title: Sentiment analysis and classification based on textual reviews

abstract: Mining is used to help people to extract valuable information from large amount of data. Sentiment analysis focuses on the analysis and understanding of the emotions from the text patterns. It identifies the opinion or attitude that a person has towards a topic or an object and it seeks to identify the viewpoint underlying a text span. Sentiment analysis is useful in social media monitoring to automatically characterize the overall feeling or mood of consumers as reflected in social media toward a specific brand or company and determine whether they are viewed positively or negatively on the web. This new form of analysis has been widely adopted in customer relation management especially in the context of complaint management. For automating the task of classifying a single topic textual review, document-level sentiment classification is used for expressing a positive or negative sentiment. So analyzing sentiment using Multi-theme document is very difficult and the accuracy in the classification is less. The document level classification approximately classifies the sentiment using Bag of words in Support Vector Machine (SVM) algorithm. In proposed work, a new algorithm called Sentiment Fuzzy Classification algorithm with parts of speech tags is used to improve the classification accuracy on the benchmark dataset of Movies reviews dataset.


Running head : ADOLESCENT SELF-ESTEEM AND ADULT DEPRESSION Low and Decreasing Self-Esteem During Adolescence Predict Adult Depression Two Decades


title: Culture, corruption, suicide, happiness and global social media use: a cross-cultural perspective

abstract: This study was conducted to answer this simple question: 'Can cultural values explain global social media use?' Along with cultural dimensions introduced by past studies we also added several demographic, socio-economic and personality variables into this study that generated quite interesting findings. We found that there are low levels of suicide, more happiness and more corruption in societies that use social media heavily. We also observed that GDP per capita and median age are negatively related with social media use. Self-esteem stood out as important variable related to social media use intensity along with emotional expressiveness, openness and conscientiousness. Contrary to the common view, nation-level social capital achievement was negatively related with social media use and there was absolutely no relationship between high-context and low-context communication characteristics and local social media use. Some other findings also indicated that conservative and collectivistic countries use social media more often than do individualistic and developed countries. Schwartz's cultural dimensions and the results of the GLOBE study accounted for a considerable amount of variation in country-level social media use where Hofstede and Trompenaars' cultural dimensions were insignificant. Since most of the cultural values failed to explain the intensity of social media use, we also developed a cross-cultural online communication framework called cross-cultural self and others' worth.


title: Reputation on Stack Exchange: Tag, You're It!

abstract: Question and Answer sites like the Stack Exchange network allow users to contribute knowledge in a variety of topics. Of particular interest are the habits of expert users, or users with high reputation scores. Expanding on previous work done by Bosu et al., we performed an exploratory analysis of the data to determine if there is a positive correlation between user reputation scores and the diversity of tags a user contributed to. For our analysis, we used data available from Stack Exchange to created a visual network of user tagging habits. We found that there is a correlation between these two measures, suggesting that expert users contribute to a wide variety of tags. Our research also shows poor community structures in the networks studied. This is consistent with the prevailing literature on Stack Exchange use and the power law. Our findings confirms one of the recommendations put forward by Bosu et al. Through the use of a much larger population and put forward ideas for new areas of exploration on question and answer sites.


title: IRSTLM: an open source toolkit for handling large scale language models.

abstract: Research in speech recognition and machine translation is boosting the use of large scale n-gram language models. We present an open source toolkit that permits to efficiently handle language models with billions of n-grams on conventional machines. The IRSTLM toolkit supports distribution of ngram collection and smoothing over a computer cluster, language model compression through probability quantization, lazy-loading of huge language models from disk. IRSTLM has been so far successfully deployed with the Moses toolkit for statistical machine translation and with the FBK-irst speech recognition system. Efficiency of the tool is reported on a speech transcription task of Italian political speeches using a language model of 1.1 billion four-grams.


title: Life Designing: A Paradigm for Career Construction in the 21st Century.

abstract: a b s t r a c t At the beginning of the 21st century, a new social arrangement of work poses a series of questions and challenges to scholars who aim to help people develop their working lives. Given the globalization of career counseling, we decided to address these issues and then to formulate potentially innovative responses in an international forum. We used this approach to avoid the difficulties of creating models and methods in one country and then trying to export them to other countries where they would be adapted for use. This article presents the initial outcome of this collaboration, a counseling model and methods. The life-designing model for career intervention endorses five presuppositions about people and their work lives: contextual possibilities, dynamic processes, non-linear progression, multiple perspectives, and personal patterns. Thinking from these five presuppositions, we have crafted a contextualized model based on the epistemology of social constructionism, particularly recognizing that an individual’s knowledge and identity are the product of social interaction and that meaning is co-constructed through discourse. The life-design


Addressing Safety and Security Contradictions in Cyber-Physical Systems


JHU-ISI Gesture and Skill Assessment Working Set ( JIGSAWS ) : A Surgical Activity Dataset for Human Motion Modeling


title: Identification of Peer-to-Peer VoIP Sessions Using Entropy and Codec Properties

abstract: Voice over Internet Protocol (VoIP) applications based on peer-to-peer (P2P) communications have been experiencing considerable growth in terms of number of users. To overcome filtering policies or protect the privacy of their users, most of these applications implement mechanisms such as protocol obfuscation or payload encryption that avoid the inspection of their traffic, making it difficult to identify its nature. The incapacity to determine the application that is responsible for a certain flow raises challenges for the effective management of the network. In this paper, a new method for the identification of VoIP sessions is presented. The proposed mechanism classifies the flows, in real-time, based on the speech codec used in the session. To make the classification lightweight, the behavioral signatures for each analyzed codec were created using only the lengths of the packets. Unlike most previous approaches, the classifier does not use the lengths of the packets individually. Instead, it explores their level of heterogeneity in real time, using entropy to emphasize such feature. The results of the performance evaluation show that the proposed method is able to identify VoIP sessions accurately and simultaneously recognize the used speech codec.


title: Convolutional Neural Networks for P300 Detection with Application to Brain-Computer Interfaces

abstract: A Brain-Computer Interface (BCI) is a specific type of human-computer interface that enables the direct communication between human and computers by analyzing brain measurements. Oddball paradigms are used in BCI to generate event-related potentials (ERPs), like the P300 wave, on targets selected by the user. A P300 speller is based on this principle, where the detection of P300 waves allows the user to write characters. The P300 speller is composed of two classification problems. The first classification is to detect the presence of a P300 in the electroencephalogram (EEG). The second one corresponds to the combination of different P300 responses for determining the right character to spell. A new method for the detection of P300 waves is presented. This model is based on a convolutional neural network (CNN). The topology of the network is adapted to the detection of P300 waves in the time domain. Seven classifiers based on the CNN are proposed: four single classifiers with different features set and three multiclassifiers. These models are tested and compared on the Data set II of the third BCI competition. The best result is obtained with a multiclassifier solution with a recognition rate of 95.5 percent, without channel selection before the classification. The proposed approach provides also a new way for analyzing brain activities due to the receptive field of the CNN models.


title: Emotion Recognition Using Multimodal Deep Learning

abstract: To enhance the performance of affective models and reduce the cost of acquiring physiological signals for real-world applications, we adopt multimodal deep learning approach to construct affective models with SEED and DEAP datasets to recognize different kinds of emotions. We demonstrate that high level representation features extracted by the Bimodal Deep AutoEncoder BDAE are effective for emotion recognition. With the BDAE network, we achieve mean accuracies of 91.01i¾?% and 83.25i¾?% on SEED and DEAP datasets, respectively, which are much superior to those of the state-of-the-art approaches. By analysing the confusing matrices, we found that EEG and eye features contain complementary information and the BDAE network could fully take advantage of this complement property to enhance emotion recognition.


title: Document image dewarping using robust estimation of curled text lines

abstract: Digital cameras have become almost ubiquitous and their use for fast and casual capturing of natural images is unchallenged. For making images of documents, however, they have not caught up to flatbed scanners yet, mainly because camera images tend to suffer from distortion due to the perspective and are therefore limited in their further use for archival or OCR. For images of non-planar paper surfaces like books, page curl causes additional distortion, which poses an even greater problem due to its nonlinearity. This paper presents a new algorithm for removing both perspective and page curl distortion. It requires only a single camera image as input and relies on a priori layout information instead of additional hardware. Therefore, it is much more user friendly than most previous approaches, and allows for flexible ad hoc document capture. Results are presented showing that the algorithm produces visually pleasing output and increases OCR accuracy, thus having the potential to become a general purpose preprocessing tool for camera based document capture.


title: Coupled deep auto-encoder with image edge information for image super-resolution

abstract: Image super-resolution aims to recover a fine-resolution image from one or more low-resolution image(s). In this paper, we propose a novel image super-resolution approach based on the recent development of coupled deep auto-encoder. In the training step, the vector of the local low resolution (LR) and high resolution image (HR) patches and the corresponding edge information are extracted to be the input of the autoencoder. Then we learn the intrinsic representations of LR and HR image patches by auto-encoders respectively. Then we learn their relationship by a two-layer neural network. In the test step, given a LR image patch, we can obtain the corresponding HR patch through the trained deep network efficiently and effectively. Expensive experimentation results show that the proposed algorithm generates higher-quality super-resolution image compared to other state-of-the-art methods.


title: CDStore: Toward Reliable, Secure, and Cost-Efficient Cloud Storage via Convergent Dispersal

abstract: We present CDStore, which disperses users' backup data across multiple clouds and provides a unified multicloud storage solution with reliability, security, and cost-efficiency guarantees. CDStore builds on an augmented secret sharing scheme called convergent dispersal, which supports deduplication by using deterministic content-derived hashes as inputs to secret sharing. We present the design of CDStore, and in particular, describe how it combines convergent dispersal with two-stage deduplication to achieve both bandwidth and storage savings and be robust against side-channel attacks. We evaluate the performance of our CDStore prototype using real-world workloads on LAN and commercial cloud testbeds. Our cost analysis also demonstrates that CDStore achieves a monetary cost saving of 70% over a baseline cloud storage solution using state-of-the-art secret sharing.


title: SIEL: Aspect Based Sentiment Analysis in Reviews

abstract: Following the footsteps of SemEval-2014 Task 4 (Pontiki et al., 2014), SemEval-2015 too had a task dedicated to aspect-level sentiment analysis (Pontiki et al., 2015), which saw participation from over 25 teams. In Aspectbased Sentiment Analysis, the aim is to identify the aspects of entities and the sentiment expressed for each aspect. In this paper, we present a detailed description of our system, that stood 4th in Aspect Category subtask (slot 1), 7th in Opinion Target Expression subtask (slot 2) and 8th in Sentiment Polarity subtask (slot 3) on the Restaurant datasets.


title: Are you tough enough? Framework for Robustness Validation of Machine Comprehension Systems

abstract: Deep Learning NLP domain lacks procedures for the analysis of model robustness. In this paper we propose a framework which validates robustness of any Question Answering model through model explainers. We propose that a robust model should transgress the initial notion of semantic similarity induced by word embeddings to learn a more human-like understanding of meaning. We test this property by manipulating questions in two ways: swapping important question word for 1) its semantically correct synonym and 2) for word vector that is close in embedding space. We estimate importance of words in asked questions with Locally Interpretable Model Agnostic Explanations method (LIME). With these two steps we compare state-of-the-art Q&A models. We show that although accuracy of state-of-the-art models is high, they are very fragile to changes in the input. Moreover, we propose 2 adversarial training scenarios which raise model sensitivity to true synonyms by up to 7% accuracy measure. Our findings help to understand which models are more stable and how they can be improved. In addition, we have created and published a new dataset that may be used for validation of robustness of a Q&A model.


The two worlds of humanitarian innovation


title: Investigation of multi-device location spoofing attacks on air traffic control and possible countermeasures

abstract: Multilateration techniques have been proposed to verify the integrity of unprotected location claims in wireless localization systems. A common assumption is that the adversary is equipped with only a single device from which it transmits location spoofing signals. In this paper, we consider a more advanced model where the attacker is equipped with multiple devices and performs a geographically distributed coordinated attack on the multilateration system. The feasibility of a distributed multi-device attack is demonstrated experimentally with a self-developed attack implementation based on multiple COTS software-defined radio (SDR) devices. We launch an attack against the OpenSky Network, an air traffic surveillance system that implements a time-difference-of-arrival (TDoA) multi-lateration method for aircraft localization based on ADS-B signals. Our experiments show that the timing errors for distributed spoofed signals are indistinguishable from the multilateration errors of legitimate aircraft signals, indicating that the threat of multi-device spoofing attacks is real in this and other similar systems. In the second part of this work, we investigate physical-layer features that could be used to detect multi-device attacks. We show that the frequency offset and transient phase noise of the attacker's radio devices can be exploited to discriminate between a received signal that has been transmitted by a single (legitimate) transponder or by multiple (malicious) spoofing sources. Based on that, we devise a multi-device spoofing detection system that achieves zero false positives and a false negative rate below 1%.


title: Hybrid Mobile Edge Computing: Unleashing the Full Potential of Edge Computing in Mobile Device Use Cases

abstract: Many different technologies fostering and supporting distributed and decentralized computing scenarios emerged recently. Edge computing provides the necessary on-demand computing power for Internet-of-Things (IoT) devices where it is needed. Computing power is moved closer to the consumer, with the effect of reducing latency and increasing fail-safety due to absent centralized structures. This is an enabler for applications requiring high-bandwidth uplinks and low latencies to computing units. In this paper, a new use case for edge computing is identified. Mobile devices can overcome their battery limitations and performance constraints by dynamically using the edge-computing-provided computational power. We call this new technology Hybrid Mobile Edge Computing. We present a general architecture and framework, which targets the mobile device use case of hybrid mobile edge computing, not only considering the improvement of performance and energy consumption, but also providing means to protect user privacy, sensitive data and computations. The achieved results are backed by the results of our analysis, targeting the energy saving potentials and possible performance improvements.


title: A 0.5V supply, 49nW band-gap reference and crystal oscillator in 40nm CMOS

abstract: This paper presents the co-design of 0.5V operational band-gap reference (BGR) and 32kHz crystal oscillator (XO) in 40nm CMOS process. The proposed BJT-based BGR offers 10× resistor area reduction compared to the conventional BGR. The proposed XO provides trans-conductance enhancement without the need for large coupling capacitor and uses a feedback loop to regulate its bias current, oscillation amplitude across PVT. The total power consumption is less than 49nW from −40°C to 120°C. Without any trimming, the BGR achieves temperature coefficients of 8ppm/°C and XO has temperature coefficient of 0.25ppm/°C. The BGR offers 5× lower area, 2× lower power consumption at 120°C compared to prior work on resistor-based, non-duty cycled BGR. The XO improves power consumption by at least 2× compared to prior work at 120°C, 0.5V supply.


Analog-to-Digital Converter Testing


title: A study on glabellar wrinkle patterns in Koreans

abstract: BACKGROUND ::: Botulinum toxin is a well-established treatment for dynamic glabellar lines. The glabella is in fact, the most common site for botulinum toxin injection in Asians. Previous studies have identified five glabellar contraction patterns according to the predominance of eyebrow approximation, depression or elevation. Unfortunately, the authors found the former classification somewhat confusing. ::: ::: ::: OBJECTIVE ::: To identify and classify glabellar wrinkle patterns in Koreans for a better treatment approach. We also aimed to provide an Asian consensus recommendation for the individual wrinkle pattern. ::: ::: ::: METHODS ::: A retrospective photographic analysis of 139 Korean patients who received botulinum toxin for the first time to treat glabellar wrinkles was conducted. The wrinkle patterns were identified and classified by a panel of experienced Korean dermatologists, based on the prevalence of perpendicular and transverse glabellar lines, the nasal wrinkles and the forehead wrinkles. The panel also convened to develop a clinical consensus on the individual wrinkle pattern in Asians. ::: ::: ::: RESULTS ::: Five patterns were identified: (1) 'U', (2) '11', (3) 'X', (4) 'π (Phi)' and (5) 'I'. The classification method allowed indentifying the most important muscles in each wrinkle pattern. The consensus recommendation addressed the most heavily used muscles to receive higher doses and to be injected into a larger number of sites. Those less solicited muscles were left untreated or received lower doses, allowing for more effective and natural results. ::: ::: ::: CONCLUSION ::: Interpersonal differences in facial animation exist among Koreans. We hope our simple glabellar wrinkles classification enables a more accurate, individualized treatment with botulinum toxin in Asians.


title: Brook for GPUs: stream computing on graphics hardware

abstract: In this paper, we present Brook for GPUs, a system for general-purpose computation on programmable graphics hardware. Brook extends C to include simple data-parallel constructs, enabling the use of the GPU as a streaming co-processor. We present a compiler and runtime system that abstracts and virtualizes many aspects of graphics hardware. In addition, we present an analysis of the effectiveness of the GPU as a compute engine compared to the CPU, to determine when the GPU can outperform the CPU for a particular algorithm. We evaluate our system with five applications, the SAXPY and SGEMV BLAS operators, image segmentation, FFT, and ray tracing. For these applications, we demonstrate that our Brook implementations perform comparably to hand-written GPU code and up to seven times faster than their CPU counterparts.


title: Low Drop-Out Voltage Regulators: Capacitor-less Architecture Comparison

abstract: Demand for system-on-chip solutions has increased the interest in low drop-out (LDO) voltage regulators which do not require a bulky off-chip capacitor to achieve stability, also called capacitor-less LDO (CL-LDO) regulators. Several architectures have been proposed; however comparing these reported architectures proves difficult, as each has a distinct process technology and specifications. This paper compares CL-LDOs in a unified matter. We designed, fabricated, and tested five illustrative CL-LDO regulator topologies under common design conditions using 0.6?m CMOS technology. We compare the architectures in terms of (1) line/load regulation, (2) power supply rejection, (3) line/load transient, (4) total on-chip compensation capacitance, (5) noise, and (6) quiescent power consumption. Insights on what optimal topology to choose to meet particular LDO specifications are provided.


title: Probabilistic Graphical Models for Semi-Supervised Traffic Classification ∗

abstract: Traffic classification using machine learning continues to be an active research area. The majority of work in this area uses off-the-shelf machine learning tools and treats them as black-box classifiers. This approach turns all the modelling complexity into a feature selection problem. In this paper, we build a problem-specific solution to the traffic classification problem by designing a custom probabilistic graphical model. Graphical models are a modular framework to design classifiers which incorporate domain-specific knowledge. More specifically, our solution introduces semi-supervised learning which means we learn from both labelled and unlabelled traffic flows. We show that our solution performs competitively compared to previous approaches while using less data and simpler features.


title: A left-handed/right-handed leaky-wave antenna derived from slotted rectangular hollow waveguide

abstract: A low-profile leaky wave antenna based on a composite left-handed/right-handed substrate integrated waveguide is presented. It utilizes the fact, that the H10 rectangular hollow waveguide mode can be represented by a lumped element equivalent circuit model, where only the series capacitor is missing as compared to the lumped element equivalent circuit model of a composite left-handed/right-handed transmission line. The series capacitor is realized by transverse slots in the upper major side of the waveguide, which are bridged by metallic strips located in a plane slightly below the upper side. Thus, sufficiently large capacitors can be realised and the periodic sections can be kept relatively short as compared to the wavelength. Moreover, the open configuration exhibits parasitic radiation leading to the leaky-wave antenna. Simulation results show an antenna with high efficiency and good matching to a 50 Ω microstrip line over a wide bandwidth, where backward and forward scanning as well as broadside radiation with an achieved maximum gain up to 10.15 dBi is possible dependent on the chosen operation frequency.


title: Fully automatic assessment of programming exercises

abstract: Automatic assessment of programming exercises has become an important method for grading students' exercises and giving feedback for them in mass courses. We describe a system called Scheme-robo, which has been designed for assessing programming exercises written in the functional programming language Scheme. The system assesses individual procedures instead of complete programs. In addition to checking the correctness of students' solutions the system provides many different tools for analysing other things in the program like its structure and running time, and possible plagiarism. The system has been in production use on our introductory programming course with some 300 students for two years with good results.


title: Opportunistic Beamforming using Dumb Antennas

abstract: Multiuser diversity is a form of diversity inherent in a wireless network, provided by independent time-varying channels across the different users. The diversity benefit is exploited by tracking the channel fluctuations of the users and scheduling transmissions to users when their instantaneous channel quality is near the peak. The diversity gain increases with the dynamic range of the fluctuations and is thus limited in environments with little scattering and/or slow fading. In such environments, we propose the use of multiple transmit antennas to induce large and fast channel fluctuations so that multiuser diversity can still be exploited. The scheme can be interpreted as opportunistic beamforming and we show that true beamforming gains can be achieved when there are sufficient users, even though very limited channel feedback is needed. Furthermore, in a cellular system, the scheme plays an additional role of opportunistic nulling of the interference created on users of adjacent cells. We discuss the design implications of implementing. this scheme in a complete wireless system.


title: A Deep Ensemble Model with Slot Alignment for Sequence-to-Sequence Natural Language Generation

abstract: Natural language generation lies at the core of generative dialogue systems and conversational agents. We describe an ensemble neural language generator, and present several novel methods for data representation and augmentation that yield improved results in our model. We test the model on three datasets in the restaurant, TV and laptop domains, and report both objective and subjective evaluations of our best model. Using a range of automatic metrics, as well as human evaluators, we show that our approach achieves better results than state-of-the-art models on the same datasets.


title: Child labour for the benefit of the family in rural Poland

abstract: Employing children to work in a family household or on a farm is a widespread phenomena in many countries worldwide. This paper presents the results of an investigation of the phenomenon of the involvement of children aged 11-14 years in work on family farms and in rural households in central and eastern Poland, a typical agricultural region. The data obtained are characterized by high reliability due to utilization of various research techniques: surveys and observations based on records of daily routines. Material collected by questionnaire survey is significant because of the relatively large group of respondents – 1,536 people, selected by stratified sampling. Records of the daily routines of 332 people randomly selected from the general sample of 1,536 people, were conducted over a total period of 2 months through all the seasons – 2 weeks in each season – provided reliable data. Analysis of the collected material allowed characterization of the phenomenon of children's engagement in the work in a family household and on a farm belonging to their parents or relatives, and an indication of the life and health hazards of children involved in work for a benefit of the family.


title: Knowledge Transfer Between Artificial Intelligence Systems

abstract: We consider the fundamental question: how a legacy "student" Artificial Intelligent (AI) system could learn from a legacy "teacher" AI system or a human expert without complete re-training and, most importantly, without requiring significant computational resources. Here "learning" is understood as an ability of one system to mimic responses of the other and vice-versa. We call such learning an Artificial Intelligence knowledge transfer. We show that if internal variables of the "student" Artificial Intelligent system have the structure of an $n$-dimensional topological vector space and $n$ is sufficiently high then, with probability close to one, the required knowledge transfer can be implemented by simple cascades of linear functionals. In particular, for $n$ sufficiently large, with probability close to one, the "student" system can successfully and non-iteratively learn $k\ll n$ new examples from the "teacher" (or correct the same number of mistakes) at the cost of two additional inner products. The concept is illustrated with an example of knowledge transfer from a pre-trained convolutional neural network to a simple linear classifier with HOG features.


title: Social engineering attacks on the knowledge worker

abstract: Social engineering has become an emerging threat in virtual communities and is an effective means to attack information systems. Today's knowledge workers make use of a number of services that leverage sophisticated social engineering attacks. Moreover, there is a trend towards BYOD (bring your own device) policies and the usage of online communication and collaboration tools in private and business environments. In globally acting companies, teams are no longer geographically co-located but staffed just-in-time. The decrease in personal interaction combined with the plethora of tools used (E-Mail, IM, Skype, Dropbox, LinkedIn, Lync, etc.) create new attack vectors for social engineering attacks. Recent attacks on companies such as the New York Times, RSA, or Apple have shown that targeted spear-phishing attacks are an effective evolution of social engineering attacks. When combined with zero-day-exploits they become a dangerous weapon, often used by advanced persistent threats. This paper provides a taxonomy of well-known social engineering attacks as well as a comprehensive overview of advanced social engineering attacks on the knowledge worker.


title: Uplink power efficiency of multiuser MIMO with very large antenna arrays

abstract: A multiplicity of autonomous terminals simultaneously transmits data streams to a compact array of antennas. The array uses imperfect channel-state information derived from transmitted pilots to extract the individual data streams. The power radiated by the terminals can be made inversely proportional to the square-root of the number of base station antennas with no reduction in performance. In contrast if perfect channel-state information were available the power could be made inversely proportional to the number of antennas. A maximum-ratio combining receiver normally performs worse than a zero-forcing receiver. However as power levels are reduced, the cross-talk introduced by the inferior maximum-ratio receiver eventually falls below the noise level and this simple receiver becomes a viable option.


title: Grounded Semantic Parsing for Complex Knowledge Extraction

abstract: Recently, there has been increasing interest in learning semantic parsers with indirect supervision, but existing work focuses almost exclusively on question answering. Separately, there have been active pursuits in leveraging databases for distant supervision in information extraction, yet such methods are often limited to binary relations and none can handle nested events. In this paper, we generalize distant supervision to complex knowledge extraction, by proposing the first approach to learn a semantic parser for extracting nested event structures without annotated examples, using only a database of such complex events and unannotated text. The key idea is to model the annotations as latent variables, and incorporate a prior that favors semantic parses containing known events. Experiments on the GENIA event extraction dataset show that our approach can learn from and extract complex biological pathway events. Moreover, when supplied with just five example words per event type, it becomes competitive even among supervised systems, outperforming 19 out of 24 teams that participated in the original shared task.


title: SmoothGrad: removing noise by adding noise

abstract: Explaining the output of a deep network remains a challenge. In the case of an image classifier, one type of explanation is to identify pixels that strongly influence the final decision. A starting point for this strategy is the gradient of the class score function with respect to the input image. This gradient can be interpreted as a sensitivity map, and there are several techniques that elaborate on this basic idea. This paper makes two contributions: it introduces SmoothGrad, a simple method that can help visually sharpen gradient-based sensitivity maps, and it discusses lessons in the visualization of these maps. We publish the code for our experiments and a website with our results.


title: Carving JPEG images and thumbnails using image pattern matching

abstract: Digital forensics is a branch of forensic science to monitor, analyze and examine digital media or devices. File carving is the art of recovering files from digital data storage with corrupted or unavailable file system metadata and it is very useful in a digital forensics investigation. However, earlier generation file carver like Scalpel and Foremost only deals with non-fragmented files. We proposed an automatic image and thumbnail carving tool called myKarve which is useful in digital forensics investigation and presentation of evidential information that is able to carve contiguous and linearly fragmented images caused by garbage. myKarve is designed on a new architecture to deal with thumbnail and fragmentation issues. The tool is tested with images obtained from the Internet. myKarve is found to be a more efficient automated image and thumbnail carver compared to the original Scalpel with the following advantages: detects more headers using validated headers; carves more images and thumbnails by using the newly introduced image patterns; and is able to discard garbage from linearly fragmented images. The results from myKarve are invaluable in the field work of digital forensic analysis that can produce technical evidence against cybercrime activities to prosecution cases.


title: Combining top-down and bottom-up dynamics in land use modeling: exploring the future of abandoned farmlands in Europe with the Dyna-CLUE model

abstract: Land use change is the result of interactions between processes operating at different scales. Simulation models at regional to global scales are often incapable of including locally determined processes of land use change. This paper introduces a modeling approach that integrates demand-driven changes in land area with locally determined conversion processes. The model is illustrated with an application for European land use. Interactions between changing demands for agricultural land and vegetation processes leading to the re-growth of (semi-) natural vegetation on abandoned farmland are explicitly addressed. Succession of natural vegetation is simulated based on the spatial variation in biophysical and management related conditions, while the dynamics of the agricultural area are determined by a global multi-sector model. The results allow an exploration of the future dynamics of European land use and landscapes. The model approach is similarly suitable for other regions and processes where large scale processes interact with local dynamics.


title: Energy trade-offs in the IBM wristwatch computer

abstract: We recently demonstrated a high function wrist watch computer prototype that runs the Linux operating system and also XII graphics libraries. In this paper we describe the unique energy related challenges and tradeoffs we encountered while building this watch. We show that the usage duty factor for the device heavily dictates which of the powers, active power or sleep power, needs to be minimized more aggressively in order to achieve the longest perceived battery life. We also describe the energy issues that percolate through several layers of software all the way from device usage scenarios, applications, user interfaces, system level software to device drivers. All of these need to be systematically addressed to achieve the battery life dictated by the hardware components and the capacity of the battery in the device.


title: Adaptive Decentralized Droop Controller to Preserve Power Sharing Stability of Paralleled Inverters in Distributed Generation Microgrids

abstract: This paper addresses the low-frequency relative stability problem in paralleled inverter-based distributed generation (DG) units in microgrids. In the sense of the small-signal dynamics of a microgrid, it can be shown that as the demanded power of each inverter changes, the low-frequency modes of the power sharing dynamics drift to new locations and the relative stability is remarkably affected, and eventually, instability can be yielded. To preserve the power sharing stability, an adaptive decentralized droop controller of paralleled inverter-based DG units is presented in this paper. The proposed power sharing strategy is based on the static droop characteristics combined with an adaptive transient droop function. Unlike conventional droop controllers, which yield 1-DOF tunable controller, the proposed droop controller yields 2-DOF tunable controller. Subsequently, the dynamic performance of the power sharing mechanism can be adjusted, without affecting the static droop gain, to damp the oscillatory modes of the power sharing controller. To account for the power modes immigration at different loading conditions, the transient droop gains are adaptively scheduled via small-signal analysis of the power sharing mechanism along the loading trajectory of each DG unit to yield the desired transient and steady-state response. The gain adaptation scheme utilizes the filtered active and reactive powers as indices; therefore, a stable and smooth power injection performance can be obtained at different loading conditions. The adaptive nature of the proposed controller ensures active damping of power oscillations at different operating conditions, and yields a stable and robust performance of the paralleled inverter system.


title: Diversity-oriented synthesis as a tool for the discovery of novel biologically active small molecules.

abstract: Biologically active molecules can be identified through the screening of small-molecule libraries. Deficiencies in current compound collections are evidenced by the continuing decline in drug-discovery successes. Typically, such collections are comprised of large numbers of structurally similar compounds. A general consensus has emerged that library size is not everything; library diversity, in terms of molecular structure and thus function, is crucial. Diversity-oriented synthesis (DOS) aims to generate such structural diversity in an efficient manner. Recent years have witnessed significant achievements in the field, which help to validate the usefulness of DOS as a tool for the discovery of novel, biologically interesting small molecules.


title: Cascaded Interactional Targeting Network for Egocentric Video Analysis

abstract: Knowing how hands move and what object is being manipulated are two key sub-tasks for analyzing first-person (egocentric) action. However, lack of fully annotated hand data as well as imprecise foreground segmentation make either sub-task challenging. This work aims to explicitly ad dress these two issues via introducing a cascaded interactional targeting (i.e., infer both hand and active object regions) deep neural network. Firstly, a novel EM-like learning framework is proposed to train the pixel-level deep convolutional neural network (DCNN) by seamlessly integrating weakly supervised data (i.e., massive bounding box annotations) with a small set of strongly supervised data (i.e., fully annotated hand segmentation maps) to achieve state-of-the-art hand segmentation performance. Secondly, the resulting high-quality hand segmentation maps are further paired with the corresponding motion maps and object feature maps, in order to explore the contextual information among object, motion and hand to generate interactional foreground regions (operated objects). The resulting interactional target maps (hand + active object) from our cascaded DCNN are further utilized to form discriminative action representation. Experiments show that our framework has achieved the state-of-the-art egocentric action recognition performance on the benchmark dataset Activities of Daily Living (ADL).


title: RPLB: A Replica Placement Algorithm in Data Grid with Load Balancing

abstract: Data grid is an infrastructure built based on internet which facilitates sharing and management of geographically distributed data resources. Data sharing in data grids is enhanced through dynamic data replication methodologies to reduce access latencies and bandwidth consumption. Replica placement is to create and place duplicate copies of the most needed file in beneficial locations in the data grid network. To reduce the make span i.e., total job execution time, storage consumption and effective network usage in data grids, a new method for replica placement is introduced. In this proposed method, all the nodes in the same region are grouped together and replica is placed in the highest degree and highest frequency node in the region. The node to place replica should be load balanced in terms of access and storage. The proposed dynamic Replica Placement algorithm with Load Balancing (RPLB) is tested using OptorSim simulator, which is developed by European Data Grid Projects. In this paper, two variants of the proposed algorithm RPLB, namely RPLBfrequency and RPLBdegree are also presented. The comparative analysis of all the three proposed algorithms is also presented in this paper. A Graphical User Interface (GUI) is designed as an interface to OptorSim to get all values for grid configuration file, job configuration file and parameters configuration file. Simulation results reveal that the performance of the proposed methodology is better in terms of makespan, storage consumption and replication count when compared to the existing algorithms in the literature.


title: Time Series Classification under More Realistic Assumptions

abstract: Most literature on time series classification assumes that the beginning and ending points of the pattern of interest can be correctly identified, both during the training phase and later deployment. In this work, we argue that this assumption is unjustified, and this has in many cases led to unwarranted optimism about the performance of the proposed algorithms. As we shall show, the task of correctly extracting individual gait cycles, heartbeats, gestures, behaviors, etc., is generally much more difficult than the task of actually classifying those patterns. We propose to mitigate this problem by introducing an alignment-free time series classification framework. The framework requires only very weakly annotated data, such as “in this ten minutes of data, we see mostly normal heartbeats...,” and by generalizing the classic machine learning idea of data editing to streaming/continuous data, allows us to build robust, fast and accurate classifiers. We demonstrate on several diverse real-world problems that beyond removing unwarranted assumptions and requiring essentially no human intervention, our framework is both significantly faster and significantly more accurate than current state-of-the-art approaches.


title: FoodLog: capture, analysis and retrieval of personal food images via web

abstract: With the increase of the number of food images on the Internet, we have been developing a food-logging system which has an automated analysis function as a Web application. It can distinguish food images from other images, analyze the food balance, and visualize the log. In this paper, we demonstrate how the performance can be improved by the personalized models. Because our Web application has an interface to review and correct the food analysis results, the generation of the personalized models can be done on-line. Experimental results using two hundred images showed that the extracted image feature vectors differ from user to user but on the other hand the feature vectors and the food balance of each user have a strong correlation. Therefore, the accuracy of the food balance estimation was improved from 37% to 42% on average by the personalized classifier.


title: Semantic-aware Query Processing for Activity Trajectories

abstract: Nowadays, users of social networks like tweets and weibo have generated massive geo-tagged records, and these records reveal their activities in the physical world together with spatio-temporal dynamics. Existing trajectory data management studies mainly focus on analyzing the spatio-temporal properties of trajectories, while leaving the understanding of their activities largely untouched. In this paper, we incorporate the semantic analysis of the activity information embedded in trajectories into query modelling and processing, with the aim of providing end users more accurate and meaningful trip recommendations. To this end, we propose a novel trajectory query that not only considers the spatio-temporal closeness but also, more importantly, leverages probabilistic topic modelling to capture the semantic relevance of the activities between data and query. To support efficient query processing, we design a novel hybrid index structure, namely ST-tree, to organize the trajectory points hierarchically, which enables us to prune the search space in spatial and topic dimensions simultaneously. The experimental results on real datasets demonstrate the efficiency and scalability of the proposed index structure and search algorithms.


A Probabilistic Geocoding System based on a National Address File


title: Rehabilitation robots assisting in walking training for SCI patient

abstract: We have been developing a series of robots to apply for each step of spinal cord injury (SCI) recovery. We describe the preliminary walking pattern assisting robot and the practical walking assisting robot, which will be applied to the incomplete type of SCI to Quadriplegia. The preliminary experimental results are performed by normal subjects to verify the basic functions of the robot assisting and to prepare the test by SCI patients.


title: Context-aware mathematical expression recognition: An end-to-end framework and a benchmark

abstract: In this paper we propose a novel end-to-end framework for mathematical expression (ME) recognition. The method uses a convolutional neural network (CNN) to perform mathematical symbol detection and recognition simultaneously incorporating spatial context, and can handle multi-part and touching symbols effectively. To evaluate the performance, we provide a benchmark that contains MEs both from real-life and synthetic data. Images in our dataset undergo multiple variations such as viewpoint, illumination and background. For training, we use pure synthetic data for saving human labeling effort. The proposed method achieved 87% accuracy of total correct for clear images and 45% for cluttered ones.


title: AUC Optimization vs. Error Rate Minimization

abstract: The area under an ROC curve (AUC) is a criterion used in many applications to measure the quality of a classification algorithm. However, the objective function optimized in most of these algorithms is the error rate and not the AUC value. We give a detailed statistical analysis of the relationship between the AUC and the error rate, including the first exact expression of the expected value and the variance of the AUC for a fixed error rate. Our results show that the average AUC is monotonically increasing as a function of the classification accuracy, but that the standard deviation for uneven distributions and higher error rates is noticeable. Thus, algorithms designed to minimize the error rate may not lead to the best possible AUC values. We show that, under certain conditions, the global function optimized by the RankBoost algorithm is exactly the AUC. We report the results of our experiments with RankBoost in several datasets demonstrating the benefits of an algorithm specifically designed to globally optimize the AUC over other existing algorithms optimizing an approximation of the AUC or only locally optimizing the AUC.


Screening of CIAT cassava germplasm diversity : the relationship between cyanogenic potential and cassava starch functional properties


title: Reconstructing Native Language Typology from Foreign Language Usage

abstract: This work was supported by the Center for Brains, Minds and Machines ::: (CBMM), funded by NSF STC award CCF - 1231216.


title: The flavonoid quercetin induces acute vasodilator effects in healthy volunteers: correlation with beta-glucuronidase activity.

abstract: UNLABELLED ::: Quercetin exerts vasodilator, antiplatelet and antiproliferative effects and reduces blood pressure, oxidative status and end-organ damage in hypertensive humans and animal models. We hypothesized that oral quercetin might induce vasodilator effects in humans and that they might be related to the deconjugation of quercetin-3-O-glucuronide (Q3GA). ::: ::: ::: DESIGN ::: double blind, randomized, placebo-controlled trial. Fifteen healthy volunteers (26±5 years, 6 female) were given a capsule containing placebo, 200 or 400mg of quercetin in random order in three consecutive weeks. At 2h a dose-dependent increase in Q3GA was observed in plasma (∼0.4 and 1μM for 200 and 400mg, respectively) with minor levels of quercetin and isorhamnetin. No changes were observed in blood pressure. At 5h quercetin induced and increase in brachial arterial diameter that correlated with the product of the levels of Q3GA by the plasma glucuronidase activity. There was an increase in urinary levels of glutathione but there was no increase in nitrites plus nitrates. Quercetin and isorhamnetin also relaxed human umbilical arteries in vitro while Q3GA was without effect. In conclusions, quercetin exerts acute vasodilator effects in vivo in normotensive, normocholesterolemic human subjects. These results are consistent with the effects being due to the deconjugation of the metabolite Q3GA.


title: Bee Colony Optimization with local search for traveling salesman problem

abstract: Many real world industrial applications involve the Traveling Salesman Problem (TSP), which is a problem that finds a Hamiltonian path with minimum cost. Examples of problems that belong to this category are transportation routing problem, scan chain optimization and drilling problem in integrated circuit testing and production. This paper presents a Bee Colony Optimization (BCO) algorithm for symmetrical TSP. The BCO model is constructed algorithmically based on the collective intelligence shown in bee foraging behaviour. The algorithm is integrated with a fixed-radius near neighbour 2-opt (FRNN 2-opt) heuristic to further improve prior solutions generated by the BCO model. To limit the overhead incurred by the FRNN 2-opt, a frequency-based pruning strategy is proposed. The pruning strategy allows only a subset of the promising solutions to undergo local optimization. Experimental results comparing the proposed BCO algorithm with existing approaches on a set of benchmark problems are presented. For 84 benchmark problems, the BCO algorithm is able to obtain an overall average solution quality of 0.31% from known optimum. The results also show that it is comparable to other algorithms such as Ant Colony Optimization and Particle Swarm Optimization.


title: Dimensions of Human Development

abstract: Summary. — If human development is ‘‘multidimensional’’ then perhaps we need to discuss what we mean by multidimensional: what is a dimension, and what are the multiple dimensions of interest? This paper develops an account of dimensions of human development, and shows its usefulness and its limitations—both in general and in relation to Amartya Sen’s capability approach. The second half of the paper surveys other major ‘‘lists’’ of dimensions that have been published in poverty studies, crosscultural psychology, moral philosophy, quality of life indicators, participatory development, and basic needs, and compares and contrasts them with the account sketched here. 2002 Elsevier Science Ltd. All rights reserved.


title: Intelligent Assistive Technology Applications to Dementia Care: Current Capabilities, Limitations, and Future Challenges

abstract: The number of older Americans afflicted by Alzheimer disease and related dementias will triple to 13 million persons by 2050, thus greatly increasing healthcare needs. An approach to this emerging crisis is the development and deployment of intelligent assistive technologies that compensate for the specific physical and cognitive deficits of older adults with dementia, and thereby also reduce caregiver burden. The authors conducted an extensive search of the computer science, engineering, and medical databases to review intelligent cognitive devices, physiologic and environmental sensors, and advanced integrated sensor networks that may find future applications in dementia care. Review of the extant literature reveals an overwhelming focus on the physical disability of younger persons with typically nonprogressive anoxic and traumatic brain injuries, with few clinical studies specifically involving persons with dementia. A discussion of the specific capabilities, strengths, and limitations of each technology is followed by an overview of research methodological challenges that must be addressed to achieve measurable progress to meet the healthcare needs of an aging America.


Mining Large-scale Parallel Corpora from Multilingual Patents: An English-Chinese example and its application to SMT


title: Recent Advances in Convolutional Neural Networks

abstract: In the last few years, deep learning has led to very good performance on a variety of problems, such as visual recognition, speech recognition and natural language processing. Among different types of deep neural networks, convolutional neural networks have been most extensively studied. Due to the lack of training data and computing power in early days, it is hard to train a large high-capacity convolutional neural network without overfitting. After the rapid growth in the amount of the annotated data and the recent improvements in the strengths of graphics processor units (GPUs), the research on convolutional neural networks has been emerged swiftly and achieved state-of-the-art results on various tasks. In this paper, we provide a broad survey of the recent advances in convolutional neural networks. Besides, we also introduce some applications of convolutional neural networks in computer vision.


title: A Semi-Supervised Batch-Mode Active Learning Strategy for Improved Statistical Machine Translation

abstract: The availability of substantial, in-domain parallel corpora is critical for the development of high-performance statistical machine translation (SMT) systems. Such corpora, however, are expensive to produce due to the labor intensive nature of manual translation. We propose to alleviate this problem with a novel, semi-supervised, batch-mode active learning strategy that attempts to maximize indomain coverage by selecting sentences, which represent a balance between domain match, translation difficulty, and batch diversity. Simulation experiments on an English-to-Pashto translation task show that the proposed strategy not only outperforms the random selection baseline, but also traditional active learning techniques based on dissimilarity to existing training data. Our approach achieves a relative improvement of 45.9% in BLEU over the seed baseline, while the closest competitor gained only 24.8% with the same number of selected sentences.


title: Attachment theory and John Bowlby: Some reflections

abstract: Abstract The 100th anniversary of Edward John Mostyn Bowlby's birth (February 26th, 1907) was celebrated at the Tavistock Clinic in London by his family and colleagues, with presentations of ongoing research as well as reflections on both the person and his theory. My own reflections include the influence of ethological thinking on the development of attachment theory, Bowlby's focus on observations followed by explanation, his appreciation of emotional communication as well as behavior, and his recognition of the role of the family as well as the child/caregiver dyad. While always remaining open to new avenues of research, John Bowlby was firmly insistent on the precise use of attachment terminology, and quite rightly too!


title: Biometric template protection using cancelable biometrics and visual cryptography techniques

abstract: Wide spread use of biometric based authentication implies the need to secure biometric reference data. Various template protection schemes have been introduced to prevent biometric forgery and identity thefts. Cancelable biometrics and visual cryptography are two recent technologies introduced to address the concerns regarding privacy of biometric data, and to improve public confidence and acceptance of biometric systems. Cancelable biometrics is an important technique that allows generation of revocable biometric templates. As the number of biometric instances are limited and once compromised they are lost forever. Cancelable biometrics allows templates to be cancelled and revoked like passwords innumerable times. Recently, various approaches that utilize visual cryptography to secure the stored template and impart privacy to the central databases have been introduced. This work attempts to summarize the existing approaches in literature making use of these two technologies to protect biometric templates.


title: Shape from Texture without Boundaries

abstract: We describe a shape from texture method that constructs an estimate of surface geometry using only the deformation of individual texture elements. Our method does not need to use either the boundary of the observed surface or any assumption about the overall distribution of elements.The method assumes that surface texture elements are drawn from a number of different types, each of fixed shape. Neither the shape of the elements nor the number of types need be known in advance. We show that, with this assumption and assuming a generic, scaled orthographic view and texture, each type of texture element can be reconstructed in a frontal coordinate system from image instances. Interest-point methods supply a method of simultaneously obtaining instances of each texture element automatically and defining each type of element. Furthermore, image instances that have been marked in error can be identified and ignored using the Expectation-Maximization algorithm. A further EM procedure yields a surface reconstruction and a relative irradiance map from the data. We provide numerous examples of reconstructions for images of real scenes, show a comparison between our reconstruction and range maps, and demonstrate that the reconstructions display geometric and irradiance phenomena that can be observed in the original image.


title: Building Competitive Direct Acoustics-to-Word Models for English Conversational Speech Recognition

abstract: Direct acoustics-to-word (A2W) models in the end-to-end paradigm have received increasing attention compared to conventional sub-word based automatic speech recognition models using phones, characters, or context-dependent hidden Markov model states. This is because A2W models recognize words from speech without any decoder, pronunciation lexicon, or externally-trained language model, making training and decoding with such models simple. Prior work has shown that A2W models require orders of magnitude more training data in order to perform comparably to conventional models. Our work also showed this accuracy gap when using the English Switchboard-Fisher data set. This paper describes a recipe to train an A2W model that closes this gap and is at-par with state-of-the-art sub-word based models. We achieve a word error rate of 8.8%/13.9% on the Hub5-2000 Switchboard/CallHome test sets without any decoder or language model. We find that model initialization, training data order, and regularization have the most impact on the A2W model performance. Next, we present a joint word-character A2W model that learns to first spell the word and then recognize it. This model provides a rich output to the user instead of simple word hypotheses, making it especially useful in the case of words unseen or rarely-seen during training.


title: Effectiveness of Immersive Videos in Inducing Awe: An Experimental Study

abstract: Awe, a complex emotion composed by the appraisal components of vastness and need for accommodation, is a profound and often meaningful experience. Despite its importance, psychologists have only recently begun empirical study of awe. At the experimental level, a main issue concerns how to elicit high intensity awe experiences in the lab. To address this issue, Virtual Reality (VR) has been proposed as a potential solution. Here, we considered the highest realistic form of VR: immersive videos. 42 participants watched at immersive and normal 2D videos displaying an awe or a neutral content. After the experience, they rated their level of awe and sense of presence. Participants’ psychophysiological responses (BVP, SC, sEMG) were recorded during the whole video exposure. We hypothesized that the immersive video condition would increase the intensity of awe experienced compared to 2D screen videos. Results indicated that immersive videos significantly enhanced the self-reported intensity of awe as well as the sense of presence. Immersive videos displaying an awe content also led to higher parasympathetic activation. These findings indicate the advantages of using VR in the experimental study of awe, with methodological implications for the study of other emotions.


title: Amodal Instance Segmentation

abstract: We consider the problem of amodal instance segmentation, the objective of which is to predict the region encompassing both visible and occluded parts of each object. Thus far, the lack of publicly available amodal segmentation annotations has stymied the development of amodal segmentation methods. In this paper, we sidestep this issue by relying solely on standard modal instance segmentation annotations to train our model. The result is a new method for amodal instance segmentation, which represents the first such method to the best of our knowledge. We demonstrate the proposed method’s effectiveness both qualitatively and quantitatively.


(Artificial) Mind over Matter: Integrating Humans and Algorithms in Solving Matching Problems


title: Fully Automated Organ Segmentation in Male Pelvic CT Images

abstract: Accurate segmentation of prostate and surrounding organs at risk is important for prostate cancer radiotherapy treatment planning. We present a fully automated workflow for male pelvic CT image segmentation using deep learning. The architecture consists of a 2D localization network followed by a 3D segmentation network for volumetric segmentation of prostate, bladder, rectum, and femoral heads. We used a multi-channel 2D U-Net followed by a 3D U-Net with encoding arm modified with aggregated residual networks, known as ResNeXt. The models were trained and tested on a pelvic CT image dataset comprising 136 patients. Test results show that 3D U-Net based segmentation achieves mean (SD) Dice coefficient values of 90 (2.0)% ,96 (3.0)%, 95 (1.3)%, 95 (1.5)%, and 84 (3.7)% for prostate, left femoral head, right femoral head, bladder, and rectum, respectively, using the proposed fully automated segmentation method.


title: Assessing test-driven development at IBM

abstract: In a software development group of IBM Retail Store Solutions, we built a non-trivial software system based on a stable standard specification using a disciplined, rigorous unit testing and build approach based on the test- driven development (TDD) practice. Using this practice, we reduced our defect rate by about 50 percent compared to a similar system that was built using an ad-hoc unit testing approach. The project completed on time with minimal development productivity impact. Additionally, the suite of automated unit test cases created via TDD is a reusable and extendable asset that will continue to improve quality over the lifetime of the software system. The test suite will be the basis for quality checks and will serve as a quality contract between all members of the team.


title: Fake Colorized Image Detection

abstract: Image forensics aims to detect the manipulation of digital images. Currently, splicing detection, copy-move detection and image retouching detection are drawing much attentions from researchers. However, image editing techniques develop with time goes by. One emerging image editing technique is colorization, which can colorize grayscale images with realistic colors. Unfortunately, this technique may also be intentionally applied to certain images to confound object recognition algorithms. To the best of our knowledge, no forensic technique has yet been invented to identify whether an image is colorized. We observed that, compared to natural images, colorized images, which are generated by three state-of-the-art methods, possess statistical differences for the hue and saturation channels. Besides, we also observe statistical inconsistencies in the dark and bright channels, because the colorization process will inevitably affect the dark and bright channel values. Based on our observations, i.e., potential traces in the hue, saturation, dark and bright channels, we propose two simple yet effective detection methods for fake colorized images: Histogram based Fake Colorized Image Detection (FCID-HIST) and Feature Encoding based Fake Colorized Image Detection (FCID-FE). Experimental results demonstrate that both proposed methods exhibit a decent performance against multiple state-of-the-art colorization approaches.


title: Type synthesis of 4-DOF parallel manipulators

abstract: The number of 4-DOF (degrees of freedom) parallel manipulators with full-cycle mobility has been small for a long time. In this paper, the type synthesis of 4-DOF parallel manipulators is performed systematically by the constraint-synthesis method. Novel 4-DOF symmetrical and asymmetrical parallel manipulators with full-cycle mobility are identified and enumerated. Moreover, structural characteristics of the 4-DOF parallel manipulators are revealed.


title: Speakers optimize information density through syntactic reduction

abstract: If language users are rational, they might choose to structure their utterances so as to optimize communicative properties. In particular, information-theoretic and psycholinguistic considerations suggest that this may include maximizing the uniformity of information density in an utterance. We investigate this possibility in the context of syntactic reduction, where the speaker has the option of either marking a higher-order unit (a phrase) with an extra word, or leaving it unmarked. We demonstrate that speakers are more likely to reduce less information-dense phrases. In a second step, we combine a stochastic model of structured utterance production with a logistic-regression model of syntactic reduction to study which types of cues speakers employ when estimating the predictability of upcoming elements. We demonstrate that the trend toward predictability-sensitive syntactic reduction (Jaeger, 2006) is robust in the face of a wide variety of control variables, and present evidence that speakers use both surface and structural cues for predictability estimation.


title: Grid-based mapping and tracking in dynamic environments using a uniform evidential environment representation

abstract: Mapping and tracking in dynamic environments for autonomously-moving robots is still challenging, despite being essential tasks. They are often done separately using occupancy grids and established object tracking algorithms. In this work, an approach is presented that estimates a uniform, low-level, grid-based world model including dynamic and static objects, their uncertainties, as well as their velocities. It does not require existing object tracks to filter out data points not used for creating and updating the map. Nor does it require that measurements can be classified into belonging to a static or to a moving object. Promising results from experiments with an autonomous vehicle equipped with a laser scanner demonstrate the usefulness of the approach.


title: The effects of video game realism on attention, retention and aggressive outcomes

abstract: This study used a between subjects, post-test only design to test the effects of video game realism (Doom 1 vs. Doom 3) and a control condition on attention, retention and aggressive outcomes. Overall, those who played Doom 3 perceived it as significantly more realistic than those who played Doom 1, thus providing validity for the manipulation. Next, those who played Doom 3 were significantly more attentive and experienced more presence than those who played Doom 1. Furthermore, playing either Doom game resulted in more aggression than playing no game and physically aggressive intentions were higher among those who played Doom 3 as compared to those who played Doom 1. Lastly, we tested for any possible interaction between realism and the attention and retention subfunctions on production and we found that, compared to the other players, those who experienced greater identification among those playing Doom 3 had higher verbal aggression. For physical aggression, those who played Doom 3 and experienced more attention and greater identification had higher aggression scores than those in the other conditions.


title: Torque Ripple Reduction of Switched Reluctance Motor by Optimizing Stator Pole Type

abstract: The torque ripple is the important restriction affecting the promotion and application of switched reluctance motor, the analytic expressions to calculate switched reluctance motors tangential force and radial force are derived in this paper, based on analyzing the cause of switched reluctance motor vibration and torque ripple. By improving the stator poles structure, the torque near the commutation points is compensated, which smooth the wave of air gap magnetic field to reduce torque ripple when the stators and the rotors salient poles coincide. In this paper, three structures that puts wedge angle on both sides of the stator poles is presented, and analysis of torque ripple is made. Studies shows that the right size of wedge angle can effectively inhibit the switched reluctance motor torque fluctuations.


title: Success rate group search optimiser

abstract: The group search optimiser (GSO) algorithm is a newly found evolutionary algorithm that is inspired by animal-searching behaviour and group living theory. The GSO algorithm follows the producer–scrounger framework that consists of producer, scrounger and ranger members. There are multiple key parameters in the GSO algorithm that directly affect the performance of the algorithm. Among these parameters, the maximum pursuit distance parameter plays an important role because it determines the step length of the producer and rangers of the GSO algorithm. In this paper, we develop a modified GSO algorithm by using the success rate model to adjust the maximum pursuit distance parameter of the algorithm. We test the proposed algorithm on a rich set of benchmark functions including 30- and 300-dimensional problems and compare the results with popular evolutionary and swarm algorithms. The experimental results demonstrate that the scanning mechanism of the proposed algorithm quickly optimises not only the 30-dimens...


title: An arousal-based neural model of infant attachment

abstract: We develop an arousal-based neural model of infant attachment using a deep learning architecture. We show how our model can differentiate between attachment classifications during strange situation-like separation and reunion episodes, in terms of both signalling behaviour and patterns of autonomic arousal, according to the sensitivity of previous interaction.


title: Consumer's Online Shopping Influence Factors and Decision-Making Model

abstract: Previous research on online consumer behavior has mostly been confined to the perceived risk which is used to explain those barriers for purchasing online. However, perceived benefit is another important factor which influences consumers’ decision when shopping online. As a result, an integrated consumer online shopping decision-making model is developed which contains three elements—Consumer, Product, and Web Site. This model proposed relative factors which influence the consumers’ intention during the online shopping progress, and divided them into two different dimensions—mentally level and material level. We tested those factors with surveys, from both online volunteers and offline paper surveys with more than 200 samples. With the help of SEM, the experimental results show that the proposed model and method can be used to analyze consumer’s online shopping decision-making process effectively.


title: TQM and firms performance: An EFQM excellence model research based survey

abstract: The purpose of this article is to develop an instrument for measuring TQM implementation following the European Foundation for Quality Management Excellence Model and to provide empirical evidence on the relationship between management practices and measures of business performance in the model. To this end, the study employs survey data collected from Spanish manufacturing and service firms. Confirmatory factor analysis is used to test the psychometric properties of the measurement scales and the hypothesized relationships between total quality management practices and organizational performance are examined using structural equation modeling. The findings of the research indicate that the adoption of the TQM practices suggested in the EFQM Excellence Model allows firms to outperform their competitors in the results criteria included in the Model. Therefore, this paper provides a valuable benchmarking data for firms as it substantiates the EFQM Enabler’s contribution to the attainment of competitive advantage.


title: Fundamental sex difference in human brain architecture

abstract: In PNAS, a report by Ingalhalikar et al. (1) has the makings of a landmark paper. Here I would like to briefly suggest why.


title: Umbilical Nodule with Cyclical Bleeding: A Case Report and Literature Review of Atypical Endometriosis

abstract: Endometriosis is defined as the presence of endometrial glands and stroma outside the uterus. It affects 3 to 10 percent of women of reproductive age. Umbilical endometriosis is rare, with an estimated incidence of 0.5–1.0% among all cases of endometriosis, and is usually secondary to prior laparoscopic surgery involving the umbilicus. In this report, we described a case of umbilical endometriosis treated with surgical resection and highlight the great importance of medical history compared to complementary diagnostic tests that can be sometimes inconclusive.


title: Knowledge management , codification and tacit knowledge

abstract: This article returns to a theme addressed in Vol. 8(1) October 2002: knowledge management and the problem of managing tacit knowledge. The article is primarily a review and analysis of the literature associated with the management of knowledge. In particular, it focuses on the works of a group of economists who have studied the transformation of knowledge into information through the process of codification and the knowledge transaction topography they have developed to describe this. The article explores the theoretical and philosophical antecedents of the economists' views. It uses this as a basis for examining the dominant views of knowledge that appear in much of the literature on knowledge management and for performing a critical evaluation of their work. The results of the analysis centre upon the question of when is it appropriate to codify knowledge. They present a basic summary of the costs and benefits of codification before looking in more detail at its desirability. The conclusions concern the implications of the above for knowledge management and the management of tacit knowledge. They deal with the nature of knowledge management, some of the reasons why knowledge management projects fail to achieve their expectations and the potential problems of codification as a strategy for knowledge management.


title: Validation of the Pain Resilience Scale in a Chronic Pain Sample.

abstract: Psychosocial factors that protect against negative outcomes for individuals with chronic pain have received increased attention in recent years. Pain resilience, or the ability to maintain behavioral engagement and regulate emotions as well as cognitions despite prolonged or intense pain, is one such factor. A measure of pain-specific resilience, the Pain Resilience Scale, was previously identified as a better predictor of acute pain tolerance than general resilience. The present study sought to validate this measure in a chronic pain sample, while also furthering understanding of the role of pain resilience compared with other protective factors. Participants with chronic pain completed online questionnaires to assess factors related to positive pain outcomes, pain vulnerability, pain intensity, and quality of life. A confirmatory factor analysis confirmed the 2-factor structure of the Pain Resilience Scale previously observed among respondents without chronic pain, although one item from each subscale was dropped in the final version. For this chronic pain sample, structural equation modeling showed that pain resilience contributes unique variance to a model including pain acceptance and pain self-efficacy in predicting quality of life and pain intensity. Further, pain resilience was a better fit in this model than general resilience, strengthening the argument for assessing pain resilience over general resilience. ::: ::: ::: PERSPECTIVE ::: A modified version of the Pain Resilience Scale retained the original factor structure when tested in a chronic pain sample. Construct validity was supported by expected relationships with pain-related protective and vulnerability measures. Further, a model including positive pain constructs showed that pain resilience accounts for unique variability when predicting quality of life and pain intensity.


title: Majority is not enough: bitcoin mining is vulnerable

abstract: The Bitcoin cryptocurrency records its transactions in a public log called the blockchain. Its security rests critically on the distributed protocol that maintains the blockchain, run by participants called miners. Conventional wisdom asserts that the mining protocol is incentive-compatible and secure against colluding minority groups, that is, it incentivizes miners to follow the protocol as prescribed. ::: ::: We show that the Bitcoin mining protocol is not incentive-compatible. We present an attack with which colluding miners' revenue is larger than their fair share. The attack can have significant consequences for Bitcoin: Rational miners will prefer to join the attackers, and the colluding group will increase in size until it becomes a majority. At this point, the Bitcoin system ceases to be a decentralized currency. ::: ::: Unless certain assumptions are made, selfish mining may be feasible for any coalition size of colluding miners. We propose a practical modification to the Bitcoin protocol that protects Bitcoin in the general case. It prohibits selfish mining by a coalition that command less than 1/4 of the resources. This threshold is lower than the wrongly assumed 1/2 bound, but better than the current reality where a coalition of any size can compromise the system.


title: Advances and challenges in log analysis

abstract: Logs contain a wealth of information to help manage systems.


title: Towards life-long visual localization using an efficient matching of binary sequences from images

abstract: Life-long visual localization is one of the most challenging topics in robotics over the last few years. The difficulty of this task is in the strong appearance changes that a place suffers due to dynamic elements, illumination, weather or seasons. In this paper, we propose a novel method (ABLE-M) to cope with the main problems of carrying out a robust visual topological localization along time. The novelty of our approach resides in the description of sequences of monocular images as binary codes, which are extracted from a global LDB descriptor and efficiently matched using FLANN for fast nearest neighbor search. Besides, an illumination invariant technique is applied. The usage of the proposed binary description and matching method provides a reduction of memory and computational costs, which is necessary for long-term performance. Our proposal is evaluated in different life-long navigation scenarios, where ABLE-M outperforms some of the main state-of-the-art algorithms, such as WI-SURF, BRIEF-Gist, FAB-MAP or SeqSLAM. Tests are presented for four public datasets where a same route is traversed at different times of day or night, along the months or across all four seasons.


Make 'em pay up


title: Spoken Language Recognition Using Ensemble Classifiers

abstract: In this paper, we study a novel approach to spoken language recognition using an ensemble of binary classifiers. In this framework, we begin by representing a speech utterance with a high-dimensional feature vector such as the phonotactic characteristics or the polynomial expansion of cepstral features. A binary classifier can be built based on such feature vectors. We adopt a distributed output coding strategy in ensemble classifier design, where we decompose a multiclass language recognition problem into many binary classification tasks, each of which addresses a language recognition subtask by using a component classifier. Then, we combine the results of the component classifiers to form an output code as a hypothesized solution to the overall language recognition problem. In this way, we effectively project high-dimensional feature vectors into a tractable low-dimensional space, yet maintaining language discriminative characteristics of the spoken utterances. By fusing the output codes from both phonotactic features and cepstral features, we achieve equal-error-rates of 1.38% and 3.20% for 30-s trials on the 2003 and 2005 NIST language recognition evaluation databases.


title: The soluble proteome of tobacco Bright Yellow-2 cells undergoing H2O2-induced programmed cell death

abstract: Plant programmed cell death (PCD) is a genetically controlled process that plays an important role in development and stress responses. Reactive oxygen species (ROS) are key inducers of PCD. The addition of 50 mM H₂O₂ to tobacco Bright Yellow-2 (TBY-2) cell cultures induces PCD. A comparative proteomic analysis of TBY-2 cells treated with 50 mM H₂O₂ for 30 min and 3 h was performed. The results showed early down-regulation of several elements in the cellular redox hub and inhibition of the protein repair-degradation system. The expression patterns of proteins involved in the homeostatic response, in particular those associated with metabolism, were consistently altered. The changes in abundance of several cytoskeleton proteins confirmed the active role of the cytoskeleton in PCD signalling. Cells undergoing H₂O₂-induced PCD fail to cope with oxidative stress. The antioxidant defence system and the anti-PCD signalling cascades are inhibited. This promotes a genetically programmed cell suicide pathway. Fifteen differentially expressed proteins showed an expression pattern similar to that previously observed in TBY-2 cells undergoing heat shock-induced PCD. The possibility that these proteins are part of a core complex required for PCD induction is discussed.


title: Face detection evaluation: a new approach based on the golden ratio {\Phi}

abstract: Face detection is a fundamental research area in computer vision field. Most of the face-related applications such as face recognition and face tracking assume that the face region is perfectly detected. To adopt a certain face detection algorithm in these applications, evaluation of its performance is needed. Unfortunately, it is difficult to evaluate the performance of face detection algorithms due to the lack of universal criteria in the literature. In this paper, we propose a new evaluation measure for face detection algorithms by exploiting a biological property called Golden Ratio of the perfect human face. The new evaluation measure is more realistic and accurate compared to the existing one. Using the proposed measure, five haar-cascade classifiers provided by Intel©OpenCV have been quantitatively evaluated on three common databases to show their robustness and weakness as these classifiers have never been compared among each other on same databases under a specific evaluation measure. A thoughtful comparison between the best haar-classifier and two other face detection algorithms is presented. Moreover, we introduce a new challenging dataset, where the subjects wear the headscarf. The new dataset is used as a testbed for evaluating the current state of face detection algorithms under the headscarf occlusion.


Neural Networks For Negation Scope Detection


title: Analytical Approach for Air-Gap Modeling of Field-Excited Flux-Switching Machine: No-Load Operation

abstract: This paper presents a general and accurate approach to determine the no-load flux of field-excited flux-switching (FE-FS) machines. These structures are inherently difficult to model due to their doubly-slotted air gap. This analytical approach is based on magnetomotive force-permeance theory. The analytical model developed is extensively compared to field distribution obtained with 2-D finite element (2-D FE) simulations. The good agreement observed between analytical model and 2-D FE results emphasizes the interest of this general approach regarding the computation time. Hence, this analytical approach is suitable for optimization process in pre-sizing loop. Furthermore, based on the field model, classical electromagnetic performances can be derived, such as flux linkage and back-electromotive force (back-EMF) and also, unbalanced magnetic force. Once again, FE results validate the analytical prediction, allowing investigations on several stator-rotor combinations, or optimization of the back-EMF.


title: Comparing Hybrid Peer-to-Peer Systems

abstract: “Peer-to-peer” systems like Napster and Gnutella have recently become popular for sharing information. In this paper, we study the relevant issues and tradeoffs in designing a scalable P2P system. We focus on a subset of P2P systems, known as “hybrid” P2P, where some functionality is still centralized. (In Napster, for example, indexing is centralized, and file exchange is distributed.) We model a file-sharing application, developing a probabilistic model to describe query behavior and expected query result sizes. We also develop an analytic model to describe system performance. Using experimental data collected from a running, publicly available hybrid P2P system, we validate both models. We then present several hybrid P2P system architectures and evaluate them using our model. We discuss the tradeoffs between the architectures and highlight the effects of key parameter values on system performance.


title: Optimization of a furniture factory layout

abstract: This paper deals with the problem of optimizing a factory floor layout in a Slovenian furniture factory. First, the current state of the manufacturing system is analyzed by constructing a discrete event simulation (DES) model that reflects the manufacturing processes. The company produces over 10,000 different products, and their manufacturing processes include approximately 30,000 subprocesses. Therefore, manually constructing a model to include every subprocess is not feasible. To overcome this problem, a method for automated model construction was developed to construct a DES model based on a selection of manufacturing orders and relevant subprocesses. The obtained simulation model provided insight into the manufacturing processes and enable easy modification of model parameters for optimizing the manufacturing processes. Finally, the optimization problem was solved: the total distance the products had to traverse between machines was minimized by devising an optimal machine layout. With the introduction of certain simplifications, the problem was best described as a quadratic assignment problem. A novel heuristic method based on force-directed graph drawing algorithms was developed. Optimizing the floor layout resulted in a significant reduction of total travel distance for the products.


title: Kek, Cucks, and God Emperor Trump: A Measurement Study of 4chan's Politically Incorrect Forum and Its Effects on the Web

abstract: The discussion-board site 4chan has been part of the Internet's dark underbelly since its inception, and recent political events have put it increasingly in the spotlight. In particular, /pol/, the "Politically Incorrect" board, has been a central figure in the outlandish 2016 US election season, as it has often been linked to the alt-right movement and its rhetoric of hate and racism. However, 4chan remains relatively unstudied by the scientific community: little is known about its user base, the content it generates, and how it affects other parts of the Web. In this paper, we start addressing this gap by analyzing /pol/ along several axes, using a dataset of over 8M posts we collected over two and a half months. First, we perform a general characterization, showing that /pol/ users are well distributed around the world and that 4chan's unique features encourage fresh discussions. We also analyze content, finding, for instance, that YouTube links and hate speech are predominant on /pol/. Overall, our analysis not only provides the first measurement study of /pol/, but also insight into online harassment and hate speech trends in social media.


title: Phenotypic analysis of autosomal dominant hereditary spastic paraplegia linked to chromosome 8q.

abstract: OBJECTIVE ::: To describe clinical, electrophysiologic, neuroimaging, and muscle biopsy features in a hereditary spastic paraplegia (HSP) kindred linked to a new HSP locus on chromosome 8q. ::: ::: ::: BACKGROUND ::: HSP is a genetically diverse group of disorders characterized by insidiously progressive spastic weakness in the legs. We recently analyzed a Caucasian kindred with autosomal dominant HSP and identified tight linkage to a novel HSP locus on chromosome 8q23-24. ::: ::: ::: METHODS ::: Clinical analysis, nerve conduction studies, electromyography, somatosensory evoked potentials, MRI of brain and spinal cord, and muscle biopsy for mitochondrial analysis were performed in members of the first HSP kindred linked to chromosome 8q. ::: ::: ::: RESULTS ::: Fifteen individuals showed insidiously progressive spastic paraparesis beginning between ages 22 and 60 years (average, 37.2 years). Spinal cord MRI in 1 moderately affected subject showed significant atrophy of the thoracic spinal cord as determined by cross-sectional area measurements. Somatosensory evoked potential recording, electromyography, nerve conduction studies, and muscle biopsy, including histochemical and biochemical analysis of mitochondrial function, were normal. ::: ::: ::: CONCLUSIONS ::: The phenotype in this family is that of typical, but severe, uncomplicated HSP. Other than apparently increased severity, there were no clinical features that distinguished this family from autosomal dominant HSP linked to loci on chromosomes 2p, 14q, and 15q. This clinical similarity between different genetic types of autosomal dominant HSP raises the possibility that genes responsible for these clinically indistinguishable disorders may participate in a common biochemical cascade. Normal results of muscle histochemical and biochemical analysis suggest that mitochondrial disturbance, a feature of chromosome 16-linked autosomal recessive HSP due to paraplegin gene mutations, is not a feature of chromosome 8q-linked autosomal dominant HSP and may not be a common factor of HSP in general.


title: Serum 25-Hydroxyvitamin D Levels and Risk of Multiple Sclerosis

abstract: CONTEXT ::: Epidemiological and experimental evidence suggests that high levels of vitamin D, a potent immunomodulator, may decrease the risk of multiple sclerosis. There are no prospective studies addressing this hypothesis. ::: ::: ::: OBJECTIVE ::: To examine whether levels of 25-hydroxyvitamin D are associated with risk of multiple sclerosis. ::: ::: ::: DESIGN, SETTING, AND PARTICIPANTS ::: Prospective, nested case-control study among more than 7 million US military personnel who have serum samples stored in the Department of Defense Serum Repository. Multiple sclerosis cases were identified through Army and Navy physical disability databases for 1992 through 2004, and diagnoses were confirmed by medical record review. Each case (n = 257) was matched to 2 controls by age, sex, race/ethnicity, and dates of blood collection. Vitamin D status was estimated by averaging 25-hydroxyvitamin D levels of 2 or more serum samples collected before the date of initial multiple sclerosis symptoms. ::: ::: ::: MAIN OUTCOME MEASURES ::: Odds ratios of multiple sclerosis associated with continuous or categorical levels (quantiles or a priori-defined categories) of serum 25-hydroxyvitamin D within each racial/ethnic group. ::: ::: ::: RESULTS ::: Among whites (148 cases, 296 controls), the risk of multiple sclerosis significantly decreased with increasing levels of 25-hydroxyvitamin D (odds ratio [OR] for a 50-nmol/L increase in 25-hydroxyvitamin D, 0.59; 95% confidence interval, 0.36-0.97). In categorical analyses using the lowest quintile (<63.3 nmol/L) as the reference, the ORs for each subsequent quintile were 0.57, 0.57, 0.74, and 0.38 (P = .02 for trend across quintiles). Only the OR for the highest quintile, corresponding to 25-hydroxyvitamin D levels higher than 99.1 nmol/L, was significantly different from 1.00 (OR, 0.38; 95% confidence interval, 0.19-0.75; P = .006). The inverse relation with multiple sclerosis risk was particularly strong for 25-hydroxyvitamin D levels measured before age 20 years. Among blacks and Hispanics (109 cases, 218 controls), who had lower 25-hydroxyvitamin D levels than whites, no significant associations between vitamin D and multiple sclerosis risk were found. ::: ::: ::: CONCLUSION ::: The results of our study suggest that high circulating levels of vitamin D are associated with a lower risk of multiple sclerosis.


title: Automated interior design using a genetic algorithm

abstract: In this paper, we present a system that automatically populates indoor virtual scenes with furniture objects and optimizes their positions and orientations with respect to aesthetic, ergonomic and functional rules called interior design guidelines. These guidelines are represented as mathematical expressions which form the cost function. Our system optimizes the set of multiple interior designs by minimizing the cost function using a genetic algorithm. Moreover, we extend the optimization to transdimensional space by enabling automatic selection of furniture objects. Finally, we optimize the assignment of materials to the furniture objects to achieve a unified design and harmonious color distribution. We investigate the capability of our system to generate sensible and livable interior designs in a perceptual study.


title: Uncooled Thermal Camera Calibration and Optimization of the Photogrammetry Process for UAV Applications in Agriculture

abstract: The acquisition, processing, and interpretation of thermal images from unmanned aerial vehicles (UAVs) is becoming a useful source of information for agronomic applications because of the higher temporal and spatial resolution of these products compared with those obtained from satellites. However, due to the low load capacity of the UAV they need to mount light, uncooled thermal cameras, where the microbolometer is not stabilized to a constant temperature. This makes the camera precision low for many applications. Additionally, the low contrast of the thermal images makes the photogrammetry process inaccurate, which result in large errors in the generation of orthoimages. In this research, we propose the use of new calibration algorithms, based on neural networks, which consider the sensor temperature and the digital response of the microbolometer as input data. In addition, we evaluate the use of the Wallis filter for improving the quality of the photogrammetry process using structure from motion software. With the proposed calibration algorithm, the measurement accuracy increased from 3.55 °C with the original camera configuration to 1.37 °C. The implementation of the Wallis filter increases the number of tie-point from 58,000 to 110,000 and decreases the total positing error from 7.1 m to 1.3 m.