted. We improve the robustness of our algorithm by tracking lane markers temporally. We test our algorithm on KITTI dataset and show results that our algorithm can detect lane markers in presence of occlusions, sharp curves, and shadows.


title: Unsupervised learning on k-partite graphs

abstract: Various data mining applications involve data objects of multiple types that are related to each other, which can be naturally formulated as a k-partite graph. However, the research on mining the hidden structures from a k-partite graph is still limited and preliminary. In this paper, we propose a general model, the relation summary network, to find the hidden structures (the local cluster structures and the global community structures) from a k-partite graph. The model provides a principal framework for unsupervised learning on k-partite graphs of various structures. Under this model, we derive a novel algorithm to identify the hidden structures of a k-partite graph by constructing a relation summary network to approximate the original k-partite graph under a broad range of distortion measures. Experiments on both synthetic and real datasets demonstrate the promise and effectiveness of the proposed model and algorithm. We also establish the connections between existing clustering approaches and the proposed model to provide a unified view to the clustering approaches.


title: The ProteomeXchange consortium in 2017: supporting the cultural change in proteomics public data deposition

abstract: The ProteomeXchange (PX) Consortium of proteomics resources (http://www.proteomexchange.org) was formally started in 2011 to standardize data submission and dissemination of mass spectrometry proteomics data worldwide. We give an overview of the current consortium activities and describe the advances of the past few years. Augmenting the PX founding members (PRIDE and PeptideAtlas, including the PASSEL resource), two new members have joined the consortium: MassIVE and jPOST. ProteomeCentral remains as the common data access portal, providing the ability to search for data sets in all participating PX resources, now with enhanced data visualization components.We describe the updated submission guidelines, now expanded to include four members instead of two. As demonstrated by data submission statistics, PX is supporting a change in culture of the proteomics field: public data sharing is now an accepted standard, supported by requirements for journal submissions resulting in public data release becoming the norm. More than 4500 data sets have been submitted to the various PX resources since 2012. Human is the most represented species with approximately half of the data sets, followed by some of the main model organisms and a growing list of more than 900 diverse species. Data reprocessing activities are becoming more prominent, with both MassIVE and PeptideAtlas releasing the results of reprocessed data sets. Finally, we outline the upcoming advances for ProteomeXchange.


title: A coherent authentication framework for mobile computing based on homomorphic signature and implicit authentication

abstract: Mobile cloud computing is an extension of cloud computing that allow the users to access the cloud service via their mobile devices. Although ::: mobile cloud computing is convenient and easy to use, the security challenges are increasing significantly.One of the major issues is unauthorized access.Identity Management enables to tackle this issue by protecting the identity of users and controlling access to resources. Although there are several IDM frameworks in place, they are vulnerable to attacks like timing ::: attacks in OAuth, malicious code attack in OpenID and huge amount of information leakage when user’s identity is compromised in Single Sign-On. ::: Our proposed framework implicitly authenticates a user based on user’s typing behavior.The authentication information is encrypted into homomorphic signature before being sent to IDM server and tokens are used to authorize users to access the cloud resources.Advantages of our proposed framework are: user’s identity protection and prevention from unauthorized access.


title: Joint Flow: Temporal Flow Fields for Multi Person Tracking

abstract: In this work we propose an online multi person pose tracking approach which works on two consecutive frames $I_{t-1}$ and $I_t$. The general formulation of our temporal network allows to rely on any multi person pose estimation approach as spatial network. From the spatial network we extract image features and pose features for both frames. These features serve as input for our temporal model that predicts Temporal Flow Fields (TFF). These TFF are vector fields which indicate the direction in which each body joint is going to move from frame $I_{t-1}$ to frame $I_t$. This novel representation allows to formulate a similarity measure of detected joints. These similarities are used as binary potentials in a bipartite graph optimization problem in order to perform tracking of multiple poses. We show that these TFF can be learned by a relative small CNN network whilst achieving state-of-the-art multi person pose tracking results.


title: A level set based method for fixing overhangs in 3D printing

abstract: Abstract 3D printers based on the additive manufacturing technology create objects layer-by-layer dropping fused material. As a consequence, strong overhangs cannot be printed because the new-come material does not find a suitable support over the last deposed layer. In these cases, one can add support structures (scaffolds) which make the object printable, to be removed at the end. In this paper, we propose a level set based method to create object-dependent support structures, specifically conceived to reduce both the amount of additional material and the printing time. We also review some open problems about 3D printing which can be of interests for the mathematical community.


title: Clustering Experiments on Big Transaction Data for Market Segmentation

abstract: This paper addresses the Volume dimension of Big Data. It presents a preliminary work on finding segments of retailers from a large amount of Electronic Funds Transfer at Point Of Sale (EFTPOS) transaction data. To the best of our knowledge, this is the first time a work on Big EFTPOS Data problem has been reported. A data reduction technique using the RFM (Recency, Frequency, Monetary) analysis as applied to a large data set is presented. Ways to optimise clustering techniques used to segment the big data set through data partitioning and parallelization are explained. Preliminary analysis on the segments of the retailers output from the clustering experiments demonstrates that further drilling down into the retailer segments to find more insights into their business behaviours is warranted.


title: Image Labeling and Segmentation using Hierarchical Conditional Random Field Model

abstract: The use of hierarchical Conditional Random Field model deal with the problem of labeling images . At the time of labeling a new image, selection of the nearest cluster and using the related CRF model to label this image. When one give input image, one first use the CRF model to get initial pixel labels then finding the cluster with most similar images. Then at last relabeling the input image by the CRF model associated with this cluster. This paper presents a approach to label and segment specific image having correct information.


title: Modified genetic algorithm for solving n-queens problem

abstract: Genetic algorithm is applicable to a wide range of constraint satisfaction problems such as n-queens problem. In the absence of specialized solution for a particular problem, genetic algorithm would be efficient. But holism and random choices cause problem for genetic algorithm in searching large state spaces. So, the efficiency of this algorithm would be demoted when the size of state space of the problem grows exponentially. In this paper, we attempt to cover this weakness by using local search algorithm like minimal conflicts algorithm. Minimal conflicts algorithm is trying to provide partial view for genetic algorithm by locally searching the state space. This may cause genetic algorithm to take longer steps toward the solution. Modified genetic algorithm, is the result of collaboration between genetic algorithm and minimal conflicts algorithm. Comparing the results of applying standard genetic algorithm and modified genetic algorithm on n-queens problem in section VI, indicates the amount of performance improvement.


title: On-chip Delay Line for Extraction of Decorrelated Phase Noise in FMCW Radar Transceiver MMICs

abstract: Estimation of phase noise (PN) has become economically feasible for integration in many of today's semiconductors. Several contributions propose concepts that obtain a good estimate of the PN's power spectrum without even requiring a reference oscillator. Differently, in this work we aim to estimate the time domain representation of the residual PN in the intermediate frequency domain of a frequency modulated continuous wave (FMCW) radar transceiver. For that, an artificial on-chip target is utilized, which is to be incorporated into an existing monolithic microwave integrated circuit (MMIC). The estimated decorrelated phase noise is required for cancelation of short-range leakage originating from an unwanted signal reflection superimposing the overall channel response of the radar. We determine the minimum required delay such that the residual PN of the on-chip target exceeds the intrinsic noise. Further, three different realizations of the delay line in the MMIC are compared. We verify our analytical derivations with a full FMCW radar system simulation.


title: Suicide Clusters: A Review of Risk Factors and Mechanisms

abstract: Suicide clusters, although uncommon, cause great concern in the communities in which they occur. We searched the world literature on suicide clusters and describe the risk factors and proposed psychological mechanisms underlying the spatio-temporal clustering of suicides (point clusters). Potential risk factors include male gender, being an adolescent or young adult, drug or alcohol abuse, and past history of self-harm. However, the majority of studies lack methodological rigor. Many different psychological mechanisms are described, including contagion, imitation, suggestion, learning, and assortative relating, but supporting empirical evidence is generally lacking. More scientifically rigorous studies are needed to improve understanding of suicide clusters.


title: A Survey of Modulation of Gut Microbiota by Dietary Polyphenols

abstract: Dietary polyphenols present in a broad range of plant foods have been related to beneficial health effects. This review aims to update the current information about the modulation of the gut microbiota by dietary phenolic compounds, from a perspective based on the experimental approaches used. After referring to general aspects of gut microbiota and dietary polyphenols, studies related to this topic are presented according to their experimental design: batch culture fermentations, gastrointestinal simulators, animal model studies, and human intervention studies. In general, studies evidence that dietary polyphenols may contribute to the maintenance of intestinal health by preserving the gut microbial balance through the stimulation of the growth of beneficial bacteria (i.e., lactobacilli and bifidobacteria) and the inhibition of pathogenic bacteria, exerting prebiotic-like effects. Combination of in vitro and in vivo models could help to understand the underlying mechanisms in the polyphenols-microbiota-host triangle and elucidate the implications of polyphenols on human health. From a technological point of view, supplementation with rich-polyphenolic stuffs (phenolic extracts, phenolic-enriched fractions, etc.) could be an effective option to improve health benefits of functional foods such as the case of dairy fermented foods.


title: Leveraging the error resilience of machine-learning applications for designing highly energy efficient accelerators

abstract: In recent years, inexact computing has been increasingly regarded as one of the most promising approaches for reducing energy consumption in many applications that can tolerate a degree of inaccuracy. Driven by the principle of trading tolerable amounts of application accuracy in return for significant resource savings - the energy consumed, the (critical path) delay and the (silicon) area being the resources - this approach has been limited to certain application domains. In this paper, we propose to expand the application scope, error tolerance as well as the energy savings of inexact computing systems through neural network architectures. Such neural networks are fast emerging as popular candidate accelerators for future heterogeneous multi-core platforms, and have flexible error tolerance limits owing to their ability to be trained. Our results based on simulated 65nm technology designs demonstrate that the proposed inexact neural network accelerator could achieve 43.91%-62.49% savings in energy consumption (with corresponding delay and area savings being 18.79% and 31.44% respectively) when compared to existing baseline neural network implementation, at the cost of an accuracy loss (quantified as the Mean Square Error (MSE) which increases from 0.14 to 0.20 on average).


title: The use of computational approaches in inhaler development.

abstract: Abstract Computational Fluid Dynamics (CFD) and Discrete Element Modelling (DEM) studies relevant to inhaled drug delivery are reviewed. CFD is widely used in device design to determine airflow patterns and turbulence levels. CFD is also used to simulate particles and droplets, which are subjected to various forces, turbulence and wall interactions. These studies can now be performed routinely because of the availability of commercial software containing high quality turbulence and particle models. DEM allows for the modelling of agglomerate break-up upon interaction with a wall or due to shear in the flow. However, the computational cost is high and the number of particles that can be simulated is minimal compared with the number present in typical inhaled formulations. Therefore DEM is currently limited to fundamental studies of break-up mechanisms. With decreasing computational limitations, simulations combining CFD and DEM that can address outstanding issues in agglomerate break-up and dispersion will be possible.


title: Atypical Human Infections by Animal Trypanosomes

abstract: The two classical forms of human trypanosomoses are sleeping sickness due to Trypanosoma brucei gambiense or T. brucei rhodesiense, and Chagas disease due to T. cruzi. However, a number of atypical human infections caused by other T. species (or sub-species) have been reported, namely due to T. brucei brucei, T. vivax, T. congolense, T. evansi, T. lewisi, and T. lewisi-like. These cases are reviewed here. Some infections were transient in nature, while others required treatments that were successful in most cases, although two cases were fatal. A recent case of infection due to T. evansi was related to a lack of apolipoprotein L-I, but T. lewisi infections were not related to immunosuppression or specific human genetic profiles. Out of 19 patients, eight were confirmed between 1974 and 2010, thanks to improved molecular techniques. However, the number of cases of atypical human trypanosomoses might be underestimated. Thus, improvement, evaluation of new diagnostic tests, and field investigations are required for detection and confirmation of these atypical cases.


title: MISP: The Design and Implementation of a Collaborative Threat Intelligence Sharing Platform

abstract: The IT community is confronted with incidents of all kinds and nature, new threats appear on a daily basis. Fighting these security incidents individually is almost impossible. Sharing information about threats among the community has become a key element in incident response to stay on top of the attackers. Reliable information resources, providing credible information, are therefore essential to the IT community, or even at broader scale, to intelligence communities or fraud detection groups. This paper presents the Malware Information Sharing Platform (MISP) and threat sharing project, a trusted platform, that allows the collection and sharing of important indicators of compromise (IoC) of targeted attacks, but also threat information like vulnerabilities or financial indicators used in fraud cases. The aim of MISP is to help in setting up preventive actions and counter-measures used against targeted attacks. Enable detection via collaborative-knowledge-sharing about existing malware and other threats.


title: MemNet: A Persistent Memory Network for Image Restoration

abstract: Recently, very deep convolutional neural networks (CNNs) have been attracting considerable attention in image restoration. However, as the depth grows, the long-term dependency problem is rarely realized for these very deep models, which results in the prior states/layers having little influence on the subsequent ones. Motivated by the fact that human thoughts have persistency, we propose a very deep persistent memory network (MemNet) that introduces a memory block, consisting of a recursive unit and a gate unit, to explicitly mine persistent memory through an adaptive learning process. The recursive unit learns multi-level representations of the current state under different receptive fields. The representations and the outputs from the previous memory blocks are concatenated and sent to the gate unit, which adaptively controls how much of the previous states should be reserved, and decides how much of the current state should be stored. We apply MemNet to three image restoration tasks, i.e., image denosing, super-resolution and JPEG deblocking. Comprehensive experiments demonstrate the necessity of the MemNet and its unanimous superiority on all three tasks over the state of the arts. Code is available at this https URL.


title: Sequence-to-Segment Networks for Segment Detection

abstract: Detecting segments of interest from an input sequence is a challenging problem which often requires not only good knowledge of individual target segments, but also contextual understanding of the entire input sequence and the relationships between the target segments. To address this problem, we propose the Sequence-to-Segment Network (S2N), a novel end-to-end sequential encoder-decoder architecture. S2N first encodes the input into a sequence of hidden states that progressively capture both local and holistic information. It then employs a novel decoding architecture, called Segment Detection Unit (SDU), that integrates the decoder state and encoder hidden states to detect segments sequentially. During training, we formulate the assignment of predicted segments to ground truth as bipartite matching and use the Earth Mover's Distance to calculate the localization errors. We experiment with S2N on temporal action proposal generation and video summarization and show that S2N achieves state-of-the-art performance on both tasks.


An algorithmic approach to concept exploration in a large knowledge network (automatic thesaurus consultation): symbolic branch-and-bound search vs. connectionist Hopfield net activation


title: The Demonstration of Dislocation-Stress Memorization Technique Stressor on Si n-FinFETs

abstract: The dislocation stress memorization technique (D-SMT) stressor is demonstrated to boost the device performance on the Si three-dimensional (3-D) FinFET device. The larger channel stress and mobility enhancement ratio are observed in the narrower gate width device, due to the effect of triple crystal re-growth directions on the 3-D FinFET device. In this paper, ∼33% mobility enhancement and ∼23% Id,sat improvement on the Si FinFET device with W/L of 100/60 nm are achieved successfully with the implement of D-SMT stressor.


title: Creating test collections from user generated content for GIR evaluation

abstract: Evaluation of the effectiveness of Geographic Information Retrieval (GIR) systems is challenging and time consuming. We describe an approach to such evaluations, where we use user generated content in the form of text and associated metadata to build a large test colletion automatically. We can thus show that the UGC test collection is useful for evaluating and exploring some of the critical aspects of a GIR, for instance by submitting large numbers of queries.


title: 3D feature points detection on sparse and non-uniform pointcloud for SLAM

abstract: In this paper, we propose a novel 3D feature point detection algorithm using Multiresolution Surface Variation (MSV). The proposed algorithm is used to extract 3D features from a cluttered, unstructured environment for use in realtime Simultaneous Localisation and Mapping (SLAM) algorithms running on a mobile robot. The salient feature of the proposed method is that, it can not only handle dense, uniform 3D point clouds (such as those obtained from Kinect or rotating 2D Lidar), but also (perhaps more importantly) handle sparse, non-uniform 3D point clouds (obtained from sensors such as 3D Lidar) and produce robust, repeatable key points that are specifically suitable for SLAM. The efficacy of the proposed method is evaluated using a dataset collected from a mobile robot with a 3D Velodyne Lidar (VLP-16) mounted on top.


title: Two Section Wideband 90° Hybrid Coupler Using Parallel-Coupled Three-Line

abstract: Two section wideband hybrid coupler with short-circuited coupled lines in the middle branch is demonstrated. The coupled lines are designed with a parallel-coupled 3-line, which has tight coupling and symmetric transmission phase over the center frequency. Without defected ground planes and a multilayer process, the proposed two-section coupler has the widest fractional bandwidth (FBW) than other two or even three section hybrid couplers modified from branch-line couplers. The designed wideband coupler has 55% FBW at the center frequency of 1.9 GHz. The bandwidth is limited by 1-dB power imbalance and the worst return loss, isolation, and phase imbalance within the bandwidth are 20.1 dB, 20.8 dB, and 3.2°, respectively.


title: Building smart cities applications using IoT and cloud-based architectures

abstract: The concept of a Smart City highlights the need to enhance quality, interconnection and performance of various urban services with the use of information and communication technologies (ICT). Smart City technologies promote cloud-based and Internet of Things (IoT) based services in which real-world user interfaces use smart phones, sensors and RFIDs. Cloud computing and IoT are presently two most important ICT models that are shaping the next generation of computing. Both concepts have major impact on how we build and deploy smart applications/solutions for smart cities. Cloud computing represents the delivery of hardware and software resources on demand over the Internet-as-a-service. On the other hand, IoT concept envisions a new generation of devices (sensors, both virtual and physical) that are connected to the Internet and provide different services for value-added applications. This paper addresses the convergent domain of cloud computing and IoT for any smart city application deployment. Dubai as a smart city is discussed with some application-based scenarios. An IoT-based healthcare framework is also proposed in the paper.


title: Semantic Mark-up of Italian Legal Texts Through NLP-based Techniques

abstract: In this paper we illustrate an approach to information extraction from legal texts using SALEM. SALEM is an NLP architecture for semantic annotation and indexing of Italian legislative texts, developed by ILC in close collaboration with ITTIG-CNR, Florence. Results of SALEM performance on a test sample of about 500 Italian law paragraphs are provided.


title: Prediction of Machine Health Condition Using Neuro-Fuzzy and Bayesian Algorithms

abstract: This paper proposes a novel approach for machine health condition prognosis based on neuro-fuzzy systems (NFSs) and Bayesian algorithms. The NFS, after training with machine condition data, is employed as a prognostic model to forecast the evolution of the machine fault state with time. An online model update scheme is developed on the basis of the probability density function (PDF) of the NFS residuals between the actual and predicted condition data. Bayesian estimation algorithms adopt the model's predicted data as prior information in combination with online measurements to update the degree of belief in the forecasting estimations. In order to simplify the implementation of the proposed approach, a recursive Bayesian algorithm called particle filtering is utilized to calculate in real time a posterior PDF by a set of random samples (or particles) with associated weights. When new data become available, the weights of all particles are updated, and then, predictions are carried out, which form the PDF of the predicted estimations. The developed method is evaluated via two experimental cases-a cracked carrier plate and a faulty bearing. The prediction performance is compared with three prevalent machine condition predictors-recurrent neural networks, NFSs, and recurrent NFSs. The results demonstrate that the proposed approach can predict machine conditions more accurately.


title: Segmenting Time Series: A Survey and Novel Approach

abstract: In recent years, there has been an explosion of interest in mining time series databases. As with most computer science problems, representation of the data is the key to efficient and effective solutions. One of the most commonly used representations is piecewise linear approximation. This representation has been used by various researchers to support clustering, classification, indexing and association rule mining of time series data. A variety of algorithms have been proposed to obtain this representation, with several algorithms having been independently rediscovered several times. In this paper, we undertake the first extensive review and empirical comparison of all proposed techniques. We show that all these algorithms have fatal flaws from a data mining perspective. We introduce a novel algorithm that we empirically show to be superior to all others in the literature.


title: Radar and stereo vision fusion for multitarget tracking on the special Euclidean group

abstract: Reliable scene analysis, under varying conditions, is an essential task in nearly any assistance or autonomous system application, and advanced driver assistance systems (ADAS) are no exception. ADAS commonly involve adaptive cruise control, collision avoidance, lane change assistance, traffic sign recognition, and parking assistance-with the ultimate goal of producing a fully autonomous vehicle. The present paper addresses detection and tracking of moving objects within the context of ADAS. We use a multisensor setup consisting of a radar and a stereo camera mounted on top of a vehicle. We propose to model the sensors uncertainty in polar coordinates on Lie Groups and perform the objects state filtering on Lie groups, specifically, on the product of two special Euclidean groups, i.e., SE ( 2 ) 2 . To this end, we derive the designed filter within the framework of the extended Kalman filter on Lie groups. We assert that the proposed approach results with more accurate uncertainty modeling, since used sensors exhibit contrasting measurement uncertainty characteristics and the predicted target motions result with banana-shaped uncertainty contours. We believe that accurate uncertainty modeling is an important ADAS topic, especially when safety applications are concerned. To solve the multitarget tracking problem, we use the joint integrated probabilistic data association filter and present necessary modifications in order to use it on Lie groups. The proposed approach is tested on a real-world dataset collected with the described multisensor setup in urban traffic scenarios. Radar and stereo camera integration for tracking in ADAS.Detection and tracking of moving objects by filtering on matrix Lie groups.State space formed as a product of two special Euclidean groups.Employed banana-shaped uncertainties typical for range-bearing sensors and vehicles in motion.JIPDA filter for multitarget tracking on matrix Lie groups.


title: RA-UNet: A hybrid deep attention-aware network to extract liver and tumor in CT scans

abstract: Automatic extraction of liver and tumor from CT volumes is a challenging task due to their heterogeneous and diffusive shapes. Recently, 2D and 3D deep convolutional neural networks have become popular in medical image segmentation tasks because of the utilization of large labeled datasets to learn hierarchical features. However, 3D networks have some drawbacks due to their high cost on computational resources. In this paper, we propose a 3D hybrid residual attention-aware segmentation method, named RA-UNet, to precisely extract the liver volume of interests (VOI) and segment tumors from the liver VOI. The proposed network has a basic architecture as a 3D U-Net which extracts contextual information combining low-level feature maps with high-level ones. Attention modules are stacked so that the attention-aware features change adaptively as the network goes"very deep"and this is made possible by residual learning. This is the first work that an attention residual mechanism is used to process medical volumetric images. We evaluated our framework on the public MICCAI 2017 Liver Tumor Segmentation dataset and the 3DIRCADb dataset. The results show that our architecture outperforms other state-of-the-art methods. We also extend our RA-UNet to brain tumor segmentation on the BraTS2018 and BraTS2017 datasets, and the results indicate that RA-UNet achieves good performance on a brain tumor segmentation task as well.


title: Detection of opinion spam with character n-grams

abstract: In this paper we consider the detection of opinion spam as a stylistic classification task because, given a particular domain, the deceptive and truthful opinions are similar in content but differ in the way opinions are written (style). Particularly, we propose using character n-grams as features since they have shown to capture lexical content as well as stylistic information. We evaluated our approach on a standard corpus composed of 1600 hotel reviews, considering positive and negative reviews. We compared the results obtained with character n-grams against the ones with word n-grams. Moreover, we evaluated the effectiveness of character n-grams decreasing the training set size in order to simulate real training conditions. The results obtained show that character n-grams are good features for the detection of opinion spam; they seem to be able to capture better than word n-grams the content of deceptive opinions and the writing style of the deceiver. In particular, results show an improvement of 2.3% and 2.1% over the word-based representations in the detection of positive and negative deceptive opinions respectively. Furthermore, character n-grams allow to obtain a good performance also with a very small training corpus. Using only 25% of the training set, a Naive Bayes classifier showed F 1 values up to 0.80 for both opinion polarities.


title: E.: Labeled faces in the wild: A database for studying face recognition in unconstrained environments

abstract: Most face databases have been created under controlled conditions to facilitate the study of specific parameters on the face recognition problem. These parameters include such variables as position, pose, lighting, background, camera quality, and gender. While there are many applications for face recognition technology in which one can control the parameters of image acquisition, there are also many applications in which the practitioner has little or no control over such parameters. This database, Labeled Faces in the Wild, is provided as an aid in studying the latter, unconstrained, recognition problem. The database contains labeled face photographs spanning the range of conditions typically encountered in everyday life. The database exhibits “natural” variability in factors such as pose, lighting, race, accessories, occlusions, and background. In addition to describing the details of the database, we provide specific experimental paradigms for which the database is suitable. This is done in an effort to make research performed with the database as consistent and comparable as possible. We provide baseline results, including results of a state of the art face recognition system combined with a face alignment system. To facilitate experimentation on the database, we provide several parallel databases, including an aligned version.


title: HaoLap: A Hadoop based OLAP system for big data

abstract: In recent years, facing information explosion, industry and academia have adopted distributed file system and MapReduce programming model to address new challenges the big data has brought. Based on these technologies, this paper presents HaoLap (Hadoop based oLap), an OLAP (OnLine Analytical Processing) system for big data. Drawing on the experience of Multidimensional OLAP (MOLAP), HaoLap adopts the specified multidimensional model to map the dimensions and the measures; the dimension coding and traverse algorithm to achieve the roll up operation on dimension hierarchy; the partition and linearization algorithm to store dimensions and measures; the chunk selection algorithm to optimize OLAP performance; and MapReduce to execute OLAP. The paper illustrates the key techniques of HaoLap including system architecture, dimension definition, dimension coding and traversing, partition, data storage, OLAP and data loading algorithm. We evaluated HaoLap on a real application and compared it with Hive, HadoopDB, HBaseLattice, and Olap4Cloud. The experiment results show that HaoLap boost the efficiency of data loading, and has a great advantage in the OLAP performance of the data set size and query complexity, and meanwhile HaoLap also completely support dimension operations.


title: Quality of service approaches in cloud computing

abstract: Context: Cloud computing is a new computing technology that provides services to consumers and businesses. Due to the increasing use of these services, the quality of service (QoS) of cloud computing has become an important and essential issue since there are many open challenges which need to be addressed related to trust in cloud services. Many research issues have been proposed in QoS approaches in the cloud computing area.Objective: The aim of this study is to survey current research on QoS approaches in cloud computing in order to identify where more emphasis should be placed in both current and future research directions.Method: A systematic mapping study was performed to find the related literature, and 67 articles were selected as primary studies that are classified in relation to the focus, research type and contribution type.Result: The majority of the articles are of the validation research type (64%). Infrastructure as a service (48%) was the largest research focus area, followed by software as a service (36%). The majority of contributions concerned methods (48%), followed by models (32%).Conclusion: The results of this study confirm that QoS approaches in cloud computing have become an important topic in the cloud computing area in recent years and there remain open challenges and gaps which require future research exploration. In particular, tools, metrics and evaluation research are needed in order to provide useful and trustworthy cloud computing services that deliver appropriate QoS. 67 primary studies addressed QoS in cloud computing.The largest of studies discussed validation.The majority of studies considered infrastructure as service.Most studies focused on methods or models.QoS approaches require further research.