title: A generalized method toward drug-target interaction prediction via low-rank matrix projection

abstract: Drug-target interaction (DTI) prediction plays a very important role in drug development and drug discovery. Biochemical experiments or \textit{in vitro} methods are very expensive, laborious and time-consuming. Therefore, \textit{in silico} approaches including docking simulation and machine learning have been proposed to solve this problem. In particular, machine learning approaches have attracted increasing attentions recently. However, in addition to the known drug-target interactions, most of the machine learning methods require extra characteristic information such as chemical structures, genome sequences, binding types and so on. Whenever such information is not available, they may perform poor. Very recently, the similarity-based link prediction methods were extended to bipartite networks, which can be applied to solve the DTI prediction problem by using topological information only. In this work, we propose a method based on low-rank matrix projection to solve the DTI prediction problem. On one hand, when there is no extra characteristic information of drugs or targets, the proposed method utilizes only the known interactions. On the other hand, the proposed method can also utilize the extra characteristic information when it is available and the performances will be remarkably improved. Moreover, the proposed method can predict the interactions associated with new drugs or targets of which we know nothing about their associated interactions, but only some characteristic information. We compare the proposed method with ten baseline methods, e.g., six similarity-based methods that utilize only the known interactions and four methods that utilize the extra characteristic information. The datasets and codes implementing the simulations are available at this https URL


title: THE BIOLOGIC BASIS OF TRANSGENDER IDENTITY: 2D:4D FINGER LENGTH RATIOS IMPLICATE A ROLE FOR PRENATAL ANDROGEN ACTIVITY.

abstract: OBJECTIVE ::: The biologic basis for gender identity is unknown. Research has shown that the ratio of the length of the second and fourth digits (2D:4D) in mammals is influenced by biologic sex in utero, but data on 2D:4D ratios in transgender individuals are scarce and contradictory. We investigated a possible association between 2D:4D ratio and gender identity in our transgender clinic population in Albany, New York. ::: ::: ::: METHODS ::: We prospectively recruited 118 transgender subjects undergoing hormonal therapy (50 female to male [FTM] and 68 male to female [MTF]) for finger length measurement. The control group consisted of 37 cisgender volunteers (18 females, 19 males). The length of the second and fourth digits were measured using digital calipers. The 2D:4D ratios were calculated and analyzed with unpaired t tests. ::: ::: ::: RESULTS ::: FTM subjects had a smaller dominant hand 2D:4D ratio (0.983 ± 0.027) compared to cisgender female controls (0.998 ± 0.021, P = .029), but a ratio similar to control males (0.972 ± 0.036, P =.19). There was no difference in the 2D:4D ratio of MTF subjects (0.978 ± 0.029) compared to cisgender male controls (0.972 ± 0.036, P = .434). ::: ::: ::: CONCLUSION ::: Our findings are consistent with a biologic basis for transgender identity and the possibilities that FTM gender identity is affected by prenatal androgen activity but that MTF transgender identity has a different basis. ::: ::: ::: ABBREVIATIONS ::: 2D:4D = 2nd digit to 4th digit; FTM = female to male; MTF = male to female.


title: Pathogen-Induced Inflammatory Environment Controls Effector and Memory CD8 + T Cell Differentiation

abstract: In response to infection, CD8(+) T cells integrate multiple signals and undergo an exponential increase in cell numbers. Simultaneously, a dynamic differentiation process occurs, resulting in the formation of short-lived effector cells (SLECs; CD127(low)KLRG1(high)) and memory precursor effector cells (CD127(high)KLRG1(low)) from an early effector cell that is CD127(low)KLRG1(low) in phenotype. CD8(+) T cell differentiation during vesicular stomatitis virus infection differed significantly than during Listeria monocytogenes infection with a substantial reduction in early effector cell differentiation into SLECs. SLEC generation was dependent on Ebi3 expression. Furthermore, SLEC differentiation during vesicular stomatitis virus infection was enhanced by administration of CpG-DNA, through an IL-12-dependent mechanism. Moreover, CpG-DNA treatment enhanced effector CD8(+) T cell functionality and memory subset distribution, but in an IL-12-independent manner. Population dynamics were dramatically different during secondary CD8(+) T cell responses, with a much greater accumulation of SLECs and the appearance of a significant number of CD127(high)KLRG1(high) memory cells, both of which were intrinsic to the memory CD8(+) T cell. These subsets persisted for several months but were less effective in recall than memory precursor effector cells. Thus, our data shed light on how varying the context of T cell priming alters downstream effector and memory CD8(+) T cell differentiation.


title: A Helix Excited Circularly Polarized Hollow Cylindrical Dielectric Resonator Antenna

abstract: In this letter, we use a helix to excite two orthogonal HE11δ modes in phase quadrature in a hollow cylindrical dielectric resonator antenna to generate circular polarization. As compared to our previous work, it is shown that replacing the cylindrical dielectric resonator antenna with a hollow cylindrical-shaped one leads to a significant enhancement in the 3-dB axial-ratio bandwidth. A design guideline is provided, and three design cases are presented. A prototype for one of the designs is fabricated and measured. Measured and simulated data are presented and discussed. It is shown that the 3-dB axial-ratio bandwidth increases as the wall thickness of the hollow cylindrical dielectric resonator decreases. Furthermore, a reduction in the wall thickness requires a longer helix exciter for similar performance .


title: Fungus Detection Through Optical Sensor System Using Two Different Kinds of Feature Vectors for the Classification

abstract: This revolutionary era emphasizes the need of novel, simple, automatic fungal detection system to control the devastation caused by fungal species. Fungus is highly notorious for human health, food, and archives. We have developed a device and algorithms to automatically detect fungus from air by employing optical sensor system. Several air samples were collected and examined under high magnification microscopic camera in order to take images of samples through the designed system. System collects three different types of images (dark-field, bright-field, and auto fluorescence) with the help of different light sources. This provided detailed temporal variation of fungus spores to effectively analyze fungus images. Two different algorithms were developed for fungus spore detection: hand crafted features and histogram of oriented gradients as features. Then, support vector machine was used for the purpose of classification. Furthermore, we compared the fungus spores count obtained with our algorithm and manual counting method. The performance of the proposed system on new images obtained by the optical sensor system was evaluated and all results of precision, recall, and accuracy are also presented in the paper. Convincing results were obtained in favor of the proposed approach for precision recall ratios. The obtained results proved the possibility of developing a device for early detection of several types of fungus spores. The system successfully detected the number of fungus spores present in the air and could estimate all possible threats.


title: Temporal window of integration in auditory-visual speech perception

abstract: Abstract Forty-three normal hearing participants were tested in two experiments, which focused on temporal coincidence in auditory visual (AV) speech perception. In these experiments, audio recordings of/ pa /and/ ba /were dubbed onto video recordings of / ba /or/ ga /, respectively (ApVk, AbVg), to produce the illusory “fusion” percepts / ta /, or / da / [McGurk, H., & McDonald, J. (1976). Hearing lips and seeing voices. Nature, 264, 746–747]. In Experiment 1, an identification task using McGurk pairs with asynchronies ranging from −467 ms (auditory lead) to +467 ms was conducted. Fusion responses were prevalent over temporal asynchronies from −30 ms to +170 ms and more robust for audio lags. In Experiment 2, simultaneity judgments for incongruent and congruent audiovisual tokens (AdVd, AtVt) were collected. McGurk pairs were more readily judged as asynchronous than congruent pairs. Characteristics of the temporal window over which simultaneity and fusion responses were maximal were quite similar, suggesting the existence of a 200 ms duration asymmetric bimodal temporal integration window.


title: High IQ Adolescents Under Stress: Do They Perform Poor in Academics

abstract: The study was aimed at finding if any relationship exists between psychological stress and academic achievement of high IQ adolescents. Subjects were high IQ adolescents having IQ 110 and above. Bisht Battery of Stress Scales was used to assess the amount of stress on these adolescents. Academic achievement was assessed on the basis of average of marks obtained in last three examinations. Correlation coefficients between stress scores and academic scores were computed. Academic achievement was found to be negatively and significantly correlated with all types of stress except existential stress. (authors)


title: Maintaining Natural Image Statistics with the Contextual Loss

abstract: Maintaining natural image statistics is a crucial factor in restoration and generation of realistic looking images. When training CNNs, photorealism is usually attempted by adversarial training (GAN), that pushes the output images to lie on the manifold of natural images. GANs are very powerful, but not perfect. They are hard to train and the results still often suffer from artifacts. In this paper we propose a complementary approach, that could be applied with or without GAN, whose goal is to train a feed-forward CNN to maintain natural internal statistics. We look explicitly at the distribution of features in an image and train the network to generate images with natural feature distributions. Our approach reduces by orders of magnitude the number of images required for training and achieves state-of-the-art results on both single-image super-resolution, and high-resolution surface normal estimation.


title: Passive and active battery balancing comparison based on MATLAB simulation

abstract: Battery systems are affected by many factors, the most important one is the cells unbalancing. Without the balancing system, the individual cell voltages will differ over time, battery pack capacity will decrease quickly. That will result in the fail of the total battery system. Thus cell balancing acts an important role on the battery life preserving. Different cell balancing methodologies have been proposed for battery pack. This paper presents a review and comparisons between the different proposed balancing topologies for battery string based on MATLAB/Simulink® simulation. The comparison carried out according to circuit design, balancing simulation, practical implementations, application, balancing speed, complexity, cost, size, balancing system efficiency, voltage/current stress … etc.


title: LBP-based degraded document image binarization

abstract: Most of the classical threshold-based methods for document image binarization use simple features carried out from the spatial pixels values of the document images. In this paper, we present a new binarization method for degraded documents, based on Local Binary Pattern (LBP) as a texture measure. The mean and variance of pixels are computed respectively from both the original document image and the LBP image. Then, these features are used within a threshold-based method. Another variant is computed by combining a contrast information with the LBP operator to overcome the drawback caused by the poor contrasted document images. Experimental results conducted on DIBCO datasets and compared against some state-of-the-art methods, prove the effective use of the LBP for binarizing historical documents.


title: SCHOOL OF BUSINESS WORKING PAPER No. 302 Using Bayesian Networks for Bankruptcy Prediction: Some Methodological Issues

abstract: Abstract This study provides operational guidance for building naive Bayes Bayesian network (BN) models for bankruptcy prediction. First, we suggest a heuristic method that guides the selection of bankruptcy predictors. Based on the correlations and partial correlations among variables, the method aims at eliminating redundant and less relevant variables. A naive Bayes model is developed using the proposed heuristic method and is found to perform well based on a 10-fold validation analysis. The developed naive Bayes model consists of eight first-order variables, six of which are continuous. We also provide guidance on building a cascaded model by selecting second-order variables to compensate for missing values of first-order variables. Second, we analyze whether the number of states into which the six continuous variables are discretized has an impact on the model’s performance. Our results show that the model’s performance is the best when the number of states for discretization is either two or three. Starting from four states, the performance starts to deteriorate, probably due to over-fitting. Finally, we experiment whether modeling continuous variables with continuous distributions instead of discretizing them can improve the model’s performance. Our finding suggests that this is not true. One possible reason is that continuous distributions tested by the study do not represent well the underlying distributions of empirical data. Finally, the results of this study could also be applicable to business decision-making contexts other than bankruptcy prediction.


title: A Ternary Weight Binary Input Convolutional Neural Network: Realization on the Embedded Processor

abstract: In image recognition, techniques using a convolutional neural network (CNN) have been extensively studied and are widely used in various applications, such as a handwritten character recognition, a face recognition, a scene determination, and an object recognition. It has an enormous amount of computational complexity and internal parameters, and it is often implemented in high-performance GPUs. However, the embedded system requires real-time image recognition with a low-power consumption. In such systems, a binarized CNN has been proposed for the embedded system. It can achieve efficient implementation by restricting the values that the parameters inside CNN treating -1 and +1, and low bit precision of operations and memory. In the paper, we extend to a ternary weight binary input CNN to further increase its performance with a low-performance embedded processor. In the ternarized CNN, values that internal weight can take -1, +1 and 0, where zero weight can be realized by a skip computation. Since the number of possible states of the ternarized CNN is larger than that of the binarized CNN, high recognition accuracy can be obtained. Furthermore, we study an optimal training algorithm in the ternarized CNN and show the results by computer experiment. Comparison with the binarized CNN, as for the ARM processor, the ternary weight CNN was 8.13 times faster than the binary weight one.


title: Graph-Based Ranking Algorithms For Sentence Extraction, Applied To Text Summarization

abstract: This paper presents an innovative unsupervised method for automatic sentence extraction using graph-based ranking algorithms. We evaluate the method in the context of a text summarization task, and show that the results obtained compare favorably with previously published results on established benchmarks.


title: Commonsense computing (episode 5): algorithm efficiency and balloon testing

abstract: This paper investigates what students understand about algorithm efficiency before receiving any formal instruction on the topic. We gave students a challenging search problem and two solutions, then asked them to identify the more efficient solution and to justify their choice. Many students did not use the standard worst-case analysis of algorithms; rather they chose other metrics, including average-case, better for more cases, better in all cases, one algorithm being more correct, and better for real-world scenarios. Students were much more likely to choose the correct algorithm when they were asked to trace the algorithms on specific examples; this was true even if they traced the algorithms incorrectly.


title: A comparison of three insect-inspired locomotion controllers

abstract: Abstract This paper compares three insect inspired controllers which were implemented on an autonomous hexapod robot. There is a growing interest in using insect locomotion schemes to control walking robots. Researchers' interest in insect-based controllers ranges from understanding the biological basis of locomotion control in insects to building real-time walking machines which require relatively little computational power. Several models for insect locomotion exist, and robotics researchers tend to adopt one approach and experiment with it. In contrast, this paper offers a comparison of three insect inspired controllers — all of which were implemented and tested on the same autonomous hexapod robot. Some of the controllers used reflex-based mechanisms whereas others used pattern-based mechanisms. Reflexive controllers exploit sensory stimulus and response reactions to produce leg motion and gait coordination. In contrast, pattern-based controllers depend more upon pre-programmed patterns of behavior which may be influenced by external events. Typically, these pre-programmed patterns of behavior are implemented using central pattern generators (CPGs). In this work, we compare gait coordination performance of three controllers on flat terrain. We extend the comparison to include leg loading considerations, disabled leg compensation, and externally applied leg perturbations. We discuss the differences between controllers with respect to inconsistent leg retraction velocities, leg design issues, sensing requirements, and computational issues. The robot performed quite differently under varying experimental conditions depending upon which controller was used. We found that controller performance was the most sensitive to robot design parameters. For our case, we had the most success with pattern-based mechanisms given the leg design of our robot and its limitations in controlling the retraction velocity of its legs. The pattern-based mechanisms allowed the robot to remain stable over a variety of gaits while the robot was subjected to loading the legs, disabling a leg, and physically disturbing the legs. The reflexive mechanisms were less successful at maintaining stability when the robot's legs were increasingly disrupted.


title: Assessment of a Smart Sensing Shoe for Gait Phase Detection in Level Walking

abstract: Gait analysis and more specifically ambulatory monitoring of temporal and spatial gait parameters may open relevant fields of applications in activity tracking, sports and also in the assessment and treatment of specific diseases. Wearable technology can boost this scenario by spreading the adoption of monitoring systems to a wide set of healthy users or patients. In this context, we assessed a recently developed commercial smart shoe—the FootMoov—for automatic gait phase detection in level walking. FootMoov has built-in force sensors and a triaxial accelerometer and is able to transmit the sensor data to the smartphone through a wireless connection. We developed a dedicated gait phase detection algorithm relying both on force and inertial information. We tested the smart shoe on ten healthy subjects in free level walking conditions and in a laboratory setting in comparison with an optical motion capture system. Results confirmed a reliable detection of the gait phases. The maximum error committed, on the order of 44.7 ms, is comparable with previous studies. Our results confirmed the possibility to exploit consumer wearable devices to extract relevant parameters to improve the subject health or to better manage his/her progressions.


title: Efficient semi-streaming algorithms for local triangle counting in massive graphs

abstract: In this paper we study the problem of local triangle counting in large graphs. Namely, given a large graph G = (V;E) we want to estimate as accurately as possible the number of triangles incident to every node υ ∈ V in the graph. The problem of computing the global number of triangles in a graph has been considered before, but to our knowledge this is the first paper that addresses the problem of local triangle counting with a focus on the efficiency issues arising in massive graphs. The distribution of the local number of triangles and the related local clustering coefficient can be used in many interesting applications. For example, we show that the measures we compute can help to detect the presence of spamming activity in large-scale Web graphs, as well as to provide useful features to assess content quality in social networks. For computing the local number of triangles we propose two approximation algorithms, which are based on the idea of min-wise independent permutations (Broder et al. 1998). Our algorithms operate in a semi-streaming fashion, using O(jV j) space in main memory and performing O(log jV j) sequential scans over the edges of the graph. The first algorithm we describe in this paper also uses O(jEj) space in external memory during computation, while the second algorithm uses only main memory. We present the theoretical analysis as well as experimental results in massive graphs demonstrating the practical efficiency of our approach.


title: SCADA system security: Complexity, history and new developments

abstract: Over the last decade, efforts from industries and research communities have been made in addressing the security of Supervisory Control and Data Acquisition (SCADA) systems. However, the SCADA security deployed for critical infrastructures is still a challenging issue today. This paper gives an overview of the complexity of SCADA security. Products and applications in control network security are reviewed. Furthermore, new developments in SCADA security, especially the trend in technical and theoretical studies are presented. Some important topics on SCADA security are identified and highlighted and this can be served as the guide for future works in this area.


title: CipherXRay: Exposing Cryptographic Operations and Transient Secrets from Monitored Binary Execution

abstract: Malwares are becoming increasingly stealthy, more and more malwares are using cryptographic algorithms (e.g., packing, encrypting C&C communication) to protect themselves from being analyzed. The use of cryptographic algorithms and truly transient cryptographic secrets inside the malware binary imposes a key obstacle to effective malware analysis and defense. To enable more effective malware analysis, forensics, and reverse engineering, we have developed CipherXRay - a novel binary analysis framework that can automatically identify and recover the cryptographic operations and transient secrets from the execution of potentially obfuscated binary executables. Based on the avalanche effect of cryptographic functions, CipherXRay is able to accurately pinpoint the boundary of cryptographic operation and recover truly transient cryptographic secrets that only exist in memory for one instant in between multiple nested cryptographic operations. CipherXRay can further identify certain operation modes (e.g., ECB, CBC, CFB) of the identified block cipher and tell whether the identified block cipher operation is encryption or decryption in certain cases. We have empirically validated CipherXRay with OpenSSL, popular password safe KeePassX, the ciphers used by malware Stuxnet, Kraken and Agobot, and a number of third party softwares with built-in compression and checksum. CipherXRay is able to identify various cryptographic operations and recover cryptographic secrets that exist in memory for only a few microseconds. Our results demonstrate that current software implementations of cryptographic algorithms hardly achieve any secrecy if their execution can be monitored.


title: On the hardware implementations of the SHA-2 (256, 384, 512) hash functions

abstract: Couple to the communications wired and unwired networks growth, is the increasing demand for strong secure data transmission. New cryptographic standards are developed, and new encryption algorithms are designed, in order to satisfy the special needs for security. SHA-2 is the newest powerful standard in the hash functions families. In this paper, a VLSI architecture for the SHA-2 family is proposed. For every hash function SHA-2 (256, 384, and 512) of this standard, a hardware implementation is presented. All the implementations are examined and compared in the supported security level and in the performance by using hardware terms. This work can substitute efficiently the previous SHA-1 standard implementations, in every integrity security scheme, with higher offered security level, and better performance. In addition, the proposed implementations could be applied alternatively in the integrations of digital signature algorithms, keyed-hash message authentication codes and in random numbers generators architectures.


title: Efficient Subgraph Matching by Postponing Cartesian Products

abstract: In this paper, we study the problem of subgraph matching that extracts all subgraph isomorphic embeddings of a query graph q in a large data graph G. The existing algorithms for subgraph matching follow Ullmann's backtracking approach; that is, iteratively map query vertices to data vertices by following a matching order of query vertices. It has been shown that the matching order of query vertices is a very important aspect to the efficiency of a subgraph matching algorithm. Recently, many advanced techniques, such as enforcing connectivity and merging similar vertices in query or data graphs, have been proposed to provide an effective matching order with the aim to reduce unpromising intermediate results especially the ones caused by redundant Cartesian products. In this paper, for the first time we address the issue of unpromising results by Cartesian products from "dissimilar" vertices. We propose a new framework by postponing the Cartesian products based on the structure of a query to minimize the redundant Cartesian products. Our second contribution is proposing a new path-based auxiliary data structure, with the size O(|E(G)| x |V(q)|), to generate a matching order and conduct subgraph matching, which significantly reduces the exponential size O(|V(G)||V(q)|-1) of the existing path-based auxiliary data structure, where V (G) and E (G) are the vertex and edge sets of a data graph G, respectively, and V (q) is the vertex set of a query $q$. Extensive empirical studies on real and synthetic graphs demonstrate that our techniques outperform the state-of-the-art algorithms by up to $3$ orders of magnitude.


title: Trauma-informed care in inpatient mental health settings: A review of the literature

abstract: Trauma-informed care is an emerging value that is seen as fundamental to effective and contemporary mental health nursing practice. Trauma-informed care, like recovery, leaves mental health nurses struggling to translate these values into day-to-day nursing practice. Many are confused about what individual actions they can take to support these values. To date, the most clearly articulated policy to emerge from the trauma-informed care movement in Australia has been the agreement to reduce, and wherever possible, eliminate the use of seclusion and restraint. Confronted with the constant churn of admissions and readmissions of clients with challenging behaviours, and seemingly intractable mental illness, the elimination of seclusion and restraint is seen to be utopian by many mental health nurses in inpatient settings. Is trauma-informed care solely about eliminating seclusion and restraint, or are there other tangible practices nurses could utilize to effect better health outcomes for mental health clients, especially those with significant abuse histories? This article summarizes the findings from the literature from 2000-2011 in identifying those practices and clinical activities that have been implemented to effect trauma-informed care in inpatient mental health settings.


title: Rotation Invariant Spherical Harmonic Representation of 3D Shape Descriptors

abstract: One of the challenges in 3D shape matching arises from the fact that in many applications, models should be considered to be the same if they differ by a rotation. Consequently, when comparing two models, a similarity metric implicitly provides the measure of similarity at the optimal alignment. Explicitly solving for the optimal alignment is usually impractical. So, two general methods have been proposed for addressing this issue: (1) Every model is represented using rotation invariant descriptors. (2) Every model is described by a rotation dependent descriptor that is aligned into a canonical coordinate system defined by the model. In this paper, we describe the limitations of canonical alignment and discuss an alternate method, based on spherical harmonics, for obtaining rotation invariant representations. We describe the properties of this tool and show how it can be applied to a number of existing, orientation dependent descriptors to improve their matching performance. The advantages of this tool are two-fold: First, it improves the matching performance of many descriptors. Second, it reduces the dimensionality of the descriptor, providing a more compact representation, which in turn makes comparing two models more efficient.


title: Learning a Deep Single Image Contrast Enhancer from Multi-Exposure Images

abstract: Due to the poor lighting condition and limited dynamic range of digital imaging devices, the recorded images are often under-/over-exposed and with low contrast. Most of previous single image contrast enhancement (SICE) methods adjust the tone curve to correct the contrast of an input image. Those methods, however, often fail in revealing image details because of the limited information in a single image. On the other hand, the SICE task can be better accomplished if we can learn extra information from appropriately collected training data. In this paper, we propose to use the convolutional neural network (CNN) to train a SICE enhancer. One key issue is how to construct a training data set of low-contrast and high-contrast image pairs for end-to-end CNN learning. To this end, we build a large-scale multi-exposure image data set, which contains 589 elaborately selected high-resolution multi-exposure sequences with 4,413 images. Thirteen representative multi-exposure image fusion and stack-based high dynamic range imaging algorithms are employed to generate the contrast enhanced images for each sequence, and subjective experiments are conducted to screen the best quality one as the reference image of each scene. With the constructed data set, a CNN can be easily trained as the SICE enhancer to improve the contrast of an under-/over-exposure image. Experimental results demonstrate the advantages of our method over existing SICE methods with a significant margin.


title: Map-Based Probabilistic Visual Self-Localization

abstract: Accurate and efficient self-localization is a critical problem for autonomous systems. This paper describes an affordable solution to vehicle self-localization which uses odometry computed from two video cameras and road maps as the sole inputs. The core of the method is a probabilistic model for which an efficient approximate inference algorithm is derived. The inference algorithm is able to utilize distributed computation in order to meet the real-time requirements of autonomous systems in some instances. Because of the probabilistic nature of the model the method is capable of coping with various sources of uncertainty including noise in the visual odometry and inherent ambiguities in the map (e.g., in a Manhattan world). By exploiting freely available, community developed maps and visual odometry measurements, the proposed method is able to localize a vehicle to 4 m on average after 52 seconds of driving on maps which contain more than 2,150 km of drivable roads.


title: HAFT: hardware-assisted fault tolerance

abstract: Transient hardware faults during the execution of a program can cause data corruptions. We present HAFT, a fault tolerance technique using hardware extensions of commodity CPUs to protect unmodified multithreaded applications against such corruptions. HAFT utilizes instruction-level redundancy for fault detection and hardware transactional memory for fault recovery. We evaluated HAFT with Phoenix and PARSEC benchmarks. The observed normalized runtime is 2x, with 98.9% of the injected data corruptions being detected and 91.2% being corrected. To demonstrate the effectiveness of HAFT, we applied it to real-world case studies including Memcached, Apache, and SQLite.


Optimizing distributions over molecular space. An Objective-Reinforced Generative Adversarial Network for Inverse-design Chemistry (ORGANIC)


title: Automatic Recognition Of Personality In Conversation

abstract: The identification of personality by automatic analysis of conversation has many applications in natural language processing, from leader identification in meetings to partner matching on dating websites. We automatically train models of the main five personality dimensions, on a corpus of conversation extracts and personality ratings. Results show that the models perform better than the baseline, and their analysis confirms previous findings linking language and personality, while revealing many new linguistic and prosodic markers.


title: ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement Learning

abstract: The recent advances in deep neural networks have led to effective vision-based reinforcement learning methods that have been employed to obtain human-level controllers in Atari 2600 games from pixel data. Atari 2600 games, however, do not resemble real-world tasks since they involve non-realistic 2D environments and the third-person perspective. Here, we propose a novel test-bed platform for reinforcement learning research from raw visual information which employs the first-person perspective in a semi-realistic 3D world. The software, called ViZDoom, is based on the classical first-person shooter video game, Doom. It allows developing bots that play the game using the screen buffer. ViZDoom is lightweight, fast, and highly customizable via a convenient mechanism of user scenarios. In the experimental part, we test the environment by trying to learn bots for two scenarios: a basic move-and-shoot task and a more complex maze-navigation problem. Using convolutional deep neural networks with Q-learning and experience replay, for both scenarios, we were able to train competent bots, which exhibit human-like behaviors. The results confirm the utility of ViZDoom as an AI research platform and imply that visual reinforcement learning in 3D realistic first-person perspective environments is feasible.


title: Blood Pressure Prediction via Recurrent Models with Contextual Layer

abstract: Recently, the percentage of people with hypertension is increasing, and this phenomenon is widely concerned. At the same time, wireless home Blood Pressure (BP) monitors become accessible in people's life. Since machine learning methods have made important contributions in different fields, many researchers have tried to employ them in dealing with medical problems. However, the existing studies for BP prediction are all based on clinical data with short time ranges. Besides, there do not exist works which can jointly make use of historical measurement data (e.g. BP and heart rate) and contextual data (e.g. age, gender, BMI and altitude). Recurrent Neural Networks (RNNs), especially those using Long Short-Term Memory (LSTM) units, can capture long range dependencies, so they are effective in modeling variable-length sequences. In this paper, we propose a novel model named recurrent models with contextual layer, which can model the sequential measurement data and contextual data simultaneously to predict the trend of users' BP. We conduct our experiments on the BP data set collected from a type of wireless home BP monitors, and experimental results show that the proposed models outperform several competitive compared methods.


title: An efficient algorithm for mining time interval-based patterns in large database

abstract: Most studies on sequential pattern mining are mainly focused on time point-based event data. Few research efforts have elaborated on mining patterns from time interval-based event data. However, in many real applications, event usually persists for an interval of time. Since the relationships among event time intervals are intrinsically complex, mining time interval-based patterns in large database is really a challenging problem. In this paper, a novel approach, named as incision strategy and a new representation, called coincidence representation are proposed to simplify the processing of complex relations among event intervals. Then, an efficient algorithm, CTMiner (Coincidence Temporal Miner) is developed to discover frequent time-interval based patterns. The algorithm also employs two pruning techniques to reduce the search space effectively. Furthermore, experimental results show that CTMiner is not only efficient and scalable but also outperforms state-of-the-art algorithms.


title: Effects of Hippotherapy on Postural Stability, in Persons with Multiple Sclerosis: A Pilot Study

abstract: PURPOSE ::: The purpose of this pilot study was to examine the effectiveness of hippotherapy as an intervention for the treatment of postural instability in individuals with multiple sclerosis (MS). ::: ::: ::: SUBJECTS ::: A sample of convenience of 15 individuals with MS (24-72 years) were recruited from support groups and assessed for balance deficits. ::: ::: ::: METHODS ::: This study was a nonequivalent pretest-posttest comparison group design. Nine individuals (4 males, 5 females) received weekly hippotherapy intervention for 14 weeks. The other 6 individuals (2 males, 4 females) served as a comparison group. All participants were assessed with the Berg Balance Scale (BBS) and Tinetti Performance Oriented Mobility Assessment (POMA) at 0, 7, and 14 weeks. ::: ::: ::: RESULTS ::: The group receiving hippotherapy showed statistically significant improvement from pretest (0 week) to posttest (14 week) on the BBS (mean increase 9.15 points (x (2) = 8.82, p = 0.012)) and POMA scores (mean increase 5.13 (x (2) = 10.38, p = 0.006)). The comparison group had no significant changes on the BBS (mean increase 0.73 (x (2) = 0.40, p = 0.819)) or POMA (mean decrease 0.13 (x (2) = 1.41, p = 0.494)). A statistically significant difference was also found between the groups' final BBS scores (treatment group median = 55.0, comparison group median 41.0), U = 7, r = -0.49. ::: ::: ::: DISCUSSION ::: Hippotherapy shows promise for the treatment of balance disorders in persons with MS. Further research is needed to refine protocols and selection criteria.


On Common Ground : Neural-Symbolic Integration and Lifelong Machine Learning


title: Joint Dependency Parsing and Multiword Expression Tokenization

abstract: Complex conjunctions and determiners are often considered as pretokenized units in parsing. This is not always realistic, since they can be ambiguous. We propose a model for joint dependency parsing and multiword expressions identification, in which complex function words are represented as individual tokens linked with morphological dependencies. Our graph-based parser includes standard second-order features and verbal subcategoriza-tion features derived from a syntactic lexicon .We train it on a modified version of the French Treebank enriched with morphological dependencies. It recognizes 81.79% of ADV+que conjunctions with 91.57% precision, and 82.74% of de+DET determiners with 86.70% precision.


title: Crop disease classification using texture analysis

abstract: With the Agriculture Sector being the backbone of not only a large number of industries but also society as a whole, there is a rising need to grow good quality crops which in turn will give a high yield. For this to happen, it is crucial to monitor the crops throughout their growth period. In this paper, Image processing is used to detect and classify sunflower crop diseases based on the image of their leaf. The images are taken through a high resolution digital camera and after preprocessing, are subjected to k-means clustering to get the diseased part of the leaf. These are then run through the various machine learning algorithms and classified based on their color and texture features. A comparison based on accuracy between various machine learning algorithms is done namely K-Nearest Neighbors, Multi-Class Support Vector Machine, Naive Bayes and Multinomial Logistic Regression to achieve maximum accuracy. The implementation has been done using MATLAB.


title: Compact planar UWB antenna array for breast cancer detection

abstract: A compact planar ultra-wideband (UWB) antenna array was developed for radar-based breast cancer detection system in the supine position. The size of the antenna element was 11 mm × 13.1 mm. A 4×4 antenna array size was 44 mm × 52.4 mm. The bandwidth and the center frequency of the antenna were 12.5 GHz and 6 GHz, respectively. The permittivity and conductivity of the human breast tumor were measured and the phantom materials were selected to fit the characteristics of the human breast tissues. Confocal imaging was carried out using the phantom. It was confirmed that the compact antenna array for breast cancer detection could resolve the two separate tumor phantoms which were located at the depth of 20 mm with the separation distance of 20 mm.


title: Bio-inspired multiobjective clustering optimization: A survey and a proposal

abstract: Multiobjective clustering techniques have been used to simultaneously consider several complementary aspects of clustering quality. They optimize two or more cluster validity indices simultaneously, they lead to high-quality results, and have emerged as attractive and robust alternatives for solving clustering problems. This paper provides a brief review of bio-Inspired multiobjective clustering, and proposes a bee-inspired multiobjective optimization (MOO) algorithm, named cOptBees-MO, to solve multiobjective data clustering problems. In its survey part, a brief tutorial on MOO and multiobjective clustering optimization (MOCO) is presented, followed by a review of the main works in the area. Particular attention is given to the many objective functions used in MOCO. To evaluate the performance of the algorithm it was executed for various datasets and the results presented high quality clusters, diverse solutions an the automatic determination of a suitable number of clusters.


title: A Forecasting Methodology Using Support Vector Regression and Dynamic Feature Selection

abstract: Various techniques have been proposed to forecast a given time series. Models from the ARIMA family have been successfully used, as well as regression approaches based on e.g. linear, non-linear regression, neural networks, and Support Vector Regression. What makes the difference in many real-world applications, however, is not the technique but an appropriate forecasting methodology. Here, we propose such a methodology for the regression-based forecasting approach. A hybrid system is presented that iteratively selects the most relevant features and constructs the regression model optimizing its parameters dynamically. We develop a particular technique for feature selection as well as for model construction. The methodology, however, is a generic one providing the opportunity to employ alternative approaches within our framework. The application to several time series underlines its usefulness.


title: Towards Integrated Verification of Timed Transition Models

abstract: This paper describes an attempt to combine theorem proving and model-checking to formally verify real-time systems in a discrete time setting. The Timed Automata Modeling Environment (TAME) has been modified to provide a formal model for Time Transition Models (TTMs) in the PVS proof checker. Strong and weak state-event observation equivalences are formalized in PVS for state-event labeled transition systems (SELTS), the underlying semantic model of TTMs. The state-event equivalences form the basis of truth value preserving abstractions for a real-time temporal logic. When appropriate restrictions are placed upon the TTMs, their PVS models can be easily translated into input for the SAL model-checker. A simple real-time control system is specified and verified using these theories. While these preliminary results indicate that the combination of PVS and SAL could provide a useful environment to perform equivalence verification, model-checking and compositional model reduction of real-time systems, the current implementation in the general purpose SAL model-checker lags well behind state of the art real-time model-checkers.


Artificial Intelligence and Cognitive Psychology Applications , Models Gabriella


title: Multiple feature hashing for real-time large scale near-duplicate video retrieval

abstract: Near-duplicate video retrieval (NDVR) has recently attracted lots of research attention due to the exponential growth of online videos. It helps in many areas, such as copyright protection, video tagging, online video usage monitoring, etc. Most of existing approaches use only a single feature to represent a video for NDVR. However, a single feature is often insufficient to characterize the video content. Besides, while the accuracy is the main concern in previous literatures, the scalability of NDVR algorithms for large scale video datasets has been rarely addressed. In this paper, we present a novel approach - Multiple Feature Hashing (MFH) to tackle both the accuracy and the scalability issues of NDVR. MFH preserves the local structure information of each individual feature and also globally consider the local structures for all the features to learn a group of hash functions which map the video keyframes into the Hamming space and generate a series of binary codes to represent the video dataset. We evaluate our approach on a public video dataset and a large scale video dataset consisting of 132,647 videos, which was collected from YouTube by ourselves. The experiment results show that the proposed method outperforms the state-of-the-art techniques in both accuracy and efficiency.


title: Retricoin: Bitcoin based on compact proofs of retrievability

abstract: Bitcoin [24] is a fully decentralized electronic cash system. The generation of the proof-of-work in Bitcoin requires large amount of computing resources. However, this huge amount of energy is wasted as one cannot make something useful out of it. In this paper, we propose a scheme called Retricoin which replaces the heavy computational proof-of-work of Bitcoin by proofs of retrievability that have practical benefits. To guarantee the availability of an important but large file, we distribute the segments of the file among the users in the Bitcoin network. Every user who wants to mine Bitcoins must store a considerable portion of this file and prove her storage to other peers in the network using proofs of retrievability. The file can be constructed at any point of time from the users storing their respective segments untampered. Retricoin is more efficient than the existing Permacoin scheme [23] in terms of storage overhead and network bandwidth required to broadcast the proof to the Bitcoin network. The verification time in our scheme is comparable to that of Permacoin and reasonable for all practical purposes. We also design an algorithm to let the miners in a group (or pool) mine collectively.


title: The Psychology of Risk: The Behavioral Finance Perspective

abstract: Since the mid-1970s, hundreds of academic studies have been conducted in risk perception-oriented research within the social sciences (e.g., nonfinancial areas) across various branches of learning. The academic foundation pertaining to the "psy- chological aspects" of risk perception studies in behavioral finance, accounting, and economics developed from the earlier works on risky behaviors and hazardous activi- ties. This research on risky and hazardous situations was based on studies performed at Decision Research (an organization founded in 1976 by Paul Slovic) on risk percep- tion documenting specific behavioral risk characteristics from psychology that can be applied within a financial and investment decision-making context. A notable theme within the risk perception literature is how an investor processes information and the various behavioral finance theories and issues that might influence a person's percep- tion of risk within the judgment process. The different behavioral finance theories and concepts that influence an individual's perception of risk for different types of financial services and investment products are heuristics, overconfidence, prospect theory, loss aversion, representativeness, framing, anchoring, familiarity bias, perceived control, expert knowledge, affect (feelings), and worry.


title: Efficient Reinforcement Learning Through Evolving Neural Network Topologies

abstract: Neuroevolution is currently the strongest method on the pole-balancing benchmark reinforcement learning tasks. Although earlier studies suggested that there was an advantage in evolving the network topology as well as connection weights, the leading neuroevolution systems evolve fixed networks. Whether evolving structure can improve performance is an open question. In this article, we introduce such a system, NeuroEvolution of Augmenting Topologies (NEAT). We show that when structure is evolved (1) with a principled method of crossover, (2) by protecting structural innovation, and (3) through incremental growth from minimal structure, learning is significantly faster and stronger than with the best fixed-topology methods. NEAT also shows that it is possible to evolve populations of increasingly large genomes, achieving highly complex solutions that would otherwise be difficult to optimize.


title: Automotive connectivity, cyber attack scenarios and automotive cyber security

abstract: The Automotive industry is changing rapidly, as classical automotive engineering and information and communication technologies (ICT) converge. Digitization, new mobility concepts, e-mobility, and autonomous driving require a permanent connection with the Internet and transform the car into a very complex cyber-physical system. However, with this connectivity the car becomes vulnerable to cyber attacks. It is very important to understand the threat scenarios underlying the mega trends that are changing the industry. The paper gives a brief overview of the main drivers of this change and discusses some of the major cyber security threats arising in the world of connected cars focussing on three important areas where cyber attacks can happen — e-mobility, car sharing and automated valet parking. Fortunately, there are various cyber security measures that can help to protect the connected car. We briefly discuss some of the main approaches.


title: Learning Predictive Features in Affordance based Robotic Perception Systems

abstract: This work is about the relevance of Gibson's concept of affordances [1] for visual perception in interactive and autonomous robotic systems. In extension to existing functional views on visual feature representations, we identify the importance of learning in perceptual cueing for the anticipation of opportunities for interaction of robotic agents. We investigate how the originally defined representational concept for the perception of affordances - in terms of using either optical flow or heuristically determined 3D features of perceptual entities - should be generalized to using arbitrary visual feature representations. In this context we demonstrate the learning of causal relationships between visual cues and predictable interactions, and emphasize on a novel framework for cueing and hypothesis verification of affordances that could play an important role in future robot control architectures. We argue that affordance based perception should enable systems to react to environment stimuli both more efficient and autonomous, and provide a potential to plan on the basis of responses to more complex perceptual configurations. We verify the concept with a concrete implementation applying state-of-the-art visual descriptors and regions of interest within a simulated robot scenario and prove that these features were successfully selected for predicting opportunities of robot interaction.


title: Geo-spatial Location Spoofing Detection for Internet of Things

abstract: We develop a new location spoofing detection algorithm for geo-spatial tagging and location-based services in the Internet of Things (IoT), called enhanced location spoofing detection using audibility (ELSA), which can be implemented at the backend server without modifying existing legacy IoT systems. ELSA is based on a statistical decision theory framework and uses two-way time-of-arrival (TW-TOA) information between the user’s device and the anchors. In addition to the TW-TOA information, ELSA exploits the implicit audibility information (or outage information) to improve detection rates of location spoofing attacks. Given TW-TOA and audibility information, we derive the decision rule for the verification of the device’s location, based on the generalized likelihood ratio test. We develop a practical threat model for delay measurements’ spoofing scenarios, and investigate in detail the performance of ELSA in terms of detection and false alarm rates. Our extensive simulation results on both synthetic and real-world datasets demonstrate the superior performance of ELSA compared to conventional non-audibility-aware approaches.


title: An empirical study on the impact of static typing on software maintainability

abstract: Static type systems play an essential role in contemporary programming languages. Despite their importance, whether static type systems impact human software development capabilities remains open. One frequently mentioned argument in favor of static type systems is that they improve the maintainability of software systems--an often-used claim for which there is little empirical evidence. This paper describes an experiment that tests whether static type systems improve the maintainability of software systems, in terms of understanding undocumented code, fixing type errors, and fixing semantic errors. The results show rigorous empirical evidence that static types are indeed beneficial to these activities, except when fixing semantic errors. We further conduct an exploratory analysis of the data in order to understand possible reasons for the effect of type systems on the three kinds of tasks used in this experiment. From the exploratory analysis, we conclude that developers using a dynamic type system tend to look at different files more frequently when doing programming tasks--which is a potential reason for the observed differences in time.


title: Social Networks and the Semantic Web

abstract: A formal, web-based representation of social networks is both a necessity in terms of infrastructure as well as a prominent application for the Semantic Web. In this paper we present three advances in exploiting the opportunity of semantically-enriched network data: (1) an ontology for the representation of social networks and relationships (2) a hybrid system for online data acquisition that combines traditional web mining techniques with the collection of Semantic Web data (2) a case study highlighting some of the possible analysis of this data using methods from Social Network Analysis, the branch of sociology concerned with relational data.


1 SOCIAL COGNITIVE THEORY OF MASS COMMUNICATION


Deep Learning and Data Labeling for Medical Applications


title: Conductive Inks with a “Built-In” Mechanism That Enables Sintering at Room Temperature

abstract: At present there is no metallic ink that enables formation of conductive patterns at room temperature by a single printing step. Printing conductive features by metallic nanoparticle-based inks must be followed by sintering while heating to elevated temperatures, thus preventing their utilization on most plastic substrates used in plastic electronics. In this report we present a new silver nanoparticle-based conductive ink, having a built-in sintering mechanism, which is triggered during drying of the printed pattern. The nanoparticles that are stabilized by a polymer undergo self-sintering spontaneously, due to the presence of a destabilizing agent, which comes into action only during drying of the printed pattern. The destabilizing agent, which contains Cl− ions, causes detachment of the anchoring groups of the stabilizer from the nanoparticles’ surface and thus enables their coalescence and sintering. It was found that the new metallic ink leads to very high conductivities, by a single printing step: u...


Radiological Grading of Spinal MRI


title: Similarity Breeds Proximity: Pattern Similarity within and across Contexts Is Related to Later Mnemonic Judgments of Temporal Proximity

abstract: Summary Experiences unfold over time, but little is known about the mechanisms that support the formation of coherent episodic memories for temporally extended events. Recent work in animals has provided evidence for signals in hippocampus that could link events across temporal gaps; however, it is unknown whether and how such signals might be related to later memory for temporal information in humans. We measured patterns of fMRI BOLD activity as people encoded items that were separated in time and manipulated the presence of shared or distinct context across items. We found that hippocampal pattern similarity in the BOLD response across trials predicted later temporal memory decisions when context changed. By contrast, pattern similarity in lateral occipital cortex was related to memory only when context remained stable. These data provide evidence in humans that representational stability in hippocampus across time may be a mechanism for temporal memory organization.


title: Prediction of heart disease using multilayer perceptron neural network

abstract: In medical field the diagnosis of heart disease is most difficult task. It depends on the careful analysis of different clinical and pathological data of the patient by medical experts, which is complicated process. Due to advancement in machine learning and information technology, the researchers and medical practitioners in large extent are interested in the development of automated system for the prediction of heart disease that is highly accurate, effective and helpful in early diagnosis. In this paper we present a prediction system for heart disease using multilayer perceptron neural network. The neural network in this system accepts 13 clinical features as input and it is trained using back-propagation algorithm to predict that there is a presence or absence of heart disease in the patient with highest accuracy of 98% comparative to other systems. The accuracy thus obtained with this system shows that it is better and efficient than other systems.


title: Avida: A Software Platform for Research in Computational Evolutionary Biology

abstract: Avida is a software platform for experiments with self-replicating and evolving computer programs. It provides detailed control over experimental settings and protocols, a large array of measurement tools, and sophisticated methods to analyze and post-process experimental data. We explain the general principles on which Avida is built, as well as its main components and their interactions. We also explain how experiments are set up, carried out, and analyzed.


title: Challenges in agile software development: A systematic literature review

abstract: Agile methodology is popular in today software development. Many studies has revealed that agile software development bring significantly positive impact on software development performance, quality, and user satisfaction. However, it also has some challenges. This study tend to develop a systematic literature review to identify the challenge of agile software development (ASD) in general. The sample totaled 20 papers that complied with the inclusion criteria. The distribution of papers based on years, countries, and other criteria is presented in this study. The result showed that there are 30 challenges in implementing ASD methodology. Among 30 challenges in ASD, the most significant challenges are team management and distributed team, followed by requirement prioritization, documentation, changing and over-scoping requirement, organization, process, and progress monitoring and feedback.


title: ConDroid: Targeted Dynamic Analysis of Android Applications

abstract: Recent years have seen the development of a multitude of tools for the security analysis of Android applications. A major deficit of current fully automated security analyses, however, is their inability to drive execution to interesting parts, such as where code is dynamically loaded or certain data is decrypted. In fact, security-critical or downright offensive code may not be reached at all by such analyses when dynamically checked conditions are not met by the analysis environment. To tackle this unsolved problem, we propose a tool combining static call path analysis with byte code instrumentation and a heuristic partial symbolic execution, which aims at executing interesting calls paths. It can systematically locate potentially security-critical code sections and instrument applications such that execution of these sections can be observed in a dynamic analysis. Among other use cases, this can be leveraged to force applications into revealing dynamically loaded code, a simple yet effective way to circumvent detection by security analysis software such as the Google Play Store's Bouncer. We illustrate the functionality of our tool by means of a simple logic bomb example and a real-life security vulnerability which is present in hunderd of apps and can still be actively exploited at this time.


title: Culture, personality, and subjective well-being: integrating process models of life satisfaction.

abstract: The authors examined the interplay of personality and cultural factors in the prediction of the affective (hedonic balance) and the cognitive (life satisfaction) components of subjective well-being (SWB). They predicted that the influence of personality on life satisfaction is mediated by hedonic balance and that the relation between hedonic balance and life satisfaction is moderated by culture. As a consequence, they predicted that the influence of personality on life satisfaction is also moderated by culture. Participants from 2 individualistic cultures (United States, Germany) and 3 collectivistic cultures (Japan, Mexico, Ghana) completed measures of Extraversion, Neuroticism, hedonic balance, and life satisfaction. As predicted, Extraversion and Neuroticism influenced hedonic balance to the same degree in all cultures, and hedonic balance was a stronger predictor of life satisfaction in individualistic than in collectivistic cultures. The influence of Extraversion and Neuroticism on life satisfaction was largely mediated by hedonic balance. The results suggest that the influence of personality on the emotional component of SWB is pancultural, whereas the influence of personality on the cognitive component of SWB is moderated by culture.


title: The impact of cyberloafing towards Malaysia employees' productivity: A conceptual framework

abstract: The increasing use of the internet has changed the way we live and work. Moreover, in countries where there is a use of internet on a large scale, changes in the content and context of work have been observed. The use of Internet has become a tool to achieve competitive advantage by organizations. Also, it has been associated with many negative consequences such as reduced employees' productivity. Despite its importance, there is a lack of cyberloafing studies in Malaysia. The paper proposes a modified version of Triandis's theory of interpersonal behaviour by incorporating a new independent variable, ability to hide cyberloafing that directly predicts employees cyberloafing. This paper also recommends examining the relationship between cyberloafing and employees' productivity. Some studies believe that cyberloafing is beneficial for employees' productivity but other studies suggest that it is harmful to employees' productivity. By understanding what causes cyberloafing and its impacts, organizations can be more effective in managing the issue.


title: Tracking cerebral white matter changes across the lifespan: insights from diffusion tensor imaging studies

abstract: Delineating the normal development of brain white matter (WM) over the human lifespan is crucial to improved understanding of underlying WM pathology in neuropsychiatric and neurological conditions. We review the extant literature concerning diffusion tensor imaging studies of brain WM development in healthy individuals available until October 2012, summarise trends of normal development of human brain WM and suggest possible future research directions. Temporally, brain WM maturation follows a curvilinear pattern with an increase in fractional anisotropy (FA) from newborn to adolescence, decelerating in adulthood till a plateau around mid-adulthood, and a more rapid decrease of FA from old age onwards. Spatially, brain WM tracts develop from central to peripheral regions, with evidence of anterior-to-posterior maturation in commissural and projection fibres. The corpus callosum and fornix develop first and decline earlier, whilst fronto-temporal WM tracts like cingulum and uncinate fasciculus have protracted maturation and decline later. Prefrontal WM is most vulnerable with greater age-related FA reduction compared with posterior WM. Future large scale studies adopting longitudinal design will better clarify human brain WM changes over time.


title: Broadband printed dipole antennas with shaped ground plane

abstract: A printed dipole antenna, in spite of its numerous advantages in terms of size, weight, aerodynamic drag etc. suffers from the inherent disadvantage of incompatibility with wide band operation. Both microstrip dipoles and printed dipoles are resonant antennas but printed dipoles are often used to replace microstrip dipoles since they offer somewhat better band width. However, the maximum band width reported to be obtained is not sufficient for many present day high-speed data applications like streaming video, real time navigation and control links, as also applications like medical imaging, broad band EMI measurements etc. A novel yet simple design of printed dipole antenna without flared arms and with shaped ground plane is presented here using which −10 dB return loss band width as high as 41% in L band and below is achieved, a feat hitherto unachieved. By flaring its arms, the same antenna provides −10dB band width as high as 51% in the same band of frequency. Antennas are designed to operate in L band and below so that these can be used in GSM and CDMA wireless applications. Measured results of both antennas show good agreement with the simulated results.


title: Machine Learning-Based Detection of Ransomware Using SDN

abstract: The growth of malware poses a major threat to internet users, governments, and businesses around the world. One of the major types of malware, ransomware, encrypts a user's sensitive information and only returns the original files to the user after a ransom is paid. As malware developers shift the delivery of their product from HTTP to HTTPS to protect themselves from payload inspection, we can no longer rely on deep packet inspection to extract features for malware identification. Toward this goal, we propose a solution leveraging a recent trend in networking hardware, that is programmable forwarding engines (PFEs). PFEs allow collection of per-packet, network monitoring data at high rates. We use this data to monitor the network traffic between an infected computer and the command and control (C&C) server. We extract high-level flow features from this traffic and use this data for ransomware classification. We write a stream processor and use a random forest, binary classifier to utilizes these rich flow records in fingerprinting malicious, network activity without the requirement of deep packet inspection. Our classification model achieves a detection rate in excess of 0.86, while maintaining a false negative rate under 0.11. Our results suggest that a flow-based fingerprinting method is feasible and accurate enough to catch ransomware before encryption.


title: Image Stitching Using Structure Deformation

abstract: The aim of this paper is to achieve seamless image stitching without producing visual artifact caused by severe intensity discrepancy and structure misalignment, given that the input images are roughly aligned or globally registered. Our new approach is based on structure deformation and propagation for achieving the overall consistency in image structure and intensity. The new stitching algorithm, which has found applications in image compositing, image blending, and intensity correction, consists of the following main processes. Depending on the compatibility and distinctiveness of the 2D features detected in the image plane, single or double optimal partitions are computed subject to the constraints of intensity coherence and structure continuity. Afterwards, specific 1D features are detected along the computed optimal partitions from which a set of sparse deformation vectors is derived to encode 1D feature matching between the partitions. These sparse deformation cues are robustly propagated into the input images by solving the associated minimization problem in gradient domain, thus providing a uniform framework for the simultaneous alignment of image structure and intensity. We present results in general image compositing and blending in order to show the effectiveness of our method in producing seamless stitching results from complex input images.


A method for standardizing MR intensities between slices and volumes


title: A new wide-band planar balun on a single-layer PCB

abstract: A new wide-band microstrip balun implemented on a single-layer printed circuit board (PCB) is presented in this letter. The proposed planar balun consists of a wide-band Wilkinson power divider and a noncoupled-line broad-band 180/spl deg/ phase shifter. To demonstrate the design methodology, one prototype is realized. The new design was simulated and validated by the measurement. Measured results show that 10-dB return loss of the unbalanced port has been achieved across the bandwidth from 1.7 GHz to 3.3 GHz, or 64%. Within the operation band, the measured return losses for both the two balanced ports are better than -10 dB, and the balanced ports isolation is below -1.5 dB. The measured amplitude and phase imbalance between the two balanced ports are within 0.3 dB and /spl plusmn/5/spl deg/, respectively, over the operating frequency band.


title: USING DATA ENVELOPMENT ANALYSIS AND STOCHASTIC FRONTIER ANALYSIS METHODS TO EVALUATE EFFICIENCY OF HYDROELECTRICITY CENTERS

abstract: This study clarifies efficiency scores and also ranks of hydroelectricity centers by using data envelopment analysis and stochastic frontier analysis methods. Applying copula technique in the stochastic frontier analysis is an advantage to our study between similar activities.


Supporting the Use of Social Media by Emergency Managers: Software Tools to Overcome Information Overload


title: Diagnosis of diabetes using classification mining techniques

abstract: Diabetes has affected over 246 million people worldwide with a majority of them being women. According to the WHO report, by 2025 this number is expected to rise to over 380 million. The disease has been named the fifth deadliest disease in the United States with no imminent cure in sight. With the rise of information technology and its continued advent into the medical and healthcare sector, the cases of diabetes as well as their symptoms are well documented. This paper aims at finding solutions to diagnose the disease by analyzing the patterns found in the data through classification analysis by employing Decision Tree and Na\"ive Bayes algorithms. The research hopes to propose a quicker and more efficient technique of diagnosing the disease, leading to timely treatment of the patients.


title: Tracking Human Pose During Robot-Assisted Dressing using Single-Axis Capacitive Proximity Sensing

abstract: Dressing is a fundamental task of everyday living and robots offer an opportunity to assist people with motor impairments. While several robotic systems have explored robot-assisted dressing, few have considered how a robot can manage errors in human pose estimation, or adapt to human motion in real time during dressing assistance. In addition, estimating pose changes due to human motion can be challenging with vision-based techniques since dressing is often intended to visually occlude the body with clothing. We present a method to track a person's pose in real time using capacitive proximity sensing. This sensing approach gives direct estimates of distance with low latency, has a high signal-to-noise ratio, and has low computational requirements. Using our method, a robot can adjust for errors in the estimated pose of a person and physically follow the contours and movements of the person while providing dressing assistance. As part of an evaluation of our method, the robot successfully pulled the sleeve of a hospital gown and a cardigan onto the right arms of 10 human participants, despite arm motions and large errors in the initially estimated pose of the person's arm. We also show that a capacitive sensor is unaffected by visual occlusion of the body and can sense a person's body through fabric clothing.


title: Evaluating the potential for improving anaerobic digestion of cellulosic waste via routine bioaugmentation and alkaline pretreatment

abstract: In this study, two methods for improving anaerobic digestion processes were investigated. The first method was routine bioaugmentation and the second was alkaline pretreatment. Both of these methods were applied to a two-phase anaerobic digestion process for treating the residuals from sweet corn processing, which have a significant lignocellulosic fraction as well as some starch from the base of the kernels. The two-phase anaerobic digestion process was proposed as one component of a larger integrated anaerobic/aerobic waste treatment process in which four co-products would be generated namely, methane-rich biogas, fertilizer, single cell protein, and algal biomass. ::: The first objective of this study was to determine whether bioaugmentation with a cellulolytic bioculture would result in increased methane production compared to a non-bioaugmented control condition. Batch tests were conducted to compare the biogas potential of sweet corn processing residues with and without bioaugmentation using a proprietary cellulolytic bioculture. The results indicated that bioaugmentation was beneficial to digestion performance, increasing the average methane production by 34% compared to non-bioaugmented controls (265 versus 199 ml/g VSadded). The average rate of methane production was also increased in the bioaugmented condition compared to non-bioaugmented controls. However, the observed total methane production was relatively low in comparison to the maximum theoretical production (415 ml CH4/g VSadded), suggesting there to be room for further improving digestion efficiency. ::: The second objective of this study was to verify whether routine bioaugmentation with cellulolytic microorganisms benefited substrate hydrolysis and subsequent methane production compared to one-time bioaugmentation. It was hypothesized that through routine bioaugmentation with cellulolytic microorganisms, a microbial population better suited for degradation of lignocellulosic material could be achieved and maintained, thereby increasing the rate of hydrolysis and ultimately increasing the rate of methane production. Pursuant to this objective, a two-phase sequencing/semi batch experiment was conducted in which routine bioaugmentation with two sources of cellulolytic microorganisms was compared to one-time bioaugmented and non-bioaugmented conditions. Neutral detergent fiber (NDF) analysis and net soluble chemical oxygen demand (sCOD) generation suggested that routine bioaugmentation improved substrate hydrolysis by 22-25% in comparison to one-time bioaugmentation after 14 days of operation. Methane yields from routine bioaugmented conditions using a proprietary cellulolytic bioculture also showed 15% higher methane production was achieved in comparison to one-time bioaugmentation after 36 days of digestion. In this experiment, bioaugmentation with a proprietary cellulolytic bioculture was compared to bioaugmentation with dairy cattle rumen fluid. The rumen bioaugmentation culture produced higher methane yields than the proprietary bioculture (16-34%).…


Quantitative Methods Inquires 288 MULTINOMIAL LOGISTIC REGRESSION : USAGE AND APPLICATION IN RISK ANALYSIS


title: Registration Error Analysis for Augmented Reality

abstract: Augmented reality AR systems typically use see-through head-mounted displays STHMDs to superimpose images of computer-generated objects onto the user's view of the real environment in order to augment it with additional information. The main failing of current AR systems is that the virtual objects displayed in the STHMD appear in the wrong position relative to the real environment. This registration error has many causes: system delay, tracker error, calibration error, optical distortion, and misalignment of the model, to name only a few. Although some work has been done in the area of system calibration and error correction, very little work has been done on characterizing the nature and sensitivity of the errors that cause misregistration in AR systems. This paper presents the main results of an end-to-end error analysis of an optical STHMD-based tool for surgery planning. The analysis was done with a mathematical model of the system and the main results were checked by taking measurements on a real system under controlled circumstances. The model makes it possible to analyze the sensitivity of the system-registration error to errors in each part of the system. The major results of the analysis are: 1 Even for moderate head velocities, system delay causes more registration error than all other sources combined; 2 eye tracking is probably not necessary; 3 tracker error is a significant problem both in head tracking and in system calibration; 4 the World or reference coordinate system adds error and should be omitted when possible; 5 computational correction of optical distortion may introduce more delay-induced registration error than the distortion error it corrects, and 6 there are many small error sources that will make submillimeter registration almost impossible in an optical STHMD system without feedback. Although this model was developed for optical STHMDs for surgical planning, many of the results apply to other HMDs as well.


title: Health of farmed fish: its relation to fish welfare and its utility as welfare indicator

abstract: This brief review focuses on health and biological function as cornerstones of fish welfare. From the function-based point of view, good welfare is reflected in the ability of the animal to cope with infectious and non-infectious stressors, thereby maintaining homeostasis and good health, whereas stressful husbandry conditions and protracted suffering will lead to the loss of the coping ability and, thus, to impaired health. In the first part of the review, the physiological processes through which stressful husbandry conditions modulate health of farmed fish are examined. If fish are subjected to unfavourable husbandry conditions, the resulting disruption of internal homeostasis necessitates energy-demanding physiological adjustments (allostasis/acclimation). The ensuing energy drain leads to trade-offs with other energy-demanding processes such as the functioning of the primary epithelial barriers (gut, skin, gills) and the immune system. Understanding of the relation between husbandry conditions, allostatic responses and fish health provides the basis for the second theme developed in this review, the potential use of biological function and health parameters as operational welfare indicators (OWIs). Advantages of function- and health-related parameters are that they are relatively straightforward to recognize and to measure and are routinely monitored in most aquaculture units, thereby providing feasible tools to assess fish welfare under practical farming conditions. As the efforts to improve fish welfare and environmental sustainability lead to increasingly diverse solutions, in particular integrated production, it is imperative that we have objective OWIs to compare with other production forms, such as high-density aquaculture. However, to receive the necessary acceptance for legislation, more robust scientific backing of the health- and function-related OWIs is urgently needed.


Confidants and Well-Being: A Note on Male Friendship in Old Age


title: Insights and Approaches for Low-Complexity 5G Small-Cell Base-Station Design for Indoor Dense Networks

abstract: This paper investigates low-complexity approaches to small-cell base-station (SBS) design, suitable for future 5G millimeter-wave (mmWave) indoor deployments. Using large-scale antenna systems and high-bandwidth spectrum, such SBS can theoretically achieve the anticipated future data bandwidth demand of 10000 fold in the next 20 years. We look to exploit small cell distances to simplify SBS design, particularly considering dense indoor installations. We compare theoretical results, based on a link budget analysis, with the system simulation of a densely deployed indoor network using appropriate mmWave channel propagation conditions. The frequency diverse bands of 28 and 72 GHz of the mmWave spectrum are assumed in the analysis. We investigate the performance of low-complexity approaches using a minimal number of antennas at the base station and the user equipment. Using the appropriate power consumption models and the state-of-the-art sub-component power usage, we determine the total power consumption and the energy efficiency of such systems. With mmWave being typified nonline-of-sight communication, we further investigate and propose the use of direct sequence spread spectrum as a means to overcome this, and discuss the use of multipath detection and combining as a suitable mechanism to maximize link reliability.


title: DeepRecon: Dynamically reconfigurable architecture for accelerating deep neural networks

abstract: Deep learning models are computationally expensive and their performance depends strongly on the underlying hardware platform. General purpose compute platforms such as GPUs have been widely used for implementing deep learning techniques. However, with the advent of emerging application domains such as internet of things, developments of custom integrated circuits capable of efficiently implementing deep learning models with low power and form factor are in high demand. In this paper we analyze both the computation and communication costs of common deep networks. We propose a reconfigurable architecture that efficiently utilizes computational and storage resources for accelerating deep learning techniques without loss of algorithmic accuracy.


title: What Do You Mean by “AI”?

abstract: Many problems in AI study can be traced back to the confusion of different research goals. In this paper, five typical ways to define AI are clarified, analyzed, and compared. It is argued that though they are all legitimate research goals, they lead the research to very different directions, and most of them have trouble to give AI a proper identity. Finally, a working definition of AI is proposed, which has important advantages over the alternatives.


title: Going the distance for TLB prefetching: an application-driven study

abstract: The importance of the Translation Lookaside Buffer (TLB) on system performance is well known. There have been numerous prior efforts addressing TLB design issues for cutting down access times and lowering miss rates. However, it was only recently that the first exploration [26] on prefetching TLB entries ahead of their need was undertaken and a mechanism called Recency Prefetching was proposed. There is a large body of literature on prefetching for caches, and it is not clear how they can be adapted (or if the issues are different) for TLBs, how well suited they are for TLB prefetching, and how they compare with the recency prefetching mechanism.This paper presents the first detailed comparison of different prefetching mechanisms (previously proposed for caches) - arbitrary stride prefetching, and markov prefetching - for TLB entries, and evaluates their pros and cons. In addition, this paper proposes a novel prefetching mechanism, called Distance Prefetching, that attempts to capture patterns in the reference behavior in a smaller space than earlier proposals. Using detailed simulations of a wide variety of applications (56 in all) from different benchmark suites and all the SPEC CPU2000 applications, this paper demonstrates the benefits of distance prefetching.


title: DAML: Domain Adaptation Metric Learning

abstract: The state-of-the-art metric-learning algorithms cannot perform well for domain adaptation settings, such as cross-domain face recognition, image annotation, etc., because labeled data in the source domain and unlabeled ones in the target domain are drawn from different, but related distributions. In this paper, we propose the domain adaptation metric learning (DAML), by introducing a data-dependent regularization to the conventional metric learning in the reproducing kernel Hilbert space (RKHS). This data-dependent regularization resolves the distribution difference by minimizing the empirical maximum mean discrepancy between source and target domain data in RKHS. Theoretically, by using the empirical Rademacher complexity, we prove risk bounds for the nearest neighbor classifier that uses the metric learned by DAML. Practically, learning the metric in RKHS does not scale up well. Fortunately, we can prove that learning DAML in RKHS is equivalent to learning DAML in the space spanned by principal components of the kernel principle component analysis (KPCA). Thus, we can apply KPCA to select most important principal components to significantly reduce the time cost of DAML. We perform extensive experiments over four well-known face recognition datasets and a large-scale Web image annotation dataset for the cross-domain face recognition and image annotation tasks under various settings, and the results demonstrate the effectiveness of DAML.


title: Reinforcement Learning: An Introduction

abstract: Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability. The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning.


title: Unsupervised Outlier Detection in Time Series Data

abstract: Fraud detection is of great importance to financial institutions. This paper is concerned with the problem of finding outliers in time series financial data using Peer Group Analysis (PGA), which is an unsupervised technique for fraud detection. The objective of PGA is to characterize the expected pattern of behavior around the target sequence in terms of the behavior of similar objects, and then to detect any difference in evolution between the expected pattern and the target. The tool has been applied to the stock market data, which has been collected from Bangladesh Stock Exchange to assess its performance in stock fraud detection. We observed PGA can detect those brokers who suddenly start selling the stock in a different way to other brokers to whom they were previously similar. We also applied t-statistics to find the deviations effectively.


title: Efficient distributed selective search

abstract: Simulation and analysis have shown that selective search can reduce the cost of large-scale distributed information retrieval. By partitioning the collection into small topical shards, and then using a resource ranking algorithm to choose a subset of shards to search for each query, fewer postings are evaluated. In this paper we extend the study of selective search into new areas using a fine-grained simulation, examining the difference in efficiency when term-based and sample-based resource selection algorithms are used; measuring the effect of two policies for assigning index shards to machines; and exploring the benefits of index-spreading and mirroring as the number of deployed machines is varied. Results obtained for two large datasets and four large query logs confirm that selective search is significantly more efficient than conventional distributed search architectures and can handle higher query rates. Furthermore, we demonstrate that selective search can be tuned to avoid bottlenecks, and thus maximize usage of the underlying computer hardware.


Measuring the power consumption of social media applications on a mobile device


title: Clustering daily patterns of human activities in the city

abstract: Data mining and statistical learning techniques are powerful analysis tools yet to be incorporated in the domain of urban studies and transportation research. In this work, we analyze an activity-based travel survey conducted in the Chicago metropolitan area over a demographic representative sample of its population. Detailed data on activities by time of day were collected from more than 30,000 individuals (and 10,552 households) who participated in a 1-day or 2-day survey implemented from January 2007 to February 2008. We examine this large-scale data in order to explore three critical issues: (1) the inherent daily activity structure of individuals in a metropolitan area, (2) the variation of individual daily activities—how they grow and fade over time, and (3) clusters of individual behaviors and the revelation of their related socio-demographic information. We find that the population can be clustered into 8 and 7 representative groups according to their activities during weekdays and weekends, respectively. Our results enrich the traditional divisions consisting of only three groups (workers, students and non-workers) and provide clusters based on activities of different time of day. The generated clusters combined with social demographic information provide a new perspective for urban and transportation planning as well as for emergency response and spreading dynamics, by addressing when, where, and how individuals interact with places in metropolitan areas.


title: Anytime solution optimization for sampling-based motion planning

abstract: Recent work in sampling-based motion planning has yielded several different approaches for computing good quality paths in high degree of freedom systems: path shortcutting methods that attempt to shorten a single solution path by connecting non-consecutive configurations, a path hybridization technique that combines portions of two or more solutions to form a shorter path, and asymptotically optimal algorithms that converge to the shortest path over time. This paper presents an extensible meta-algorithm that incorporates a traditional sampling-based planning algorithm with offline path shortening techniques to form an anytime algorithm which exhibits competitive solution lengths to the best known methods and optimizers. A series of experiments involving rigid motion and complex manipulation are performed as well as a comparison with asymptotically optimal methods which show the efficacy of the proposed scheme, particularly in high-dimensional spaces.


title: A Broadband Noise-Canceling CMOS LNA for 3.1–10.6-GHz UWB Receivers

abstract: An ultra-wideband 3.1-10.6-GHz low-noise amplifier employing a broadband noise-canceling technique is presented. By using the proposed circuit and design methodology, the noise from the matching device is greatly suppressed over the desired UWB band, while the noise from other devices performing noise cancellation is minimized by the systematic approach. Fabricated in a 0.18-mum CMOS process, the IC prototype achieves a power gain of 9.7 dB over a -3 dB bandwidth of 1.2-11.9-GHz and a noise figure of 4.5-5.1 dB in the entire UWB band. It consumes 20 mW from a 1.8-V supply and occupies an area of only 0.59 mm2


title: Determination of primary structure of amberjack myosin heavy chain and its relationship with structural stability of various fish myosin rods

abstract: The structural stability of fish myosin depends upon species and temperatures of water in which fish live. Primary, secondary, and quaternary structures of myosin heavy chain (MyHC) from three species of fish living at different temperature ranges have been compared with those of rabbit MyHC in order to investigate the differences in stability. Primary structure of MyHC, although being accessible for warm-water and cold-water fish (carp and walleye pollack), was not available in previous for tropical-water fish literature; so in this study primary structure of MyHC of the tropical-water fish amberjack has been determined by cloning and sequencing its cDNA. The MyHC has 1938 amino acid residues (AA), which are almost as much as as those of carp and walleye pollack. The amberjack MyHC is 91–95% homologous with other fish and rabbit MyHCs. There is a discernible difference between animal species with stable myosin rod (amberjack, carp, and rabbit) and walleye pollack with unstable rod. Stable rod species have a high probability of forming coiled-coil around the COOH-terminal end of the rod, while the pollack has a low coiled-coil formation probability. In addition, the average scores of the coiled-coil for myosin rod were rabbit (1.738) > amberjack (1.691) > carp (1.680) > walleye pollack (1.674) which correlated exactly with the observed stability. The results suggest that coiled-coil forming ability, particularly around the COOH-terminal end, directs structural stability of fish myosin rod.


title: Guidelines for haptic interpersonal communication applications: an exploration of foot interaction styles

abstract: A new method for researching haptic interaction styles is presented, based on a layered interaction model and a classification of existing devices. The method is illustrated by designing a new foot interaction device. The aim of which is to enhance non-verbal communication over a computer network. A layered protocols interaction model allows to consider all aspects of the haptic communication process: the intention to perform an action, limitations of the human body, and specifications of the communication device and the network. We demonstrate how this model can be used to derive design-guidelines by analyzing and classifying existing communication devices. By designing and evaluating a foot interaction device, we not only demonstrate that feet are suited for personal, concealed communication over a network, but also show the added value of the design-guidelines. Results of user tests provide clues for designing stimuli for foot interaction and indicate applications of foot communication devices.


title: Gate sizing: finFETs vs 32nm bulk MOSFETs

abstract: FinFET devices promise to replace traditional MOSFETs because of superior ability in controlling leakage and minimizing short channel effects while delivering a strong drive current. We investigate in this paper the gate sizing of finFET devices, and we provide a comparison with 32nm bulk CMOS. Wider finFET devices are built utilizing multiple parallel fins between the source and drain. Independent gating of the finFET's double gates allows significant reduction in leakage current. We perform temperature-aware circuit optimization by modeling delay using temperature-dependent parameters, and by imposing constraints that limit the maximum allowable number of parallel fins. We show that finFET circuits are superior in performance and produce less static power when compared to 32nm circuits.