title: How useful are your comments?: analyzing and predicting youtube comments and comment ratings

abstract: An analysis of the social video sharing platform YouTube reveals a high amount of community feedback through comments for published videos as well as through meta ratings for these comments. In this paper, we present an in-depth study of commenting and comment rating behavior on a sample of more than 6 million comments on 67,000 YouTube videos for which we analyzed dependencies between comments, views, comment ratings and topic categories. In addition, we studied the influence of sentiment expressed in comments on the ratings for these comments using the SentiWordNet thesaurus, a lexical WordNet-based resource containing sentiment annotations. Finally, to predict community acceptance for comments not yet rated, we built different classifiers for the estimation of ratings for these comments. The results of our large-scale evaluations are promising and indicate that community feedback on already rated comments can help to filter new unrated comments or suggest particularly useful but still unrated comments.


title: Fully Homomorphic Encryption over the Integers with Shorter Public Keys

abstract: At Eurocrypt 2010 van Dijk et al. described a fully homomorphic encryption scheme over the integers. The main appeal of this scheme (compared to Gentry's) is its conceptual simplicity. This simplicity comes at the expense of a public key size in O(λ10) which is too large for any practical system. In this paper we reduce the public key size to O(λ7) by encrypting with a quadratic form in the public key elements, instead of a linear form. We prove that the scheme remains semantically secure, based on a stronger variant of the approximate-GCD problem, already considered by van Dijk et al. ::: ::: We alsodescribe the first implementation of the resulting fully homomorphic scheme. Borrowing some optimizations from the recent Gentry-Halevi implementation of Gentry's scheme, we obtain roughly the same level of efficiency. This shows that fully homomorphic encryption can be implemented using simple arithmetic operations.


title: Ecdysozoan phylogeny and Bayesian inference: first use of nearly complete 28S and 18S rRNA gene sequences to classify the arthropods and their kin.

abstract: Relationships among the ecdysozoans, or molting animals, have been difficult to resolve. Here, we use nearly complete 28S+18S ribosomal RNA gene sequences to estimate the relations of 35 ecdysozoan taxa, including newly obtained 28S sequences from 25 of these. The tree-building algorithms were likelihood-based Bayesian inference and minimum-evolution analysis of LogDet-transformed distances, and hypotheses were tested wth parametric bootstrapping. Better taxonomic resolution and recovery of established taxa were obtained here, especially with Bayesian inference, than in previous parsimony-based studies that used 18S rRNA sequences (or 18S plus small parts of 28S). In our gene trees, priapulan worms represent the basal ecdysozoans, followed by nematomorphs, or nematomorphs plus nematodes, followed by Panarthropoda. Panarthropoda was monophyletic with high support, although the relationships among its three phyla (arthropods, onychophorans, tardigrades) remain uncertain. The four groups of arthropods-hexapods (insects and related forms), crustaceans, chelicerates (spiders, scorpions, horseshoe crabs), and myriapods (centipedes, millipedes, and relatives)-formed two well-supported clades: Hexapoda in a paraphyletic crustacea (Pancrustacea), and 'Chelicerata+Myriapoda' (a clade that we name 'Paradoxopoda'). Pycnogonids (sea spiders) were either chelicerates or part of the 'chelicerate+myriapod' clade, but not basal arthropods. Certain clades derived from morphological taxonomy, such as Mandibulata, Atelocerata, Schizoramia, Maxillopoda and Cycloneuralia, are inconsistent with these rRNA data. The 28S gene contained more signal than the 18S gene, and contributed to the improved phylogenetic resolution. Our findings are similar to those obtained from mitochondrial and nuclear (e.g., elongation factor, RNA polymerase, Hox) protein-encoding genes, and should revive interest in using rRNA genes to study arthropod and ecdysozoan relationships.


title: IRON file systems

abstract: Commodity file systems trust disks to either work or fail completely, yet modern disks exhibit more complex failure modes. We suggest a new fail-partial failure model for disks, which incorporates realistic localized faults such as latent sector errors and block corruption. We then develop and apply a novel failure-policy fingerprinting framework, to investigate how commodity file systems react to a range of more realistic disk failures. We classify their failure policies in a new taxonomy that measures their Internal RObustNess (IRON), which includes both failure detection and recovery techniques. We show that commodity file system failure policies are often inconsistent, sometimes buggy, and generally inadequate in their ability to recover from partial disk failures. Finally, we design, implement, and evaluate a prototype IRON file system, Linux ixt3, showing that techniques such as in-disk checksumming, replication, and parity greatly enhance file system robustness while incurring minimal time and space overheads.


Functions of Code-Switching in Tweets : An Annotation Scheme and Some Initial Experiments


title: Image processing techniques for quality inspection of gelatin capsules in pharmaceutical applications

abstract: Machine vision systems provide quality control and real-time feedback for industrial processes, overcoming physical limitations and subjective judgment of humans. In this paper, the image processing techniques for developing low-cost machine vision system for pharmaceutical capsule inspection is explored. By developing image processing techniques, and using PCs, custom USB 2.0 cameras with minimal hardware, a low-cost flexible system is developed. This paper discusses the two-part gelatin capsule inspection system that belongs to USB camera 2.0 and associated hardware, the PCs to acquire the image data of the capsule, image processing techniques using border tracing and approximating the capsule to a circle to perform inspection and a custom system controller to pass the accepted and rejected capsules to the appropriate bin.


title: Elsevier Subscription Fraud Prevention in Telecommunications using Fuzzy Rules and Neural Networks

abstract: A system to prevent subscription fraud in fixed telecommunications with high impact on long-distance carriers is proposed. The system consists of a classification module and a prediction module. The classification module classifies subscribers according to their previous historical behavior into four different categories: subscription fraudulent, otherwise fraudulent, insolvent and normal. The prediction module allows us to identify potential fraudulent customers at the time of subscription. The classification module was implemented using fuzzy rules. It was applied to a database containing information of over 10,000 real subscribers of a major telecom company in Chile. In this database a subscription fraud prevalence of 2.2% was found. The prediction module was implemented as a multilayer perceptron neural network. It was able to identify 56.2% of the true fraudsters, screening only 3.5% of all the subscribers in the test set. This study shows the feasibility of significantly preventing subscription fraud in telecommunications by analyzing the application information and the customer antecedents at the time of application.


Difference between a testis and an ovary


title: A Simple and Global Optimization Algorithm for Engineering Problems: Differential Evolution Algorithm

abstract: Differential Evolution (DE) algorithm is a new heuristic approach mainly having three advantages; finding the true global minimum regardless of the initial parameter values, fast convergence, and using few control parameters. DE algorithm is a population based algorithm like genetic algorithms using similar operators; crossover, mutation and selection. In this work, we have compared the performance of DE algorithm to that of some other well known versions of genetic algorithms: PGA, Grefensstette, Eshelman. In simulation studies, De Jong's test functions have been used. From the simulation results, it was observed that the convergence speed of DE is significantly better than genetic algorithms. Therefore, DE algorithm seems to be a promising approach for engineering optimization problems.


title: Effective Focused Crawling Based on Content and Link Structure Analysis

abstract: A focused crawler traverses the web selecting out relevant pages to a predefined topic and neglecting those out of concern. While surfing the internet it is difficult to deal with irrelevant pages and to predict which links lead to quality pages. In this paper a technique of effective focused crawling is implemented to improve the quality of web navigation. To check the similarity of web pages w.r.t. topic keywords a similarity function is used and the priorities of extracted out links are also calculated based on meta data and resultant pages generated from focused crawler. The proposed work also uses a method for traversing the irrelevant pages that met during crawling to improve the coverage of a specific topic.


title: An exoskeleton for gait rehabilitation: Prototype design and control principle

abstract: Research in robotic gait rehabilitation still faces many challenges regarding ankle assistance, body weight support and human-robot interaction. This paper reports on the development, focusing on these challenges, of a gait rehabilitation exoskeleton powered by pleated pneumatic artificial muscles. The first prototype is intended as a platform for the evaluation of design and control concepts. The mechanical design procedure is explained with the emphasis on optimization. A proxy-based sliding mode control approach is proposed and evaluated by means of simulation. Simulation results indicate good tracking performance and safe system behavior, encouraging experimental validation on the prototype.


REMOTE SENSING OF VEGETATION FROM UAV PLATFORMS USING LIGHTWEIGHT MULTISPECTRAL AND THERMAL IMAGING SENSORS


title: Automatic Generation of Visual-Textual Presentation Layout

abstract: Visual-textual presentation layout (e.g., digital magazine cover, poster, Power Point slides, and any other rich media), which combines beautiful image and overlaid readable texts, can result in an eye candy touch to attract users’ attention. The designing of visual-textual presentation layout is therefore becoming ubiquitous in both commercially printed publications and online digital magazines. However, handcrafting aesthetically compelling layouts still remains challenging for many small businesses and amateur users. This article presents a system to automatically generate visual-textual presentation layouts by investigating a set of aesthetic design principles, through which an average user can easily create visually appealing layouts. The system is attributed with a set of topic-dependent layout templates and a computational framework integrating high-level aesthetic principles (in a top-down manner) and low-level image features (in a bottom-up manner). The layout templates, designed with prior knowledge from domain experts, define spatial layouts, semantic colors, harmonic color models, and font emotion and size constraints. We formulate the typography as an energy optimization problem by minimizing the cost of text intrusion, the utility of visual space, and the mismatch of information importance in perception and semantics, constrained by the automatically selected template and further preserving color harmonization. We demonstrate that our designs achieve the best reading experience compared with the reimplementation of parts of existing state-of-the-art designs through a series of user studies.


title: Study of Subjective and Objective Quality Assessment of Video

abstract: We present the results of a recent large-scale subjective study of video quality on a collection of videos distorted by a variety of application-relevant processes. Methods to assess the visual quality of digital videos as perceived by human observers are becoming increasingly important, due to the large number of applications that target humans as the end users of video. Owing to the many approaches to video quality assessment (VQA) that are being developed, there is a need for a diverse independent public database of distorted videos and subjective scores that is freely available. The resulting Laboratory for Image and Video Engineering (LIVE) Video Quality Database contains 150 distorted videos (obtained from ten uncompressed reference videos of natural scenes) that were created using four different commonly encountered distortion types. Each video was assessed by 38 human subjects, and the difference mean opinion scores (DMOS) were recorded. We also evaluated the performance of several state-of-the-art, publicly available full-reference VQA algorithms on the new database. A statistical evaluation of the relative performance of these algorithms is also presented. The database has a dedicated web presence that will be maintained as long as it remains relevant and the data is available online.


title: Tree Filtering: Efficient Structure-Preserving Smoothing With a Minimum Spanning Tree

abstract: We present a new efficient edge-preserving filter-"tree filter"-to achieve strong image smoothing. The proposed filter can smooth out high-contrast details while preserving major edges, which is not achievable for bilateral-filter-like techniques. Tree filter is a weighted-average filter, whose kernel is derived by viewing pixel affinity in a probabilistic framework simultaneously considering pixel spatial distance, color/intensity difference, as well as connectedness. Pixel connectedness is acquired by treating pixels as nodes in a minimum spanning tree (MST) extracted from the image. The fact that an MST makes all image pixels connected through the tree endues the filter with the power to smooth out high-contrast, fine-scale details while preserving major image structures, since pixels in small isolated region will be closely connected to surrounding majority pixels through the tree, while pixels inside large homogeneous region will be automatically dragged away from pixels outside the region. The tree filter can be separated into two other filters, both of which turn out to have fast algorithms. We also propose an efficient linear time MST extraction algorithm to further improve the whole filtering speed. The algorithms give tree filter a great advantage in low computational complexity (linear to number of image pixels) and fast speed: it can process a 1-megapixel 8-bit image at ~ 0.25 s on an Intel 3.4 GHz Core i7 CPU (including the construction of MST). The proposed tree filter is demonstrated on a variety of applications.


title: Shape-Based Object Localization for Descriptive Classification

abstract: Discriminative tasks, including object categorization and detection, are central components of high-level computer vision. However, sometimes we are interested in a finer-grained characterization of the object's properties, such as its pose or articulation. In this paper we develop a probabilistic method (LOOPS) that can learn a shape and appearance model for a particular object class, and be used to consistently localize constituent elements (landmarks) of the object's outline in test images. This localization effectively projects the test image into an alternative representational space that makes it particularly easy to perform various descriptive tasks. We apply our method to a range of object classes in cluttered images and demonstrate its effectiveness in localizing objects and performing descriptive classification, descriptive ranking, and descriptive clustering.


title: Forecasting Using Robust Exponential Smoothing with Damped Trend and Seasonal Components

abstract: We provide a framework for robust exponential smoothing. For a class of exponential smoothing variants, we present a robust alternative. The class includes models with a damped trend and/or seasonal components. We provide robust forecasting equations, robust starting values, robust smoothing parameter estimation and a robust information criterion. The method is implemented in the R package robets, allowing for automatic forecasting. We compare the standard non-robust version with the robust alternative in a simulation study. Finally, the methodology is tested on data.


title: Socially Driven News Recommendation

abstract: The participatory Web has enabled the ubiquitous and pervasive access of information, accompanied by an increase of speed and reach in information sharing. Data dissemination services such as news aggregators are expected to provide up-to-date, real-time information to the end users. News aggregators are in essence recommendation systems that filter and rank news stories in order to select the few that will appear on the users front screen at any time. One of the main challenges in such systems is to address the recency and latency problems, that is, to identify as soon as possible how important a news story is. In this work we propose an integrated framework that aims at predicting the importance of news items upon their publication with a focus on recent and highly popular news, employing resampling strategies, and at translating the result into concrete news rankings. We perform an extensive experimental evaluation using real-life datasets of the proposed framework as both a stand-alone system and when applied to news recommendations from Google News. Additionally, we propose and evaluate a combinatorial solution to the augmentation of official media recommendations with social information. Results show that the proposed approach complements and enhances the news rankings generated by state-of-the-art systems.


title: Fresh-Phish: A Framework for Auto-Detection of Phishing Websites

abstract: Denizens of the Internet are coming under a barrage of phishing attacks of increasing frequency and sophistication. Emails accompanied by authentic looking websites are ensnaring users who, unwittingly, hand over their credentials compromising both their privacy and security. Methods such as the blacklisting of these phishing websites become untenable and cannot keep pace with the explosion of fake sites. Detection of nefarious websites must become automated and be able to adapt to this ever evolving form of social engineering. We develop a framework, called ""Fresh-Phish"", for creating current machine learning data for phishing websites. Using 30 different website features that we query using python, we build a large labeled dataset and analyze several machine learning classifiers against this dataset to determine which is the most accurate. We analyze not just the accuracy of the technique, but also how long it takes to train the model.


title: A Survey On Automated Dynamic Malware Analysis Evasion and Counter-Evasion: PC, Mobile, and Web

abstract: Automated dynamic malware analysis systems are important in combating the proliferation of modern malware. Unfortunately, malware can often easily detect and evade these systems. Competition between malware authors and analysis system developers has pushed each to continually evolve their tactics for countering the other. In this paper we systematically review i) "fingerprint"-based evasion techniques against automated dynamic malware analysis systems for PC, mobile, and web, ii) evasion detection, iii) evasion mitigation, and iv) offensive and defensive evasion case studies. We also discuss difficulties in experimental evaluation, highlight future directions in offensive and defensive research, and briefly survey related topics in anti-analysis.


title: Stop Clickbait: Detecting and preventing clickbaits in online news media

abstract: Most of the online news media outlets rely heavily on the revenues generated from the clicks made by their readers, and due to the presence of numerous such outlets, they need to compete with each other for reader attention. To attract the readers to click on an article and subsequently visit the media site, the outlets often come up with catchy headlines accompanying the article links, which lure the readers to click on the link. Such headlines are known as Clickbaits. While these baits may trick the readers into clicking, in the long-run, clickbaits usually don't live up to the expectation of the readers, and leave them disappointed. In this work, we attempt to automatically detect clickbaits and then build a browser extension which warns the readers of different media sites about the possibility of being baited by such headlines. The extension also offers each reader an option to block clickbaits she doesn't want to see. Then, using such reader choices, the extension automatically blocks similar clickbaits during her future visits. We run extensive offline and online experiments across multiple media sites and find that the proposed clickbait detection and the personalized blocking approaches perform very well achieving 93% accuracy in detecting and 89% accuracy in blocking clickbaits.


Implementation of the ArabiQA Question Answering System's Components


title: Study and Analysis of ECG Signal Using MATLAB &LABVIEW as Effective Tools

abstract: This paper deals with the study and analysis of ECG signal processing by means of MATLAB tool effectively. Study of ECG signal includes generation & simulation of ECG signal, acquisition of real time ECG data, ECG signal filtering & processing, feature extraction, comparison between different ECG signal analysis algorithms & techniques (i.e. Wavlet transform or so), detection of any abnormalities in ECG, calculating beat rate and so on using the most familiar and multipurpose MATLAB software along with LABVIEW. The proper utilization of MATLAB functions (both built-in and user defined), toolbox and Simulink can lead us to work with ECG signals for processing and analysis both in real time and by simulation with great accuracy and convenience.


title: Digital Libraries and Document Image Retrieval Techniques: A Survey

abstract: Nowadays, Digital Libraries have become a widely used service to store and share both digital born documents and digital versions of works stored by traditional libraries. Document images are intrinsically non-structured and the structure and semantic of the digitized documents is in most part lost during the conversion. Several techniques related to the Document Image Analysis research area have been proposed in the past to deal with document image retrieval applications. In this chapter a survey about the more recent techniques applied in the field of recognition and retrieval of text and graphical documents is presented. In particular we describe techniques related to recognition-free approaches.


title: A ZVS-PWM Single-Phase Inverter Using a Simple ZVS-PWM Commutation Cell

abstract: A new zero-voltage-switching (ZVS) pulsewidth-modulated (PWM) single-phase inverter using a simple ZVS-PWM commutation cell is presented in this paper. Except for the auxiliary switches, all switching devices in the ZVS-PWM single-phase inverter operate at ZVS turn on and turn off. The auxiliary switches operate at zero-current-switching turned-on and turned-off. Besides operating at constant frequency, the proposed inverter has no overvoltage across the switches on the main switch compared to the hard switching inverter counterpart. Auxiliary components rated at very small current are used. The principle of operation, theoretical analysis, and experimental results of the new ZVS-PWM single-phase inverter, rated 1 kW and operated at 40 kHz, are provided in this paper to verify the performance.


title: A balanced scorecard based framework for assessing the strategic impacts of ERP systems

abstract: Although there is no analytical framework for assessing the organizational benefits of ERP systems, several researchers have indicated that the balanced scorecard (BSC) approach may be an appropriate technique for evaluating the performance of ERP systems. This paper fills this gap in the literature by providing a balanced-scorecard based framework for valuing the strategic contributions of an ERP system. Using a successful SAP implementation by a major international aircraft engine manufacturing and service organization as a case study, this paper illustrates that an ERP system does indeed impacts the business objectives of the firm and derives a new innovative ERP framework for valuing the strategic impacts of ERP systems. The ERP valuation framework, called here an ERP scorecard, integrates the four Kaplan and Norton's balanced scorecard dimensions with Zuboff's automate, informate and transformate goals of information systems to provide a practical approach for measuring the contributions and impacts of ERP systems on the strategic goals of the company.


title: Designing Procedurally Generated Levels

abstract: We aim to improve on the design of procedurally generated game levels. We propose a method which empowers game designers to author and control level generators, by expressing gameplay-related design constraints. Following a survey conducted on recent procedural level generation methods, we argue that gameplay-based control is currently the most natural control mechanism available for generative methods. Our method uses graph grammars, the result of the designer-expressed constraints, to generate sequences of desired player actions. These action graphs are used as the basis for the spatial structure and content of game levels; they guide the layout process and indicate the required content related to such actions. We showcase our approach with a case study on a 3D dungeon crawler game. Results allow us to conclude that our control mechanisms are both expressive and powerful, effectively supporting designers to procedurally generate game levels.


title: Time delay deep neural network-based universal background models for speaker recognition

abstract: Recently, deep neural networks (DNN) have been incorporated into i-vector-based speaker recognition systems, where they have significantly improved state-of-the-art performance. In these systems, a DNN is used to collect sufficient statistics for i-vector extraction. In this study, the DNN is a recently developed time delay deep neural network (TDNN) that has achieved promising results in LVCSR tasks. We believe that the TDNN-based system achieves the best reported results on SRE10 and it obtains a 50% relative improvement over our GMM baseline in terms of equal error rate (EER). For some applications, the computational cost of a DNN is high. Therefore, we also investigate a lightweight alternative in which a supervised GMM is derived from the TDNN posteriors. This method maintains the speed of the traditional unsupervised-GMM, but achieves a 20% relative improvement in EER.


title: Understanding Organizational Agility: A Work-Design Perspective

abstract: Abstract : This paper introduces a unified theoretical model of organizational agility and investigates the attributes of knowledge-intensive work-design systems, which contribute to achieving and sustaining organizational agility. Even though there has been considerable research on the topic of agility, these studies are not unified regarding their conceptualizations of agility and/or tend to adopt fairly limited views of agility dimensionality. Here, we organize a review of existing definitions and conceptual models of organizational agility, and proceed to advance a relatively comprehensive model built from a work-design perspective. This new model offers a theoretical platform for understanding organizational agility. This paper further investigates those attributes of a work design system that contribute to organizational agility. A knowledge-intensive work-design system is an example of an edge organization. Its governance mechanism "participant engagement governance, network governance, and system dynamic governance" involves three work-design levels: strategic, operational, and episodic. We contend that an entrepreneurial governance pattern has attributes contributing to organization agility, whereby the impetus for its work-design efforts stem not from some deep hierarchical authority pattern, but rather is distributed among participants and through their networking dynamics. These attributes allow each participant positioned at the edge of the system to stay alert and respond to environing trends and forces, on behalf of the system and in concert with the system. Results of an illustrative case study are reported.


title: Generating brand awareness in Online Social Networks

abstract: Display Omitted Virtual interactivity positively affects brand awareness.Reward for activities positively affects brand awareness.System quality positively affects brand awareness.Information quality positively affects brand awareness.Brand awareness positively affects WOM in social media. With their exceptional interactive and communicative capabilities, Online Social Networks (OSNs) allow destinations and companies to heighten their brand awareness. Many tourist destinations and hospitality brands are exploring the use of OSNs to form brand awareness and generate positive WOM. The purpose of this research is to propose and empirically test a theory-driven model of brand awareness in OSNs. A survey among 230 OSN users was deployed to test the theoretical model. The data was analyzed using SEM. Study results indicate that building brand awareness in OSNs increases WOM traffic. In order to foster brand awareness in OSN, it is important to create a virtually interactive environment, enabling users to exchange reliable, rich and updated information in a timely manner. Receiving financial and/or psychological rewards and accessing exclusive privileges in OSNs are important factors for users. Both system quality and information quality were found to be important precursors of brand awareness in OSNs. Study results support the importance of social media in online branding strategies. Virtual interactivity, system quality, information content quality, and rewarding activities influence and generate brand awareness, which in return, triggers WOM.


title: Design and Implementation of TARF: A Trust-Aware Routing Framework for WSNs

abstract: The multihop routing in wireless sensor networks (WSNs) offers little protection against identity deception through replaying routing information. An adversary can exploit this defect to launch various harmful or even devastating attacks against the routing protocols, including sinkhole attacks, wormhole attacks, and Sybil attacks. The situation is further aggravated by mobile and harsh network conditions. Traditional cryptographic techniques or efforts at developing trust-aware routing protocols do not effectively address this severe problem. To secure the WSNs against adversaries misdirecting the multihop routing, we have designed and implemented TARF, a robust trust-aware routing framework for dynamic WSNs. Without tight time synchronization or known geographic information, TARF provides trustworthy and energy-efficient route. Most importantly, TARF proves effective against those harmful attacks developed out of identity deception; the resilience of TARF is verified through extensive evaluation with both simulation and empirical experiments on large-scale WSNs under various scenarios including mobile and RF-shielding network conditions. Further, we have implemented a low-overhead TARF module in TinyOS; as demonstrated, this implementation can be incorporated into existing routing protocols with the least effort. Based on TARF, we also demonstrated a proof-of-concept mobile target detection application that functions well against an antidetection mechanism.


title: Learning-based feature extraction for active 3D scan with reducing color crosstalk of multiple pattern projections

abstract: 3D reconstruction methods based on active stereo technique have been widely used for many practical systems. Many of these systems are configured with a single camera and a single projector. Since such systems can only capture one side of the target object, several attempts have been conducted to enlarge the captured area, especially multi-projector systems attract many researchers. For multi-projector based systems, overlap between multiple pattern projections is a serious problem. Even if different color channels are used for each projector, complete separation is not possible because of color crosstalks. Another open problem is decoding errors of the projected patterns, which causes a failure on extracting positional information of the projected pattern form the captured image. Among several reasons for such errors, color crosstalks are crucial because their features are similar to the main signal and difficult to be decomposed. In this paper, we solve these problems by utilizing machine learning techniques where a convolutional neural network is trained to extract low dimensional pattern features for each projector. In addition, it is trained to suppress the color crosstalks from different projectors. Using this new technique, we succeeded in reconstructing 3D shapes from images where multiple patterns are overlapped.


title: Aesthetic quality assessment of consumer photos with faces

abstract: Automatically assessing the subjective quality of a photo is a challenging area in visual computing. Previous works study the aesthetic quality assessment on a general set of photos regardless of the photo's content and mainly use features extracted from the entire image. In this work, we focus on a specific genre of photos: consumer photos with faces. This group of photos constitutes an important part of consumer photo collections. We first conduct an online study on Mechanical Turk to collect ground-truth and subjective opinions for a database of consumer photos with faces. We then extract technical features, perceptual features, and social relationship features to represent the aesthetic quality of a photo, by focusing on face-related regions. Experiments show that our features perform well for categorizing or predicting the aesthetic quality.


title: Improved Relation Extraction with Feature-rich Compositional Embedding Models

abstract: Compositional embedding models build a representation (or embedding) for a linguistic structure based on its component word embeddings. We propose a Feature-rich Compositional Embedding Model (FCM) for relation extraction that is expressive, generalizes to new domains, and is easy-to-implement. The key idea is to combine both (unlexicalized) hand-crafted features with learned word embeddings. The model is able to directly tackle the difficulties met by traditional compositional embeddings models, such as handling arbitrary types of sentence annotations and utilizing global information for composition. We test the proposed model on two relation extraction tasks, and demonstrate that our model outperforms both previous compositional models and traditional feature rich models on the ACE 2005 relation extraction task, and the SemEval 2010 relation classification task. The combination of our model and a log-linear classifier with hand-crafted features gives state-of-the-art results.


title: MetaFac: community discovery via relational hypergraph factorization

abstract: This paper aims at discovering community structure in rich media social networks, through analysis of time-varying, multi-relational data. Community structure represents the latent social context of user actions. It has important applications in information tasks such as search and recommendation. Social media has several unique challenges. (a) In social media, the context of user actions is constantly changing and co-evolving; hence the social context contains time-evolving multi-dimensional relations. (b) The social context is determined by the available system features and is unique in each social media website. In this paper we propose MetaFac (MetaGraph Factorization), a framework that extracts community structures from various social contexts and interactions. Our work has three key contributions: (1) metagraph, a novel relational hypergraph representation for modeling multi-relational and multi-dimensional social data; (2) an efficient factorization method for community extraction on a given metagraph; (3) an on-line method to handle time-varying relations through incremental metagraph factorization. Extensive experiments on real-world social data collected from the Digg social media website suggest that our technique is scalable and is able to extract meaningful communities based on the social media contexts. We illustrate the usefulness of our framework through prediction tasks. We outperform baseline methods (including aspect model and tensor analysis) by an order of magnitude.


title: Scene-LSTM: A Model for Human Trajectory Prediction

abstract: We develop a human movement trajectory prediction system that incorporates the scene information (Scene-LSTM) as well as human movement trajectories (Pedestrian movement LSTM) in the prediction process within static crowded scenes. We superimpose a two-level grid structure (scene is divided into grid cells each modeled by a scene-LSTM, which are further divided into smaller sub-grids for finer spatial granularity) and explore common human trajectories occurring in the grid cell (e.g., making a right or left turn onto sidewalks coming out of an alley; or standing still at bus/train stops). Two coupled LSTM networks, Pedestrian movement LSTMs (one per target) and the corresponding Scene-LSTMs (one per grid-cell) are trained simultaneously to predict the next movements. We show that such common path information greatly influences prediction of future movement. We further design a scene data filter that holds important non-linear movement information. The scene data filter allows us to select the relevant parts of the information from the grid cell's memory relative to a target's state. We evaluate and compare two versions of our method with the Linear and several existing LSTM-based methods on five crowded video sequences from the UCY [1] and ETH [2] datasets. The results show that our method reduces the location displacement errors compared to related methods and specifically about 80% reduction compared to social interaction methods.


title: Structured light in scattering media

abstract: Virtually all structured light methods assume that the scene and the sources are immersed in pure air and that light is neither scattered nor absorbed. Recently, however, structured lighting has found growing application in underwater and aerial imaging, where scattering effects cannot be ignored. In this paper, we present a comprehensive analysis of two representative methods - light stripe range scanning and photometric stereo - in the presence of scattering. For both methods, we derive physical models for the appearances of a surface immersed in a scattering medium. Based on these models, we present results on (a) the condition for object detectability in light striping and (b) the number of sources required for photometric stereo. In both cases, we demonstrate that while traditional methods fail when scattering is significant, our methods accurately recover the scene (depths, normals, albedos) as well as the properties of the medium. These results are in turn used to restore the appearances of scenes as if they were captured in clear air. Although we have focused on light striping and photometric stereo, our approach can also be extended to other methods such as grid coding, gated and active polarization imaging.


ChemTS: an efficient python library for de novo molecular generation


title: A Better Balun? Done The Design of a 4:1 Wideband Balun Using a Parallel-Connected Transmission-Line Balun

abstract: This article presents a 4:1 wide-band balun that won the student design competition for wide-band baluns held during the 2016 IEEE Microwave Theory and Techniques Society (MTT-S) International Microwave Symposium (IMS2016) in San Francisco, California. For this contest, sponsored by Technical Committee MTT-17, participants were required to implement and evaluate their own baluns, with the winning entry achieving the widest bandwidth while satisfying the conditions of the competition rules during measurements at IMS2016. Some of the conditions were revised for this year's competition compared with previous competitions as follows.


Modelling metaphor with attribute-based semantics


title: Detecting Spam and Promoting Campaigns in Twitter

abstract: Twitter has become a target platform for both promoters and spammers to disseminate their messages, which are more harmful than traditional spamming methods, such as email spamming. Recently, large amounts of campaigns that contain lots of spam or promotion accounts have emerged in Twitter. The campaigns cooperatively post unwanted information, and thus they can infect more normal users than individual spam or promotion accounts. Organizing or participating in campaigns has become the main technique to spread spam or promotion information in Twitter. Since traditional solutions focus on checking individual accounts or messages, efficient techniques for detecting spam and promotion campaigns in Twitter are urgently needed. In this article, we propose a framework to detect both spam and promotion campaigns. Our framework consists of three steps: the first step links accounts who post URLs for similar purposes; the second step extracts candidate campaigns that may be for spam or promotion purposes; and the third step classifies the candidate campaigns into normal, spam, and promotion groups. The key point of the framework is how to measure the similarity between accounts' purposes of posting URLs. We present two measure methods based on Shannon information theory: the first one uses the URLs posted by the users, and the second one considers both URLs and timestamps. Experimental results demonstrate that the proposed methods can extract the majority of the candidate campaigns correctly, and detect promotion and spam campaigns with high precision and recall.


title: Neural Response Ranking for Social Conversation: A Data-Efficient Approach

abstract: The overall objective of 'social' dialogue systems is to support engaging, entertaining, and lengthy conversations on a wide variety of topics, including social chit-chat. Apart from raw dialogue data, user-provided ratings are the most common signal used to train such systems to produce engaging responses. In this paper we show that social dialogue systems can be trained effectively from raw unannotated data. Using a dataset of real conversations collected in the 2017 Alexa Prize challenge, we developed a neural ranker for selecting 'good' system responses to user utterances, i.e. responses which are likely to lead to long and engaging conversations. We show that (1) our neural ranker consistently outperforms several strong baselines when trained to optimise for user ratings; (2) when trained on larger amounts of data and only using conversation length as the objective, the ranker performs better than the one trained using ratings -- ultimately reaching a Precision@1 of 0.87. This advance will make data collection for social conversational agents simpler and less expensive in the future.


title: Goliath's Curse: Coercive Threats and Asymmetric Power

abstract: States typically issue compellent threats against considerably weaker adversaries, yet their threats often fail+ Why? Expanding on a standard model of inter- national crisis bargaining, I argue that a theory of reputation-building can help shed light on this puzzle+ The model casts reputation as a strategic problem, showing that challengers issuing compellent threats have incentives to anticipate the reputation costs that target states incur when appeasing aggressors+ If challengers can recognize these costs and offset them with side payments or smaller demands, then even reputation-conscious targets will acquiesce+ I argue, however, that military strength contributes to information problems that make challengers more likely to underesti- mate their targets' reputation costs and insufficiently compensate them+ In this way, military power can undermine the effectiveness of compellent threats+ The logic is illustrated by the 1939 Russo-Finnish crisis, and the argument's implications for the study of coercive diplomacy are explored+


title: Deep Texture Manifold for Ground Terrain Recognition

abstract: We present a texture network called Deep Encoding Pooling Network (DEP) for the task of ground terrain recognition. Recognition of ground terrain is an important task in establishing robot or vehicular control parameters, as well as for localization within an outdoor environment. The architecture of DEP integrates orderless texture details and local spatial information and the performance of DEP surpasses state-of-the-art methods for this task. The GTOS database (comprised of over 30,000 images of 40 classes of ground terrain in outdoor scenes) enables supervised recognition. For evaluation under realistic conditions, we use test images that are not from the existing GTOS dataset, but are instead from hand-held mobile phone videos of similar terrain. This new evaluation dataset, GTOS-mobile, consists of 81 videos of 31 classes of ground terrain such as grass, gravel, asphalt and sand. The resultant network shows excellent performance not only for GTOS-mobile, but also for more general databases (MINC and DTD). Leveraging the discriminant features learned from this network, we build a new texture manifold called DEP-manifold. We learn a parametric distribution in feature space in a fully supervised manner, which gives the distance relationship among classes and provides a means to implicitly represent ambiguous class boundaries. The source code and database are publicly available1.


title: An Intelligent Interface for Organizing Online Opinions on Controversial Topics

abstract: An enormous amount of posts and comments are shared in online social forums, which often organize these online social opinions based on semantic contents. However, for controversial topics, people with different attitudes and stances often have very distinct perspectives, reactions, and emotions to the same post. Organization by semantic contents often encourages selective exposure to information, which may exacerbate opinion polarization. To address this problem, we design a novel interface that allows people to better understand and appreciate people with different stances in social forums. Our interface was developed to allow interactive visualization and categorization of original posts about a controversial topic with crowd workers' reactions and emotions from different stances. We evaluated the interface using Reddit posts about US presidential candidates. Results demonstrate that the interface can mitigate selective exposure and help users to adopt a broader spectrum of opinions than the traditional Reddit interface.


title: Internet Traffic Analysis Using Community Detection and Apache Spark

abstract: With the rapid development of Internet, Internet traffic and end hosts continue to grow in size. Traffic behavior analysis for a large-scale network is becoming more and more difficult. To address these challenges, this paper proposes an Internet traffic analysis approach based on community detection to discover community consisted of end hosts with similar traffic behavior in a large campus network. First, we use only the IP-to-IP information without packet payloads to model the similarity of end hosts in campus network. Then the similarity graph which represent the social behavior similarity of all end hosts is constructed. Finally, we leverage Label Propagation algorithm to discover end hosts community on the similarity graph. To satisfy demands for the scalable analysis of ever-growing Internet traffic data, a Spark-based Internet traffic analysis system is developed, including implementing the above algorithm. The experimental results based on real campus network traffic show the benefits of the proposed approach in analyzing traffic behavior of a large-scale network on host community level and detecting potential anomalous traffic behavior. The proposed approach reduces the complexity of analyzing the traffic behavior of a large network compare with analyzing individual host. In addition, the experimental results also demonstrate the Spark-based Internet traffic analysis system can analyze Internet traffic efficiently.


title: Real-time prediction of meme burst

abstract: Predicting meme burst is of great relevance to develop security-related detecting and early warning capabilities. In this paper, we propose a feature-based method for real-time meme burst predictions, namely “Semantic, Network, and Time” (SNAT). By considering the potential characteristics of bursty memes, such as the semantics and spatio-temporal characteristics during their propagation, SNAT is capable of capturing meme burst at the very beginning and in real time. Experimental results prove the effectiveness of SNAT in terms of both fixed-time and real-time meme burst prediction tasks.


title: Open-view human action recognition based on linear discriminant analysis

abstract: In the last decades, action recognition task has evolved from single view recording to unconstrained environment. Recently, multi-view action recognition has become a hot topic in computer vision. However, we notice that only a few works have focused on the open-view action recognition, which is a common problem in the real world. Open-view action recognition focus on doing action recognition in unseen view without using any information from it. To address this issue, we firstly introduce a novel multi-view surveillance action dataset and benchmark several state-of-the-art algorithms. From the results, we observe that the performance of the state-of-the-art algorithms would drop a lot under open-view constraints. Then, we propose a novel open-view action recognition method based on the linear discriminant analysis. This method can learn a common space for action samples under different view by using their category information, which can achieve a good result in open-view action recognition.


title: The learning curve in microtia surgery.

abstract: Reconstruction of the auricle is known to be complex. Our objective was to evaluate the improvement of the outcome of the lobulus-type microtia reconstruction. Patient satisfaction was also evaluated. There are no previous reports of the learning process in this field. Postoperative photographs of 51 microtia reconstructions were assessed and rated by a panel made up of six surgeons. The ratings were gathered to generate learning curves. Twenty-two patients assessed the outlook of their reconstructed ears, and the results were analyzed as a self-assessment group. The reliability of the rating by a panel was tested by intraclass correlations. There is a highly significant increasing trend in learning ( P = 0.000001). This trend is not constantly upward, and the steady state was not reached during the study. In the self-assessment group, females were significantly more critical than males ( P = 0.014). Intraclass correlation for six panel members was 0.90, and the rating was considered reliable. Thus, a long and gentle learning curve does exist in microtia reconstruction. To secure good quality and continuity, centralization of the operations and trainee arrangements are highly advisable. Outcomes of plastic surgery can reliably be rated by an evaluation panel.


title: µ-Calculus Synthesis

abstract: In system synthesis, we transform a specification into a system that is guaranteed to satisfy the specification. When the system is open, it interacts with an environment via input and output signals and its behavior depends on this interaction. An open system should satisfy its specification in all possible environments. In addition to the input signals that the system can read, an environment can also have internal signals that the system cannot read. In the above setting, of synthesis with incomplete information, we should transform a specification that refers to both readable and unreadable signals into a system whose behavior depends only on the readable signals. In this work we solve the problem of synthesis with incomplete information for specifications in μ-calculus. Since many properties of systems are naturally specified by means of fixed points, the μ-calculus is an expressive and important specification language. Our results and technique generalize and simplify previous work on synthesis. In particular, we prove that the problem of μ-calculus synthesis with incomplete information is EXPTIME-complete. Thus, it is not harder than the satisfiability or the synthesis problems for this logic.


Ontology-based context modeling for a smart living room


Colour Constancy: Developing Empirical Tests of Computational Models


title: Physiotherapy Exercises Recognition Based on RGB-D Human Skeleton Models

abstract: In the Western world, aging is a growing problem of the society and computer assisted treatments can facilitate the telemedicine for old people or it can help in rehabilitations of patients after sport accidents in far locations. Physical exercises play an important role in physiotherapy and RGB-D devices can be utilized to recognize them in order to make interactive computer healthcare applications in the future. A practical model definition is introduced in this paper to recognize different exercises with Asus Xtion camera. One of the contributions is the extendable recognition models to detect other human activities with noisy sensors, but avoiding heavy data collection. The experiments show satisfactory detection performance without any false positives which is unique in the field to the best of the author knowledge. The computational costs are negligible thus the developed models can be suitable for embedded systems.


title: Action unit detection with segment-based SVMs

abstract: Automatic facial action unit (AU) detection from video is a long-standing problem in computer vision. Two main approaches have been pursued: (1) static modeling — typically posed as a discriminative classification problem in which each video frame is evaluated independently; (2) temporal modeling — frames are segmented into sequences and typically modeled with a variant of dynamic Bayesian networks. We propose a segment-based approach, kSeg-SVM, that incorporates benefits of both approaches and avoids their limitations. kSeg-SVM is a temporal extension of the spatial bag-of-words. kSeg-SVM is trained within a structured output SVM framework that formulates AU detection as a problem of detecting temporal events in a time series of visual features. Each segment is modeled by a variant of the BoW representation with soft assignment of the words based on similarity. Our framework has several benefits for AU detection: (1) both dependencies between features and the length of action units are modeled; (2) all possible segments of the video may be used for training; and (3) no assumptions are required about the underlying structure of the action unit events (e.g., i.i.d.). Our algorithm finds the best k-or-fewer segments that maximize the SVM score. Experimental results suggest that the proposed method outperforms state-of-the-art static methods for AU detection.


title: ConvNet Architecture Search for Spatiotemporal Feature Learning

abstract: Learning image representations with ConvNets by pre-training on ImageNet has proven useful across many visual understanding tasks including object detection, semantic segmentation, and image captioning. Although any image representation can be applied to video frames, a dedicated spatiotemporal representation is still vital in order to incorporate motion patterns that cannot be captured by appearance based models alone. This paper presents an empirical ConvNet architecture search for spatiotemporal feature learning, culminating in a deep 3-dimensional (3D) Residual ConvNet. Our proposed architecture outperforms C3D by a good margin on Sports-1M, UCF101, HMDB51, THUMOS14, and ASLAN while being 2 times faster at inference time, 2 times smaller in model size, and having a more compact representation.


An adaptive FEC algorithm using hidden Markov chains


title: Overview of the torque-controlled humanoid robot TORO

abstract: This paper gives an overview on the torque-controlled humanoid robot TORO, which has evolved from the former DLR Biped. In particular, we describe its mechanical design and dimensioning, its sensors, electronics and computer hardware. Additionally, we give a short introduction to the walking and multi-contact balancing strategies used for TORO.


title: Analytical Modeling of Human Choice Complexity in a Mixed Model Assembly Line Using Machine Learning-Based Human in the Loop Simulation

abstract: Despite the recent advances in manufacturing automation, the role of human involvement in manufacturing systems is still regarded as a key factor in maintaining higher adaptability and flexibility. In general, however, modeling of human operators in manufacturing system design still considers human as a physical resource represented in statistical terms. In this paper, we propose a human in the loop (HIL) approach to investigate the operator’s choice complexity in a mixed model assembly line. The HIL simulation allows humans to become a core component of the simulation, therefore influencing the outcome in a way that is often impossible to reproduce via traditional simulation methods. At the initial stage, we identify the significant features affecting the choice complexity. The selected features are in turn used to build a regression model, in which human reaction time with regard to different degree of choice complexity serves as a response variable used to train and test the model. The proposed method, along with an illustrative case study, not only serves as a tool to quantitatively assess and predict the impact of choice complexity on operator’s effectiveness, but also provides an insight into how complexity can be mitigated without affecting the overall manufacturing throughput.


title: The influence of synovial inflammation and hyperplasia on symptomatic outcomes up to 2 years post-operatively in patients undergoing partial meniscectomy.

abstract: OBJECTIVE ::: Synovitis is associated with pain and other symptoms in patients with knee osteoarthritis (OA), and in patients with meniscal tears even in the absence of radiographic OA. Patients undergoing arthroscopic partial meniscectomy were followed for 2 years to determine whether synovitis predicts post-operative symptoms. ::: ::: ::: DESIGN ::: Thirty-three patients scheduled for arthroscopy were recruited for this pilot study. Symptoms were assessed using a knee pain scale, the Lysholm score, and the short form-12 (SF-12(®)) pre-operatively and at 16 weeks, 1 year and 2 years post-operatively. Synovial inflammation and hyperplasia were graded on surgical biopsies. Linear mixed effects models were tested to determine whether inflammation or hyperplasia is associated with outcome scores over time. ::: ::: ::: RESULTS ::: Lysholm scores and SF-12(®) physical component sub-scores were worse pre-operatively in patients with inflammation (Lysholm: 52.42 [95% confidence interval (CI) 42.37, 62.47] vs 72.38 [66.03, 78.72], P < 0.001; SF-12: 36.81 [28.26, 45.37] vs 46.23 [40.14, 52.32], P < 0.05). Up to 2-years post-operatively, patients with inflammation achieved mean scores similar to those without inflammation. As a result, the mean improvement in Lysholm scores was 13.01 [1.48-24.53] points higher than patients without inflammation, P = 0.03. 33% (4/12) of patients with inflammation still had fair to poor Lysholm scores 2 years after surgery compared to 7% (1/15, P=0.14) without inflammation. No association between hyperplasia and symptoms was noted. ::: ::: ::: CONCLUSIONS ::: In this pilot study of patients undergoing partial meniscectomy, synovial inflammation was associated with worse pre-operative symptoms, but not with poorer outcomes in the first 2 years post-arthroscopy. Larger cohorts and longer follow-up should be pursued to confirm this relationship, and determine if the initial response is sustained.


title: Receiver Design for Downlink Non-Orthogonal Multiple Access (NOMA)

abstract: Non-orthogonal multiple access (NOMA) is one of the promising radio access techniques for further cellular enhancements toward 5G. Compared to orthogonal multiple access (OMA) such as orthogonal frequency-division multiple access (OFDMA), large performance gains were confirmed via system-level simulations. However, NOMA link-level simulations and the design of the receiver remain of great importance to validate NOMA performance gains. In this paper, we evaluate downlink NOMA link-level performance with multiple receiver designs and propose a novel NOMA transmitter and receiver design, where the signals of multi-users are jointly modulated at transmitter side and detected at receiver side. The predominant advantage of the proposed scheme is that at receiver side interference cancellation to the interference signal is not needed, thus low complexity is achieved. The performances of codeword-level SIC, symbol-level SIC and the proposed receiver are evaluated and compared with ideal SIC. Simulation results show that compared with ideal SIC, downlink NOMA link-level performance depends on actual receiver design and the difference in the power ratio split between the cell edge user and cell center user. In particular, it is shown that codeword-level SIC and the proposed receiver can both provide a good performance even when the power ratio difference between the cell center user and cell edge user is small and with real channel estimation.


title: Practical Riemannian Neural Networks

abstract: We provide the first experimental results on non-synthetic datasets for the quasi-diagonal Riemannian gradient descents for neural networks introduced in [Ollivier, 2015]. These include the MNIST, SVHN, and FACE datasets as well as a previously unpublished electroencephalogram dataset. The quasi-diagonal Riemannian algorithms consistently beat simple stochastic gradient gradient descents by a varying margin. The computational overhead with respect to simple backpropagation is around a factor $2$. Perhaps more interestingly, these methods also reach their final performance quickly, thus requiring fewer training epochs and a smaller total computation time. We also present an implementation guide to these Riemannian gradient descents for neural networks, showing how the quasi-diagonal versions can be implemented with minimal effort on top of existing routines which compute gradients.


title: Knowledge management modeling in public sector organizations: a case study

abstract: Purpose – The purpose of this paper is to describe the successful use of a knowledge management (KM) model in a public sector organization.Design/methodology/approach – Building on the theoretical foundation of others, this case study demonstrates the value of KM modeling in a science‐based initiative in the Canadian public service.Findings – The Inukshuk KM model, which comprises the five elements of technology, leadership, culture, measurement, and process, provides a holistic approach in public service KM.Practical implications – The proposed model can be employed by other initiatives to facilitate KM planning and implementation.Originality/value – This the first project to consider how KM models may be implemented in a Canadian public service environment.


title: Plantar fasciitis

abstract: Heel pain, mostly caused by plantar fasciitis (PF), is a common complaint of many patients who requiring professional orthopedic care and are mostly suffering from chronic pain beneath their heels. The present article reviews studies done by preeminent practitioners related to the anatomy of plantar fasciitis and their histo-pathological features, factors associated with PF, clinical features, imaging studies, differential diagnoses, and diverse treatment modalities for treatment of PF, with special emphasis on non-surgical treatment. Anti-inflammatory agents, plantar stretching, and orthosis proved to have highest priority; corticosteroid injection, night splints and extracorporeal shock wave therapy were of next priority, in patients with PF. In patients resistant to the mentioned treatments surgical intervention should be considered.


title: Secure communication for smart IoT objects: Protocol stacks, use cases and practical examples

abstract: In this paper we discuss security procedures for constrained IoT devices. We start with the description of a general security architecture along with its basic procedures, then discuss how its elements interact with the constrained communication stack and explore pros and cons of popular security approaches at various layers of the ISO/OSI model. We also discuss a practical example for the establishment of end-to-end secure channels between constrained and unconstrained devices. The proposed method is lightweight and allows the protection of IoT devices through strong encryption and authentication means, so that constrained devices can benefit from the same security functionalities that are typical of unconstrained domains, without however having to execute computationally intensive operations. To make this possible, we advocate using trusted unconstrained nodes for the offloading of computationally intensive tasks. Moreover, our design does not require any modifications to the protocol stacks of unconstrained nodes.


title: Learning Human Motion Models for Long-Term Predictions

abstract: We propose a new architecture for the learning of predictive spatio-temporal motion models from data alone. Our approach, dubbed the Dropout Autoencoder LSTM (DAELSTM), is capable of synthesizing natural looking motion sequences over long-time horizons1 without catastrophic drift or motion degradation. The model consists of two components, a 3-layer recurrent neural network to model temporal aspects and a novel autoencoder that is trained to implicitly recover the spatial structure of the human skeleton via randomly removing information about joints during training. This Dropout Autoencoder (DAE) is then used to filter each predicted pose by a 3-layer LSTM network, reducing accumulation of correlated error and hence drift over time. Furthermore to alleviate insufficiency of commonly used quality metric, we propose a new evaluation protocol using action classifiers to assess the quality of synthetic motion sequences. The proposed protocol can be used to assess quality of generated sequences of arbitrary length. Finally, we evaluate our proposed method on two of the largest motion-capture datasets available and show that our model outperforms the state-of-the-art techniques on a variety of actions, including cyclic and acyclic motion, and that it can produce natural looking sequences over longer time horizons than previous methods.


title: An Overview of 3D Object Grasp Synthesis Algorithms

abstract: This overview presents computational algorithms for generating 3D object grasps with autonomous multi-fingered robotic hands. Robotic grasping has been an active research subject for decades, and a great deal of effort has been spent on grasp synthesis algorithms. Existing papers focus on reviewing the mechanics of grasping and the finger-object contact interactions Bicchi and Kumar (2000) [12] or robot hand design and their control Al-Gallaf et al. (1993) [70]. Robot grasp synthesis algorithms have been reviewed in Shimoga (1996) [71], but since then an important progress has been made toward applying learning techniques to the grasping problem. This overview focuses on analytical as well as empirical grasp synthesis approaches.


Master Thesis Data-Driven De-Anonymization in Bitcoin


title: An integrated framework for clinical grading of cataract

abstract: Cataract is the most common cause of global blindness. Early detection and grading of cataract helps reduce the vision loss. Clinical grading of cataract is subjective and time consuming process but it has a vital role in treatment planning for cataract patients. Automatic detection and grading of cataract greatly improved the health care services for the cataract patients. A number of medical imaging techniques and methods have been developed in past few years for automatic diagnosis of cataract, but the need of fully automated clinical grading system for early cataract detection still exist. With this demand, we have proposed an integrated framework for clinical grading of cataract. The aim of the framework is to integrate a set of standard grading protocols for accurate and improved diagnosis of cataract on the basis of its severity levels. It provides an easy to use and flexible software application framework for the organization and analysis of clinical grading of cataract.


title: Multi-attention Recurrent Network for Human Communication Comprehension

abstract: Human face-to-face communication is a complex multimodal signal. We use words (language modality), gestures (vision modality) and changes in tone (acoustic modality) to convey our intentions. Humans easily process and understand face-to-face communication, however, comprehending this form of communication remains a significant challenge for Artificial Intelligence (AI). AI must understand each modality and the interactions between them that shape the communication. In this paper, we present a novel neural architecture for understanding human communication called the Multi-attention Recurrent Network (MARN). The main strength of our model comes from discovering interactions between modalities through time using a neural component called the Multi-attention Block (MAB) and storing them in the hybrid memory of a recurrent component called the Long-short Term Hybrid Memory (LSTHM). We perform extensive comparisons on six publicly available datasets for multimodal sentiment analysis, speaker trait recognition and emotion recognition. MARN shows state-of-the-art results performance in all the datasets.


title: Haze Visibility Enhancement: A Survey and Quantitative Benchmarking

abstract: This paper provides a comprehensive survey of methods dealing with visibility enhancement of images taken in hazy or foggy scenes. The survey begins with discussing the optical models of atmospheric scattering media and image formation. This is followed by a survey of existing methods, which are grouped to multiple image methods, polarizing filters based methods, methods with known depth, and single-image methods. We also provide a benchmark of a number of well known single-image methods, based on a recent dataset provided by Fattal and our newly generated scattering media dataset that contains ground truth images for quantitative evaluation. To our knowledge, this is the first benchmark using numerical metrics to evaluate dehazing techniques. This benchmark allows us to objectively compare the results of existing methods and to better identify the strengths and limitations of each method.


title: A fusion framework based on fuzzy integrals for passive-blind image tamper detection

abstract: The innate complexity and uncertainty in the domain of image forensics has made the application of fusion technology an obligatory requirement. Fuzzy integrals, that provide a meaningful formalism for combining different information sources has gained limited consideration in image forensics. The current paper presents a fuzzy integral based fusion framework for image tamper detection that can exploit the interaction between multiple forensic tools for collaborative decision making. Four tools that expose traces of semantic manipulation based on specific statistical cues are designed that work cohesively to allow detection of forgeries in single image (copy-move), composite image (splicing) and two generic artefacts (double JPEG compression and noise inconsistency). The measurement level fusion of tool outcome is carried out with Sugneo and Choquet integrals as the underlying aggregation operators. The classification competency of each tool is evaluated on a specialized dataset of forged images with the respective forgery trace. The empirical evaluation of the fusion framework for blind tamper detection is carried out on a combined master dataset comprising the various forgery traces under study. The Choquet integral based aggregation exhibits an enhanced classification competency on comparison with other fusion approaches like Feature level, Fuzzy Logic based on if-else rules, Behaviour Knowledge Space, Dempster-Shafer combination and classifier ensemble architecture based on Wolpkart’s stacked generalization.


title: An Ultra-Low-Voltage Ultra-Low-Power CMOS Miller OTA With Rail-to-Rail Input/Output Swing

abstract: An ultra-low-voltage ultra-low-power CMOS Miller operational transconductance amplifier (OTA) with rail-to-rail input/output swing is presented. The topology is based on combining bulk-driven differential pair and dc level shifters, with the transistors work in weak inversion. The improved Miller OTA has been successfully verified in a standard 0.35-mum CMOS process. Experimental results have confirmed that, at a minimum supply voltage of 600 mV, lower than the threshold voltage, the topology presents almost rail-to-rail input and output swings and consumes only 550 nW.


title: International Conference "30 Years of Doi Moi: Success, Lessons and Prospects"

abstract: On 8th October 2016, the International Conference " 30 Years of Doi Moi: Success, Lessons and Prospects " was organised by VNU University of Social Sciences and Humanities, Hanoi (VNU-USSH), supported by Konrad Adenauer (KAS), Germany. Many researchers participated in this event came from Germany and Vietnam, such as Associate Prof Nguyen Hong Quan, Institute of National Defense Strategies, Ministry of National Defense, Mr Vu Khoan-Former Deputy Prime Minister of Vietnam, Dr Stefan Kaufmann, member of Geman's Parliament; Prof.Dr Martin Grossheim (Passau University, Germany), Mr Peeter Girke, Head Representative of KAS in Vietnam, Prof Pham Quang Minh, Rector of VNU-USSH, and other researchers and students from Faculty of International Studies, VNU-USSH. The conference consists of 5 sections with keynote speakers (Doi Moi as the key for Germany and Vietnam Relationship), Doi Moi from comparison perspectives (international integration of Vietnam: Comparison with regional countries, Unended history: Doi Moi in the view of comparison research", Doi Moi in terms of economics, national defense and diplomacy (International cooperation and military in foreign affairs: 30 years of retrospect; Doi Moi in classroom: Vietnam history in Vietnamese historical textbooks; 30 years of Vietnam-US relations: from enemy to comprehensive partnership; Outcomes of multilateral foreign policy: the confirmation of Vietnam status in the international arena); Doi Moi in culture, society and education (30 years of Doi Moi in Cuture; Evaluating the legal impacts: the renovation in legal making in Vietnan; human development in Vietnam after 30 years of Doi Moi: Achievements and limitations; Evaluative model on higher education quality: an innovation of Vietnam education system); Challenges and opportunities of Doi Moi (30 years of Doi Moi: toward the second Doi Moi with intensive speed and wide area; Cultural Renew in Vietnam: implications from achievements and future development directions).


title: Query Expansion by Local Context Analysis

abstract: Query expansion (QE) aims at improving information retrieval (IR) effectiveness by enhancing the query formulation. Because users' queries are generally short and because of the language ambiguity, some information needs are difficult to answer. Query reformulation and QE methods have been developed to face this issue. Relevance feedback (RF) is one of the most popular QE techniques. In its manual version, the system uses the information on the relevance -manually judged- of retrieved documents in order to expand the initial query. Rather than using users' judgment on the document relevance, blind RF considers the first retrieved documents as relevant. Generally speaking, RF methods consider the terms that cooccur with query terms within positive feedback documents as candidates for the expansion. Rather than considering feedback documents in their all, it is possible to analyze local information. This paper presents a new method that uses local context from feedback documents for QE. The method uses POS information as well as the remoteness from query terms within feedback documents. We show that the method significantly improves precision on TREC collections.


title: Managing conflicts in goal-driven requirements engineering

abstract: A wide range of inconsistencies can arise during requirements engineering as goals and requirements are elicited from multiple stakeholders. Resolving such inconsistencies sooner or later in the process is a necessary condition for successful development of the software implementing those requirements. The paper first reviews the main types of inconsistency that can arise during requirements elaboration, defining them in an integrated framework and exploring their interrelationships. It then concentrates on the specific case of conflicting formulations of goals and requirements among different stakeholder viewpoints or within a single viewpoint. A frequent, weaker form of conflict called divergence is introduced and studied in depth. Formal techniques and heuristics are proposed for detecting conflicts and divergences from specifications of goals/requirements and of domain properties. Various techniques are then discussed for resolving conflicts and divergences systematically by the introduction of new goals or by transforming the specifications of goals/objects toward conflict-free versions. Numerous examples are given throughout the paper to illustrate the practical relevance of the concepts and techniques presented. The latter are discussed in the framework of the KAOS methodology for goal-driven requirements engineering.


title: Faceted metadata for image search and browsing

abstract: There are currently two dominant interface types for searching and browsing large image collections: keyword-based search, and searching by overall similarity to sample images. We present an alternative based on enabling users to navigate along conceptual dimensions that describe the images. The interface makes use of hierarchical faceted metadata and dynamically generated query previews. A usability study, in which 32 art history students explored a collection of 35,000 fine arts images, compares this approach to a standard image search interface. Despite the unfamiliarity and power of the interface (attributes that often lead to rejection of new search interfaces), the study results show that 90% of the participants preferred the metadata approach overall, 97% said that it helped them learn more about the collection, 75% found it more flexible, and 72% found it easier to use than a standard baseline system. These results indicate that a category-based approach is a successful way to provide access to image collections.


title: Blockchain Technologies: The Foreseeable Impact on Society and Industry

abstract: The authors describe blockchain’s fundamental concepts, provide perspectives on its challenges and opportunities, and trace its origins from the Bitcoin digital cash system to recent applications.


The Potential of Blockchain in Education and Health Care


title: Anxiety, Depression, and the Microbiome: A Role for Gut Peptides

abstract: The complex bidirectional communication between the gut and the brain is finely orchestrated by different systems, including the endocrine, immune, autonomic, and enteric nervous systems. Moreover, increasing evidence supports the role of the microbiome and microbiota-derived molecules in regulating such interactions; however, the mechanisms underpinning such effects are only beginning to be resolved. Microbiota-gut peptide interactions are poised to be of great significance in the regulation of gut-brain signaling. Given the emerging role of the gut-brain axis in a variety of brain disorders, such as anxiety and depression, it is important to understand the contribution of bidirectional interactions between peptide hormones released from the gut and intestinal bacteria in the context of this axis. Indeed, the gastrointestinal tract is the largest endocrine organ in mammals, secreting dozens of different signaling molecules, including peptides. Gut peptides in the systemic circulation can bind cognate receptors on immune cells and vagus nerve terminals thereby enabling indirect gut-brain communication. Gut peptide concentrations are not only modulated by enteric microbiota signals, but also vary according to the composition of the intestinal microbiota. In this review, we will discuss the gut microbiota as a regulator of anxiety and depression, and explore the role of gut-derived peptides as signaling molecules in microbiome-gut-brain communication. Here, we summarize the potential interactions of the microbiota with gut hormones and endocrine peptides, including neuropeptide Y, peptide YY, pancreatic polypeptide, cholecystokinin, glucagon-like peptide, corticotropin-releasing factor, oxytocin, and ghrelin in microbiome-to-brain signaling. Together, gut peptides are important regulators of microbiota-gut-brain signaling in health and stress-related psychiatric illnesses.


title: Localizing and segmenting text in images and videos

abstract: Many images, especially those used for page design on Web pages, as well as videos contain visible text. If these text occurrences could be detected, segmented, and recognized automatically, they would be a valuable source of high-level semantics for indexing and retrieval. We propose a novel method for localizing and segmenting text in complex images and videos. Text lines are identified by using a complex-valued multilayer feed-forward network trained to detect text at a fixed scale and position. The network's output at all scales and positions is integrated into a single text-saliency map, serving as a starting point for candidate text lines. In the case of video, these candidate text lines are refined by exploiting the temporal redundancy of text in video. Localized text lines are then scaled to a fixed height of 100 pixels and segmented into a binary image with black characters on white background. For videos, temporal redundancy is exploited to improve segmentation performance. Input images and videos can be of any size due to a true multiresolution approach. Moreover, the system is not only able to locate and segment text occurrences into large binary images, but is also able to track each text line with sub-pixel accuracy over the entire occurrence in a video, so that one text bitmap is created for all instances of that text line. Therefore, our text segmentation results can also be used for object-based video encoding such as that enabled by MPEG-4.


title: Light Chisel: 6DOF Pen Tracking

abstract: We present a novel interaction device tracked in 6 degrees of freedom by two commodity cameras. The inexpensive Light Chisel is statically illuminated with two LEDs, and uses no additional sensor e.g. inertial or magnetic or means of communication or synchronization. Its form factor is well suited for a screwdriver or chisel grip, allowing the Light Chisel to be rolled between the fingers. The position and orientation of the tool is tracked absolutely, making the Light Chisel suited for complex interaction, e.g. geometric modeling in augmented reality. The Light Chisel is physically small, limiting the physical and optical collisions with the real world. The orientation of the tool is tracked in a wide range of angles: pitch and yaw ±90i¾?, roll ±180i¾?. We evaluated our system against the OptiTrack optical tracking system. Our system achieved mean differences from OptiTrack reference of 2.07 mm in position, 1.06i¾? in yaw and pitch, and 5.26i¾? in roll using a pair of VGA cameras. We demonstrate usefulness of our Light Chisel in four applications: character animation, modeling by swirls, volumetric modeling, and docking of CAD models.


title: Vaccine Delivery to the Oral Cavity Using Coated Microneedles Induces Systemic and Mucosal Immunity

abstract: PURPOSE ::: The objective of this study is to evaluate the feasibility of using coated microneedles to deliver vaccines into the oral cavity to induce systemic and mucosal immune responses. ::: ::: ::: METHOD ::: Microneedles were coated with sulforhodamine, ovalbumin and two HIV antigens. Coated microneedles were inserted into the inner lower lip and dorsal surface of the tongue of rabbits. Histology was used to confirm microneedle insertion, and systemic and mucosal immune responses were characterized by measuring antigen-specific immunoglobulin G (IgG) in serum and immunoglobulin A (IgA) in saliva, respectively. ::: ::: ::: RESULTS ::: Histological evaluation of tissues shows that coated microneedles can penetrate the lip and tongue to deliver coatings. Using ovalbumin as a model antigen it was found that the lip and the tongue are equally immunogenic sites for vaccination. Importantly, both sites also induced a significant (p < 0.05) secretory IgA in saliva compared to pre-immune saliva. Microneedle-based oral cavity vaccination was also compared to the intramuscular route using two HIV antigens, a virus-like particle and a DNA vaccine. Microneedle-based delivery to the oral cavity and the intramuscular route exhibited similar (p > 0.05) yet significant (p < 0.05) levels of antigen-specific IgG in serum. However, only the microneedle-based oral cavity vaccination group stimulated a significantly higher (p < 0.05) antigen-specific IgA response in saliva, but not intramuscular injection. ::: ::: ::: CONCLUSION ::: In conclusion, this study provides a novel method using microneedles to induce systemic IgG and secretory IgA in saliva, and could offer a versatile technique for oral mucosal vaccination.


title: Exploring exploratory search: a user study with linked semantic data

abstract: The maturation of semantic technologies and the growing popularity of the Linked Open Data (LOD) cloud make it possible to expose linked semantic data sets to end users in order to empower a range of analytical tasks taking advantage of knowledge integration and semantic linking. Linked semantic data appears to offer a great potential for exploratory search, which is open-ended, multi-faceted, and iterative in nature. However, there is limited insight into how browsing through linked semantic data sets can support exploratory search. This paper presents a user study with a uni-focal semantic browsing interface for exploratory search through several data sets linked via domain ontologies. The study, which is qualitative and exploratory in nature and uses music as an illustrative domain, examines (i) obstacles and challenges related to user exploratory search in LOD and (ii) the serendipitous learning effect and the role semantics plays in that. The approach and lessons learnt can benefit future human factor studies to evaluate interactive exploration of linked semantic data, as well as technology developers to become aware of issues that have to be addressed in to facilitate exploratory search with LOD.


title: A Review of Emotion Recognition Using Physiological Signals

abstract: Emotion recognition based on physiological signals has been a hot topic and applied in many areas such as safe driving, health care and social security. In this paper, we present a comprehensive review on physiological signal-based emotion recognition, including emotion models, emotion elicitation methods, the published emotional physiological datasets, features, classifiers, and the whole framework for emotion recognition based on the physiological signals. A summary and comparation among the recent studies has been conducted, which reveals the current existing problems and the future work has been discussed.


title: Object tracking using SIFT features and mean shift

abstract: A scale invariant feature transform (SIFT) based mean shift algorithm is presented for object tracking in real scenarios. SIFT features are used to correspond the region of interests across frames. Meanwhile, mean shift is applied to conduct similarity search via color histograms. The probability distributions from these two measurements are evaluated in an expectation-maximization scheme so as to achieve maximum likelihood estimation of similar regions. This mutual support mechanism can lead to consistent tracking performance if one of the two measurements becomes unstable. Experimental work demonstrates that the proposed mean shift/SIFT strategy improves the tracking performance of the classical mean shift and SIFT tracking algorithms in complicated real scenarios.


title: Robust automatic speech recognition with missing and unreliable acoustic data

abstract: Abstract Human speech perception is robust in the face of a wide variety of distortions, both experimentally applied and naturally occurring. In these conditions, state-of-the-art automatic speech recognition (ASR) technology fails. This paper describes an approach to robust ASR which acknowledges the fact that some spectro-temporal regions will be dominated by noise. For the purposes of recognition, these regions are treated as missing or unreliable. The primary advantage of this viewpoint is that it makes minimal assumptions about any noise background. Instead, reliable regions are identified, and subsequent decoding is based on this evidence. We introduce two approaches for dealing with unreliable evidence. The first – marginalisation – computes output probabilities on the basis of the reliable evidence only. The second – state-based data imputation – estimates values for the unreliable regions by conditioning on the reliable parts and the recognition hypothesis. A further source of information is the bounds on the energy of any constituent acoustic source in an additive mixture. This additional knowledge can be incorporated into the missing data framework. These approaches are applied to continuous-density hidden Markov model (HMM)-based speech recognisers and evaluated on the TIDigits corpus for several noise conditions. Two criteria which use simple noise estimates are employed as a means of identifying reliable regions. The first treats regions which are negative after spectral subtraction as unreliable. The second uses the estimated noise spectrum to derive local signal-to-noise ratios, which are then thresholded to identify reliable data points. Both marginalisation and state-based data imputation produce a substantial performance advantage over spectral subtraction alone. The use of energy bounds leads to a further increase in performance for both approaches. While marginalisation outperforms data imputation, the latter technique allows the technique to act as a preprocessor for conventional recognisers, or in speech-enhancement applications.


title: The language of emotion in short blog texts

abstract: Emotion is central to human interactions, and automatic detection could enhance our experience with technologies. We investigate the linguistic expression of fine-grained emotion in 50 and 200 word samples of real blog texts previously coded by expert and naive raters. Content analysis (LIWC) reveals angry authors use more affective language and negative affect words, and that joyful authors use more positive affect words. Additionally, a co-occurrence semantic space approach (LSA) was able to identify fear (which naive human emotion raters could not do). We relate our findings to human emotion perception and note potential computational applications.


title: ViewpointWhy "open source" misses the point of free software

abstract: Decoding the important differences in terminology, underlying philosophy, and value systems between two similar categories of software.


title: A Micromachined Dual-Band Orthomode Transducer

abstract: In this paper, an orthomode transducer (OMT) for dual-band operation and optimized for stacked micromachined layers implementation is presented. The proposed design avoids the use of septums, irises, pins, or small features and minimizes the number of equal-thickness micromachined layers required. In this way, the micromachining fabrication is simplified, making the proposed design a very attractive candidate for high frequency applications and for low-cost batch production. A W-band dual-band design (one different polarization in each frequency band) with more than 10% fractional bandwidth for each band and 30% separation between bands is presented. In addition, proper routing and layered bends are designed for an optimum standard interfacing with the same orientation of the input/output ports. Two OMTs in a back-to-back configuration are fabricated using a thick SU-8 photo-resist micromachining process. A total of six stacked SU-8 layers, all of them with the same thickness of 635 μm, are used. The experimental results are coherent with the tolerance and misalignment of the process, validating the proposed novel OMT design.


title: Development and application of monoclonal antibodies against avian influenza virus nucleoprotein.

abstract: Rapid and accurate diagnosis of avian influenza (AI) infection is important for an understanding epidemiology. In order to develop rapid tests for AI antigen and antibody detection, two monoclonal antibodies (mAbs) against influenza nucleoprotein (NP) were produced. These mAbs are designated as F26-9 and F28-73 and able to recognize whole AI virus particles as well as the recombinant NP. Both of the mAbs were tested in a slot blot for their reactivity against 15 subtypes of influenza virus; F28-73 reacted with all tested 15 subtypes, while F26-9 failed to react with H13N6 and H15N8. The mAb binding epitopes were identified using truncated NP recombinant proteins and peptide array techniques. The mAb F26-9 reacted with NP-full, NP-1 (638bp), NP-2 (315bp), NP-4 (488bp), and NP-5 (400bp) in the Western blot. The peptide array results demonstrated that the mAb F26-9 reacted with NP peptides 15-17 corresponding to amino acids 71-96. The mAb F28-73 recognized the NP-full, -1 and -4 fragments, but failed bind to NP-2, -3, -5, and any peptides. This antibody-binding site is expected to be contained within 1-162 amino acids of AI NP, although the exact binding epitope could not be determined. The two mAbs showed reactivity with AI antigen in immunofluorescence, immunohistochemistry and immune plaque assays. Immune response of AI infected animals was determined using the mAb F28-73 in a cELISA. All tested chickens were positive at 11 days post-infection and remained positive until the end of the experiment on day 28 (>50% inhibition). The two mAbs with different specificities are appropriate for developing various tests for diagnosis of AI infection.


A Report on NACE's 2002 Graduating Student & Alumni Survey--How College Job Seekers View Their Prospects.


title: Internet of Things and LoRa™ low-power wide-area networks challenges

abstract: Nowadays there is a lot of effort on the study, analysis and finding of new solutions related to high density sensor networks used as part of the IoT (Internet of Things) concept. LoRa (Long Range) is a modulation technique that enables the long-range transfer of information with a low transfer rate. This paper presents a review of the challenges and the obstacles of IoT concept with emphasis on the LoRa technology. A LoRaWAN network (Long Range Network Protocol) is of the Low Power Wide Area Network (LPWAN) type and encompasses battery powered devices that ensure bidirectional communication. The main contribution of the paper is the evaluation of the LoRa technology considering the requirements of IoT. In conclusion LoRa can be considered a suitable candidate in addressing the IoT challenges.


title: Reply to Commentary by Trentham et al. on "Caged Phosphate and the Slips and Misses in Determination of Quantum Yields for Ultraviolet-A-Induced Photouncaging" by Gasser et al.

abstract: Following a recent communication on the determination of quantum yields for ultraviolet-A-induced photouncaging written by a few of us (P.A. , A.L. , and G.G.), Dr. Trentham contacted ChemPhysChem because he doubted the experimental results presented in that article. In a commentary co-authored with several colleagues, Dr. Trentham has argued in favor of the initial quantum yields determined by Kaplan et al. 37 years ago, that our measurements disproved. The arguments of the commentary are based on several photouncaging studies conducted since the pioneering work of Kaplan et al. However, the commentary by Trentham and his colleagues could not pinpoint the errors that could have led to the markedly different results obtained in our group, apart from suggesting an instrumentation failure. This explanation can be ruled out with a relative certitude, as different instruments were used in our (P.A. , A.L. , and G.G.) initial study to exclude this as possible source of error. Nevertheless, this commentary has sparked our motivation to continue our investigations and to find an explanation for the issues around the determination of uncaging quantum yields. We therefore contacted a colleague with experience in the field of caged compounds, Prof. Christian Bochet (C.B.), and asked him and his coworker Dr. Elia Janett (E.J.) to independently determine the uncaging quantum yield of caged phosphate in his laboratories, using his experimental protocols and equipment, and this without the presence of P.A, A.L. , or G.G. We would like to point out at this stage that both project investigators (C.B. and G.G.) are not related and have not been involved in any common projects to date. In this article, we (P.A. , A.L, E.J. , C.B. , and G.G.) not only present these new results, but also speculate on potential explanations for the divergences in the determined uncaging quantum yields. The new measurements were performed on an Atlas Photonics LUMOS 43 A photoreactor equipped with LEDs with a center-wavelength of 360 nm and using a standard 1 cm absorbance quartz cell. The emission spectrum of these LEDs is quite narrow (bandwidth <10 nm) and thus close to monochromatic. The total photon flux was determined by phenylglyoxylic acid actinometry. Generally, quantum yields are defined as described by Equation (1):