title: The action of leptin in the ventral tegmental area to decrease food intake is dependent on Jak-2 signaling

abstract: Recent evidence suggests that leptin reduces food intake via actions in the brain circuitry of food reward, such as the ventral tegmental area (VTA), as leptin receptors are present in the VTA, and leptin injection in the VTA reduces food intake. In the hypothalamus, leptin-induced anorexia requires signaling via Janus kinase-signal transducer and activator of transcription (Jak-STAT), insulin receptor substrate (IRS)-phosphatidylinositol 3-kinase (PI 3-kinase), and mammalian target of rapamycin (mTOR). In this study, we determined whether leptin activates each of these signal transduction pathways in the VTA and whether these signaling pathways are required for VTA-leptin induced anorexia. Here, we show that pSTAT3-Tyr 705 , a marker of leptin activation, was induced in a midbrain region containing the VTA and substantia nigra following either intracerebroventricular leptin or direct administration of leptin to the VTA, but these interventions failed to increase levels of either pAKT-Ser 473 or phospho-p70S6K-Thr 389 , markers of IRS-PI 3-kinase and mTOR signaling, respectively. Moreover, the effect of intra-VTA leptin administration to reduce 4- and 20-h food intake and 20-h body weight was blocked by an inhibitor of Jak-2, at a dose that had no effect on food intake or body weight by itself, but not by local inhibition of either PI 3-kinase (LY-294002) or mTOR (rapamycin) in this timeframe. Taken together, these data support the hypothesis that leptin signaling in the VTA is involved in the regulation of energy balance, but, in contrast to the leptin signaling in the hypothalamus, these effects are mediated predominantly via Jak-2 signaling rather than via the IRS-PI 3-kinase or mTOR signaling pathway.


title: Deep Spatio-Temporal Random Fields for Efficient Video Segmentation

abstract: In this work we introduce a time- and memory-efficient method for structured prediction that couples neuron decisions across both space at time. We show that we are able to perform exact and efficient inference on a densely connected spatio-temporal graph by capitalizing on recent advances on deep Gaussian Conditional Random Fields (GCRFs). Our method, called VideoGCRF is (a) efficient, (b) has a unique global minimum, and (c) can be trained end-to-end alongside contemporary deep networks for video understanding. We experiment with multiple connectivity patterns in the temporal domain, and present empirical improvements over strong baselines on the tasks of both semantic and instance segmentation of videos.


title: Design of a capacitive power transfer system for charging of electric vehicles

abstract: Electric Vehicles (EV) nowadays are an attractive means of transportation, as they do not emit harmful gasses. Charging of EVs can be achieved through either direct contact or contactless approaches. Contactless charging approach, as Inductive Power Transfer (IPT) system, provides several merits. Yet, the IPT system has several problems; the most severe one is that it causes health issues, since it is based on electromagnetic induction. The alternative method of transferring power wirelessly is the Capacitive Power Transfer (CPT). The basic concept of a CPT system is based on the power transferal via electric fields between the plates. In this paper, a 5kW CPT system is designed with SiC MOSFET-based inverter. A compensating network, connected to the coupling capacitor, is used. The system is then connected to a rectifier to feed a battery. CPT different aspects are studied in this paper. The system is simulated using MATLAB/Simulink and tested experimentally to examine the performance.


title: A Framework for Analyzing Big Social Data and Modelling Emotions in Social Media

abstract: Social media constitute a rich source of information. The analysis of the big volume of user-generated content in social media can provide meaningful information for gleaning people's emotions and deeper understanding public attitude and mood. In this work, we present a generic framework for the analysis of big social data and the modeling of public emotions and mood. The framework consists of two main parts. Initially an ensemble classifier schema, which combines a generic domain independent, knowledge-based tool with machine learning methods, is used to recognize emotional content in user generated social data. Then, a graph based method is utilized to model the emotional level of a topic based on the emotions recognized and then it creates the topic's emotional graph visualizing public emotions and mood on the topic. The results from the case study are quite encouraging.


title: Blockchain as a Service: An Autonomous, Privacy Preserving, Decentralized Architecture for Deep Learning

abstract: Deep learning algorithms have recently gained attention due to their inherent capabilities and the application opportunities that they provide. Two of the main reasons for the success of deep learning methods are the availability of processing power and big data. Both of these two are expensive and rare commodities that present limitations to the usage and implementation of deep learning. Decentralization of the processing and data is one of the most prevalent solutions for these issues. This paper proposes a cooperative decentralized deep learning architecture. The contributors can train deep learning models with private data and share them to the cooperative data-driven applications initiated elsewhere. Shared models are fused together to obtain a better model. In this work, the contributors can both design their own models or train the models provided by the initiator. In order to utilize an efficient decentralized learning algorithm, blockchain technology is incorporated as a method of creating an incentive-compatible market. In the proposed method, Ethereum blockchain's scripting capabilities are employed to devise a decentralized deep learning mechanism, which provides much higher, collective processing power and grants access to large amounts of data, which would be otherwise inaccessible. The technical description of the mechanism is described and the simulation results are presented.


title: Characterization, Stability and Convergence of Hierarchical Clustering Methods

abstract: We study hierarchical clustering schemes under an axiomatic view. We show that within this framework, one can prove a theorem analogous to one of Kleinberg (2002), in which one obtains an existence and uniqueness theorem instead of a non-existence result. We explore further properties of this unique scheme: stability and convergence are established. We represent dendrograms as ultrametric spaces and use tools from metric geometry, namely the Gromov-Hausdorff distance, to quantify the degree to which perturbations in the input metric space affect the result of hierarchical methods.


title: The serializability of concurrent database updates

abstract: A sequence of interleaved user transactions in a database system may not be ser:ahzable, t e, equivalent to some sequential execution of the individual transactions Using a simple transaction model, it ~s shown that recognizing the transaction histories that are serlahzable is an NP-complete problem. Several efficiently recognizable subclasses of the class of senahzable histories are therefore introduced; most of these subclasses correspond to senahzabdity principles existing in the hterature and used in practice Two new principles that subsume all previously known ones are also proposed Necessary and sufficient conditions are given for a class of histories to be the output of an efficient history scheduler, these conditions imply that there can be no efficient scheduler that outputs all of senahzable histories, and also that all subclasses of senalizable histories studied above have an efficient scheduler Finally, it is shown how these results can be extended to far more general transaction models, to transactions with partly interpreted functions, and to distributed database systems


title: A more globally accurate dimensionality reduction method using triplets

abstract: We first show that the commonly used dimensionality reduction (DR) methods such as t-SNE and LargeVis poorly capture the global structure of the data in the low dimensional embedding. We show this via a number of tests for the DR methods that can be easily applied by any practitioner to the dataset at hand. Surprisingly enough, t-SNE performs the best w.r.t. the commonly used measures that reward the local neighborhood accuracy such as precision-recall while having the worst performance in our tests for global structure. We then contrast the performance of these two DR method against our new method called TriMap. The main idea behind TriMap is to capture higher orders of structure with triplet information (instead of pairwise information used by t-SNE and LargeVis), and to minimize a robust loss function for satisfying the chosen triplets. We provide compelling experimental evidence on large natural datasets for the clear advantage of the TriMap DR results. As LargeVis, TriMap scales linearly with the number of data points.


title: Overview of the Recognizing Inference in Text (RITE-2) at NTCIR-10

abstract: This paper describes an overview of RITE-2 (Recognizing Inference in TExt) task in NTCIR-10. We evaluated systems that automatically recognize semantic relations between sentences such as paraphrase, entailment, contradiction in Japanese, Simplified Chinese and Traditional Chinese. The tasks in RITE-2 are Binary Classification of entailment (BC Subtask), Multi-Class Classification including paraphrase and contradiction (MC Subtask), Entrance Exam Subtasks (Exam BC and Exam Search), Unit Test, and RITE4QA Subtask. We had 28 active participants, and received 215 formal runs (110 Japanese runs, 53 Traditional Chinese runs, 52 Simplified Chinese runs). This paper also describes how the datasets for RITE-2 had been developed, how the systems were evaluated, and reports RITE-2 formal run results.


title: A software defined radio receiver for ILS localizer signal-in-space parameters inspection using Chirp-Z transform and self-adaptive AGC

abstract: In aviation navigation, a localizer of Instrument Landing System (ILS) provides lateral guidance to keep aircrafts on a runway centerline during approaching and landing. A Very High Frequency (VHF) signal is transmitted on ILS localizer channel to allow the airborne avionics equipment to determine its position from the centerline. In compliance with International Civil Aviation Organization (ICAO) standards, this ILS localizer signal must be inspected to assess the transmitted signal-in-space parameters. In this paper, we have developed a Software Defined Radio (SDR) receiver for preflight inspection stage to assist ground personnel to inspect the ILS localizer signal. The SDR receiver uses a novel design to measure the ILS localizer signal. Firstly, chirp-z transform algorithm is proposed to estimate frequency magnitudes of the ILS localizer signal for modulation index calculation. This algorithm aims to enlarge the input spectrum around a selected center frequency for more accurate frequency magnitude estimation. Secondly, the iterative searching is presented to handle a signal level control of hardware front-end. This algorithm iteratively adjusts a software controllable gain of Automatic Gain Control (AGC) for more extensive range of SDR input power. In the experiments, performance evaluation was carried out through three commercial off-the-shelf (COTS) ILS generators and a COTS ILS receiver to benchmark our SDR-based receiver. Test results showed that our approach achieves accurate measurement and wide range of the received ILS localizer signal.


title: Statistics on Password Re-use and Adaptive Strength for Financial Accounts

abstract: Multiple studies have demonstrated that users select weak passwords. However, the vast majority of studies on password security uses password lists that only have passwords for one site, which means that several important questions cannot be studied. For example, how much stronger are password choices for different categories of sites? We use a dataset which we extracted from a large dump of malware records. It contains multiple accounts (and passwords) per user and thus allows us to study both password re-use and the correlation between the value of an account and the strength of the passwords for those accounts.


title: Hexagonal-Shaped Broadband Compact Scrimp Horn Antenna for Operation in $C$ -Band

abstract: The performance of compact linearly polarized Short Circular Ring-loaded horn with Minimized cross-Polarization (Scrimp) horns with different aperture sizes is first evaluated using an in-house finite-difference time-domain (FDTD) computer-aided design (CAD) tool. Then, a very short hexagonal Scrimp horn with an aperture size of 1.5λ0 is designed in C -band; its height is only 1.26λ0 . The experimental results demonstrate that the aperture efficiency is higher than 80% with a reflection coefficient lower than -17 dB over a 12.6% frequency bandwidth. Radiation patterns of excellent quality are measured with a cross-polarization level lower than - 28 dB over the entire bandwidth. These antennas are very attractive candidates for high-power focal array configurations in multifeed systems.


Love, Reality, and Illusion in Intimate Relationships


DEVELOPING ( UTAUT 2 ) MODEL OF ADOPTION MOBILE HEALTH APPLICATION IN JORDAN E-GOVERNMENT


title: k-meansNet: When k-means Meets Differentiable Programming

abstract: In this paper, we study two challenging problems. The first one is how to implement \textit{k}-means in the neural network, which enjoys efficient training based on the stochastic algorithm. The second one is how to enhance the interpretability of network design for clustering. To solve the problems, we propose a neural network which is a novel formulation of the vanilla $k$-means objective. Our contribution is in twofold. From the view of neural networks, the proposed \textit{k}-meansNet is with explicit interpretability in neural processing. We could understand not only why the network structure is presented like itself but also why it could perform data clustering. Such an interpretable neural network remarkably differs from the existing works that usually employ visualization technique to explain the result of the neural network. From the view of \textit{k}-means, three highly desired properties are achieved, i.e. robustness to initialization, the capability of handling new coming data, and provable convergence. Extensive experimental studies show that our method achieves promising performance comparing with 12 clustering methods on some challenging datasets.


title: Neural Latent Extractive Document Summarization

abstract: Extractive summarization models require sentence-level labels, which are usually created heuristically (e.g., with rule-based methods) given that most summarization datasets only have document-summary pairs. Since these labels might be suboptimal, we propose a latent variable extractive model where sentences are viewed as latent variables and sentences with activated variables are used to infer gold summaries. During training the loss comes \emph{directly} from gold summaries. Experiments on the CNN/Dailymail dataset show that our model improves over a strong extractive baseline trained on heuristically approximated labels and also performs competitively to several recent models.


title: Corpus Annotation through Crowdsourcing: Towards Best Practice Guidelines

abstract: Crowdsourcing is an emerging collaborative approach that can be used for the acquisition of annotated corpora and a wide range of other linguistic resources. Although the use of this approach is intensifying in all its key genres (paid-for crowdsourcing, games with a purpose, volunteering-based approaches), the community still lacks a set of best-practice guidelines similar to the annotation best practices for traditional, expert-based corpus acquisition. In this paper we focus on the use of crowdsourcing methods for corpus acquisition and propose a set of best practice guidelines based in our own experiences in this area and an overview of related literature. We also introduce GATE Crowd, a plugin of the GATE platform that relies on these guidelines and offers tool support for using crowdsourcing in a more principled and efficient manner.


title: PCAS: Pruning Channels with Attention Statistics for Deep Network Compression

abstract: Compression techniques for deep neural networks are important for implementing them on small embedded devices. In particular, channel-pruning is a useful technique for realizing compact networks. However, many conventional methods require manual setting of compression ratios in each layer. It is difficult to analyze the relationships between all layers, especially for deeper models. To address these issues, we propose a simple channel-pruning technique based on attention statistics that enables to evaluate the importance of channels. We improved the method by means of a criterion for automatic channel selection, using a single compression ratio for the entire model in place of per-layer model analysis. The proposed approach achieved superior performance over conventional methods with respect to accuracy and the computational costs for various models and datasets. We provide analysis results for behavior of the proposed criterion on different datasets to demonstrate its favorable properties for channel pruning.


title: Masquelet's procedure and bone morphogenetic protein in congenital pseudarthrosis of the tibia in children: a case series and meta-analysis.

abstract: PURPOSE ::: A type 2 recombinant human bone morphogenetic protein (rhBMP2) and Masquelet's procedure were used in three children presenting with congenital pseudarthrosis of the tibia (CPT). Recent studies on CPT suggested the presence in situ of pathologic tissues promoting pseudarthrosis. The authors hypothesized that large segmental resection of pseudarthrosis could improve prognosis of the CPT. Masquelet's procedure and rhBMP2 have been advocated for the treatment of long bone defect. ::: ::: ::: METHOD ::: The authors report three cases of CPT in children treated with Masquelet's procedure and application of rhBMP2. They analyzed all published cases of CPT similarly treated. ::: ::: ::: RESULTS ::: In the present study, Masquelet's procedure did not improve the results in the treatment of CPT, but segmental bone reconstruction was possible. Bone healing was obtained in three out of the five applications of rhBMP2. In one case, the patient's parents asked for leg amputation. Analysis of the 33 published cases with the application of BMP in CPT points to a 62 % healing rate in this pathology. ::: ::: ::: CONCLUSION ::: The authors confirmed that segmental bone reconstruction is possible in CPT using Masquelet's procedure. In the literature, the success rate of the application of rhBMP in CPT appears to be lower than the healing rate usually reported without BMP. Nevertheless, the strict selection of patients, limited number of cases, and their heterogeneity make interpreting the results difficult. However, the theoretical risk which the children are exposed to during the use of BMP makes rigorous selection of the indications necessary. Finally, the interest of rhBMP2 application in Masquelet's procedure remained to be proven.


title: The mnist database of handwritten digits

abstract: Disclosed is an improved articulated bar flail having shearing edges for efficiently shredding materials. An improved shredder cylinder is disclosed with a plurality of these flails circumferentially spaced and pivotally attached to the periphery of a rotatable shaft. Also disclosed is an improved shredder apparatus which has a pair of these shredder cylinders mounted to rotate about spaced parallel axes which cooperates with a conveyer apparatus which has a pair of inclined converging conveyer belts with one of the belts mounted to move with respect to the other belt to allow the transport of articles of various sizes therethrough.


title: End-to-End Simulation of 5G mmWave Networks

abstract: Due to its potential for multi-gigabit and low latency wireless links, millimeter wave (mmWave) technology is expected to play a central role in 5th generation (5G) cellular systems. While there has been considerable progress in understanding the mmWave physical layer, innovations will be required at all layers of the protocol stack, in both the access and the core network. Discrete-event network simulation is essential for end-to-end, cross-layer research and development. This paper provides a tutorial on a recently developed full-stack mmWave module integrated into the widely used open-source ns–3 simulator. The module includes a number of detailed statistical channel models as well as the ability to incorporate real measurements or ray-tracing data. The physical and medium access control layers are modular and highly customizable, making it easy to integrate algorithms or compare orthogonal frequency division multiplexing numerologies, for example. The module is interfaced with the core network of the ns–3 Long Term Evolution (LTE) module for full-stack simulations of end-to-end connectivity, and advanced architectural features, such as dual-connectivity, are also available. To facilitate the understanding of the module, and verify its correct functioning, we provide several examples that show the performance of the custom mmWave stack as well as custom congestion control algorithms designed specifically for efficient utilization of the mmWave channel.


title: Noise Adaptive Fuzzy Switching Median Filter for Salt-and-Pepper Noise Reduction

abstract: This letter presents a novel two-stage noise adaptive fuzzy switching median (NAFSM) filter for salt-and-pepper noise detection and removal. Initially, the detection stage will utilize the histogram of the corrupted image to identify noise pixels. These detected ?noise pixels? will then be subjected to the second stage of the filtering action, while ?noise-free pixels? are retained and left unprocessed. Then, the NAFSM filtering mechanism employs fuzzy reasoning to handle uncertainty present in the extracted local information as introduced by noise. Simulation results indicate that the NAFSM is able to outperform some of the salt-and-pepper noise filters existing in literature.


title: Discovering Discriminative Graphlets for Aerial Image Categories Recognition

abstract: Recognizing aerial image categories is useful for scene annotation and surveillance. Local features have been demonstrated to be robust to image transformations, including occlusions and clutters. However, the geometric property of an aerial image (i.e., the topology and relative displacement of local features), which is key to discriminating aerial image categories, cannot be effectively represented by state-of-the-art generic visual descriptors. To solve this problem, we propose a recognition model that mines graphlets from aerial images, where graphlets are small connected subgraphs reflecting both the geometric property and color/texture distribution of an aerial image. More specifically, each aerial image is decomposed into a set of basic components (e.g., road and playground) and a region adjacency graph (RAG) is accordingly constructed to model their spatial interactions. Aerial image categories recognition can subsequently be casted as RAG-to-RAG matching. Based on graph theory, RAG-to-RAG matching is conducted by comparing all their respective graphlets. Because the number of graphlets is huge, we derive a manifold embedding algorithm to measure different-sized graphlets, after which we select graphlets that have highly discriminative and low redundancy topologies. Through quantizing the selected graphlets from each aerial image into a feature vector, we use support vector machine to discriminate aerial image categories. Experimental results indicate that our method outperforms several state-of-the-art object/scene recognition models, and the visualized graphlets indicate that the discriminative patterns are discovered by our proposed approach.


title: Home automation using raspberry Pi through Siri enabled mobile devices

abstract: Home automation is a system that has the technology to control devices automatically in order to convene the desires of security, comfort and efficiency. On the other hand, voice-based digital assistant such as Apple's Siri provides a location independent access to the Internet and local networks. This study focuses on implementing a home automation system through Siri's capability of speech recognition and through Raspberry Pi as a low cost control system to automate home devices, namely the air cooler, door, lights, TV and window. SiriProxy is installed on the Raspberry Pi as a proxy server for Siri. By developing a SiriProxy plug-in, a set of commands for home automation could be custom-made by the user. The system has been tested and verified through speech recognition's accuracy tests, response latency tests and success rate tests. A total of 34 subjects with different English accents tested a total of 14 voice commands with a total of 390 trials. The tests verified that the system's average latency is at 2.12 seconds with an overall success rate at 93.3333% on at least 3 trials and 87.381% success rate on at least one trial.


title: Structural Diversity and Homophily: A Study Across More Than One Hundred Big Networks

abstract: A widely recognized organizing principle of networks is structural homophily, which suggests that people with more common neighbors are more likely to connect with each other. However, what influence the diverse structures embedded in common neighbors have on link formation is much less well-understood. To explore this problem, we begin by characterizing the structural diversity of common neighborhoods. Using a collection of 120 large-scale networks, we demonstrate that the impact of the common neighborhood diversity on link existence can vary substantially across networks. We find that its positive effect on Facebook and negative effect on LinkedIn suggest different underlying networking needs in these networks. We also discover striking cases where diversity violates the principle of homophily---that is, where fewer mutual connections may lead to a higher tendency to link with each other. We then leverage structural diversity to develop a common neighborhood signature (CNS), which we apply to a large set of networks to uncover unique network superfamilies not discoverable by conventional methods. Our findings shed light on the pursuit to understand the ways in which network structures are organized and formed, pointing to potential advancement in designing graph generation models and recommender systems.


title: Video and Image based Emotion Recognition Challenges in the Wild: EmotiW 2015

abstract: The third Emotion Recognition in the Wild (EmotiW) challenge 2015 consists of an audio-video based emotion and static image based facial expression classification sub-challenges, which mimics real-world conditions. The two sub-challenges are based on the Acted Facial Expression in the Wild (AFEW) 5.0 and the Static Facial Expression in the Wild (SFEW) 2.0 databases, respectively. The paper describes the data, baseline method, challenge protocol and the challenge results. A total of 12 and 17 teams participated in the video based emotion and image based expression sub-challenges, respectively.


title: Comparison of controllable transmission ratio type variable stiffness actuator with antagonistic and pre-tension type actuators for the joints exoskeleton robots

abstract: Exoskeleton robots are used as assistive limbs for elderly persons, rehabilitation for paralyzed persons or ::: ::: power augmentation purposes for healthy persons. The similarity of the exoskeleton robots and human body ::: ::: neuro-muscular system maximizes the device performance. Human body neuro-muscular system provides a ::: ::: flexible and safe movement capability with minimum energy consumption by varying the stiffness of the ::: ::: human joints regularly. Similar to human body, variable stiffness actuators should be used to provide a ::: ::: flexible and safe movement capability in exoskeletons. In the present day, different types of variable ::: ::: stiffness actuator designs are used, and the studies on these actuators are still continuing rapidly. As ::: ::: exoskeleton robots are mobile devices working with the equipment such as batteries, the motors used in the ::: ::: design are expected to have minimal power requirements. In this study, antagonistic, pre-tension and ::: ::: controllable transmission ratio type variable stiffness actuators are compared in terms of energy efficiency ::: ::: and power requirement at an optimal (medium) walking speed for ankle joint. In the case of variable ::: ::: stiffness, the results show that the controllable transmission ratio type actuator compared with the ::: ::: antagonistic design is more efficient in terms of energy consumption and power requirement.


title: Sentiment Predictability for Stocks

abstract: In this work, we present our findings and experiments for stock-market prediction using various textual sentiment analysis tools, such as mood analysis and event extraction, as well as prediction models, such as LSTMs and specific convolutional architectures.


title: Zero-Order Reverse Filtering

abstract: In this paper, we study an unconventional but practically meaningful reversibility problem of commonly used image filters. We broadly define filters as operations to smooth images or to produce layers via global or local algorithms. And we raise the intriguingly problem if they are reservable to the status before filtering. To answer it, we present a novel strategy to understand general filter via contraction mappings on a metric space. A very simple yet effective zero-order algorithm is proposed. It is able to practically reverse most filters with low computational cost. We present quite a few experiments in the paper and supplementary file to thoroughly verify its performance. This method can also be generalized to solve other inverse problems and enables new applications.


title: Finding the missing link for big biomedical data.

abstract: It has been argued that big data will enable efficiencies and accountability in health care.1,2 However, to date, other industries have been far more successful at obtaining value from large-scale integration and analysis of heterogeneous data sources. What these industries have figured out is that big data becomes transformative when disparate data sets can be linked at the individual person level. In contrast, big biomedical data are scattered across institutions and intentionally isolated to protect patient privacy. Both technical and social challenges to linking these data must be addressed before big biomedical data can have their full influence on health care. It is this linkage challenge that we address in this Viewpoint.


title: Stock price prediction using LSTM, RNN and CNN-sliding window model

abstract: Stock market or equity market have a profound impact in today's economy. A rise or fall in the share price has an important role in determining the investor's gain. The existing forecasting methods make use of both linear (AR, MA, ARIMA) and non-linear algorithms (ARCH, GARCH, Neural Networks), but they focus on predicting the stock index movement or price forecasting for a single company using the daily closing price. The proposed method is a model independent approach. Here we are not fitting the data to a specific model, rather we are identifying the latent dynamics existing in the data using deep learning architectures. In this work we use three different deep learning architectures for the price prediction of NSE listed companies and compares their performance. We are applying a sliding window approach for predicting future values on a short term basis. The performance of the models were quantified using percentage error.


title: Dual Averaging and Proximal Gradient Descent for Online Alternating Direction Multiplier Method

abstract: We develop new stochastic optimization methods that are applicable to a wide range of structured regularizations. Basically our methods are combinations of basic stochastic optimization techniques and Alternating Direction Multiplier Method (ADMM). ADMM is a general framework for optimizing a composite function, and has a wide range of applications. We propose two types of online variants of ADMM, which correspond to online proximal gradient descent and regularized dual averaging respectively. The proposed algorithms are computationally efficient and easy to implement. Our methods yield O(1/√T) convergence of the expected risk. Moreover, the online proximal gradient descent type method yields O(log(T)/T) convergence for a strongly convex loss. Numerical experiments show effectiveness of our methods in learning tasks with structured sparsity such as overlapped group lasso.


title: Emotional State Recognition Using Advanced Machine Learning Techniques on EEG Data

abstract: This study investigates the discrimination between calm, exciting positive and exciting negative emotional states using EEG signals. Towards this direction, a publicly available dataset from eNTERFACE Workshop 2006 was used having as stimuli emotionally evocative images. At first, EEG features were extracted based on literature review. Then, a computational framework is proposed using machine learning techniques, performing feature selection and classification into two at a time emotional states. The procedure described in this paper investigates and assess the effectiveness of selection and classification techniques providing improved classification accuracy. The proposed methodology is able to obtain accuracy of 75.12% in classifying the two emotional states comparing with similar studies using the same dataset.


title: Adopting Continuous Delivery and Deployment: Impacts on Team Structures, Collaboration and Responsibilities

abstract: Context: Continuous Delivery and Deployment (CD) practices aim to deliver software features more frequently and reliably. While some efforts have been made to study different aspects of CD practices, a little empirical work has been reported on the impact of CD on team structures, collaboration and team members' responsibilities. Goal: Our goal is to empirically investigate how Development (Dev) and Operations (Ops) teams are organized in software industry for adopting CD practices. Furthermore, we explore the potential impact of practicing CD on collaboration and team members' responsibilities. Method:We conducted a mixed-method empirical study, which collected data from 21 in-depth, semi-structured interviews in 19 organizations and a survey with 93 software practitioners. Results: There are four common types of team structures (i.e., (1) separate Dev and Ops teams with higher collaboration; (2) separate Dev and Ops teams with facilitator(s) in the middle; (3) small Ops team with more responsibilities for Dev team; (4) no visible Ops team) for organizing Dev and Ops teams to effectively initiate and adopt CD practices. Our study also provides insights into how software organizations actually improve collaboration among teams and team members for practicing CD. Furthermore, we highlight new responsibilities and skills (e.g., monitoring and logging skills), which are needed in this regard.


title: When the job is a calling: The role of applying one's signature strengths at work

abstract: This study investigates the role of applying the individual signature strengths at work for positive experiences at work (i.e. job satisfaction, pleasure, engagement, and meaning) and calling. A sample of 111 employees from various occupations completed measures on character strengths, positive experiences at work, and calling. Co-workers (N = 111) rated the applicability of character strengths at work. Correlations between the applicability of character strengths and positive experiences at work decreased with intraindividual centrality of strengths (ranked strengths from the highest to the lowest). The level of positive experiences and calling were higher when four to seven signature strengths were applied at work compared to less than four. Positive experiences partially mediated the effect of the number of applied signature strengths on calling. Implications for further research and practice will be discussed.


title: Multimode Digital Controller for Synchronous Buck Converters Operating Over Wide Ranges of Input Voltages and Load Currents

abstract: This paper describes a digital pulsewidth modulation/pulse-frequency modulation (PWM/PFM) controller with input-voltage feedforward (IVFF) for synchronous buck dc-dc converters. The controller includes automatic PWM/PFM mode switching and effective synchronous operation with a minimum number of active components and without the need for current sensing in PFM mode of operation. IVFF improves efficiency and dynamic performance over a wide range of input voltages. Controller parameters, including the PWM switching frequency, the PFM pulse period, and the mode transition point are programmable, which enables efficiency optimization. Experimental results are shown for a synchronous buck converter with 5-12 V input voltage, and 1.3 V, 0-10 A output.


title: Voxel Cloud Connectivity Segmentation - Supervoxels for Point Clouds

abstract: Unsupervised over-segmentation of an image into regions of perceptually similar pixels, known as super pixels, is a widely used preprocessing step in segmentation algorithms. Super pixel methods reduce the number of regions that must be considered later by more computationally expensive algorithms, with a minimal loss of information. Nevertheless, as some information is inevitably lost, it is vital that super pixels not cross object boundaries, as such errors will propagate through later steps. Existing methods make use of projected color or depth information, but do not consider three dimensional geometric relationships between observed data points which can be used to prevent super pixels from crossing regions of empty space. We propose a novel over-segmentation algorithm which uses voxel relationships to produce over-segmentations which are fully consistent with the spatial geometry of the scene in three dimensional, rather than projective, space. Enforcing the constraint that segmented regions must have spatial connectivity prevents label flow across semantic object boundaries which might otherwise be violated. Additionally, as the algorithm works directly in 3D space, observations from several calibrated RGB+D cameras can be segmented jointly. Experiments on a large data set of human annotated RGB+D images demonstrate a significant reduction in occurrence of clusters crossing object boundaries, while maintaining speeds comparable to state-of-the-art 2D methods.


title: A Surgeless Solid-State DC Circuit Breaker for Voltage-Source-Converter-Based HVDC Systems

abstract: This paper proposes a dc circuit breaker for voltage source converter (VSC) based high-voltage dc transmission (HVDC) systems. Technical challenges for applying dc circuit breakers are to increase blocking voltage and to suppress surge voltage at the current clearing. The proposed dc circuit breaker is a solid-state breaker which consists of a number of semiconductor devices in series. It maintains equal voltage balancing among the devices to apply it to high-voltage applications. Moreover, the surge voltage across the circuit breaker is reduced by employing a freewheeling diode. Considering a system rated at 300 MW in power and 250 kV in dc voltage, the conduction loss of the proposed circuit breaker is estimated to be 0.045% of the rated power. The value is smaller than the power loss of the VSCs. A downscaled HVDC system rated at 10 kW in power and 360 V in dc voltage was built and tested. A series of experimental results demonstrates the dc fault clearing and rapid restoration of power transmission.


title: Working through the pain: Working memory capacity and differences in processing and storage under pain

abstract: It has been suggested that pain perception and attention are closely linked at both a neural and a behavioural level. If pain and attention are so linked, it is reasonable to speculate that those who vary in working memory capacity (WMC) should be affected by pain differently. This study compares the performance of individuals who differ in WMC as they perform processing and memory span tasks while under mild pain and not. While processing performance under mild pain does not interact with WMC, the ability to store information for later recall does. This suggests that pain operates much like an additional processing burden, and that the ability to overcome this physical sensation is related to differences in WMC.


title: Design Practice in Human Computer Interaction Design Education

abstract: As the need for Human Computer Interaction (HCI) designers increases so does the need for courses that best prepare students for their future work life. Multidisciplinary teamwork is what very frequently meets the graduates in their new work situations. Preparing students for such multidisciplinary work through education is not easy to achieve. In this paper, we investigate ways to engage computer science students, majoring in design, use, and interaction (with technology), in design practices through an advanced graduate course in interaction design. Here, we take a closer look at how prior embodied and explicit knowledge of HCI that all of the students have, combined with understanding of design practice through the course, shape them as human-computer interaction designers. We evaluate the results of the effort in terms of increase in creativity, novelty of ideas, body language when engaged in design activities, and in terms of perceptions of how well this course prepared the students for the work practice outside of the university.


title: The relationship of sweat gland count to electrodermal activity

abstract: This study assessed whether greater skin conductance activity at the distal versus medial site (Scerbo, Freedman, Raine, Dawson, & Venables, 1992) is attributable to a greater number of active (open) sweat glands at the distal site. The number of sweat glands was measured using the Palmar Sweat Index (PSI). Twenty-four subjects were exposed to 10 auditory stimuli. Electrodes were placed on the fore and middle fingers of each hand, using distal sites on one hand and medial sites on the other. The PSI was measured at the medial and distal phalanges adjacent to the electrode placement sites. The distal site contained more open and total sweat glands. Open gland count had the strongest correlations with skin conductance. Multivariate analyses of covariance revealed that site effects for nonspecific and orienting response frequency and trials to habituation were associated with site differences in open glands. Skin conductance measures and the PSI reveal greater electrodermal activity at the distal site. In addition, the number of open glands may be a useful measure related to electrodermal response frequency when polygraph measurement is unavailable.


Proper nouns recognition in Arabic crime text using machine learning approach


title: An Empirical Analysis of Different Machine Learning Techniques for Classification of EEG Signal to Detect Epileptic Seizure

abstract: Electroencephalogram (EEG) signal is a modest measure of electric flow in a human brain. It is responsible for information flow through the neurons in the brain which controls and monitors the full torso. Hence, to widening and in-depth understanding of effectiveness in EEG signal analysis is the primary focus of this paper. Moreover, machine learning techniques often proven as more efficacious compared to other techniques. To this effect, the present study primarily focuses on the analysis of EEG signal through the classification of the processed data by discrete wavelet transform (DWT) for identification of epileptic seizures using machine learning techniques. Machine learning techniques like neural networks and support vector machine (SVM) are the focus of this paper for classification of EEG signals to label epilepsy patients. In neural networks, the empirical analysis gives focus on multi-layer perceptron, probabilistic neural network, radial basis function neural networks, and recurrent neural networks. Further, for multi-layer neural networks different propagation training algorithms are examined such as BackPropagation, Resilient-Propagation, and Quick-Propagation. For SVM, several kernel methods are considered such as Linear, Polynomial, and RBF for empirical analysis. The analysis confirms with the present setting that, recurrent neural network performs poor in all the cases of prepared epilepsy data. However, SVM and probabilistic neural networks are quite effective and competitive.


title: A Survey on Feature Extraction Techniques for Shape based Object Recognition

abstract: Robotics is one of the research area in computer age. So, to make the robots as capable as humans, to allow them to interact with real environment so many algorithms are developed and will be developed. Some of those algorithms are developed in the area of computer vision to allow the robots for accurate recognition. In all those algorithms feature extraction technique is most important part of the algorithm. As the features are robust to different affine transformations like translation, scale, rotation, flipped, etc. the algorithm will be more robust to those transformations. So, feature extraction techniques are one of the important part of the image retrieval systems. Some of those feature extraction techniques, with their invariance properties are discussed here for the image retrieval system.


title: Universal representations:The missing link between faces, text, planktons, and cat breeds

abstract: With the advent of large labelled datasets and high-capacity models, the performance of machine vision systems has been improving rapidly. However, the technology has still major limitations, starting from the fact that different vision problems are still solved by different models, trained from scratch or fine-tuned on the target data. The human visual system, in stark contrast, learns a universal representation for vision in the early life of an individual. This representation works well for an enormous variety of vision problems, with little or no change, with the major advantage of requiring little training data to solve any of them.


title: An Integrated Method for Urban Main-Road Centerline Extraction From Optical Remotely Sensed Imagery

abstract: Road information has a fundamental role in modern society. Road extraction from optical satellite images is an economic and efficient way to obtain and update a transportation database. This paper presents an integrated method to extract urban main-road centerlines from satellite optical images. The proposed method has four main steps. First, general adaptive neighborhood is introduced to implement spectral-spatial classification to segment the images into two categories: road and nonroad groups. Second, road groups and homogeneous property, measured by local Geary's C, are fused to improve road-group accuracy. Third, road shape features are used to extract reliable road segments. Finally, local linear kernel smoothing regression is performed to extract smooth road centerlines. Road networks are then generated using tensor voting. The proposed method is tested and subsequently validated using a large set of multispectral high-resolution images. A comparison with several existing methods shows that the proposed method is more suitable for urban main-road centerline extraction.


title: Unsupervised Morphology Rivals Supervised Morphology for Arabic MT

abstract: If unsupervised morphological analyzers could approach the effectiveness of supervised ones, they would be a very attractive choice for improving MT performance on low-resource inflected languages. In this paper, we compare performance gains for state-of-the-art supervised vs. unsupervised morphological analyzers, using a state-of-the-art Arabic-to-English MT system. We apply maximum marginal decoding to the unsupervised analyzer, and show that this yields the best published segmentation accuracy for Arabic, while also making segmentation output more stable. Our approach gives an 18% relative BLEU gain for Levantine dialectal Arabic. Furthermore, it gives higher gains for Modern Standard Arabic (MSA), as measured on NIST MT-08, than does MADA (Habash and Rambow, 2005), a leading supervised MSA segmenter.


title: Real-Time, Full 3-D Reconstruction of Moving Foreground Objects From Multiple Consumer Depth Cameras

abstract: The problem of robust, realistic and especially fast 3-D reconstruction of objects, although extensively studied, is still a challenging research task. Most of the state-of-the-art approaches that target real-time applications, such as immersive reality, address mainly the problem of synthesizing intermediate views for given view-points, rather than generating a single complete 3-D surface. In this paper, we present a multiple-Kinect capturing system and a novel methodology for the creation of accurate, realistic, full 3-D reconstructions of moving foreground objects, e.g., humans, to be exploited in real-time applications. The proposed method generates multiple textured meshes from multiple RGB-Depth streams, applies a coarse-to-fine registration algorithm and finally merges the separate meshes into a single 3-D surface. Although the Kinect sensor has attracted the attention of many researchers and home enthusiasts and has already appeared in many applications over the Internet, none of the already presented works can produce full 3-D models of moving objects from multiple Kinect streams in real-time. We present the capturing setup, the methodology for its calibration and the details of the proposed algorithm for real-time fusion of multiple meshes. The presented experimental results verify the effectiveness of the approach with respect to the 3-D reconstruction quality, as well as the achieved frame rates.


title: LBP − TOP Based Countermeasure against Face Spoofing Attacks

abstract: User authentication is an important step to protect information and in this field face biometrics is advantageous. Face biometrics is natural, easy to use and less human-invasive. Unfortunately, recent work has revealed that face biometrics is vulnerable to spoofing attacks using low-tech cheap equipments. This article presents a countermeasure against such attacks based on the LBP−TOP operator combining both space and time information into a single multiresolution texture descriptor. Experiments carried out with the REPLAY ATTACK database show a Half Total Error Rate (HTER) improvement from 15.16% to 7.60%.


title: Building an Encrypted, Distributed, and Searchable Key-value Store

abstract: Modern distributed key-value stores are offering superior performance, incremental scalability, and fine availability for data-intensive computing and cloud-based applications. Among those distributed data stores, the designs that ensure the confidentiality of sensitive data, however, have not been fully explored yet. In this paper, we focus on designing and implementing an encrypted, distributed, and searchable key-value store. It achieves strong protection on data privacy while preserving all the above prominent features of plaintext systems. We first design a secure data partition algorithm that distributes encrypted data evenly across a cluster of nodes. Based on this algorithm, we propose a secure transformation layer that supports multiple data models in a privacy-preserving way, and implement two basic APIs for the proposed encrypted key-value store. To enable secure search queries for secondary attributes of data, we leverage searchable symmetric encryption to design the encrypted secondary indexes which consider security, efficiency, and data locality simultaneously, and further enable secure query processing in parallel. For completeness, we present formal security analysis to demonstrate the strong security strength of the proposed designs. We implement the system prototype and deploy it to a cluster at Microsoft Azure. Comprehensive performance evaluation is conducted in terms of Put/Get throughput, Put/Get latency under different workloads, system scaling cost, and secure query performance. The comparison with Redis shows that our prototype can function in a practical manner.


title: A Real-Time Deep Learning Pedestrian Detector for Robot Navigation

abstract: A real-time Deep Learning based method for Pedestrian Detection (PD) is applied to the Human-Aware robot navigation problem. The pedestrian detector combines the Aggregate Channel Features (ACF) detector with a deep Convolutional Neural Network (CNN) in order to obtain fast and accurate performance. Our solution is firstly evaluated using a set of real images taken from onboard and offboard cameras and, then, it is validated in a typical robot navigation environment with pedestrians (two distinct experiments are conducted). The results on both tests show that our pedestrian detector is robust and fast enough to be used on robot navigation applications.


title: Mapping the knowledge domain and the theme evolution of appropriability research between 1986 and 2016: a scientometric review

abstract: The scholars in the research domains of innovation and strategic management concerned about the appropriability for about 30 years or more. They focused on appropriability research and constantly evolving. In this paper, we analyze 30 years (1986–2016) literature on appropriability studies from Web of Science Core Collection database. A cited reference clustering map of different periods and terms co-occurrence map have been generated using bibliometric analysis and content analysis. Based on this, we study the evolutionary trajectory, mechanisms and theoretical architecture of appropriability research and explore further research directions. The results indicate that the essence of the appropriability research evolution is the perception changes in opening and sharing, value creation and value growth, and future research is focusing on role of appropriability in the platform governance, generative appropriability and the evolution of the problem-solving mechanisms.


title: Detecting Linguistic Characteristics of Alzheimer's Dementia by Interpreting Neural Models

abstract: Alzheimer's disease (AD) is an irreversible and progressive brain disease that can be stopped or slowed down with medical treatment. Language changes serve as a sign that a patient's cognitive functions have been impacted, potentially leading to early diagnosis. In this work, we use NLP techniques to classify and analyze the linguistic characteristics of AD patients using the DementiaBank dataset. We apply three neural models based on CNNs, LSTM-RNNs, and their combination, to distinguish between language samples from AD and control patients. We achieve a new independent benchmark accuracy for the AD classification task. More importantly, we next interpret what these neural models have learned about the linguistic characteristics of AD patients, via analysis based on activation clustering and first-derivative saliency techniques. We then perform novel automatic pattern discovery inside activation clusters, and consolidate AD patients' distinctive grammar patterns. Additionally, we show that first derivative saliency can not only rediscover previous language patterns of AD patients, but also shed light on the limitations of neural models. Lastly, we also include analysis of gender-separated AD data.


title: Iris Image Classification Based on Hierarchical Visual Codebook

abstract: Iris recognition as a reliable method for personal identification has been well-studied with the objective to assign the class label of each iris image to a unique subject. In contrast, iris image classification aims to classify an iris image to an application specific category, e.g., iris liveness detection (classification of genuine and fake iris images), race classification (e.g., classification of iris images of Asian and non-Asian subjects), coarse-to-fine iris identification (classification of all iris images in the central database into multiple categories). This paper proposes a general framework for iris image classification based on texture analysis. A novel texture pattern representation method called Hierarchical Visual Codebook (HVC) is proposed to encode the texture primitives of iris images. The proposed HVC method is an integration of two existing Bag-of-Words models, namely Vocabulary Tree (VT), and Locality-constrained Linear Coding (LLC). The HVC adopts a coarse-to-fine visual coding strategy and takes advantages of both VT and LLC for accurate and sparse representation of iris texture. Extensive experimental results demonstrate that the proposed iris image classification method achieves state-of-the-art performance for iris liveness detection, race classification, and coarse-to-fine iris identification. A comprehensive fake iris image database simulating four types of iris spoof attacks is developed as the benchmark for research of iris liveness detection.


title: Strength-based interventions: Their importance in application to the gifted

abstract: Positive psychology has revived psychology’s abandoned interest in the study of morally positively valued traits (the so-called character strengths) and virtues. We review lit- erature generated on strength-based approaches and focus on applications in the so-called positive psychology interventions. There seems to be great potential in this approach for research in the field of giftedness and, of course, also when practically working with gifted children and adolescents. We highlight some ideas for future research directions.


title: Spectral Affine‐Kernel Embeddings

abstract: In this paper, we propose a controllable embedding method for high- and low-dimensional geometry processing through sparse matrix eigenanalysis. Our approach is equally suitable to perform non-linear dimensionality reduction on big data, or to offer non-linear shape editing of 3D meshes and pointsets. At the core of our approach is the construction of a multi-Laplacian quadratic form that is assembled from local operators whose kernels only contain locally-affine functions. Minimizing this quadratic form provides an embedding that best preserves all relative coordinates of points within their local neighborhoods. We demonstrate the improvements that our approach brings over existing nonlinear dimensionality reduction methods on a number of datasets, and formulate the first eigen-based as-rigid-as-possible shape deformation technique by applying our affine-kernel embedding approach to 3D data augmented with user-imposed constraints on select vertices.


title: Pedestrian Detection using Infrared images and Histograms of Oriented Gradients

abstract: Thispaperpresents acomplete methodforpedes- trian detection applied toinfrared images. First, westudy an imagedescriptor basedonhistograms oforiented gradients (HOG),associated witha Support Vector Machine(SVM) classifier andevaluate itsefficiency. Afterhaving tunedthe HOG descriptor andtheclassifier, weinclude this methodin acomplete system, whichdeals withstereo infrared images. Thisapproach gives goodresults forwindowclassification, andapreliminary testapplied onavideosequence proves thatthis approach isverypromising. I.INTRODUCTION Since thelast fewyears now,thedevelopment ofdriving assistance systems hasbeenveryactive inorder toin- crease thevehicle anditsenvironment safety. Atthe present time, themainobjective inthisdomainisto provide thedrivers withsomeinformation concerning its environment andanypotential hazard. Oneamongall useful information isthedetection andlocalization ofa pedestrian infront ofavehicle. Thisproblem ofdetecting pedestrians isaverydifficult problemthathasessentially beenaddressed using vision sensors, imageprocessing andpattern recognition techniques. Inparticular, detecting pedestrians thanks to images isacomplex challenge duetotheir appearance andposevariability. Inthecontext ofdaylight vision, several approaches havebeenproposed andarebased on different imageprocessing techniques ormachine learning (9), (5), (12). Recently, owingtothedevelopment oflow-cost infrared cameras, night vision systems havegained moreand moreinterest, thusincreasing theneedofautomatic detection ofpedestrians atnight. Thisproblem of detecting pedestrians frominfrared images hasbeen investigated byvarious research teamsinthelast years. Themainmethodology isbasedon extracting cues (symmetry, shape-independent


title: Feed-Forward Networks with Attention Can Solve Some Long-Term Memory Problems

abstract: We propose a simplified model of attention which is applicable to feed-forward neural networks and demonstrate that the resulting model can solve the synthetic "addition" and "multiplication" long-term memory problems for sequence lengths which are both longer and more widely varying than the best published results for these tasks.


title: Proximity-coupled L-band patch array antenna fed by binomial power distribution

abstract: The development of proximity-coupled L-band patch array antenna fed by using binomial power distribution is presented. The used of binomial power distribution with Pascal coefficient is to suppress side lobe level (SLL) of the array antenna useful for some wireless communications including radar application. The array antenna which is intended to operate at the L-band frequency of 1.27GHz is designed using two layers of FR4 epoxy dielectric substrate with the total thickness of 1.6mm. It is constructed of five square patches printed on the top side of first layer of dielectric substrate which are fed by using proximity-coupled feeding deployed on the top side of second layer of grounded-dielectric substrate. To demonstrate the feasibility of SLL suppression, an array antenna with uniform power distribution is also developed and characterized. From the characterization result, it shows that the binomial power distribution can suppress SLL of the realized patch array antenna up to 6dB comparable to the uniform power distribution patch array antenna.


title: Dose–response relationship in music therapy for people with serious mental disorders: Systematic review and meta-analysis

abstract: Abstract Serious mental disorders have considerable individual and societal impact, and traditional treatments may show limited effects. Music therapy may be beneficial in psychosis and depression, including treatment-resistant cases. The aim of this review was to examine the benefits of music therapy for people with serious mental disorders. All existing prospective studies were combined using mixed-effects meta-analysis models, allowing to examine the influence of study design (RCT vs. CCT vs. pre-post study), type of disorder (psychotic vs. non-psychotic), and number of sessions. Results showed that music therapy, when added to standard care, has strong and significant effects on global state, general symptoms, negative symptoms, depression, anxiety, functioning, and musical engagement. Significant dose–effect relationships were identified for general, negative, and depressive symptoms, as well as functioning, with explained variance ranging from 73% to 78%. Small effect sizes for these outcomes are achieved after 3 to 10, large effects after 16 to 51 sessions. The findings suggest that music therapy is an effective treatment which helps people with psychotic and non-psychotic severe mental disorders to improve global state, symptoms, and functioning. Slight improvements can be seen with a few therapy sessions, but longer courses or more frequent sessions are needed to achieve more substantial benefits.


Tag Recommendations in StackOverflow


title: Spectral Mapping Using Artificial Neural Networks for Voice Conversion

abstract: In this paper, we use artificial neural networks (ANNs) for voice conversion and exploit the mapping abilities of an ANN model to perform mapping of spectral features of a source speaker to that of a target speaker. A comparative study of voice conversion using an ANN model and the state-of-the-art Gaussian mixture model (GMM) is conducted. The results of voice conversion, evaluated using subjective and objective measures, confirm that an ANN-based VC system performs as good as that of a GMM-based VC system, and the quality of the transformed speech is intelligible and possesses the characteristics of a target speaker. In this paper, we also address the issue of dependency of voice conversion techniques on parallel data between the source and the target speakers. While there have been efforts to use nonparallel data and speaker adaptation techniques, it is important to investigate techniques which capture speaker-specific characteristics of a target speaker, and avoid any need for source speaker's data either for training or for adaptation. In this paper, we propose a voice conversion approach using an ANN model to capture speaker-specific characteristics of a target speaker and demonstrate that such a voice conversion approach can perform monolingual as well as cross-lingual voice conversion of an arbitrary source speaker.


title: Social Consequences of Grindr Use: Extending the Internet-Enhanced Self-Disclosure Hypothesis

abstract: Grindr, a location-based real-time dating application, provides sexual-minority men (SMM) a space through which they can identify, access, and communicate with one another. Although previous research has examined user motivations and public self-disclosure patterns on Grindr, we investigate the effects intimate self-disclosure and sexting via the application's private messaging on internalized homophobia and loneliness. Using the Internet-enhanced self-disclosure hypothesis (ISDH) as a framework, we conducted an online survey of 274 Grindr users. Serial mediation analysis showed support for the ISDH, suggesting that Grindr use was negatively associated with loneliness. Intimate self-disclosure and internalized homophobia mediated the relationship between Grindr use and loneliness, but sexting had no relationship with internalized homophobia or loneliness. We discuss implications for the ISDH, Grindr, self-disclosure, and sexting.


title: Optimum harmonic reduction with a wide range of modulation indexes for multilevel converters

abstract: This paper proposes a novel modulation technique to be applied to multilevel voltage-source converters suitable for high-voltage power supplies and flexible AC transmission system devices. The proposed technique can generate output stepped waveforms with a wide range of modulation indexes and minimized total voltage harmonic distortion. The main power devices switch only once per cycle, as is suitable for high-power applications. In addition to meeting the minimum turn-on and turn-off time requirements for high-power semiconductor switches, the proposed technique excludes from the synthesized waveform any pulses that are either too narrow or too wide. By using a systematic method, only the polarities and the number of levels need to be determined for different modulation levels. To verify the theory and the simulation results, a cascaded converter-based hardware prototype, including an 8-b microcontroller as well as modularized power stage and gate driver circuits, is implemented. Experimental results indicate that the proposed technique is effective for the reduction of harmonics in multilevel converters, and both the theoretical and simulation results are well validated.


title: A Security Analysis of the Composition of ChaCha20 and Poly1305.

abstract: This note contains a security reduction to demonstrate that Langley’s composition of Bernstein’s ChaCha20 and Poly1305, as proposed for use in IETF protocols, is a secure authenticated encryption scheme. The reduction assumes that ChaCha20 is a PRF, that Poly1305 is -almost-∆-universal, and that the adversary is nonce respecting.


title: Geographic perspectives on development goals : Constructive engagements and critical perspectives on the MDGs and the SDGs

abstract: The successes and failures of the UN Millennium Development Goals and the establishment of a new set of Sustainable Development Goals provide many opportunities for geographic engagement and critic...


title: Learning Numerical Attributes in Knowledge Bases

abstract: Knowledge bases (KB) are often represented as a collection of facts in the form (HEAD, PREDICATE, TAIL), where HEAD and TAIL are entities while PREDICATE is a binary relationship that links the two. It is a well-known fact that knowledge bases are far from complete, and hence the plethora of research on KB completion methods, specifically on link prediction. However, though frequently ignored, these repositories also contain numerical facts. Numerical facts link entities to numerical values via numerical predicates; e.g., (PARIS, LATITUDE, 48.8). Likewise, numerical facts also suffer from the incompleteness problem. To address this issue, we introduce the numerical attribute prediction problem. This problem involves a new type of query where the relationship is a numerical predicate. Consequently, and contrary to link prediction, the answer to this query is a numerical value. We argue that the numerical values associated with entities explain, to some extent, the relational structure of the knowledge base. Therefore, we leverage knowledge base embedding methods to learn representations that are useful predictors for the numerical attributes. An extensive set of experiments on benchmark versions of FREEBASE and YAGO show that our approaches largely outperform sensible baselines. We make the datasets available under a permissive BSD-3 license.


title: Dynamic terrain traversal skills using reinforcement learning

abstract: The locomotion skills developed for physics-based characters most often target flat terrain. However, much of their potential lies with the creation of dynamic, momentum-based motions across more complex terrains. In this paper, we learn controllers that allow simulated characters to traverse terrains with gaps, steps, and walls using highly dynamic gaits. This is achieved using reinforcement learning, with careful attention given to the action representation, non-parametric approximation of both the value function and the policy; epsilon-greedy exploration; and the learning of a good state distance metric. The methods enable a 21-link planar dog and a 7-link planar biped to navigate challenging sequences of terrain using bounding and running gaits. We evaluate the impact of the key features of our skill learning pipeline on the resulting performance.


title: A Survey of Millimeter Wave (mmWave) Communications for 5G: Opportunities and Challenges

abstract: With the explosive growth of mobile data demand, the fifth generation (5G) mobile network would exploit the enormous amount of spectrum in the millimeter wave (mmWave) bands to greatly increase communication capacity. There are fundamental differences between mmWave communications and existing other communication systems, in terms of high propagation loss, directivity, and sensitivity to blockage. These characteristics of mmWave communications pose several challenges to fully exploit the potential of mmWave communications, including integrated circuits and system design, interference management, spatial reuse, anti-blockage, and dynamics control. To address these challenges, we carry out a survey of existing solutions and standards, and propose design guidelines in architectures and protocols for mmWave communications. We also discuss the potential applications of mmWave communications in the 5G network, including the small cell access, the cellular access, and the wireless backhaul. Finally, we discuss relevant open research issues including the new physical layer technology, software-defined network architecture, measurements of network state information, efficient control mechanisms, and heterogeneous networking, which should be further investigated to facilitate the deployment of mmWave communication systems in the future 5G networks.


title: Inverse Classification for Comparison-based Interpretability in Machine Learning

abstract: In the context of post-hoc interpretability, this paper addresses the task of explaining the prediction of a classifier, considering the case where no information is available, neither on the classifier itself, nor on the processed data (neither the training nor the test data). It proposes an instance-based approach whose principle consists in determining the minimal changes needed to alter a prediction: given a data point whose classification must be explained, the proposed method consists in identifying a close neighbour classified differently, where the closeness definition integrates a sparsity constraint. This principle is implemented using observation generation in the Growing Spheres algorithm. Experimental results on two datasets illustrate the relevance of the proposed approach that can be used to gain knowledge about the classifier.


title: Human gamma-frequency oscillations associated with attention and memory

abstract: Both theoretical and experimental animal work supports the hypothesis that transient oscillatory synchronization of neuronal assemblies at gamma frequencies (30-100 Hz) is closely associated with sensory processing. Recent data from recordings in animals and humans have suggested that gamma-frequency activity also has an important role in attention and both working and long-term memory. The involvement of gamma-band synchronization in various cognitive paradigms in humans is currently being investigated using intracranial and high-density electro- and magnetoencephalography recordings. Here, we discuss recent findings demonstrating human gamma-frequency activity associated with attention and memory in both sensory and non-sensory areas. Because oscillatory gamma-frequency activity has an important role in neuronal communication and synaptic plasticity, it could provide a key for understanding neuronal processing in both local and distributed cortical networks engaged in complex cognitive functions. This review is part of the INMED/TINS special issue Physiogenic and pathogenic oscillations: the beauty and the beast, based on presentations at the annual INMED/TINS symposium (http://inmednet.com).


title: Beyond Concern: Understanding Net Users' Attitudes About Online Privacy

abstract: People are concerned about privacy, particularly on the Internet. While many studies have provided evidence of this concern, few have explored the nature of the concern in detail, especially for the online environment. With this study, we have tried to better understand the nature of online privacy concerns; we look beyond the fact that people are concerned and attempt to understand how they are concerned. We hope our results will help inform both policy decisions as well as the development of technology tools that can assist Internet users in protecting their privacy. ::: We present results here from the analysis of 381 questionnaires completed between November 6 and November 13, 1998 by American Internet users. The sample was drawn from the FamilyPC magazine/Digital Research, Inc. Family Panel. While this is not a statistically representative sample of US Internet users, our respondents are heavy Internet users, and quite possibly lead innovators. As such, we believe that this sample is important for understanding the future Internet user population.


title: The scale of edges

abstract: Although the scale of isotropic visual elements such as blobs and interest points, e.g. SIFT[12], has been well studied and adopted in various applications, how to determine the scale of anisotropic elements such as edges is still an open problem. In this paper, we study the scale of edges, and try to answer two questions: 1) what is the scale of edges, and 2) how to calculate it. From the points of human cognition and physical interpretation, we illustrate the existence of the scale of edges and provide a quantitative definition. Then, an automatic edge scale selection approach is proposed. Finally, a cognitive experiment is conducted to validate the rationality of the detected scales. Moreover, the importance of identifying the scale of edges is also shown in applications such as boundary detection and hierarchical edge parsing.


title: Utterance-Level Multimodal Sentiment Analysis

abstract: During real-life interactions, people are naturally gesturing and modulating their voice to emphasize specific points or to express their emotions. With the recent growth of social websites such as YouTube, Facebook, and Amazon, video reviews are emerging as a new source of multimodal and natural opinions that has been left almost untapped by automatic opinion analysis techniques. This paper presents a method for multimodal sentiment classification, which can identify the sentiment expressed in utterance-level visual datastreams. Using a new multimodal dataset consisting of sentiment annotated utterances extracted from video reviews, we show that multimodal sentiment analysis can be effectively performed, and that the joint use of visual, acoustic, and linguistic modalities can lead to error rate reductions of up to 10.5% as compared to the best performing individual modality.


title: Survey of Sparse and Non-Sparse Methods in Source Separation

abstract: Source separation arises in a variety of signal process- ing applications, ranging from speech processing to medical image analysis. The separation of a superposition of multiple signals is accomplished by taking into account the structure of the mixing proc- ess and by making assumptions about the sources. When the infor- mation about the mixing process and sources is limited, the problem is called ''blind'. By assuming that the sources can be represented sparsely in a given basis, recent research has demonstrated that sol- utions to previously problematic blind source separation problems can be obtained. In some cases, solutions are possible to problems intractable by previous non-sparse methods. Indeed, sparse meth-


title: Biology of Bone Tissue: Structure, Function, and Factors That Influence Bone Cells

abstract: Bone tissue is continuously remodeled through the concerted actions of bone cells, which include bone resorption by osteoclasts and bone formation by osteoblasts, whereas osteocytes act as mechanosensors and orchestrators of the bone remodeling process. This process is under the control of local (e.g., growth factors and cytokines) and systemic (e.g., calcitonin and estrogens) factors that all together contribute for bone homeostasis. An imbalance between bone resorption and formation can result in bone diseases including osteoporosis. Recently, it has been recognized that, during bone remodeling, there are an intricate communication among bone cells. For instance, the coupling from bone resorption to bone formation is achieved by interaction between osteoclasts and osteoblasts. Moreover, osteocytes produce factors that influence osteoblast and osteoclast activities, whereas osteocyte apoptosis is followed by osteoclastic bone resorption. The increasing knowledge about the structure and functions of bone cells contributed to a better understanding of bone biology. It has been suggested that there is a complex communication between bone cells and other organs, indicating the dynamic nature of bone tissue. In this review, we discuss the current data about the structure and functions of bone cells and the factors that influence bone remodeling.


Triage amalgamated dermoscopic algorithm (TADA) for skin cancer screening


title: A virtual reconstruction approach for archaeoastronomical research

abstract: Archaeoastronomical research often suffers from lack of data about the three-dimensional situation of prehistorical monuments in their respective landscapes. In our project, we have reconstructed, in virtual models, a series of similar Neolithic monuments with alleged astronomical orientation pattern in their respective landscape. Astronomical extensions to the modelling software and to the models, or the direct inclusion of the models into desktop astronomy software, opens up valuable insights into topics long debated only on flat paper maps.


title: Object detection using image reconstruction with PCA

abstract: In this paper, we present an object detection system and its application to pedestrian detection in still images, without assuming any a priori knowledge about the image. The system works as follows: in a first stage a classifier examines each location in the image at different scales. Then in a second stage the system tries to eliminate false detections based on heuristics. The classifier is based on the idea that Principal Component Analysis (PCA) can compress optimally only the kind of images that were used to compute the principal components (PCs), and that any other kind of images will not be compressed well using a few components. Thus the classifier performs separately the PCA from the positive examples and from the negative examples; when it needs to classify a new pattern it projects it into both sets of PCs and compares the reconstructions, assigning the example to the class with the smallest reconstruction error. The system is able to detect frontal and rear views of pedestrians, and usually can also detect side views of pedestrians despite not being trained for this task. Comparisons with other pedestrian detection systems show that our system has better performance in positive detection and in false detection rate. Additionally, we show that the performance of the system can be further improved by combining the classifier based on PCA reconstruction with a conventional classifier using a Support Vector Machine.


An Analysis on the Correlation and Gender Difference between College Students' Internet Addiction and Mobile Phone Addiction in Taiwan


title: Endothelial-to-mesenchymal transition contributes to cardiac fibrosis

abstract: Cardiac fibrosis, associated with a decreased extent of microvasculature and with disruption of normal myocardial structures, results from excessive deposition of extracellular matrix, which is mediated by the recruitment of fibroblasts. The source of these fibroblasts is unclear and specific anti-fibrotic therapies are not currently available. Here we show that cardiac fibrosis is associated with the emergence of fibroblasts originating from endothelial cells, suggesting an endothelial-mesenchymal transition (EndMT) similar to events that occur during formation of the atrioventricular cushion in the embryonic heart. Transforming growth factor-beta1 (TGF-beta1) induced endothelial cells to undergo EndMT, whereas bone morphogenic protein 7 (BMP-7) preserved the endothelial phenotype. The systemic administration of recombinant human BMP-7 (rhBMP-7) significantly inhibited EndMT and the progression of cardiac fibrosis in mouse models of pressure overload and chronic allograft rejection. Our findings show that EndMT contributes to the progression of cardiac fibrosis and that rhBMP-7 can be used to inhibit EndMT and to intervene in the progression of chronic heart disease associated with fibrosis.


title: Development of CR2-Haptic: A compact and portable rehabilitation robot for wrist and forearm training

abstract: Stroke has now become the leading cause of severe disability. Rehabilitation robots are gradually becoming popular for stroke rehabilitation to improve motor recovery, as robotic technology can assist, enhance, and further quantify rehabilitation training for stroke patients. However, most of the available rehabilitation robots are complex and involve multiple degrees-of-freedom (DOFs) causing it to be very expensive and huge in size. Rehabilitation robots should be useful but also need to be affordable and portable enabling more patients to afford and train independently at home. This paper presents a development of an affordable, portable and compact rehabilitation robot that implements different rehabilitation strategies for stroke patient to train forearm and wrist movement in an enhanced virtual reality environment with haptic feedback.


title: Introduction GROMACS: Fast, Flexible, and Free

abstract: This article describes the software suite GROMACS (Groningen MAchine for Chemical Simulation) that was developed at the University of Groningen, The Netherlands, in the early 1990s. The software, w ...


title: Text-level Discourse Dependency Parsing

abstract: Previous researches on Text-level discourse parsing mainly made use of constituency structure to parse the whole document into one discourse tree. In this paper, we present the limitations of constituency based discourse parsing and first propose to use dependency structure to directly represent the relations between elementary discourse units (EDUs). The state-of-the-art dependency parsing techniques, the Eisner algorithm and maximum spanning tree (MST) algorithm, are adopted to parse an optimal discourse dependency tree based on the arcfactored model and the large-margin learning techniques. Experiments show that our discourse dependency parsers achieve a competitive performance on text-level discourse parsing.


title: Video Highlight Shot Extraction with Time-Sync Comment

abstract: Benefit from abundance of mobile applications, portability of large-screen mobile devices and accessibility of media resources, users nowadays much more prefer to watch videos on their mobiles no matter whether they are at home or on the way. However, constrained by available time and network flow, users may only choose to watch some hot video segments that are manually annotated by video editors. In this paper, we aim to automatically extract video highlight shot with the help of video sentimental feature of time-sync comments. First, analyzing statistical feature of real data. After, we simulate the generation process of time-sync comment after. Then, we propose a shot boundary detection method to extract highlight shot, which is proved to be more effective than traditional methods based on comment density. This experiment attests the time-sync comment is particularly suitable for sentiment-based video segment extraction for 2 reasons. 1) Text-based similarity calculation of is much faster than image-based process depending on every frame of video; 2) Time-sync comment reflects user subjective emotion therefore is useful in personalised video recommendation.


title: Plasmonic and metamaterial cloaking: physical mechanisms and potentials

abstract: Artificial materials, metamaterials and plasmonic media have recently received tremendous attention from the scientific communities, media and the general public, following novel ideas and suggestions for their potential use in a variety of applications such as cloaking. Here we briefly review and highlight some of the available solutions for invisibility and cloaking that employ metamaterials and plasmonic materials at various frequencies. We briefly overview some of the different cloaking mechanisms recently proposed in the literature, such as plasmonic cloaking based on scattering cancelation, coordinate-transformation cloaking and anomalous localized resonances for cloaking, in particular providing some details for scattering-cancelation-based plasmonic cloaking. We mention the main analogies and differences among these various approaches and we discuss some possible ideas for realizations and applications of these results, with particular attention to the physical phenomena involved.


title: A Review of Traditional and Novel Treatments for Seizures in Autism Spectrum Disorder: Findings from a Systematic Review and Expert Panel

abstract: Despite the fact that seizures are commonly associated with autism spectrum disorder (ASD), the effectiveness of treatments for seizures has not been well studied in individuals with ASD. This manuscript reviews both traditional and novel treatments for seizures associated with ASD. Studies were selected by systematically searching major electronic databases and by a panel of experts that treat ASD individuals. Only a few anti-epileptic drugs (AEDs) have undergone carefully controlled trials in ASD, but these trials examined outcomes other than seizures. Several lines of evidence point to valproate, lamotrigine, and levetiracetam as the most effective and tolerable AEDs for individuals with ASD. Limited evidence supports the use of traditional non-AED treatments, such as the ketogenic and modified Atkins diet, multiple subpial transections, immunomodulation, and neurofeedback treatments. Although specific treatments may be more appropriate for specific genetic and metabolic syndromes associated with ASD and seizures, there are few studies which have documented the effectiveness of treatments for seizures for specific syndromes. Limited evidence supports L-carnitine, multivitamins, andN-acetyl-L-cysteine in mitochondrial disease and dysfunction, folinic acid in cerebral folate abnormalities and early treatment with vigabatrin in tuberous sclerosis complex. Finally, there is limited evidence for a number of novel treatments, particularly magnesium with pyridoxine, omega-3 fatty acids, the glutenfree casein-free diet, and low-frequency repetitive transcranial magnetic simulation. Zinc and L-carnosine are potential novel treatments supported by basic research but not clinical studies. This review demonstrates the wide variety of treatments used to treat seizures in individuals with ASD as well as the striking lack of clinical trials performed to support the use of these treatments. Additional studies concerning these treatments for controlling seizures in individuals with ASD are warranted.


title: Inventory, evolution and expression profiling diversity of the LEA (late embryogenesis abundant) protein gene family in Arabidopsis thaliana

abstract: We analyzed the Arabidopsis thaliana genome sequence to detect Late Embryogenesis Abundant (LEA) protein genes, using as reference sequences proteins related to LEAs previously described in cotton or which present similar characteristics. We selected 50 genes representing nine groups. Most of the encoded predicted proteins are small and contain repeated domains that are often specific to a unique LEA group. Comparison of these domains indicates that proteins with classical group 5 motifs are related to group 3 proteins and also gives information on the possible history of these repetitions. Chromosomal gene locations reveal that several LEA genes result from whole genome duplications (WGD) and that 14 are organized in direct tandem repeats. Expression of 45 of these genes was tested in different plant organs, as well as in response to ABA and in mutants (such as abi3, abi5, lec2 and fus3) altered in their response to ABA or in seed maturation. The results demonstrate that several so-called LEA genes are expressed in vegetative tissues in the absence of any abiotic stress, that LEA genes from the same group do not present identical expression profile and, finally, that regulation of LEA genes with apparently similar expression patterns does not systematically involve the same regulatory pathway.


title: Evaluation Criteria for Computational Quran Search

abstract: This paper reviews search tools constructed for Information Retrieval from the Holy Quran. This paper evaluates these different search tools against 13 criteria depending on: search features, output features, precision of the retrieved verses, recall database size and types of database contents. Based on this survey, we conclude that most of the existing Quran search tools still cannot solve the problem of ambiguity in the retrieved results because these tools use traditional query analysis and make limited usage of Quran ontologies.


title: FairAccess: a new Blockchain-based access control framework for the Internet of Things

abstract: Security and privacy are huge challenges in Internet of Things (IoT) environments, but unfortunately, the harmonization of the IoT-related standards and protocols is hardly and slowly widespread. In this paper, we propose a new framework for access control in IoT based on the blockchain technology. Our first contribution consists in providing a reference model for our proposed framework within the Objectives, Models, Architecture and Mechanism specification in IoT. In addition, we introduce FairAccess as a fully decentralized pseudonymous and privacy preserving authorization management framework that enables users to own and control their data. To implement our model, we use and adapt the blockchain into a decentralized access control manager. Unlike financial bitcoin transactions, FairAccess introduces new types of transactions that are used to grant, get, delegate, and revoke access. As a proof of concept, we establish an initial implementation with a Raspberry PI device and local blockchain. Finally, we discuss some limitations and propose further opportunities. Copyright © 2017 John Wiley & Sons, Ltd.


title: A Novel Digital Control Technique for Brushless DC Motor Drives

abstract: Brushless DC (BLDC) motor drives are continually gaining popularity in motion control applications. Therefore, it is necessary to have a low cost, but effective BLDC motor speed/torque regulator. This paper introduces a novel concept for digital control of trapezoidal BLDC motors. The digital controller was implemented via two different methods, namely conduction-angle control and current-mode control. Motor operation is allowed only at two operating points or states. Alternating between the two operating points results in an average operating point that produces an average operating speed. The controller design equations are derived from Newton's second law. The novel controller is verified via computer simulations and an experimental demonstration is carried out with the rapid prototyping and real-time interface system dSPACE.


title: Drawing and Analyzing Causal DAGs with DAGitty

abstract: DAGitty is a software for drawing and analyzing causal diagrams, also known as directed acyclic graphs (DAGs). Functions include identification of minimal sufficient adjustment sets for estimating causal effects, diagnosis of insufficient or invalid adjustment via the identification of biasing paths, identification of instrumental variables, and derivation of testable implications. DAGitty is provided in the hope that it is useful for researchers and students in Epidemiology, Sociology, Psychology, and other empirical disciplines. The software should run in any web browser that supports modern JavaScript, HTML, and SVG. This is the user manual for DAGitty version 2.3. The manual is updated with every release of a new stable version. DAGitty is available at dagitty.net.


title: Analyzing the past to prepare for the future: Writing a literature review

abstract: A review of prior, relevant literature is an essential feature of any academic project. An effective review creates a firm foundation for advancing knowledge. It facilitates theory development, closes areas where a plethora of research exists, and uncovers areas where research is needed.


title: AI Researchers, Video Games Are Your Friends!

abstract: Artificial intelligence and games go way back. At least to Turing, who re-invented the Minimax algorithm to play Chess even before he had a computer, and to Samuel, who invented a predecessor of TD-learning in order to build a Checkers-playing program in the 1950s. Games are important for AI because they are designed to challenge and train human cognitive capabilities, and are thus uniquely relevant benchmark problems. They are also uniquely convenient benchmark problems, as they allow unbiased comparison between algorithms and can be executed thousands of times fast than realtime. But one should also not forget the financial clout of the games industry and games' appeal for new students. While research on board games such as Chess and Go has been part of AI research since its inception, the last decade has seen the rise of a research community around AI for video games, and not only for playing them. In this talk I will outline some of the most important trends in recent years. One is General Video Game Playing: developing controllers that can learn to play not just a single game, but a large variety variety of them. Another is Procedural Content Generation, where AI algorithms are used to generate content for games, or even design the games themselves. Yet another trend is AI-assisted design tools, which provide the game designer with instant feedback and suggestions and thus scaffolds the game design process. These research topics inform each other, with general video game playing algorithms being important for procedural content generation and AI-assisted design tools. Finally, I will try to convince you that your own research is important for this endeavor and that you should consider steering your research towards AI for games.


title: BalanceFlow: Controller load balancing for OpenFlow networks

abstract: In the discussion about Future Internet, Software-Defined Networking (SDN), enabled by OpenFlow, is currently seen as one of the most promising paradigm. While the availability and scalability concerns rises as a single controller could be alleviated by using replicate or distributed controllers, there lacks a flexible mechanism to allow controller load balancing. This paper proposes BalanceFlow, a controller load balancing architecture for OpenFlow networks. By utilizing CONTROLLER X action extension for OpenFlow switches and cross-controller communication, one of the controllers, called “super controller”, can flexibly tune the flow-requests handled by each controller, without introducing unacceptable propagation latencies. Experiments based on real topology show that BalanceFlow can adjust the load of each controller dynamically.